Polymorphism of Six Loci in Major

Histocompatibility Complex Class I Region of

Indonesian Javanese and Comparative

Assessment of the POALINs with Arab Bedouin

Population

Windy Joanmawanti (BSc, GDipForSci)

Centre for Forensic Science

University of Western Australia

This thesis is presented in partial fulfilment of the requirements for the

Master of Forensic Science

2013

DECLARATION

I declare that the research presented in this thesis, for the Master of Forensic Science at

the University of Western Australia, is my own work except where due

acknowledgment has been made in the text. The results of the work have not been

submitted for assessment, in part or full, within any other tertiary institutes.

_______________________

Windy Joanmawanti

iii

ABSTRACT

Backgrounds: The settlement of the ethnic groups in Indonesia involved several waves

of human migration and subsequent colonization. Explorers from the East and West,

who were motivated by trade, migrated towards the Malay and Indonesia archipelago.

During that period, philosophy, theology and knowledge were spread throughout the

region. This included the spread of Islam from its birthplace in Arabian Peninsula, and

had made one of the most dramatic social and cultural changes in Indonesia’s history.

As traders sailed to the Straits of Malacca, many Indonesian and Malaysian ports were

established on the route. As some traders settled in the archipelago, genetic sequences

were thought to be deposited. Consequently, relationships between Indonesia

populations with the Arab traders could therefore be reasonably expected.

In this study, the possible genetic relationship between Indonesia Javanese and Arab

Bedouin were examined. Specifically, polymorphism at HLA-A, HLA-B, and four Alu

Insertions (POALINs) in the MHC were studied. Alleles of 6 MHC class I loci were

analyzed in the Javanese, and their frequencies and distribution were compared to the

results obtained from the Arab Bedouin. The aim of the study was to elucidate the

genetic relationship (if any) between these two populations separated by distance but

linked through historical trading activities and a common faith.

Methods: The HLA-A and HLA-B alleles, assigned by Sequence Based Typing, were

obtained from DNA samples of 100 Javanese individuals.

The polymorphism of Alu insertions (POALINs), assigned by a PCR-based method

using specific primers, were also obtained from 100 Javanese DNA samples. Specific

primers were designed based on previous population studies using the Alu elements as

the genetic marker. The primers flank the region containing the absence (allele 1;

designated as *1) or presence (allele 2; designated as *2) of Alu insertion. In the

presence of Alu insertion, a PCR product is relatively larger than sequences without the

insertion. The relative size difference is observable by agarose gel electrophoresis.

The distribution of the HLA alleles and Alu polymorphism obtained from the Javanese

were compared with those from the Arab Bedouin. Comparison was also made between

the Javanese and populations studied elsewhere and published in the literature.

Populations previously studied included ethnic groups from Asia and Caucasians from

Australia. A phylogenetic tree was constructed to analyze the relationship between the

iv

Javanese, the Arab Bedouin and these other populations. The degree of linkage

between the HLA alleles and polymorphism of Alu insertion were also examined to

observe the linkage disequilibrium between the loci. Haplotype frequencies of six loci

in MHC class I were analyzed using the Arlequin software.

Results: From the study, HLA-A*24:07 (19.6%) and HLA-B*15:02 (18.5%) were

identified as the most frequent alleles for HLA-A and HLA-B in the Javanese samples,

respectively. Haplotype frequency of these two loci showed two of the most frequent

haplotypes comprised the serology group of A24-B15. Several possible novel alleles

were also observed from these two loci. However, further verification is required.

Of the four POALINs, the AluyHJ insertion (33%) was observed as the most frequent,

and the AluyHF insertion (2%) was observed as the least frequent. The haplotype

frequency of Alu insertions showed haplotype with no Alu insertion as the most

frequent.

The strong percentage of association (100%) was observed between HLA-A*24:07

allele with the AluyHJ insertion with tight and high value of linkage disequilibrium. In

contrast, no strong linkage was observed between HLA-B alleles and Alu insertions.

There were two most frequent haplotypes observed all six loci, whereas both of the

most frequent haplotype consist of HLA-A*24:07 allele and the insertion of AluyHJ.

The Alu data compiled from the study of Javanese group was compared to Arab

Bedouins and other populations in Asia and Australia. The phylogenetic tree was

constructed based on the POALINs showed that the Javanese and the Arab Bedouin

clustered differently. The Javanese showed greater similarity to ethnic groups in

Southeast and East Asia. The Arab Bedouin clustered with the Caucasian Australia.

The Javanese and several populations from Southeast and East Asia were grouped into a

cluster which formed a series of continuous cluster.

Limitations: The study only successfully obtained alleles from 50 Javanese DNA

samples for HLA-A and HLA-B loci. The study also lacks data from the AluyHG locus

in POALINs analysis. Efforts are currently underway to fill this gap.

Conclusions: Although, at face value, the Javanese adoption of the Moslem culture

brought by the Arab traders, this study examined the working hypothesis that is

suggested that the Arab Bedouin had left, at the very least, a genetic footprint during the

ancient trading era. However, no genetic relationship was observed between these two

v

distinct Moslem populations. The Javanese, however, has greater similarity to ethnic

groups in Southeast and East Asia. Although, the Javanese is the dominant population

on the island of Java, relationships between other Arab and Indonesian populations

cannot be excluded from the data collected in this study.

vi

ACKNOWLEDGMENTS

In the name of God, The Merciful and The Clement.

Praise be to God, Lords of the Worlds, and prayer and peace upon the Prophet

Muhammad, and upon his family and companions prayer and peace perpetually required

until the Day of Judgment.

The amazing journey has come to an end. I have realized that having the opportunity to

study in UWA is a blessing for any reasons. Therefore, first and foremost, I would like

to thank GOD for all the blessings within the two years of the excitement of study, and

also throughout my life.

I may not be able to name everyone separately and to thank for everything they did

during my study. However, I would like to take the opportunity to express my gratitude

to my supervisors, best colleagues, family and friends.

My sincere gratitude to Dr. Guan Tay to help me through the toughest challenge during

my short time of research, and also to give me the opportunity to meet Dr. Al-Safar in

UAE. I am grateful to Dr. Al Safar for her warmth welcome during my research at

Khalifa University, and not to forget the staffs and students who had helped me getting

along at the University. This study would not have been possible without the general

support from Prof. Ian Dadour and all the staff of Centre for Forensic Science.

I am also grateful to the Eijkman Institute for all the supports. In particular, Prof.

Herawati Sudoyo, Dr. Helena Suryadi and Dr. Safarina Malik, who have given me

generous encouragement and valuable advices. To all my laboratory colleagues at the

Eijkman Institute, thank you for all your support through a very warm friendship.

I would like to acknowledge the sources of financial support for this research: AusAID,

Eijkman Institute, UWA and Khalifa University. Without them, this study would not

have been possible.

My whole study in UWA would not have been achieved successfully without the

support of my beloved family and best friends. Thank you Papa, Mama, my brother

Putra and Mumun for always be there for me through every pray, phone call and Skype.

Thank you for all your understanding, patience and faith on my every step.

vii

I would also thank my faithful friend Eva for sharing my tears and laughter, for keeping

me up over the worst. You’re such a friend in need and that makes you a friend indeed.

Lastly, to my beloved group BIIOS, my housemates Meitha, Nurul and Siti, and also

Annisa and Ika, thank you for every moment we have shared together in Perth. I know I

always have all of you to count on when times are rough.

viii

ABBREVIATIONS

°C Degree Celsius

µl micro liter

A Adenine

APC Antigen -presenting Cell

AMRS Amplification Refractory Mutation System

BC Before century

C Cytosine

CFS Centre for Forensic Science

CSA Central-South Asia

DNA Deoxyribonucleic acid

dNTPs Deoxyribonucleotide triphosphates

EA East Asia

ER Endoplasmic reticulum

G Guanine

HLA Human Leukocyte Antigen

HREO Human Research Ethics Office

HW Hardy Weinberg

HWE Hardy Weinberg equilibrium

IDDM Insulin-dependent diabetes mellitus

IMGT Immunogenetics

LINEs Long Interspersed Nucleotide Elements

mM mili Molar

ix

MHC Major Histocompatibility Complex

min Minute

mtDNA Mitochondrial DNA

NE Northeastern

ng nano gram

PCR Polymerase Chain Reaction

PNG Papua New Guinea

POALINs Polymorphism of Alu Insertions

RNA Ribonucleic Acid

SBT Sequence Based Typing

SEA Southeast Asia

sec second

SINEs Short Interspersed Nucleotide Elements

SSO Sequence Specific Oligonucleotides

SSP Sequence Specific Priming

T Thymine

TCR T-cell Receptor

UWA University of Western Australia

x

LIST OF CONTENTS

ABSTRACT ……………………………………………………………………....….. iii

ACKNOWLEDGEMENTS ……………………………………………….……....… vi

ABBREVIATIONS …………………………………………………………......….. viii

LIST OF CONTENTS ……………………………………………………….........….. x

LIST OF FIGURES …………………………………………….…………......…… xiii

LIST OF TABLES ………………………………………………………...…...….… xv

CHAPTER 1: INTRODUCTION …….……………………………………....……... 1

1.1 BACKGROUND …………………………………………………………....…….. 1

1.2 LITERATURE REVIEW ………………………………………………....…...… 6

1.2.1 Major Histocompatibility Complex (MHC) ……………………….…………...... 6

1.2.1.1 Structure of human MHC ……………………………………….………....…... 6

1.2.1.1.1 MHC class I region ………………………………………………..…..…...… 7

1.2.1.1.2 MHC class II region ……………………….…………………..………….…. 9

1.2.1.2 The role of human MHC ………………………….…………….…………...… 9

1.2.1.3 Linkage disequilibrium ……………………………………………....…..…… 12

1.2.1.4 Nomenclature of MHC ……………………………………………....…..…… 12

1.2.1.5 MHC typing …………………………………………………………....….….. 14

1.2.1.5.1 Serological typing of MHC …………………………………………....…… 14

1.2.1.5.2 Molecular typing of MHC …………………………………………….......... 15

1.2.1.5.3 Typing ambiguities ……………………………………………………......... 17

1.2.1.6 Alu repetitive elements …………………………………………………....….. 17

1.2.1.6.1 The role of Alu elements ………………………………………….……....… 19

xi

1.2.1.6.2 Polymorphism of Alu insertions (POALINs) in population studies ……....... 20

1.2.2 The Javanese and the Arab Bedouin populations …………………………...… 21

1.2.2.1 The Javanese and Indonesia populations …………………………………....... 21

1.2.2.2 The Arab Bedouin and the spread of Islam ……………………………....….. 26

1.2.3 HLA and Alu elements in forensic science ………………………………....…... 28

1.3 RESEARCH OBJECTIVE ……………………………………………….......… 29

CHAPTER 2: MATERIALS AND METHODS ……………………………......…. 30

2.1 ETHICAL STATEMENT …………………………………………………....…. 30

2.2 SUBJECTS ……………………………………………………………….....…… 30

2.3 HLA-A AND HLA-B TYPING …………………………………………....….... 30

2.3.1 PCR-sequence based typing of HLA-A and HLA-B loci ……………….....…… 30

2.3.2 DNA sequencing reaction ……………………………………………….....…… 31

2.3.3 Allele frequency and haplotype analyses ……………………………....………. 33

2.4 POLYMORPHISM OF Alu INSERTIONS (POALINs) TYPING …......……. 33

2.4.1 POALINs PCR assay ……………………………………………………....…… 33

2.4.2 Genetic analysis of the POALINs …………………………………………....… 35

2.4.3 Phylogenetic analysis of POALINs ..…………….……………………..….....… 35

2.5 ANALYSIS OF SIX POINT HAPLOTYPES ..................................................... 36

CHAPTER 3: RESULTS …………………………………………………….......…. 37

3.1 HLA TYPING …………………………………………………………....……… 37

3.1.1 HLA-A typing ………………………………………………………….......…… 37

3.1.2 HLA-B typing …………………………………………………………....….….. 42

3.1.3 Haplotypes of HLA-A and HLA-B in the Javanese population ..……….......….. 46

3.2 POALINs IN MHC CLASS I OF THE JAVANESE ……………………....…. 47

xii

3.2.1 The association between four Alu insertions with HLA-A alleles …...……....… 50

3.2.2 The association between four Alu insertions with HLA-B alleles ………....….... 54

3.3 SIX POINTS HAPLOTYPE AND PHYLOGENETIC TREE …………....….. 54

3.3.1 Six points haplotype of MHC class I of the Javanese …………………..…....… 54

3.3.2 Phylogenetic tree of POALINs ……………………………………….…....…… 54

CHAPTER 4: DISCUSSION ………………………………………………....…….. 61

4.1 HLA TYPING ……………………………………………………………....…… 61

4.2 DISTRIBUTION OF POALINs IN THE JAVANESE ………………..…....… 64

4.3 SIX POINTS HAPLOTYPES AND PHYLOGENETIC TREE

OF Alu INSERTIONS ……………..……………………………………….…… 68

BIBLIOGRAPHY ………………………....………………………………………… 72

APPENDICES .............................................................................................................. 83

xiii

LIST OF FIGURES

CHAPTER 1

Figure 1. The migration pattern of early humans out of the Africa through southern

and northern routes between 60,000 to 40,000 years ago (reproduced from

Cavalli-Sforza and Feldman (2003)) ............................................................... 1

Figure 2. Ancient trade routes which crossed Indonesia archipelago (reproduced

from Worall et al. (2009)) .....…...….…………….……………………...….. 2

Figure 3. Location of six loci in human MHC class I region used in the study

(adapted from Dunn, Inoko & Kulski (2003)) .…………...…..………...…... 4

Figure 4. The structure of HLA class I and class II molecules (adapted

from Throsby (1999)) ……...……………………………….....….…………. 8

Figure 5. The structure of Alu element (adapted from Batzer & Deininger (2002)) .... 18

Figure 6. The location of five Alu elements in the human MHC (adapted from

Dunn, Inoko & Kulski (2003)) ...……………...………………….……..…. 21

Figure 7. Three different regions of the southeast archipelago in the Pleistocene era

(adapted from Bellwood (2007) and Voris (2000)) ...................................... 22

Figure 8. Language family tree of Austronesian

(Lewis, Simon & Fennig 2013; Tyron 1995) ............................................... 23

Figure 9. The maps of Indonesia

(reproduced from Lewis, Simon and Fennig (2013)) ................................... 24

Figure 10. The percentage of population in Indonesia based on the year 2000

population census of Badan Pusat Statistik

(reproduced from Suryadinata, Arifin and Ananta (2003)) .......................... 25

Figure 11. The map of Arab regions (adapted from Teebi (2010)) .............................. 26

Figure 12. Language family tree of Afro-Asiatic (adapted from Lewis (2009)) ........... 27

xiv

CHAPTER 3

Figure 13. Poor DNA electropherograms which were caused by (a) dye-blobs, (b) and

(c) sequencing reaction failures ................................................................... 37

Figure 14. Percentage of HLA-A allele frequencies in the Javanese population ….…. 41

Figure 15. Percentage of HLA-B allele frequencies in the Javanese population …..… 45

Figure 16. Gel visualization of four Alu insertions in MHC class I region of

Javanese samples ……...……………………….………………...….......... 48

Figure 17. Genetic distance values of ten populations .................................................. 59

Figure 18. Phylogenetic tree of ten populations using four Alu insertions in

MHC class I region ...................................................................................... 60

xv

LIST OF TABLES

CHAPTER 2

Table 1. Primers used in HLA-A and HLA-B typing of Javanese DNA samples ....... 32

Table 2. POALINs primers and annealing temperature …………....………………... 34

Table 3. Expected PCR products of Alu elements ………………...….……………… 36

CHAPTER 3

Table 4. HLA-A allele assignments of Javanese samples …………....……………… 39

Table 5. The allele frequencies show a preponderance of HLA-A24 alleles ….......… 40

Table 6. HLA-B allele assignments of Javanese DNA samples collected at the

Eijkman Institute ………………………………………………………....…. 43

Table 7. The HLA-B15 alleles occur frequently in the Javanese samples ………....... 44

Table 8. The haplotype frequencies of HLA-A and HLA-B …………………....…… 46

Table 9. The observed genotypes, allele frequencies, HWE significance and

heterozigosity of four Alu insertions in the Javanese population …….....…. 49

Table 10. The haplotype frequencies of four POALINs in MHC class I region …...… 50

Table 11. The associations between four Alu insertions with HLA-A alleles of

Javanese samples …………………………………………………......……. 52

Table 12. The associations between four Alu insertions with HLA-B alleles of

Javanese samples ……………………………………………………....…... 55

Table 13. Haplotypes of six loci in MHC class I region of the Javanese

population ……………...............…………………………………...…....… 57

Table 14. Allele frequencies of Alu insertions in Javanese (Indonesia) and nine other

populations ………………………………………………..……..…....……. 58

xvi

CHAPTER 4

Table 15. The genotypes, allele frequencies and HW equilibrium of Arab

Bedouin population ………………………....………….………………..… 66

1

CHAPTER 1: INTRODUCTION

1.1 BACKGROUND

There have been several possible hypotheses proposed regarding early human migration

out of the Africa. The first human expansion had occurred in the Pleistocene era

approximately 60,000 to 40,000 B.C. The expansion followed southern route to the

south and southeast Asia (Figure 1) (Cavalli-Sforza & Feldman 2003; Bellwood, Fox &

Tyron 1995). The second human migration had occurred in the Holocene era

approximately 4,000 to 3,500 B.C. The Austronesian speaking people migrated from

the north through Southern China, Taiwan and Philippines to southeast Asia and

Oceania (Bellwood, Fox & Tyron 1995; Oppenheimer & Richards 2001).

Figure 1. The migration pattern of early humans out of the Africa through southern and

northern routes between 60,000 to 40,000 years ago (reproduced from Cavalli-Sforza

and Feldman (2003)).

In relatively recent history at approximately 200 B.C, human migration was motivated

by commerce. One of the ancient trade routes was the Silk Road, which was established

to connect the East and the West continent (Comas et al. 1998). During trading

activities, cultural information was freely exchanged, including religious texts and

philosophical teachings. The spread of Islam from its birthplace in the Middle East is

evident in the South East Asia, particularly in countries such as Indonesia and Malaysia.

The trade route from the Middle East to South East Asia was one of the early trade

2

routes which crossed the Indonesian archipelago (Figure 2), particularly the Sumatera

and Java Islands.

Figure 2. Early trade routes which crossed Indonesia archipelago (reproduced from

Worrall et al. (2009)). The Silk Road was one of the early trade routes which connected

the East and the West.

Through human migrations, genetic materials are taken along and passed on to the

descendent in the new destinations. Therefore, it would be appropriate to analyze

human migration patterns using genetic markers. Furthermore, genetic analyses can

also be used to determine how recent populations share common ancestor as well as the

extent and timing of their contacts (Owens & King 1999; Cox 2008). There have been

several studies describing gene flow and human migrations using genetic markers such

as mitochondrial DNA (mtDNA), Y chromosome and major histocompatibility complex

(MHC) to trace ancestral relationship (Hagelberg et al. 1999; Karafet et al. 2010; Mona

et al. 2009).

3

The human MHC is located in the short arm of chromosome 6 and considered as one of

the most polymorphic region in the human genome. The complex consists of Class I,

Class II and Class II region (Beck & Trowsdale 2000; Little, Marsh & Madrigal 2007).

In human, the complex is best known for the Human Leukocyte Antigen (HLA). The

HLA has been used as a marker to study the population structure (Sanchez-Mazas et al.

2005; Solberg et al. 2008). There have been previous population studies of several loci

of the HLA cluster. In China (Han and Uyghur populations), for instance, the HLA-A,

HLA-B and HLA-DRB1 have been analyzed (Shen et al. 2010a; Shen et al. 2008).

HLA class I and II polymorphism have also been studied for Northeast Thais ethnic,

Thailand (Romphruk et al. 2010). In Indonesia, HLA polymorphism of western

Javanese (Sundanese) had been studied with the HLA-A, HLA-B and HLA-DRB1 as

the focus of the study (Yuliwulandari et al. 2008).

The HLA, moreover, has been mainly used in the study of diseases and human organ

transplantation. Prior to human organ transplantation, a matching process between the

donors and the recipients has to be performed. The process is necessary due to the

major role of HLA in the immune systems. In relation to the immune systems, the

association between HLA and particular diseases has also been observed in several

studies (Little, Marsh & Madrigal 2007). Moreover, in previous studies of HLA, the

linkages between HLA, disease, and population have been observed (Inoko 2006; Man

et al. 2007; McCormack et al. 2011).

Six loci in human MHC Class I region (HLA-A, HLA-B, AluyMICB, AluyTF, AluyHF

and AluyHJ in Figure 3) were analyzed in the present study to determine the

polymorphism of MHC class I region of the Indonesian Central-Javanese. The Javanese

occupies the Java Island which is considered as the most populous island in Indonesia

archipelago (Suryadinata, Arifin & Ananta 2003). Indonesia, across the continents of

Asia and Australia, consists of 17,500 islands with more than 300 ethnic groups who

speak more than 700 languages (Lewis 2009; Karafet et al. 2010). However, there have

been very few studies exploring the allelic distribution of HLA in Indonesia

populations. Therefore, the present study was proposed to contribute in the HLA allelic

distribution in Indonesia.

4

Figure 3. Location of six loci in human MHC class I region used in the study (adapted from Dunn, Inoko and Kulski

(2003)). The six loci analyzed in the study included HLA-A, HLA-B, AluyMICB, AluyTF, AluyHJ and AluyHF.

5

A previous study in the Sundanese, conducted by Yuliwulandari et al. (2008), observed

three loci in MHC using Sequence Specific Oligonucleotides (SSO) method. The

method requires specific primer which probed by oligonucleotides. Thus, novel allele

cannot be determined using the SSO method (Little, Marsh & Madrigal 2007). The

present study, however, was conducted using the Sequence Based Typing (SBT) which

have a high resolution level and can determine novel alleles (Little, Marsh & Madrigal

2007).

Another four Alu markers in MHC Class I region were also analyzed in the present

study. These four Alu markers have been used in several population studies previously.

For example, the study conducted by Dunn and colleagues (Dunn et al. 2005; Dunn,

Inoko & Kulski 2003; Kulski et al. 2002a; Dunn et al. 2002) used Aluy markers in MHC

class I region of Northern Thais, Japanese and Caucasian Australia populations.

Previous research using four Alu markers (POALINs) was also conducted by Dr Al-

Safar at the Centre for Forensic Science, UWA. The markers were used to study the

Arab Bedouin population.

The people described as Arab Bedouin are those who speak Arabic strictly. The word

Arab in Hebrew literally means desert people who live in waterless and treeless regions

(Salibi 1980). There are several views of the origin and background of the Arabs.

However, it is thought that the Arabian Peninsula is the origin of the Arab people

(Hunter-Zinck et al. 2010). A specific subpopulation, the Bedouin clans of that region,

is thought to have lived as desert nomads since before the birth of Arabian Babylonia,

and is believed to be the forefathers of the contemporary Arab.

Based on genetic studies of the human Y chromosome, the Indian and Arab influences,

which were distributed through commercial activities in the historical era, were

restricted to western Indonesia such as Java and Bali (Karafet et al. 2010). Therefore,

the study was also performed to determine whether similarities exist between the

Javanese and the Arab Bedouin by using the four Alu markers.

6

1.2 LITERATURE REVIEW

1.2.1 Major Histocompatibility Complex (MHC)

The Major Histocompatibility Complex (MHC) is one of the most complex regions in

the human genome with an overall size of approximately 3.5 million base pairs. It is

located in the short arm of chromosome 6 (6p21.3), and contains a myriad of genes,

some of which exhibit extreme levels of polymorphism (Beck & Trowsdale 2000). The

MHC consists of more than 220 genes, of which at least 10 per cent have functions

related to the immune system (Milner, Campbell & Trowsdale 2000). In fact, the MHC

genes are arguably the most polymorphic genes in the genome (Murphy et al. 2012).

The existence of human MHC polymorphism has been explained based on diverse

theories. Generally, alleles which acquire disadvantageous mutations have a high

possibility of being deleted as a result of negative selection. Therefore, regarding

evolutionary stability, the MHC class I and class II regions encode different types of

lineages. The HLA-A, -DR and -DQ encode lineages which are more or less conserved,

while other classical loci (HLA-B, and -DP) are subjected to frequent change (Bontrop

2000). In human, it is also known as Human Leukocyte Antigen (HLA) gene cluster. It

was first discovered through antigenic differences between white blood cells (leukocyte)

from different individuals (Kulski et al. 2002b; Murphy et al. 2012).

1.2.1.1 Structure of human MHC

The human MHC is divided into three sub-regions which are Class I, Class II and Class

III or the central MHC region. Within these 3.5 million base pairs, the class I and class

II regions each spread over approximately one third of the length. Therefore, the

remaining region is the central region which contains loci responsible for various

different functions such as intracellular peptide processing, complement, hormones and

other development characteristics. The central region differs from Class I and II regions

of the MHC complex due to its components being either related to the functions of

MHC molecules or under similar control mechanisms to the HLA molecules

(Shankarkumar 2004; Little, Marsh & Madrigal 2007). Due to the extreme level of

polymorphism in human MHC, individual MHC alleles can differ by 20 amino acids

from one to another, thus each allele becomes quite distinct. Most of the differences are

located in the peptide-binding cleft as the polymorphic residues in the peptide-binding

cleft determine the variety of MHC molecules (Eren & Travers 2000).

7

Structural information of MHC molecules is important to evolutionary studies due to

different efficiency of the molecules in stimulating immune response to a particular

peptide. Thus, the selective pressures can be evaluated by performing a comparison

between MHC peptide-binding properties and peptide sequences in pathogens to which

a population is exposed. Moreover, structural information provides predictions on how

selection acts on a functionally distinct region of a molecule by interpreting

polymorphism occurrances at each region of the molecule (Meyer & Thomson 2001).

1.2.1.1.1 MHC class I region

Class I region of MHC in human genome comprises of several HLA loci which are

HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G and HLA-J. Three of these loci

(HLA-A, HLA-B and HLA-C) are classified as classical molecules due to its high

polymorphism and sequence diversity. It is expressed at relatively high level in certain

cell types and it presents antigen to T lymphocytes (Kaufman 1996). On the other hand,

the non-classical loci including HLA-E, HLA-F, HLA-G and HLA-J, are generally

much less polymorphic than the classical loci (Bontrop 2000; Little, Marsh & Madrigal

2007).

The human MHC class I molecules are present in every nucleated cell and synthesized

in the endoplasmic reticulum (ER). The non-classical loci, however, show restricted

tissue distribution. HLA-E, for instance, is expressed in a number of tissues at low

surface levels and it is retained at the endoplasmic reticulum (ER) unless it receives a

peptide from another class I molecule. The trophoblast is known to express various

forms of HLA-G molecules but lack the expression of HLA-A and HLA-B (Milner,

Campbell & Trowsdale 2000; Bontrop 2000). The structure of human MHC class I

molecules reveals a cleft on its outermost surface which bounds a peptide antigen and

known as peptide binding cleft (Male et al. 2006; Thorsby 1999).

The human MHC or HLA class I molecules are heterodimers which are associated non-

covalently. It consists of α heavy chain and β (β2-microglobulin) polypeptide chain.

The α chain, however, forms a peptide binding cleft. Thus the wall consists of two α-

helices, whilst the β chain acts as the floor to one of the α-helices and plays an

important role in the structural supports of the heavy α chain (Figure 4).

8

Figure 4. The structure of HLA class I and class II molecules (adapted from Little,

Marsh and Madrigal (2007)). The peptide binding groove of class I molecules comprise

of α chain, while class II molecules comprise of α chain and β chain.

The α polypeptides in class I molecules are encoded by a gene in HLA complex. The β

chain, however, is encoded by a gene in chromosome 15. The peptide binding cleft of

class I molecules has closed ends. Therefore, it binds short peptides with approximately

8 to 9 amino acids long (Thorsby 2009; Meyer & Thomson 2001). Different amino acid

sequence in the cleft provides different structure of the peptide binding cleft as well as

different antigen, thus provides polymorphism and sequence diversity. As the

polymorphism of HLA is considered population specific, hence discrepancy of HLA

allele frequencies can also be expected (Male et al. 2006). By October 2012, there were

2013 alleles of HLA-A and 2605 alleles of HLA-B in the IMGT (IMmunoGeneTics)

database (http://www.ebi.ac.uk/imgt/hla/). The HLA-B, moreover, is considered as the

most polymorphic class I locus in the human MHC (Pozzi, Longo & Ferrara 1999).

1.2.1.1.2 MHC class II region

The cell surface polypeptide antigens of HLA-DP, HLA-DQ and HLA-DR loci are

contained within the MHC class II region (Male et al. 2006). HLA class II molecules

are also classical molecules since it has high polymorphism and sequence diversity,

9

expressed at relatively high level in certain cell types, and presents antigen to T

lymphocytes (Kaufman 1996). The HLA class II region spans approximately 800 kb

and encodes heterodimers molecules of α and β heavy chains (Milner, Campbell &

Trowsdale 2000). In class II region, however, both chains contribute to the peptide

binding cleft which has more open ends than the class I molecules. Hence, it binds

longer amino acids with approximately 10 to 25 amino acids (Thorsby 2009; Male et al.

2006; Meyer & Thomson 2001). Due to heterodimers heavy chains, which contribute to

the peptide binding cleft, the possibility of polymorphism in class II region are slightly

bigger than class I region. The combination of α and β heavy chains are more than the

combination of just α chain in class I region. The α-and β-chain are all arranged as

matched pairs such as DPA and DPB, DRA and DRB, and DQA and DQB (Trowsdale

1996).

Similar to the HLA class I, the HLA class II molecules are also synthesized in

endoplasmic reticulum (ER). In humans, the MHC class II molecules are expressed on

the surfaces of antigen-presenting cell (APC), where they may be recognized by CD+ T

cells (Thorsby 1999; Milner, Campbell & Trowsdale 2000).

1.2.1.2 The role of human MHC

Human MHC as a genomic region contains a group of closely linked genes which are

functionally involved in the immune systems. There are, essentially, four different

categories of functions of the MHC genes. The first is antigen processing and

presentation encoded by the HLA class I and HLA class II genes, while the second one

is innate immunity, inflammation and regulation of immunity regulated by MHC class

III genes. The third function is intercellular interaction via MHC receptors and ligands,

and the fourth one is other functions which is unrelated to immunity (Kulski et al.

2002b). There are genes in MHC which function is unrelated to immunity, for

examples the olfactory receptor genes and Zinc-finger genes. The olfactory receptor

gene cluster provides the basis of odor perception which is essential as a survival tool in

behavioral process including reproduction (Ehlers et al. 2000). The products of Zinc-

finger genes can function as enzymes, storage proteins, replication proteins and

transcription factors (Horton et al. 2004). Specific immune recognition is the first step

of any acquired immune response, and immune response is the result of specific

surveillance which is conducted continuously in human cells and tissues. Specific

10

extracellular surveillance is carried out by the B cells, while specific intracellular

surveillance is taken care by the T cells (Thorsby 1999; Male et al. 2006).

Many MHC genes have a significant function in the immune system. It specifically

facilitates infected cells to bind viral peptides (short fragments of viral proteins) thus the

viral peptides can be recognized by T cells. However, the immune response is to be

MHC restricted in the sense that T cells recognition of infected cells requires signal

combination from both MHC molecules and pathogen peptides. HLA class I molecules

(HLA-A, -B and -C), which are normally found in all nucleated cells, bind the peptides

in their peptide-binding groove. Binding of the peptides to class I molecules creates

stability to the class I molecules, which then travel with the bond peptide to the cell

membrane where they may be recognized by the T Cell Receptor (TCR) of CD8+ T

cells (Thorsby 1999; Meyer & Thomson 2001).

T cells survey surfaces of cells in our bodies for any signs signaling pathogens, or any

disruptions of the cell’s normal function. T cells will be triggered when such signals

appear (Meyer & Thomson 2001). In other words, a T cell will only respond to a

complex of viral peptides-MHC molecule as it is recognized as foreign. As a response

to pathogen infection, the infected cells may be lysed thus pathogen replication is

halted.

Peptides of extracellular origin are bound by the HLA class II molecules. The HLA

class II molecules (HLA-DQ, -DR and -DP) have a more restricted distribution in

human tissue. The molecules, which are usually called antigen-presenting cells (APC),

are normally found on monocytes, dendritic cells, macrophages and B cells (Male et al.

2006; Meyer & Thomson 2001). Extracellular peptides with 10 to 25 amino acids long

bind to HLA class II binding groove. The complex then travels to cell membrane of the

APC, where they may be recognized by TCR of CD4+ T cells. If the complex is

recognized as a foreign molecule, then the production of antibodies as well as

stimulation of macrophages are induced through the secretion of cytokines (Meyer &

Thomson 2001; Bhoosreddy & Wadher 2010).

The human MHC or HLA molecules could bind any kind of peptides which might fit to

its binding groove including foreign proteins of bacteria or viruses, or self-proteins. In

non-infected cells, self-proteins peptides are found in the binding groove and create the

undesirable situation of autoimmune. The autoimmune response can occur where there

are T cells around that are able to recognize and be activated by the self-proteins

11

peptides. In order to avoid autoimmune reactivity, the individual should establish

mechanisms of self-tolerance to determine self-proteins and foreign proteins (Thorsby

1999; Male et al. 2006). Moreover, T cells that are able to recognize self-proteins are

normally deleted during maturation of thymic to avoid autoimmune responses. In spite

of that T cells can only recognize peptide fragments which bind to the HLA molecules,

thus the set of HLA molecules carried by an individual should extend to several

different HLA molecules (Thorsby 1999).

The extensive polymorphisms of human MHC have occurred due to its immune system

related function. As individuals have been exposed to different peptides of each

pathogen, hence different MHC molecule combinations can appear. Therefore, it is

unlikely for individuals in a population to be equally susceptible to any given pathogen

(Eren & Travers 2000). In relation to human organ transplantation, the function of

human MHC molecules has been considered as a barrier between the donor and the

recipient. Currently, interpretation of tissue rejection is based on the knowledge that

TCR interacts with the complex formed by human MHC molecules and peptides, which

together determine the specificity of interaction with the T cell. Therefore, in the case

that the transplanted tissue carries HLA molecules which against the HLA molecules of

the host, then the HLA-peptides complexes will be recognized as foreign and the

response will be a rejection of the tissue (Meyer & Thomson 2001; Male et al. 2006).

The ability of the T cells to discriminate self or foreign peptides (allorecognition) in

organ transplantations can be indirect or direct. Indirect allorecognition is similar to T

cells recognition of any foreign molecules. It occurs when proteins from cells of the

donor is taken up by Antigen Presenting Cell (APC), and peptides from these proteins

are presented to recipient CD4+ T cells by HLA class II molecules, and may be

recognized as foreign. Other proteins from the donor may also enter the cytosol of the

recipient and be presented to recipient CD8+ T cells by HLA class I molecules. The

HLA molecules of the donor are a main source of foreign proteins. Hence, when

differences of HLA molecules between the donor and the recipient occur, more T cells

will recognize the donor as foreign (Thorsby 1999; Dazzi 2010). Direct allorecognition

occurs occasionally where foreign peptides in the donor’s cell membrane are directly

recognized by T cells CD4+ or CD8

+ (Thorsby 1999; Male et al. 2006; Dazzi 2010).

Therefore, the HLA typing conducted prior to organ transplantation is essential. The

HLA typing determines specific alleles of individuals before human organ

transplantations can minimize the allorejection.

12

1.2.1.3 Linkage disequilibrium

Based on basic Mendelian genetics (Law of Independent Segregation), the allele

frequencies at one locus do not influence the allele frequencies at another locus. Despite

this, there are some examples where HLA alleles at different loci occur together more

frequently than would be expected by chance. This phenomenon is known as linkage

disequilibrium (Bontrop 2000; Patterson 2000). An example of linkage disequilibrium

was observed in Caucasian population where HLA-A1 and HLA-B8 occurred together

in a frequency of 9.8%, while the expected frequency of both alleles occurring together

was 4.8% (Milner, Campbell & Trowsdale 2000; Shankarkumar 2004).

Due to linkage disequilibrium, a certain combination of HLA molecules will be

inherited together more frequently than would normally occur. Therefore, a certain set

of alleles may be advantageous with regard to immune system and positive selective

advantage (Shankarkumar 2004). Many pathogens are complex organisms which

experience both intracellular and extracellular life cycles. Therefore, linkages between

certain HLA class I alleles and HLA class II alleles provide protective responses to

particular pathogens, and then the alleles may be subjected to positive selection. Since

pathogenic pressures differ in time and fluctuate among populations, therefore, linkage

disequilibrium may also vary among human ethnic populations (Bontrop 2000).

1.2.1.4 Nomenclature of MHC

A nomenclature committee is responsible for the designation of the HLA loci, antigens

and alleles since 1967. The committee, which is comprised of geneticists,

immunologists and specialists in histocompatibility testing, then established notation for

HLA loci in 1975 (Leffell 2002). Further, a four-digit of notation has been used to

distinguish HLA alleles conventionally since 1987 Nomenclature Report. Since then

additional digits have been added and currently an allele can be assigned by four, six or

eight digits depending on its sequence (Leffell 2002; Marsh et al. 2010). The notation is

an alphanumeric system which designates the locus, the related specific immunology

and the allele. The locus is notated by the letter for example A is the notation for HLA-

A locus or B for HLA-B locus, and followed by an asterisk. The first two digits

following the asterisk describe the allele family, which often corresponds to the

serological antigen carried by the allotype. The third and the fourth digits are assigned

in the order in which the sequence has been determined (eg. A*0206), while the fifth

13

and the sixth digits are assigned to distinguish alleles which differ only by synonymous

nucleotide substitutions within the coding region (Leffell 2002; Little, Marsh &

Madrigal 2007; Marsh et al. 2010).

There is also the seventh digit number designating alleles differing in sequence outside

the coding region (or the intron). However, alleles which differ in the first four digits

must differ in one or more nucleotide substitutions that alter the amino-acid sequence of

the encoded protein. Due to the increasing number of HLA alleles, by April 2010, an

update of HLA nomenclature was officially introduced. It has been decided to use

colon (:) into the allele names as delimiters of the separate fields. Therefore, for

example, the allele A*3301 becomes A*33:01. The HLA typing technologies used

today may not achieve the level of resolution to allow a single HLA allele to be

unambiguously assigned. Thus, it is often only possible to determine the presence of a

number of closely related alleles which are referred to as an ambiguous of alleles

(Leffell 2002; Marsh et al. 2010).

The increasing discovery of new alleles forced the histocompatibility specialists to be

well versed in the intricacies of HLA nomenclature. The international

ImMunoGeneTics database (IMGT)/HLA Database is the official repository for HLA

sequences, and updated monthly. Therefore, the database is on-line and permits

submission of new and confirmatory HLA sequences directly. In relation to patients,

continual growth of alleles in the database requires their typing and/or allele

assignments to be updated periodically (Leffell 2002).

Many of the recognized HLA alleles have only been assigned by molecular typing.

Their antigenicity, however, is not always defined. Currently, 64% to 70% of serologic

equivalents have been defined of known alleles at major HLA loci. HLA alleles are

generally assigned to a group based on overall sequence homology. However, it is

possible for an allele to have considerable homology with other members of a group,

but to also have a sequence encoding a different antigen motif (Leffell 2002).

1.2.1.5 MHC typing

MHC was discovered more than 50 years ago, and typing for MHC gene has been

applied to transplantation matching since the late 1960s (Thorsby 2009; Leffell 2002).

The diversity and the degree of polymorphism within the MHC genes were not fully

appreciated until sequencing of the MHC genome was performed. The first tissue

14

typing, however, was typed by serology for the class I molecules of HLA-A, HLA-B

and HLA-C and was first applied to bone marrow and renal transplantation (Leffell

2002). The HLA-A, HLA-B and HLA-DRB1 loci are now routinely typed for organ

transplantation and additional loci may need to be typed as well. For instance, when a

patient is presented as having antibodies against HLA-C or - DQ or -DP then typing the

appropriate locus needs to be performed (Parham & Ohta 1996; Dunn 2011).

1.2.1.5.1 Serological typing of MHC

The term serology applies to the use of anti-sera in the approach. Therefore, serological

typing of MHC is based on the reaction between specific antibodies with specific HLA

antigens. The serological typing, historically, has been used to type HLA for a long

time. However, this approach does not distinguish between all alleles and some other

problems are also encountered in performing serotyping such as cross-reactivity and

non-availability of certain antibodies (Parham & Ohta 1996; Dyer, Martin & Stanford

2000). Cross reactivity is a condition where one antibody reacts with several antigens,

and it commonly occurs as the HLA molecules share the same amino acid sequence for

most of their molecular structure (Shankarkumar 2004).

In some population studies, the data sets obtained by the serological method have

systematically underestimated population differences (Parham & Ohta 1996).

Therefore, HLA typing based on molecular and nucleotide sequences currently replaces

serology. However, a role for HLA typing using a serological approach may remain

important for the investigation of the cell-surface expression of HLA alleles as well as

variant defined at the DNA level. The serological approach may also be used to

elucidate whether the expression of an HLA antigen and also the presence of the allele

is associated with a particular disease and also as an educational tool. Therefore, to

maintain the capability to perform serological typing in HLA typing laboratories is still

necessary (Dyer, Martin & Stanford 2000; Dunn 2011).

1.2.1.5.2 Molecular typing of MHC

Issues of HLA typing using serological methods have led to the introduction of the

molecular approach. Generally, the approach is considered more direct to analyze

sequence polymorphism of HLA molecules. Moreover, the DNA-based analysis allows

15

a more accurate and precise method of typing than serology but the method also uses

synthetic and standardized reagents (Erlich 2000).

In addition, molecular HLA typing permits extensive types of samples to obtain the

DNA such as blood, hairs or buccal swabs. In contrast to the molecular typing,

serological typing requires viability of cells or the expression of the relevant antigen on

the cell surface. Therefore, the molecular typing has made possible valuable population

genetic studies, disease association and clinical transplantation studies as well as

forensic applications (Erlich 2000).

There is a variety of molecular approaches in MHC typing. Currently, three most

popular methods are PCR-SSP (Sequence Specific Priming), PCR-SSO (Sequence

Specific Oligonucleotides) and SBT (Sequence Based Typing). Since SSP is a PCR-

based approach, it requires a set of different primers which are specific for different

HLA molecules. The method is also known variously as allele-specific amplification

(ASA) and the amplification refractory mutation system (ARMS) (Erlich 2000; Apple

& Erlich 1996; Erlich 2012).

In PCR-SSP typing, specific primer pairs are designed for each polymorphic sequence

motif, and the presence of targeted sequence in a sample is ascertained as a positive

PCR which is identified as a band on the electrophoresis gel. In contrast, a negative

PCR shows no band on the electrophoresis gel thus the sample is assumed to lack one or

both specific motifs. Currently, however, detection methods which are not based on

visualization using gel electrophoresis have been developed (Erlich 2000; Apple &

Erlich 1996). PCR-SSP allows an individual sample to be analyzed in one step, rather

than multiple hybridizations required for SSO procedures. Moreover, the method is

also relatively fast and informative for small numbers of samples. However, the method

is not sensitive enough to perform high-resolution HLA typing as the method cannot

discriminate several combinations of heterozygous alleles. Therefore, it requires

separate PCR (nested PCR) to achieve intermediate or high level of HLA typing

(Moribe, Kaneshige & Inoko 1997; Erlich 2000; Krausa & Browning 1996). However,

there are many SSP commercial kits available today performing low-resolution and

high-resolution HLA typing (Dunn 2011). As the method requires specifically designed

primers to determine the polymorphism, new HLA alleles are unidentified.

Another molecular approach for HLA typing is PCR-SSO. The method is also a PCR-

based which requires specifically designed primers to detect HLA polymorphism.

16

However, the method is more amenable to high-throughput HLA typing than PCR-SSP.

The primers are probed with oligonucleotides which have specificity for particular

polymorphism. Thus, the typing relies on hybridization reactivity between particular

sequence motifs with specific labeled oligonucleotides (Krausa & Browning 1996;

Dunn 2011).

The complexity and full extent of HLA polymorphism has only been revealed by DNA

Sequence Based Typing (SBT). The method is considered to have a high level of

resolution, while PCR-SSP and PCR-SSO are limited within the context of testing for

known sequence polymorphism. Therefore, polymorphism which cannot be detected

using PCR-SSP or PCR-SSO can be determined by the SBT method, even though the

possibility of sequencing errors can occur (Krausa & Browning 1996). The SBT

method can be lengthy and labor intensive and thus unsuitable for routine tissue typing.

However, as the facilities for automated sequencing have improved, as has the

availability of commercial kits, SBT may become a routine method for HLA typing

(Krausa & Browning 1996; Erlich 2000; Dunn 2011).

New sequencing techniques which have been available recently will allow the

sequencing of HLA in a faster, more automated and more cost effective way. Namely a

next-generation sequencing system such as Ion Torrent has also been developed to

achieve longer sequence reads (Erlich 2012; Dunn 2011). The next-generation

sequencing technologies include steps to obtain clonal, or single-molecule, sequencing.

The clonal PCR of single DNA fragments is sequenced using fluorescence or

chemiluminescence (Dunn 2011).

1.2.1.5.3 Typing ambiguities

Typing result of HLA, whether using molecular or serological methods, has the

potential to produce ambiguous data. Concisely, ambiguity occurs when the HLA

typing data consistent with more than one pair of alleles. Ambiguity is generally

derived from extreme complexity and diversity of HLA allele which poses a major

challenge to generate and interpret the MHC typing data. Allele ambiguities can result

from polymorphism outside the region that is being typed, while genotype ambiguities

can result from the inability to set phase for linked polymorphism (Erlich 2012; Erlich

2000).

17

As more PCR-based HLA typing is being performed in more populations, new alleles

are being identified leading to the increase of the number of alleles (Erlich 2000). The

increase of allele sequence database has an effect on the increase of ambiguity

problems. Hence, the growing list of HLA ambiguities has forced additional testing a

necessity, especially in clinical cases. The inclusion of additional exons and/or addition

of alternative typing methods, therefore, have become routine for most HLA

laboratories. The extensive allelic diversity of HLA and the ambiguities that attach to

it, made and continues to make high-resolution HLA DNA typing very challenging

(Erlich 2012; Erlich 2000).

1.2.1.6 Alu repetitive elements

These repetitive elements can be described as various sequences of DNA which are

present in the genome with multiple copies. Generally, the elements are classified into

two different types, the elements which are arrayed in pairs (that is microsatellites) and

the elements that are interspersed within the genome (Batzer & Deininger 2002).

Furthermore, the interspersed elements can be subdivided based on the size of the

element, such as the long interspersed nucleotide elements (LINEs) and the short

interspersed nucleotide elements (SINEs). The Alu repetitive element belongs to SINEs

with approximate sizes of 300 base pairs and is the most abundant SINEs (Batzer &

Deininger 2002; Yao et al. 2010). The Alu elements are thought to have proliferated

over the past 65 million years of primate genome evolution. Detailed sequence analysis

of the structure of Alu has indicated that the element has evolved from the 7SLRNA

gene which forms part of the ribosome complex (Yao et al. 2010; Batzer & Deininger

2002). The term of Alu element was given due to the element containing the

recognition site of AluI (AGCT) restriction enzyme, and it was first discovered by AluI

restriction enzyme approximately 30 years ago. In addition, the locations or sites of the

restriction enzyme are different among the elements (Batzer & Deininger 2002; Ray,

Walker & Batzer 2007; Abdurashitov et al. 2008). The Alu elements are also known as

transposable elements (TE). It has the ability to “jump” or be mobile within the

genome. However, “jumping” process and duplication of Alu elements are

intermediated by the form of RNA which then reverse-transcribed before it is inserted at

a new genomic location. Thus, the Alu elements are classified as retrotransposon

(Cordaux & Batzer 2009; Konkel, Walker & Batzer 2010).

18

The Alu element displays several specific structural characteristics. On its left arm,

there are 140 base pairs which are linked through the center of the element to the right

arm. The right arm is 31 base pairs longer than the left arm due to a deletion which

took place in the evolutionary stages. The central region of the element is an A-rich

region, as well as in the tail region. It contains poly adenine (poly-A) and is flanked by

short intact direct repeats which are derived from the site insertion (Figure 5). The left

monomer contains two promoters, blocks A and B, for RNA polymerase III. The region

of blocks A and B are 10 to 25 and 70 to 90 base pairs, respectively. Initiation of

transcription process is promoted by the block A, while the precision of initiation is

determined by block B (Batzer & Deininger 2002; Khitrinskaya, Stepanov & Puzyrev

2003).

Figure 5. The structure of Alu element (adapted from Batzer and Deininger (2002)).

The element, which belongs to the SINEs family, has poly Adenine in the central region

and tail.

Based on the evolutionary age, the elements are divided into subfamilies. Three main

branches of Alu element subfamilies are designated by letters which indicate an age of

the element. The letter J is to indicate the old subfamily, while the letter S indicates the

intermediate subfamily and the letter Y indicates the young subfamily of Alu element.

In addition, lowercase letter and numerical symbols are often used (Abdurashitov et al.

2008; Grover et al. 2004). The most ancient Alu subfamily (AluJ), is thought to be

functionally extinct. Based on a previous study, the AluJ happens to be completely

inactive in the human genome (Bennett et al. 2008). On the other hand, the intermediate

Alu subfamily (AluS) has been discovered to still have functionally Alu core elements.

The youngest Alu subfamily (AluY), however, contains the highest number of

functionally Alu core elements. Futhermore, the youngest Alu has also been considered

as the most biologically active Alu elements (Grover et al. 2004; Bennett et al. 2008).

In addition, the AluY has a specific characteristic with respect to AluI restriction site,

Poly(A) in central region Poly(A) tail

19

which is that the location of AluI restriction site is at 216 base pairs, while the other Alu

subfamilies do not have the restriction site at the particular region (Abdurashitov et al.

2008). Another characteristic of AluY is that the youngest Alu is not found in any

position in the genomes of other primates. Therefore, AluY subfamily is a human-

specific subfamily (Batzer & Deininger 2002).

1.2.1.6.1 The role of Alu elements

The Alu element was once thought to be the “junk” DNA or selfish DNA due to an

observed lack of significant function in the human genome. By the progress of human

genome project, the understanding of human genome has increased. Accordingly,

several possible roles of Alu elements in the human genome have been determined

(Makalowski 2000). The Alu element has several roles in the gene regulation. As a

mobile element, the insertion of Alu at a new genomic region may introduce new

transcription factor-binding sites which could alter the regulation of gene expression. In

addition, Alu elements are rich in CpG nucleotides which represent the substrate for

genomic methylation. Thus, when insertion of Alu elements occurs, the CpG

nucleotides of new Alu increases the mutation rate (Batzer & Deininger 2002; Deininger

& Batzer 1999). The Alu elements have also been found to contain functional promoter

elements for several steroid hormone receptors (Deininger & Batzer 1999).

Insertion of Alu elements into the 3’ noncoding regions of genes commonly occurs and

produces few negative effects to the genes. In contrast, there is low number of Alu

elements which are found within the 5’ coding or non-coding regions of exons due to

insertions in the particular region and presumably are too disruptive to the function of

the gene. Hence, when Alu elements happen to be inserted into coding exons or into

introns relatively near an exon, it can alter splicing and lead to human disease. There

are at least 16 Alu-based insertion mutations in the Human Genetic Mutation Database

(Deininger & Batzer 1999). Furthermore, distribution of Alu elements in human

genome increases the opportunity for unequal homologous recombination due to

sequence similarity of the elements. The recombination creates higher levels of

mutations and genetic exchanges such as duplications, deletions and translocations

(Deininger & Batzer 1999; Batzer & Deininger 2002; Makalowski 2000). Various

human diseases which have been caused by Alu elements include Tay Sachs, α-

thalasemia, breast cancer and leukemia (Batzer & Deininger 2002; Deininger & Batzer

1999). Recombination, however, has positive evolutionary effects. For instance,

20

duplication steps which involve recombination between Alu elements have developed

the human glycophorin gene family (Makalowski 2000).

1.2.1.6.2 Polymorphism of Alu insertions (POALINs) in population studies

Despite the associations between Alu elements and disease, there are other

characteristics of Alu element which can be used as human genetic marker. Alu

elements are inherited from a common ancestor, and the absence of Alu insertion is

known to be the ancestral state of Alu dimorphism. Thus, the Alu insertion alleles are

considered identical by descent. Moreover, the presence or absence (dimorphism) of

Alu insertions in the genome is relatively easy to assay (Batzer & Deininger 2002;

Deininger & Batzer 1999).

Another characteristic of Alu elements is homoplasmy-free. Homoplasmy-free which

occurs due to the probability of two independent Alu insertions appear in the same

genomic region in the human population is essentially very small or even zero, based on

short evolutionary time frame. In addition, there is no specific mechanism observed for

removing Alu element once it is inserted which makes the element a very stable marker.

Those characteristics make the Alu elements a prospective marker in human population

studies. Furthermore, the ancestral state of Alu elements allows the construction of a

phylogenetic tree without making too many assumptions (Batzer & Deininger 2002;

Deininger & Batzer 1999; Ray, Walker & Batzer 2007).

Analysis of polymorphism of Alu dimorphism has been used to address several

questions of human geographic ancestry (Batzer & Deininger 2002; Ray, Walker &

Batzer 2007). With respect to human MHC, there are several polymorphic Alu

insertions within human MHC, which are useful to investigate the origins and genomic

diversity of human populations (Kulski, Shigenari & Inoko 2011; Yao et al. 2010).

There are at least five dimorphic Alu insertions which have been identified and

characterized in the human MHC regions and used to analyze human populations. The

five young Alu elements in human MHC are AluyMICB, AluyTF, AluyHJ, AluyHG and

AluyHF (Yao et al. 2010; Kulski, Shigenari & Inoko 2011). The AluyMICB is young

Alu elements located in intron 1 of MICB gene within the human MHC. The Aluy

elements were thought to be inserted into the MICB gene about 19 million years ago

(Kulski et al. 2002a).

21

Another young Alu element, AluyTF, has been inserted between the CDSN and TFIIH

genes with approximately 268 kilo base of the HLA-C gene (Dunn, Inoko & Kulski

2003). The other three young Alu elements are located in the alpha block of human

MHC. The AluyHJ has been inserted approximately 18 kilo base centromeric of the

pseudogene HLA-J, while the AluyHF is located within a HERV-16 sequence

approximately 7.5 kilo base telomeric of HLA-F. Another Alu element in the alpha

block is AluyHG which is located between the HLA-H and HLA-G, and approximately

88 kilo base telomeric of the HLA-A gene (Figure 6) (Dunn et al. 2002; Kulski et al.

2001).

Figure 6. The location of five Alu elements in the human MHC (adapted from Dunn,

Inoko and Kulski (2003)). Three (AluyHJ, AluyHG and AluyHF) of five elements are

located in the alpha block.

Several studies have constructed a phylogenetic tree of several populations such as

Australia-Caucasian, Japanese, North-Eastern Thais and, Malaysia-Chinese. Based on

those previous studies of human populations, the Alu elements in human MHC have

also been determined to have association with HLA alleles (Dunn et al. 2005; Dunn et

al. 2002; Dunn et al. 2007).

1.2.2 The Javanese and the Arab Bedouin populations

1.2.2.1 The Javanese and Indonesia populations

Indonesia as the world’s largest archipelago comprises of more than 17,500 islands

spanning between the continents of Asia and Australia with approximately total area of

3 million square kilometers (Wallace 1869; Karafet et al. 2010). In 2000, there were

Class II Class III Class I

Beta

centromere

Alpha

telomere

Kappa

22

approximately 205.8 million inhabitants in Indonesia based on the population census as

cited in Suryadinata, Arifin and Ananta (2003). There are extremely diverse indigenous

languages with approximately 712 recorded from more than 300 ethnic groups (Karafet

et al. 2010; Lewis 2009).

The population history of the archipelago using linguistic comparisons (ethnology)

suggested that there were two major prehistoric migrations into the Southeast Asia. The

first was the migration out of Africa about 60,000 to 40,000 B.C (Cavalli-Sforza &

Feldman 2003). During the first wave of human migration in the Pleistocene era, the

southeast archipelago was divided into three different regions which were the

Sundaland, the Wallacea, and the Sahulland (Figure 7) (Bellwood 2007; Voris 2000).

The people who occupied the archipelago at the particular time is known as Australo-

melanesian who then survived and settled in the island of Papua (Bellwood, Fox &

Tyron 1995). The changing of sea level during the Holocene era had transformed the

Sundaland into three major islands (Sumatera, Java and Borneo) in Indonesia

archipelago. The increase of sea level had also transformed the Sahulland into Papua

and Australia (Voris 2000).

Figure 7. Three different regions of the southeast archipelago in the Pleistocene era

(adapted from Bellwood (2007) and Voris (2000)). The Sundaland had become

Sumatera, Java and Borneo islands in Indonesia archipelago during the early Holocene.

23

The second human migration of Austronesian speaking people occurred in the Holocene

era. The Austronesian language family is considered as the world’s largest, comprising

about 1200 languages and at least 270 million speakers (Figure 8) (Bellwood, Fox &

Tyron 1995). The language family is spoken by tens of millions of speakers including

Indonesian/Malaysian, Javanese and Tagalog. The human migration pattern of the first

Austronesians are believed to have originated in Southern China, then moved and

settled in Taiwan approximately 5000 to 6000 years ago. The Austronesian in Taiwan

eventually moved through the Philippines archipelago to Borneo, Sumatera and Java

islands in the Indonesia archipelago. The basic method to classify the Austronesian

languages is systematic comparison of regular sound correspondences between

languages, then reconstruction to trace the possible derivation of daughter languages

(Bellwood, Fox & Tyron 1995; Tyron 1995).

Indonesia archipelago is also inhabited by Papua speakers who also known as the non-

Austronesian. The non-Austronesian languages are used in several regions located at

the east side of the archipelago such as west Papua and the east coast of North Moluccas

(Lewis 2009). The Wallacea, moreover, becomes the admixture region of the

Austronesian and non-Austronesian (Figure 9). Therefore, the complexity of languages

and cultures throughout the archipelago had created significant genetic diversity

(Karafet et al. 2010; Keyser et al. 2006).

Figure 8. Language family tree of Austronesian (Lewis, Simon & Fennig 2013; Tyron

1995). The Javanese language belongs to the Western Malayo Polynesian of the

Austronesian

24

Figure 9. The maps of Indonesia (reproduced from Lewis, Simon and Fennig (2013)). The Austronesian speakers inhabit most of the

archipelago including Java and Bali, while the non-Austronesia speakers inhabit the Papua.

25

By the fifth century, Indonesia archipelago had been a trading network due to its

geography which connected India and China. However, the coming of Islam to

Indonesia as a consequence of maritime commerce was started approximately in the

twelfth century (Drakeley 2005; Reid 1995). The spread of Islam in Indonesia by the

Muslim traders occurred during commercial activities, and the introduction of Islam in

Indonesia was peaceful through the trading activities. Islam, however, required

significant changes such as the burial system which requires simplicity. In contrary,

before the coming of Islam, burial sites contained of valuable ceramics and gold to be

buried with the dead to ensure a comfortable passage to the afterlife. Thus, Islamization

had created the most principal separation between Austronesians (Bathia, Easteal &

Kirk 1995; Reid 1995). The territorial expansion of Islam from its birthplace in Arabian

Peninsula have made Indonesia the home of the world’s largest Muslim population

(Drakeley 2005).

In recent date, the majority of the Indonesian population lives in the Java Island, thus

create the Java Island as the most populous island in Indonesia. The Javanese, who

belongs to the Autronesian speakers, inhabits big cities and it is characterized by

significant ethnic and linguistic diversity. The Javanese comprised approximately 42%

of the population in Indonesia by the year 2000, while the Sundanese comprised 15.4%

based on the population census cited in Suryadinata, Arifin and Ananta (2003) (Figure

10).

Figure 10. The percentage of population in Indonesia based on the year 2000 population

census of Badan Pusat Statistik (reproduced from Suryadinata, Arifin and Ananta

(2003)).

26

1.2.2.2 The Arab Bedouin and the spread of Islam

The first expansion of modern human out of Africa occurred through two routes. The

southern route along the coast had been passed by the early modern humans to reach

south and southeast Asia. In contrary, the northern route had taken the Middle East,

Arabian Peninsula to reach Europe, east and northeast Asia in 40,000 B.C. (Cavalli-

Sforza & Feldman 2003).

The geographical area of the Arab region covers approximately 14 million kilometer

square. It spans through two continents from Rabat on the Atlantic to Muscat on the

Persian Gulf (Figure 11). The Arab is considered rich in diversity. There are many

populations inhabit the Arab region. However, language is what unites the Arabs

(Teebi 2010). Base on the linguistic classification, Arabic language is classified into the

Afro-Asiatic (Figure 12) (Lewis 2009). The word Arab literally means desert people

who live in waterless and treeless regions (Salibi 1980). Fifteen hundred years ago, the

term Arab referred to people residing in the Arabian Peninsula (Gablinger 2005).

Figure 11. The map of Arab regions (adapted from Teebi (2010)). The Arab regions

spans approximately 14 million kilometer square. Red circles indicate at least four

countries where the Bedouin is still exist.

27

Figure 12. Language family tree of Afro-Asiatic (adapted from Lewis (2009)). There

are 35 different spoken Arabic languages in the family including Saudi Arabia and

United Arab Emirates.

There are several different views as to the background of the Arabs. However, it is

thought that the Arabian Babylonia was the place where the Arab people had begun to

settle (Hunter-Zinck et al. 2010; Teebi 2010). Currently, in most part of the Arab

region, the populations are the result of admixture with other populations. However,

despite the heterogeneous populations, homogeneous or isolated populations exist

including Bedouin, Nubians and Druze (Teebi 2010).

A specific subpopulation, the Bedouin clans of that region, is thought to have lived as

desert nomads since before the birth of Arabian Babylonia. The Bedouin is believed to

be the forefathers of the contemporary Arab and also known as the “true” Arabs of old

(Gablinger 2005; Kark & Frantzman 2012). Bedouin is derived from the Arabic as

“desert-dweller”, and the word “Badu” in Bedouin is the anonym of sedentary and

urban (Cole 2003). The Bedouin as nomadic people, brought many goods during their

travelling (Gablinger 2005).

Between 1858 to 1917, the Bedouin in the Arabian Gulf were split into several alliances

which resulted the formation of Saudi Arabia, Qatar, Kuwait, Bahrain and United Arab

Emirate (UAE). In recent date, there have been rejections of the Bedouin in several

28

Arab countries such as Iraq and Egypt. However, there are various clans of Bedouin

exist in the UAE (Kark & Frantzman 2012).

The Bedouin, made up a considerable portion of the population in the Arabian

Peninsula around the seventh century when major international routes between Persia

and Byzantine occurred (Berkey 2003). The commercial activities in Arabian

Peninsula, however, were influenced by conflicts between Persian and Byzantine.

Those events created diversions of trade through the sea and the desert (Lewis 1993).

The spread of Islam from its birthplace in Arabian Peninsula to most of the

Austronesians of Southeast Asia, including Indonesia, was one of the consequences of

the involvement in commercial activities (Reid 1995). The strategic location of

Indonesia archipelago had facilitated the development of trade in the region. One

Indonesia-governing empire during the trading era was Srivijaya, which had control of

commercial trade routes in Southeast Asia. Located in the south of Sumatera Island,

Srivijaya provided an essential link between the South China Sea and the Indian Ocean.

The empire, however, was destroyed by another governing empire, Majapahit (Jayaram

2005). It was through contacts with Arab and Indian traders during this period that

Islam made its way to Sumatera. However, there has been possibility that the Arab

traders who crossed Indonesia archipelago were not the Bedouin. The Bedouin, as

nomadic people, have also been associated with raising livestock (Cole 2003).

1.2.3 HLA and Alu elements in forensic science

The use of Human Leukocyte Antigen and Alu elements may not be as popular as other

genetic markers such as microsatellite in forensic science. However, the HLA and Alu

elements can be applied as alternative methods when ambiguous identification occurs.

There are several utilities of Alu elements in forensic science, namely for identification

and quantitation of human or non-human DNA, gender determination of human DNA

and the study of human ancestry (Ray, Walker & Batzer 2007). The Alu elements have

high copy number in the human genome, as the elements have amplified over 1 million

elements during primate evolution (Batzer & Deininger 2002).

In forensic cases, determination of human gender is routinely performed. The most

widely used method is based on the Amelogenin Y region. The method yields different

size of PCR amplicons for X and Y chromosome of the Amelogenin gene. There are,

however, some reported cases of misidentification of males as females due to deletion in

29

the Amelogenin gene occurred in Sri Lanka, Austria, Indians and Caucasians (Hedges et

al. 2003; Ray, Walker & Batzer 2007; Steinlechner et al. 2002; Thangaraj, Reddy &

Singh 2002). The frequency of misidentified gender determination is relatively low.

However, in some cases such as rape and prenatal gender identification, misidentified

gender can create legitimate error. Fixed Alu insertion on either chromosome X

(AluSTXa) or Y (AluSTYa) has shown relatively high accuracy for gender determination

of 778 diverse DNA samples in several populations. Therefore, the use of an alternative

marker such as Alu elements to determine gender is recommended (Hedges et al. 2003).

1.3 RESEARCH OBJECTIVE

The study is aimed to analyze the allele frequencies of six loci in human MHC of

Indonesian Javanese population and compare to the Arab Bedouin in order to identify a

possible relationship or ancestral linkage between these two contemporary Muslim

communities. Further study on the Indonesia HLA population database will enhance

our knowledge on the understanding of the Indonesia population structure and historical

relationship, as well as disease susceptibility that may have association with HLA allele

frequency.

30

CHAPTER 2: MATERIALS AND METHODS

2.1 ETHICAL STATEMENT

This study was approved by the ethics committee of University of Western Australia,

Australia-Human Research Ethics Office (HREO) with reference number RA/4/1/5238

prior to commencing work involving human subject. All samples were de-identified

and no information was provided to allow the samples used to be matched to the

specific donor.

2.2 SUBJECTS

There were one hundred (n=100) archival DNA samples of healthy anonymous

unrelated individuals of Javanese (a distinct ethnic group distinguished by language and

geographic) recruited from populations located in the Central Java. These archival

samples were from the Eijkman Institute, collected for the purpose of their study in the

Human Genetic Diversity and Disease (Tumonggor et al. 2013). Prior to sample

collection, each individual had provided details of their pedigree at least two

generations into the past to ensure no ethnic admixture. The previously donated DNA

samples were available and had been stored at -80°C at the Eijkman Institute.

2.3 HLA-A AND HLA-B TYPING

The typing of the HLA-A and HLA-B loci consisted of three main stages which were a

Polymerase Chain Reaction (PCR) step, sequencing followed by analysis, and allele

frequency and haplotype analysis. All extracted DNA samples from the Javanese

volunteers were typed for HLA-A and HLA-B loci. Sequence Based Typing (SBT) of

exon 2 and exon 3 at HLA-A and HLA-B loci were performed according to Kurz et al.

(1999) and Pozzi, Longo and Ferrara (1999) with modifications.

2.3.1 PCR-sequence based typing of HLA-A and HLA-B loci

PCR amplification was performed to obtain specific HLA-A and HLA-B gene products.

Specific primers used to amplify HLA-A and HLA-B loci of Javanese samples are listed

in Table 1. The amplification of HLA-A locus was carried out using primers 5Aln1-46

and 3Aln3-66 (10 pmol/μl), with the PCR solution (25 µl) containing 100 ng of DNA

template, 0.2 mM of mix deoxyribonucleotide triphosphates (dNTPs), 1 unit of

31

AmpliTaq Gold®

360 DNA Polymerase (Applied Biosystems, Foster City, CA, USA), 3

mM MgCl2, 2.5 μl of 10× AmpliTaq Gold®

360 buffer, and 0.5 μl of 360 GC Enhancer

(Applied Biosystems, Foster City, CA, USA). The amplification was performed using a

GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA, USA) with a single

hot start step at 95°C for 10 min. A total of 35 cycles were used, each consisting of 30

sec denaturation at 95°C, a 1 min annealing step at 67°C and 1 min, 30 sec extension

step at 72°C. A final extension step of 72°C for 7 min completed the reaction. The

PCR products were visualized on 1% agarose gels stained with ethidium bromide to

confirm the presence of template for DNA sequencing.

Amplification of HLA-B locus was performed using primers Bx1 and BINT3 (10

pmol/μl). The PCR solution (25 µl) contained 100 ng of DNA template, 0.2 mM of mix

deoxyribonucleotide triphosphates (dNTPs), 1 unit of AmpliTaq Gold® 360 DNA

Polymerase, 3 mM MgCl2, 2.5 μl of 10× AmpliTaq Gold® 360 buffer, and 0.5 μl of 360

GC Enhancer (Applied Biosystems, Foster City, CA, USA). PCR was performed using

a GeneAmp PCR System 9700 with a single hot start step at 95°C for 5 min. A total of

35 cycles were used, each consisting of 30 sec denaturation at 95°C, a 30 sec annealing

step at 66°C and 2 min extension step at 72°C. A final extension step of 72°C for 10

min completed the reaction. The PCR products were visualized on 1% agarose gels

stained with ethidium bromide. Purification of all PCR products was carried out as post

PCR treatment. It was performed using QIAquick PCR Purification Kit (QIAGEN,

Hilden, Germany).

2.3.2 DNA sequencing reaction

Purified PCR products were sequenced with ABI PRISM BigDye Terminator Cycle

Sequencing Ready Reaction Kits (Applied Biosystems, Foster City, CA, USA) using

3031-xl Genetic Alayzer (Applied Biosystems, Foster City, CA, USA), according to the

manufacturer’s instructions. Sequencing was performed in the forward and reverse

directions using the primers summarized in Table 1. DNA sequence was analysed using

software Sequencing Analysis (Applied Biosystems, Foster City, CA, US) and alleles of

HLA-A and HLA-B were assigned by SBTengine® software version 2.20.0.0;

IMGT/HLA release 3.9.0 (Genome Diagnostic B.V., Utrecht, Netherlands).

32

Table 1. Primers used in HLA-A and HLA-B typing of Javanese DNA samples

Target Primer Primer sequence

HLA-A Locus 5Aln1-46 (PCR and sequencing - forward) 5’ GAA ACS GCC TCT GYG GGG AGA AGC AA 3’

3Aln3-66 (PCR and sequencing - reverse) 5’ TGT TGG TCC CAA TTG TCT CCC CTC 3’

HLA-B Locus

Bx1 (PCR - forward) 5’ GGG AGG AGC GAG GGG ACC SCA G 3’

BINT3 (PCR - reverse) 5’ GGA GGC CAT CCC CGG CGA CCT AT 3’

BEX2F (sequencing - forward) 5’ GGG CGC AGG ACC YGR GGA 3’

18CINT3 (sequencing - reverse) 5’ CCC ACT GCC CCT GGT ACC 3’

33

2.3.3 Allele frequency and haplotype analyses

Allele frequencies were calculated using direct counting method (Romphruk et al.

2010). Haplotype frequencies of the two loci (HLA-A and HLA-B) were estimated

using maximum likelihood method with Arlequin software version 3.5.1.2 (University

of Berne, Switzerland) with default standard setting as recommended by Excoffier and

Lischer (2010). Arlequin software is a package which integrates several basic and

advanced methods for population genetics data analysis, such as estimation of alleles

and haplotype frequencies. The enhanced and updated Arlequin software, version 3.5,

includes a graphical (WINARL35) and a command-line which allows the software to be

applied on Windows and Linux (Excoffier & Lischer 2010; Excoffier, Laval &

Schneider 2005).

2.4 POLYMORPHISM OF Alu INSERTIONS (POALINs) TYPING

The biallelic polymorphism of the four Alu Insertions (POALINs) was determined by

identifying the presence or absence of a specific Alu motif at each of four loci based on

the predicted size of PCR product.

2.4.1 POALINs PCR assay

The POALINs were amplified using four pairs of primers, one pair for each Alu locus.

Table 2 summarizes the primer sequences for each locus (Kulski et al. 2002a; Dunn,

Inoko & Kulski 2003; Dunn et al. 2002). All PCR reaction of POALINs was performed

in 25 μl PCR solution, containing 50 to 100 ng of DNA template, a 0.2 mM mix of

deoxyribonucleotide triphosphates (dNTPs), 1.25 units of Taq Polymerase, 3 mM

MgCl2, and 2.5 μl of 10× PCR buffer (600 mM Tris-HCl, pH 8.3; 250 mM KCl; 1%

Triton X100; 100 mM β-mercaptoethanol), and 10 pmol/μl primer. PCR was carried

out using a GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA, USA)

with a single hot start step at 95°C for 10 min. A total of 35 cycles were used, each

consisting of 30 sec denaturation at 95°C, a 30 sec annealing step and a 45 sec

extension step at 72°C. A final extension step of 72°C for 10 min completed the

reaction. There were, however, three different annealing temperatures used in the

amplification as summarized in Table 2. The PCR products were visualized on 2%

agarose gels stained in the presence of ethidium bromide.

34

Table 2. POALINs primers and annealing temperature

Target Primer Primer sequence Annealing

Temperature (°C)

Aluy-MICB

AluyMICB.F (forward)

5’ GCC TTC CAA TGC CAT TCA CAG 3’

59

AluyMICB.R (reverse)

5’ CTC AGC CCT GCT TTC CCA TCT 3’

Aluy-TF

AluyTF.F (forward)

5’ GTG CCT GGT AAA AAT TTA AGA GCT GTA 3’

55

AluyTF.R (reverse)

5’ TGC ACC CGG CCT AAA ACC ACT GGT T 3’

Aluy-HJ

AluyHJ.F (forward)

5’ AAG AAA CCC ATA ACT CAC TTG 3’

52

AluyHJ.R (reverse)

5’ TGT GTC CAG GTT AAA CTT CAG 3’

Aluy-HF

AluyHF.F (forward)

5’ GCC TCA TGG CCT GAA TCT GCC AGT GTC CTT 3’

59

AluyHF.R (reverse)

5’ GTA ACT GAC CTG CCC TCT ATA GCA TAG TCT 3’

35

2.4.2 Genetic analysis of the POALINs

The PCR assays were designed to distinguish whether the Alu elements of the MHC

POALINs are present or absent in each DNA sample that was tested. A larger PCR

product band indicated the presence of the Alu element (referred to as allele*2), while

the smaller band indicated the absence of the insertion (allele*1). The expected sizes of

each allele are summarized in Table 3. Allele frequencies were obtained using R-

statistical software version 2.14.1 (R Foundation for Statistical Computing, Vienna,

Austria) as well as genotype frequency and heterozigosity of each Alu element. R is a

free statistical software for computing and graphics. The statistical software is

distributed under the GNU General Public License (R-Development-Core-Team ;

Santori et al. 2009).

HLA associations were determined by calculating the percentage of individuals who

shared the same HLA allele and an Alu element. Linkage disequilibrium was

represented as the delta measurement which was developed by Bengtsson & Thompson

in 1981 (Dunn et al. 2005). The delta was defined as (pA - pB)/(1 - pB), where pA refers

to the frequency of HLA alleles in individuals with the Alu element and pB referes to the

frequency of HLA alleles in individuals without the Alu element. In a case where a

negative delta value occurred, a rearrangement of the variables was performed as the

delta is defined as (pB - pA)/(1 - pB) (Dunn et al. 2005). Haplotype frequencies of the

POALINs and the six points haplotypes were estimated using maximum likelihood

method with Arlequin software version 3.5.1.2 (University of Berne, Switzerland) with

default standard setting (Excoffier & Lischer 2010).

2.4.3 Phylogenetic analysis of POALINs

The Gendist software, a component of the Phylip program (version 3.69), was used to

compare Nei's genetic distance values of the Javanese and nine previously studied

populations including the Arab Bedouin. Data of nine studied populations of POALINs

were obtained from published scientific journals (Dunn et al. 2007; Dunn et al. 2002;

Dunn et al. 2005; Kulski & Dunn 2005; Tian et al. 2008) and a thesis research (Al-Safar

2009). The distance matrix was converted to MEGA format, and a neighbor-joining

phylogenetic tree was constructed in MEGA (version 4) (Kumar, Tamura & Nei 1994;

Kumar et al. 2008). Bootstrap 1000 replicate, seed = 64,238 values were selected to

indicate the reliability of the tree topology. MEGA software is an application designed

36

for comparative analysis of homologous gene sequences, estimating evolutionary

distances, reconstructing phylogenetic trees and computing basic statistical quantities

from molecular data. MEGA facilitates sequence data to be assembled from files or

web-based repositories (Kumar, Tamura & Nei 1994; Kumar et al. 2008).

Table 3. Expected PCR products of Alu elements

Aluy Loci Allele*1 Allele*2

AluyMICB 503 bp 665 bp

AluyTF 422 bp 710 bp

AluyHJ 162 bp 500 bp

AluyHF 455 bp 605 bp

2.5 ANALYSIS OF SIX POINT HAPLOTYPES

The Arlequin software version 3.5.1.2 (University of Berne, Switzerland) was used to

analyze six point haplotypes of HLA-A, HLA-B and four Alu insertions. Haplotype

frequencies of these six loci were estimated using maximum likelihood method with

default standard setting as recommended by Excoffier and Lischer (2010).

37

CHAPTER 3: RESULTS

3.1 HLA TYPING

A total of 51 and 46 out of 100 DNA samples were successfully analyzed for HLA-A

and HLA-B, respectively. The samples produced 29 different alleles of HLA-A and 31

different alleles of HLA-B. However, alleles from the other samples were unidentified

due to several causes such as poor DNA sequences quality.

Review of the poor quality of DNA sequences recognized dye-blobs, multiple

overlapping peaks or background noise resulting poor data and reaction failure (Figure

13). The most common reasons for poor quality DNA or a reaction failure are an

insufficient amount of DNA template, inadequate cleanup resulting poor purity and the

presence of PCR inhibitors (Church 2013).

Figure 13. Poor DNA electropherograms which were caused by (a) dye-blobs, (b) and

(c) sequencing reaction failures.

3.1.1 HLA-A typing

The allele assignments for the 51 samples for HLA-A locus are provided in Table 4.

There were 46 samples that contained heterozygous alleles, while only five samples

contained homozygous alleles of A*24:07 or A*24:02.

38

A total of 28 different alleles in HLA-A locus have been assigned for 51 samples of the

Javanese population. The frequency of the 28 assigned alleles of HLA-A were

calculated using direct counting method. The results revealed that the most frequent

allele of HLA-A was A*24:07 with allele frequency of 0.196, followed by A*24:02

with allele frequency of 0.176 as shown in Table 5 and Figure 14. No significant

frequency was observed from the other HLA-A alleles.

There were, however, 38 DNA samples which produced poor DNA sequences quality.

Thus, the alleles could not be assigned and remain unidentified. In order to obtain the

allele profiles, these 38 DNA samples have to be retyped in future studies. In contrast

to the samples with poor quality of DNA sequences, the other eleven samples produced

high quality of DNA sequences but the alleles remain unidentified due to no allele in the

SBTengine software (Genome Diagnostic B.V., Utrecht, Netherlands) matched the

samples. The SBTengine software contained regularly updated data from the IMGT

database. Therefore, those alleles are presumed to be novel. In future studies, further

analysis of the eleven samples will need to be performed to describe the exact sequence

of these previously uncharacterized alleles.

39

Table 4. HLA-A allele assignments of Javanese samples

No Sample ID Allele 1 Allele 2 No Sample ID Allele 1 Allele 2

1 EI1560 A*24:02:05 A*24:02:54 27 EI1620 A*02:01 A*24:07

2 EI1562 A*24:02 A*24:07 28 EI1621 A*11:01 A*24:02

3 EI1563 A*24:02 A*33:03 29 EI1622 A*02:03:01 A*24:02

4 EI1564 A*11:10 A*33:03 30 EI1623 A*02:03:01 A*24:114

5 EI1566 A*24:02 A*24:07 31 EI1627 A*11:119 A*24:02

6 EI1567 A*24:07 A*33:03:05 32 EI1628 A*11:01 A*33:59

7 EI1570 A*24:02 A*24:10 33 EI1629 A*02:06 A*24:07

8 EI1572 A*02:54 A*24:02:58 34 EI1631 A*24:07 A*34:01:01

9 EI1573 A*02:01:66 A*26:01 35 EI1632 A*23:51 A*33:03

10 EI1574 A*24:07 A*34:01:01 36 EI1635 A*24:02:54 A*34:01:01

11 EI1587 A*26:16 A*29:01:02 37 EI1637 A*24:07 A*24:07

12 EI1588 A*24:07 A*33:03:07 38 EI1639 A*24:02 A*24:02

13 EI1589 A*02:01 A*24:02 39 EI1644 A*24:02 A*24:02

14 EI1591 A*02:01 A*34:01:01 40 EI1646 A*11:01:01 A*11:01:01

15 EI1593 A*02:01 A*24:02 41 EI1647 A*02:06 A*11:119

16 EI1595 A*11:01 A*33:18 42 EI1648 A*24:07 A*24:07

17 EI1598 A*02:03:01 A*11:119 43 EI1649 A*33:03:01 A*34:01:01

18 EI1600 A*11:01 A*33:03 44 EI1652 A*24:02 A*33:03:01

19 EI1601 A*02:03:01 A*30:01 45 EI1654 A*02:01 A*24:07

20 EI1602 A*02:06 A*24:07 46 EI1655 A*02:06 A*33:03:01

21 EI1604 A*24:07 A*33:03 47 EI1656 A*11:01 A*24:02

22 EI1605 A*11:119 A*24:07 48 EI1657 A*11:01 A*33:59

23 EI1606 A*02:01 A*24:02 49 EI1658 A*24:02 A*24:07

24 EI1608 A*24:07 A*24:07 50 EI1659 A*24:02 A*24:07

25 EI1615 A*11:01 A*24:07 51 EI1626 A*02:03:01 A*11:119

26 EI1616 A*33:03:01 A*34:01:01

40

Table 5. The allele frequencies show a preponderance of HLA-A24 alleles

No. HLA-A

allele

Allele

frequency No.

HLA-A

allele

Allele

frequency

1 A*02:01 0.059 16 A*11:119 0.049

2 A*02:06 0.039 17 A*02:03:01 0.049

3 A*02:54 0.010 18 A*02:01:66 0.010

4 A*11:01 0.068 19 A*24:114 0.010

5 A*11:10 0.010 20 A*11:01:01 0.029

6 A*23:51 0.010 21 A*24:02:05 0.010

7 A*24:02 0.176 22 A*24:02:54 0.020

8 A*24:07 0.196 23 A*24:02:58 0.010

9 A*24:10 0.010 24 A*29:01:02 0.010

10 A*26:16 0.010 25 A*33:03:01 0.039

11 A*26:01 0.010 26 A*33:03:05 0.010

12 A*30:01 0.010 27 A*33:03:07 0.010

13 A*33:03 0.049 28 A*34:01:01 0.059

14 A*33:18 0.010

15 A*33:59 0.020

Bold letters and numbers show the 1st and 2

nd most frequent alleles

41

Figure 14. Percentage of HLA-A allele frequencies in the Javanese population. The HLA-A*24:07 allele is observed most

frequently in the population, followed by HLA-A*24:02 allele.

A*24:07

A*24:02

A*11:01

A*02:01/34:01:01

A*02:03:01/33:03/11:119

A*02:06/33:03:01

A*11:01:01

A*33:59/24:02:54

Others17.6%

19.6%

A*2402 (0.3)

3.9%

5.9%

4.9%

1.9% 2.9%

6.8%

42

3.1.2 HLA-B typing

There were 46 samples for which HLA-B alleles could be assigned using the

SBTengine software (Table 6). For the HLA-B locus, homozygous alleles were

observed more frequently than the HLA-A. There were 23 samples that carried

homozygous HLA-B alleles, while the rest of the samples contained heterozygous

alleles. The homozygous alleles of HLA-B found in the Javanese samples were also

observed to be more diverse compared to the HLA-A locus. Among the homozygous

alleles were B*18:01, B*15:02, B*52:01, B*40:01 and B*27:06.

A total of 31 alleles in HLA-B locus have been assigned from 46 Javanese DNA

samples. Direct counting to ascertain the allele frequencies (Table 7) revealed the allele

B*15:02 and B*18:01 were the two most frequent alleles in the Javanese population

with frequency of 0.185 and 0.109, respectively. The percentage of allele frequencies is

shown in Figure 15.

However, a number of 45 samples could not be assigned due to poor quality of

sequencing data. Alleles of the other nine samples were also failed to be identified due

to a non-match of alleles to the IMGT database in the SBTengine software. Similar to

HLA-A alleles, the unidentified samples need to be retyped in future studies to confirm

the possibility of new HLA-B alleles which previously were uncharacterized.

43

Table 6. HLA-B allele assignments of Javanese DNA samples

No Sample ID Allele 1 Allele 2 No Sample ID Allele 1 Allele 2

1 EI1559 B*15:02 B*15:13:01 24 EI1613 B*40:01 B*40:01

2 EI1560 B*18:01 B*18:01 25 EI1615 B*40:67 B*40:67

3 EI1562 B*40:01 B*40:160 26 EI1617 B*38:02 B*44:03:02

4 EI1563 B*15:02 B*15:02 27 EI1619 B*40:01 B*40:01

5 EI1566 B*18:01 B*18:01 28 EI1620 B*18:01 B*18:01

6 EI1575 B*15:21 B*44:03:02 29 EI1621 B*15:02:06 B*15:25:03

7 EI1577 B*40:01 B*40:87:02 30 EI1624 B*15:21 B*38:20

8 EI1579 B*15:02 B*15:02 31 EI1630 B*27:06 B*27:06

9 EI1580 B*15:13:01 B*15:21 32 EI1633 B*07:05:01 B*07:05:01

10 EI1586 B*15:02 B*15:25 33 EI1636 B*52:01 B*52:01

11 EI1588 B*44:03:02 B*44:37:02 34 EI1637 B*15:213 B*15:223

12 EI1592 B*15:02 B*53:08:01 35 EI1638 B*52:01 B*52:01

13 EI1593 B*15:13:01 B*51:02:02 36 EI1642 B*18:01 B*18:01

14 EI1594 B*52:01 B*52:01 37 EI1643 B*15:21 B*15:21

15 EI1597 B*15:02 B*15:02 38 EI1644 B*52:01 B*52:01

16 EI1599 B*15:02 B*15:24 39 EI1646 B*15:13:01 B*27:06

17 EI1601 B*40:158 B*40:160 40 EI1647 B*15:13:01 B*57:01

18 EI1605 B*07:05:05 B*07:05:05 41 EI1650 B*15:13:01 B*38:02

19 EI1607 B*15:02 B*15:02 42 EI1651 B*15:02:06 B*15:21

20 EI1608 B*15:02:06 B*15:02:06 43 EI1653 B*15:02 B*15:13:01

21 EI1610 B*15:02 B*15:02 44 EI1654 B*15:13:02 B*15:02:02

22 EI1611 B*15:13:01 B*15:21 45 EI1655 B*51:06:02 B*51:21

23 EI1612 B*18:01 B*18:01 46 EI1658 B*15:02 B*15:02

44

Table 7. The HLA-B15 alleles occur frequently in the Javanese samples

No. HLA-B

allele

Allele

frequency No.

HLA-B

allele

Allele

frequency

1 B*15:02 0.185 17 B*15:02:06 0.033

2 B*15:13:01 0.087 18 B*40:67 0.022

3 B*18:01 0.109 19 B*38:02 0.022

4 B*40:01 0.065 20 B*15:25:03 0.011

5 B*40:160 0.022 21 B*38:20 0.011

6 B*15:21 0.076 22 B*27:06 0.033

7 B*44:03:02 0.033 23 B*07:05:01 0.022

8 B*40:87:02 0.011 24 B*15:213 0.011

9 B*15:25 0.011 25 B*15:223 0.011

10 B*44:37:02 0.011 26 B*57:01 0.011

11 B*53:08:01 0.011 27 B*15:89 0.011

12 B*51:02:02 0.011 28 B*38:01:01 0.011

13 B*52:01 0.087 29 B*15:13:02 0.011

14 B*15:24 0.011 30 B*51:06:02 0.011

15 B*40:158 0.011 31 B*51:21 0.011

16 B*07:05:05 0.022

Bold letters and numbers show the 1st and 2

nd most frequent alleles

45

Figure 15. Percentage of HLA-B allele frequencies in the Javanese population. Among the identified HLA B alleles, the B*15:02 allele

was observed most frequently in the population.

B*15:02

B*18:01

B*15:13:01/52:01

B*15:21

B*40:01

B*44:03:02/15:02:06/27:06

B*40:160/07:05:05/07:05:01/38:02/40:67

Others

18.5%

B*1502 (0.185)

10.9%

(0.087)

7.6%

8.7%

6.5%

3.3%

2.2%

46

3.1.3 Haplotypes of HLA-A and HLA-B in the Javanese population

The haplotype frequencies which combined the HLA-A and HLA-B loci were analyzed

using Arlequin software version 3.5.1.2 with default standard setting, as summarized in

Table 8. There were 78 subjects used in the haplotype analysis. A total of 90 possible

haplotypes were obtained between these two loci. The highest frequency of 0.013 was

observed for four possible haplotypes, which were A*24:02-B*15:02, A*24:02-B52:01,

A*24:07-B*18:01, and A*24:07-B*15:02:06.

Table 8. The haplotype frequencies of HLA-A and HLA-B

MHC Class I Haplotype Haplotype

Haplotype ID HLA-A HLA-B frequency

1A A*02:01 B*15:13:01 0.006

1B A*02:01 B*15:13:02 0.006

1C A*02:01 B*18:01 0.006

1D A*24:02 B*15:02 0.013

1E A*24:02 B*52:01 0.013

1F A*24:02 B*18:01 0.006

1G A*24:02 B*51:02:02 0.006

1H A*24:07 B*07:05:05 0.006

1I A*24:07 B*15:02 0.006

1J A*24:07 B*15:02:06 0.013

1K A*24:07 B*18:01 0.013

1L A*02:06 B*57:01 0.006

1M A*02:06 B*51:06:02 0.006

1N A*11:01 B*15:02:06 0.006

1O A*11:01 B*40:67 0.006

1P A*11:01:01 B*27:06 0.006

1Q A*11:01:01 B*15:13:01 0.006

1R A*30:01 B*40:158 0.006

1S A*33:03 B*15:02 0.006

1T A*33:03:01 B*51:21 0.006

Bold letters and numbers show combinations with the highest haplotype frequencies

47

3.2 POALINs IN MHC CLASS I OF THE JAVANESE

Figure 16 shows the sizes of amplification products of POALINs in MHC class I. For

each locus, a smaller band refers to the allele*1, while the larger band refers to the

allele*2. The larger product contains the sequence insertion of the size difference

between both products. Homozygous alleles would be expected to show only one band

of allele*1 or allele*2, while heterozygous alleles would be expected to have both

alleles.

The observed genotypes and allele frequencies of four POALINs, which were obtained

using R-statistics software (Free Software Foundation, Inc., Boston, MA, USA), are

listed in Table 9. The AluyHJ (0.33) was ascertained as having the most frequent

allele*2 in Javanese population, while the least frequent allele*2 observed was AluyHF

(0.02). Two (AluyHJ and AluyTF) of the four POALINs deviated from the Hardy-

Weinberg equilibrium. The AluyHJ was also observed to have the highest

heterozigosity, followed by the AluyTF.

48

Figure 16. Gel visualization of four Alu insertions in MHC class I region of Javanese samples. The larger products contain the Alu

insertions. Heterozygous alleles were indicated by two bands products, while homozygous alleles were indicated by one band products.

AluyMICB

422 bp

710 bp

603 bp

872 bp

503 bp

665 bp

Φx 1 2 3 4

AluyHF

455 bp

605 bp

603 bp

872 bp

Φx 1 2 3 4 5

AluyTF

603 bp

872 bp

Φx 1 2 3 4 5

AluyHJ

500 bp

162 bp

603 bp

194 bp

1 2 3 4 5 6 Φx

49

Table 9. The observed genotypes, allele frequencies, HWE significance and heterozigosity of four Alu insertions in the Javanese

population

Alu Locus

Genotypes

observed Allele frequencies

Genotype

frequencies

p value

(HWE)

Heterozigosity

n 11 12 22 Allele*1 Allele*2 1/1 1/2 2/2

AluyTF 90 77 6 7 0.89 0.11 0.86 0.07 0.08 1.043e-06 0.198

AluyMICB 100 93 7 0 0.96 0.04 0.93 0.07 NA 1 0.067

AluyHF 100 96 4 0 0.98 0.02 0.96 0.04 NA 1 0.039

AluyHJ 100 52 30 18 0.67 0.33 0.52 0.30 0.18 0.00151 0.444

50

The result of haplotypes of four POALINs which were obtained from 100 subjects and

determined using Arlequin software is summarized in Table 10. Based on the

haplotypes, the most frequent four-loci POALINs (0.514) was 2A which consisted of no

Alu insertion in all loci, followed by the AluyHJ single insertion (0.250) then by the

AluyTF single insertion (0.052). The most frequent haplotype with multiple insertions

contained AluyHJ and AluyTF (0.039).

Table 10. The haplotype frequencies of four POALINs in MHC class I region

Alu

Haplotype

ID

Alu Haplotypes Haplotype

frequencies TF MICB HJ HF

2A 1 1 1 1 0.514

2B 1 1 1 2 0.010

2C 1 1 2 1 0.250

2D 1 2 1 1 0.019

2E 2 1 1 1 0.052

2F 1 2 2 1 0.002

2G 2 1 2 1 0.039

2H 2 2 2 1 0.014

Bold letters and numbers show the Alu allele combination that was most common

3.2.1 The association between four Alu insertions with HLA-A alleles

The number, percentage and delta values of POALINs associated with HLA-A is

summarized in Table 11. An association between POALINs and the HLA-A alleles was

considered not significant if only one example of an HLA allele was observed in the

population (Dunn et al. 2007; Dunn et al. 2005).

Strong percentages (≥70%) (Kulski, Shigenari & Inoko 2011) were observed in several

associations between AluyHJ*2 and HLA-A alleles. The strongest percentage of

association (100%) was observed between AluyHJ*2 with allele A*24:07. All

individuals in Javanese samples with allele A*24:07 were found to also have the AluyHJ

insertion. Other strongly associated combinations were between AluyHJ*2 with

A*24:02 (78.9%), A*02:01 (83.3%) and A*02:06 (75%).

51

Strong association was also observed between AluyTF*2 and allele A*02:06 (75%).

Large delta values (>0.50) (Dunn et al. 2005; Dunn et al. 2007) were obtained from the

association between AluyHJ*2 and those four HLA-A alleles. However, there were no

significant associations between the other two POALINs (AluyHF and AluyMICB) with

HLA-A alleles, even though large delta values were observed.

52

Table 11. The associations between four Alu insertions with HLA-A alleles of Javanese samples

HLA-A

Alleles

Number

of alleles

AluyTF AluyMICB

No. with Aluy

insertion % delta

No. with Aluy

insertion % delta

A*02:01 6 3 50.0 0 1 16.7 0.800#

A*02:06 4 3 75.0 0.667 2 50.0 0

A*11:01 5 0 0.0 - 1 20.0 0.750#

A*23:51 1 0 0.0 - 0 0.0 0

A*24:02 19 6 31.6 0.538# 2 10.5 0.882

#

A*24:07 20 4 20.0 0.750# 4 20.0 0.750

#

A*24:10 1 0 0.0 - 0 0.0 -

A*26:16 1 0 0.0 - 0 0.0 -

A*02:01:66 1 0 0.0 - 0 0.0 -

A*33:03 5 0 0.0 - 0 0.0 -

A*11:119 5 1 20.0 0.503# 0 0.0 -

A*02:03:01 5 0 0.0 - 0 0.0 -

A*33:03:01 4 2 50.0 0 0 0.0 -

A*33:03:05 1 0 0.0 - 0 0.0 -

A*34:01:01 6 1 16.7 0.800# 0 0.0 -

Aluy*2 represents the presence of Alu insertion; #delta values less than zero, thus delta’ calculated

53

Table 11. The associations between four Alu insertions with HLA-A alleles of Javanese samples (continued)

HLA-A

Alleles

Number

of alleles

AluyHJ AluyHF

No. with Aluy

insertion % delta

No. with Aluy

insertion % delta

A*02:01 6 5 83.3 0.800 1 16.7 0.800#

A*02:06 4 3 75.0 0.667 0 0.0 -

A*11:01 5 3 60.0 0.333 0 0.0 -

A*23:51 1 1 100.0 0 0 0.0 -

A*24:02 19 15 78.9 0.733 0 0.0 -

A*24:07 20 20 100.0 1 0 0.0 -

A*24:10 1 1 100.0 1 0 0.0 -

A*26:16 1 0 0.0 - 1 100.0 1

A*02:01:66 1 0 0.0 - 1 100.0 1

A*33:03 5 2 40.0 0.667# 0 0.0 -

A*11:119 5 3 60.0 0.333 0 0.0 -

A*02:03:01 5 0 0.0 - 0 0.0 -

A*33:03:01 4 1 25.0 0.667# 0 0.0 -

A*33:03:05 1 1 100.0 1 0 0.0 -

A*34:01:01 6 3 50.0 0 0 0.0 -

Aluy*2 represents the presence of Alu insertion; #delta values less than zero, thus delta’ calculated

54

3.2.2 The association between four Alu insertions with HLA-B alleles

Table 12 summarizes the number, percentage and delta value of POALINs associations

with HLA-B alleles. Similar to HLA-A alleles, an association between POALINs and

the HLA-B alleles was also considered not significant when only one example of an

HLA allele was observed in the population.

There were less than 70% associations between the Alu insertions and HLA-B alleles.

The associations between AluyHJ*2 and allele B*1801 was 60%, B*27:06 was 66.7%

and B*15:02:06 was 66.7%, whilst the association between AluyTF*2 and B*15:13:01

was 62.5%. Furthermore, significant delta values (>0.50) were only observed in the

associations between AluyHJ*2 with B*27:06 and B*15:02:06.

3.3 SIX POINTS HAPLOTYPE AND PHYLOGENETIC TREE

3.3.1 Six points haplotype of MHC class I of the Javanese

Six loci in the MHC class I region were analyzed to obtain the haplotype frequencies

(Table 13). From the number of 205 possible haplotypes, the two most frequent

haplotype frequencies were A*24:07-B*15:02:06-AluyTF*1-AluyMICB*1-AluyHJ*2-

Aluy*HF*1 and A*24:07-B*18:01-AluyTF*1-AluyMICB*1-AluyHJ*2-Aluy*HF*1 with

the frequency of 0.013. Both of the most frequent haplotypes consist of HLA allele

A*24:07 and the insertion of AluHJ. The other haplotypes produced the same

frequency value which was 0.006.

3.3.2 Phylogenetic tree of POALINs

The allele frequencies of the four POALINs in nine populations (Table 14) were used to

produce the genetic distance values as shown in Figure 17. Subsequently, the values

were used to construct the phylogenetic tree in Figure 18. A theoretical out-group with

a frequency close to zero, which was the ancestral state of each POALINs, was used to

root the tree (Dunn et al. 2007; Yao et al. 2009). Therefore, based on the ancestral form

of no insertion being the root of the tree, the phylogenetic tree of the POALINs

indicated that Javanese-Indonesia was clustered with Japanese (Dunn et al. 2002), NE-

Thais (Dunn et al. 2005), Chinese from Malaysia (Dunn et al. 2007), and Mongolian

Khan (Tian et al. 2008), while the Arab Bedouin (Al-Safar 2009) formed a cluster with

Australian-Caucasian (Dunn et al. 2002).

55

Table 12. The associations between four Alu insertions with HLA-B alleles of Javanese samples

HLA-B

Alleles

Number

of alleles

AluyTF AluyMICB

No. with Aluy

insertion

% delta No. with Aluy

insertion % delta

B*15:02 17 2 11.8 0.866 0 0 -

B*15:13:01 8 5 62.5 0.400 0 0 -

B*18:01 10 2 20.0 0.750# 0 0 -

B*15:21 7 0 0 - 0 0 -

B*44:03:02 3 0 0 - 0 0 -

B*44:37:02 1 0 0 - 0 0 -

B*53:08:01 1 0 0 - 0 0 -

B*51:02:02 1 1 100 1 1 100 1

B*52:01 8 2 25.0 0.667# 0 0 -

B*15:24 1 0 0 - 0 0 -

B*15:02:06 3 0 0 - 0 0 -

B*40:67 2 0 0 - 0 0 -

B*38:02 2 1 50.0 0 1 50 0

B*27:06 3 1 33.3 0.505# 0 0 -

B*57:01 1 1 100 1 0 0 -

Aluy*2 represents the presence of Alu insertion; #delta values less than zero, thus delta’ calculated

56

Table 12. The associations between four Alu insertions with HLA-B alleles of Javanese samples (continued)

HLA-B

Alleles

Number

of alleles

AluyHJ AluyHF

No. with Aluy

insertion % delta

No. with Aluy

insertion % delta

B*15:02 17 9 52.9 0.110 0 0 -

B*15:13:01 8 4 50.0 0 0 0 -

B*18:01 10 6 60.0 0.333 0 0 -

B*15:21 7 2 28.6 0.599# 0 0 -

B*44:03:02 3 1 33.3 0.505# 0 0 -

B*44:37:02 1 1 100 1 0 0 -

B*53:08:01 1 1 100 1 0 0 -

B*51:02:02 1 1 100 1 0 0 -

B*52:01 8 2 25.0 0.667# 0 0 -

B*15:24 1 1 100 1 0 0 -

B*15:02:06 3 2 66.7 0.505 0 0 -

B*40:67 2 2 100 1 0 0 -

B*38:02 2 1 50.0 0 0 0 -

B*27:06 3 2 66.7 0.505 0 0 -

B*57:01 1 1 100 1 0 0 -

Aluy*2 represents the presence of Alu insertion; #delta values less than zero, thus delta’ calculated

57

Table 13. Haplotypes of six loci in MHC class I region of the Javanese population

Bold letters and numbers show significant haplotype frequencies

MHC Class I

Haplotype ID

Haplotypes Haplotype

frequency HLA-A HLA-B AluyTF AluyMICB AluyHJ AluyHF

3A A*33:03:01 B*51:21 2 1 1 1 0.006

3B A*33:03:07 B*44:03:02 1 1 1 1 0.006

3C A*30:01 B*40:160 1 1 1 1 0.006

3D A*24:02 B*15:02 1 1 1 1 0.006

3E A*24:02 B*15:02 2 1 2 1 0.006

3F A*24:02 B*18:01 1 1 2 1 0.006

3G A*24:02 B*52:01 1 1 2 1 0.006

3H A*24:02:05 B*18:01 1 1 2 1 0.006

3I A*24:07 B*15:02 2 1 2 1 0.006

3J A*24:07 B*15:02:06 1 1 2 1 0.013

3K A*24:07 B*18:01 1 1 2 1 0.013

3L A*11:119 B*07:05:05 1 1 1 1 0.006

58

Table 14. Allele frequencies of Alu insertions in Javanese (Indonesia) and nine other populations

Populations POALIN allele frequencies

Population size AluyMICB*2 AluyTF*2 AluyHJ*2 AluyHF*2

Australian 0.157 0.107 0.252 0.203 105

Japanese 0.118 0.083 0.376 0.064 87

North Eastern Thai 0.117 0.086 0.292 0.018 192

Malaysian Chinese 0.170 0.040 0.300 0.030 50

Mongolian Khan 0.378 0.220 0.293 0.098 41

South African South Eastern Bantu 0.030 0.100 0.070 0.090 50

South African Kung San 0.036 0.283 0.107 0.060 42

South African Sekele San 0.050 0.034 0.050 0.083 60

Arab Bedouin 0.146 0.110 0.242 0.225 91

Indonesian Javanese 0.040 0.110 0.330 0.020 100

(Dunn et al. 2007; Dunn et al. 2002; Dunn et al. 2005; Al-Safar 2009; Tian et al. 2008; Kulski & Dunn 2005)

59

Figure 17. Genetic distance values obtained from ten populations. The Gendist software, a

component of the Phylip program (version 3.69), was used to compare Nei's genetic distance

values of the Javanese and nine previously studied populations

Genetic Distance Value

Populations 1 2 3 4 5 6 7 8 9 10 11

Australian-Caucasian

Japanese 0.012

North-Eastern Thai 0.011 0.003

Malaysian-Chinese 0.011 0.004 0.002

Mongolian-Khan 0.027 0.031 0.027 0.023

SA-South Eastern Bantu 0.015 0.030 0.018 0.023 0.053

SA-Kung San 0.027 0.038 0.026 0.037 0.050 0.010

SA-Sekele San 0.016 0.031 0.019 0.022 0.054 0.001 0.018

Bedouin from Arabia 0.000 0.015 0.014 0.014 0.031 0.015 0.027 0.016

Indonesian-Javanese 0.015 0.003 0.002 0.007 0.041 0.021 0.025 0.025 0.018

Root 0.028 0.039 0.024 0.028 0.068 0.004 0.021 0.002 0.028 0.029

60

Figure 18. A phylogenetic tree of ten populations using four Alu insertions in the MHC class I region. The absence of Alu insertion

as the ancestral state of each POALINs was used to root the tree. The neighbor -joining phylogenetic tree was established using

MEGA software with 1000 bootstrap replications, and seed = 64,238 values.

61

CHAPTER 4: DISCUSSION

4.1 HLA TYPING

The HLA alleles of Javanese population were obtained from only 51 and 46 DNA

samples for HLA-A and HLA-B loci respectively, while alleles from the other samples

were unable to be assigned. There were several possible causes of the unidentified

alleles. Poor condition of archive DNA samples might create poor sequencing result,

thus the alleles could not be assigned by the SBTengine software. Moreover,

inadequate cleanup of PCR products or cycle-sequencing reactions can also create poor-

quality sequencing data (Church 2013). Therefore, to perform a thorough cleanup

process at each step is considered necessary as there are many commercial purifying

kits available to date (Applied-Biosystems 2009).

Another possible cause could have been that the DNA samples contained new alleles

which have not been entered into the IMGT database as yet. However, to confirm a

new allele, further research should be conducted. As required by the custodians of the

IMGT database (http://www.ebi.ac.uk/ipd/imgt/hla/subs/submit), there are some

important conditions for a new allele to be approved and accepted for inclusion in the

database. For example, sequencing of the sample should always be performed in both

directions, and if possible confirmation of a novel sequence should be carried out by

using a method such as PCR-SSP or PCR-SSO with specifically designed primers or

probes which cover the new mutation. Cloning of a particular sequence which contains

new mutation prior to sequencing is also recommended (Bugawan et al. 1999; Pyo et al.

2001).

A common problem in HLA typing, the ambiguity, also occurred in DNA samples of

Javanese volunteers that were sequenced. As more PCR-based HLA typing is being

performed in more populations, the number of alleles also increase (Erlich 2000). The

increase of allele sequence database has an effect on the increase of ambiguity problem

(Erlich 2012). Ambiguity occurs where there are more than one pairs of alleles

consistent with the HLA database. Thus, the more alleles are cataloged in the database

the more possibilities of these alleles having similar profiles and create ambiguity. In

relation to organ or cell transplantation, allele ambiguities have to be eliminated to

increase match percentage between the donors and the recipients. Therefore, the

growing list of HLA ambiguities has made additional testing a necessity (Erlich 2012;

62

Erlich 2000). The SBTengine software used in the present study, however, suggested a

resolving strategy to address ambiguity problems which was additional sequencing of

the exon 4 of HLA-A and HLA-B.

The HLA alleles obtained from HLA-A locus showed allele A*24:07 (19.6%) to be the

most frequent allele in the Javanese, followed by allele A*24:02 (17.6%). The results

correspond to the previous studies of several Asian populations such as Malays, Han

and Uyghur-Chinese, and Thais where the allele A*24 was commonly found in

relatively high frequency (Shen et al. 2010b; Shen et al. 2010a; Dhaliwal et al. 2007;

Chandanayingyong et al. 1997). The allele A*24:07 as the most frequent allele in the

Javanese was also found to be the most common allele in Western-Javanese (known as

Sundanese) with allele frequency of 21.6% (Yuliwulandari et al. 2008). However, in

other Asian populations, the allele A*24:02 was observed more frequently than the

A*24:07 (Hoa et al. 2008; Ogata et al. 2007; Tabbada et al. 2010; Itoh et al. 2005).

The Javanese and Sundanese populations, which belong to the Austronesian, are both

located in the western part of Indonesia. Interestingly, in a previous study of the non-

Austronesian such as Moluccas and Nusa Tenggara (sic) (Lewis, Simon & Fennig

2013), HLA-A*24:02 allele was found most frequently. In populations of Papua New

Guinea (PNG), moreover, HLA-A*24:07 allele was rarely found (Bugawan et al. 1999).

Taking historical linguistic classification into consideration, Indonesia consists of two

major language families, the Austronesian and the non-Austronesian which is also

called Papua. Therefore, based on the observations, it is possible that the A*24:07

allele is commonly found in the Austronesian speakers while rarely found in non-

Austronesian speakers. Hence, as Indonesia constitutes more than 300 ethnic groups,

more populations need to be analyzed using HLA genetic markers to confirm this

association.

The HLA-A24 serology group, particularly allele A*24:02, has been reported to have a

strong association with complete β-cell destruction of insulin-dependent diabetes

mellitus (IDDM) patients in Japanese (Inoko 2006). Therefore, with respect to the high

frequency of the HLA-A24 serology group, the possible association between β-cell

destruction IDDM and the alleles in Javanese (Indonesia) is worthy of further

investigation.

The present study identified more alleles in the HLA-B locus rather than the HLA-A

locus. There were 31 alleles of HLA-B assigned from 46 DNA samples. Therefore,

63

based on the observation of these results and published data on other populations, the

HLA-B locus is more polymorphic than the HLA-A locus. The HLA-B locus, is

considered to be the most polymorphic locus in HLA (Consortium 1999).

At the HLA-B locus, the most frequent allele in the Javanese population was B*15:02

(18.5%), followed by B*18:01 (10.9%). The allele B*15:02 which belongs to B15

serology group has been reported to be relatively common in other Asian populations

such as Malays (Dhaliwal et al. 2007), Japanese (Itoh et al. 2005), Taiwanese (Lai et al.

2010), Vietnamese (Hoa et al. 2008), and also the Han-Chinese (Shen et al. 2010b;

Ogata et al. 2007). The allele is considered to be the most common allele in South-East

and East Asians (Ogata et al. 2007). Another common allele in Asia is B*46.

However, allele B*46 was not observed in the present study, while this particular allele

was commonly found at high frequency in other East Asian countries (Shen et al.

2010b; Lai et al. 2010; Ogata et al. 2007; Hoa et al. 2008; Romphruk et al. 2010). This

observation also corresponds to the previous study of HLA in Sundanese (Indonesia)

(Yuliwulandari et al. 2008). There are other alleles which belong to B15 serology

group observed in the present study such as B*15:21 (7.6%), B*15:24 (1.1%) and

B*15:25 (B1.1%).

The B*15:02 allele has been reported to have a strong association with carbamazepine

which induces Stevens Johnson syndrome in Taiwan Han-Chinese and other Asian

populations such as Thailand, Malaysia and India (Man et al. 2007; Chung et al. 2004;

McCormack et al. 2011). Another study also reported the association between this

allele and Toxic Epidermal Necrolysis induced by carbamazepine in Han-Chinese (Man

et al. 2007; Hung et al. 2006). However, there was no association between allele

B*15:02 with carbamazepine-induced hypersensitivity reaction in Europeans, yet the

association was between allele A*31:01 (McCormack et al. 2011). The association

between the disease and the alleles is possibly related to ethnicity. Therefore, the

possibility of an association between allele B*15:02 with carbamazepine-induced

hypersensitivity reaction in Javanese is worthy to be explored further.

Based on the haplotype frequencies of HLA-A and HLA-B, two of the most frequent

haplotypes comprised the serology group of A24-B15. The haplotype was also

observed in another western Indonesia population (Sundanese) and Jiangsu Han-

Chinese populations with the frequency of approximately 3% and 2.24% respectively

(Miao et al. 2007; Yuliwulandari et al. 2008). The findings, therefore, suggest that the

64

Indonesian populations (Sundanese and Javanese) show greater similarity to ethnic

groups in Southeastern and Eastern Asia populations (Yuliwulandari et al. 2008). The

present study of HLA-A and HLA-B loci, however, does not provide relatedness of the

populations in the phylogenetic tree. Thus, to provide the relationship and linkage

among the populations, genetic distance and phylogenetic tree of HLA-A and HLA-B

alleles need to be performed in the future study.

4.2 DISTRIBUTION OF POALINs IN JAVANESE

To confirm the relationships established by HLA-A and HLA-B loci, the series of

POALINs in the MHC cluster that are physically linked to the HLA-A and HLA-B were

studied. The alleles of the four POALINs and their genotype frequencies were counted

for the Javanese (Indonesia) population. The AluyHJ*2 was the most frequent allele in

the Javanese with allele frequency of 33% of the population, while the lowest frequency

was AluyHF*2 (2%).

In comparison to a previous study of Arab Bedouin conducted at the University of

Western Australia’s Center for Forensic Science (CFS) (Table 15), the AluyHJ*2 was

also the most frequent allele, followed by AluyHF, AluyTF and AluyMICB. The allele

frequency of AluyHJ*2 in the Javanese, however, was found to be higher than in the

Arab Bedouin. Further comparison with different populations in Asian countries

(Northeastern Thais, Chinese-Malays and Japanese), insertion of AluyHJ was also

commonly observed at relatively high frequency (Dunn et al. 2005; Dunn et al. 2007;

Dunn et al. 2002). The allele frequency of AluyHJ*2 of Javanese, however, was higher

than Australia-Caucasian, NE Thais and Malaysian-Chinese, yet lower than Japanese.

The AluyHF*2, which was found at the lowest frequency in the Javanese, was also

observed at the lowest frequency in other Asian populations such as Japanese, NE

Thais, Chinese-Malays, and Mongolian Khan. There was, however, a significant

difference of AluyMICB*2 allele frequency between the Javanese and other Asian

populations. In the Javanese, the allele frequency of AluyMICB*2 was observed in

relatively low frequency (4%), while in other Asian populations the allele frequency

was found more than 10% (Dunn et al. 2007; Dunn et al. 2005; Dunn et al. 2002).

The most diverse Alu insertion in Javanese was the AluyHJ as the insertion was found to

have the highest heterozigosity, followed by AluyTF. Similarly, Malaysian-Chinese and

NE Thais also showed a high degree heterozigosity for the AluyHJ locus (Dunn et al.

65

2005; Dunn et al. 2007). In comparison to Arab Bedouin (Table 15), the AluyHJ

(0.367) was observed as the highest heterozigosity, yet followed by AluyHF (0.349).

Generally, however, other Asia populations involved the analysis of AluyHG which

located in the alpha block of HLA class I region together with AluyHJ and AluyHF

(Dunn et al. 2007; Dunn et al. 2005). The present study does not include this locus, and

future studies involving the analysis of AluyHG locus in the POALINs in the Javanese

population needs to be considered.

The distribution of two Alu insertions (AluyMICB and AluyHF) in the Javanese showed

no deviation from the Hardy-Weinberg equilibrium which suggests that these two Alu

insertions are distributed normally in the population. The other two Alu insertions

(AluyHJ and AluyTF), however, showed significant deviation (<0.05) from the HW

equilibrium, especially the AluyTF locus. These two loci, however, had relatively high

allele frequencies of Alu insertions.

Deviation from HW equilibrium of these two Alu insertions can be due to disease

association or selection (Balding 2006). A previous study of Alu insertions in Western

Australia, has reported that the AluyTF insertion was strongly associated with Non-

Melanoma Skin Cancer (NMSC). Therefore, it was suggested that AluyTF insertion in

HLA class I region had a potential role in NMSC (Dunn, Inoko & Kulski 2006).

However, apparent deviations from HW equilibrium can also arise due to a mutation in

the PCR-primer site or a possibility to miscall heterozygotes as homozygotes (Balding

2006).

Haplotypes using the four Alu loci from the MHC class I region were constructed.

There were thirteen possible haplotypes obtained using the Arlequin software. Based

on the haplotype frequencies of Javanese, the highest frequency (51.4%) was the

haplotype deficient in Alu insertions (2A), while the most frequent haplotype with

multiple insertions was the haplotype 2G with AluyHJ and AluyTF inserted. However,

comparison of haplotype frequencies could not be performed with the Arab Bedouin

population due to the absence of four Alu haplotypes data available from the previous

study. Further comparison between the Javanese and other populations also could not

be performed, as the previous population studies included the AluyHG for haplotype

analysis.

66

Table 15. The genotypes, allele frequencies and HW equilibrium of Arab Bedouin population (Al-Safar 2009)

Alu Locus Genotypes observed Allele frequencies

p value

(HWE)

Heterozigosity

n 1/1 1/2 2/2 Allele*1 Allele*2

AluyMICB 89 65 22 2 0.854 0.146 0.931 0.249

AluyTF 91 70 20 1 0.890 0.110 0.745 0.196

AluyHJ 91 50 38 3 0.758 0.242 0.185 0.367

AluyHF 91 53 35 3 0.775 0.225 0.330 0.349

67

The haplotype with the single Alu insertion was more frequent than those with multiple

Alu insertions. The probability of independent insertions of two or more Alu elements

at the same region was presumably estimated to be close to zero (Batzer et al. 1990).

Therefore, the probability of Alu elements inserted at different loci within the same

individual (haplotype) is considered rare. However, the occurance of multiple

insertions of Alu elements is most probably due to recombination of haplotypes with

single but different polymorphic elements (Dunn et al. 2005).

As well as being the most frequent Alu insertion, the AluyHJ was also observed to have

strong associations with HLA-A alleles as well as HLA-B alleles. The strongest

percentage of association (100%) was observed between AluyHJ*2 with allele A*24:07.

Another strong percentage was also found between the insertion with allele A*24:02.

This corresponds to observations from previous studies conducted in several

populations, strong correlation between AluyHJ and A24 was also observed (Dunn et al.

2005; Dunn et al. 2007; Dunn et al. 2002; Yao et al. 2009). Therefore, it was suggested

that HLA-A24 was a founder allele in which the AluyHJ*2 insertion occurred (Dunn et

al. 2005).

However, there were strong percentages of correlation between AluyHJ*2 and allele

A*02:01 (83.3%) and A*02:06 (75%) in the Javanese samples. In previous studies, the

HLA-A*02 allele was strongly associated with AluyHG*2, and therefore HLA-A02 was

likely to be the founder of AluyHG*2 insertion (Dunn et al. 2005; Dunn et al. 2007). As

the present study does not include the AluyHG, thus the findings need to be analyzed

further to identify the association between AluyHG and HLA-A alleles. The plausible

elucidation for the unexpected association between AluyHJ*2 and allele HLA-A02 is

the likelihood of allele ambiguity, which requires sequencing of exon 4 to resolve the

problem. However, the possibility of Javanese population is distinctive as compared to

other populations cannot be excluded at this stage.

The delta value is a statistical correlation of linkage disequilibrium (LD) between two

loci. The value 0 indicates that the two loci are in complete equilibrium, while 1

indicates highest value of disequilibrium. Positive delta values were obtained where the

observed frequency was higher than the expected frequency (Kulski, Shigenari & Inoko

2011). Large and positive delta values (>0.50) (Dunn et al. 2005; Dunn et al. 2007),

which indicate tight linkage disequilibrium, were obtained from the strong associations

between AluyHJ*2 and HLA-A alleles in Javanese. The findings, therefore, correspond

68

to the strong percentage of associations obtained. It is suggested that the frequency of

AluyHJ*2 occurs together with allele HLA-A24 or HLA-A02 more often than would be

expected by chance.

In contrast to HLA-A alleles, there was no significant percentage of association (≥70%)

between the Alu insertions with HLA-B alleles. However, several alleles of HLA-B

were observed to have decent percentage of associations with AluyHJ*2 and AluyTF*2.

The result showed no significant association between HLA-B alleles with AluyMICB*2

in Javanese. In comparison to other populations, AluyMICB*2 was the element that

frequently associated with HLA-B alleles (Dunn et al. 2005; Dunn et al. 2007; Dunn et

al. 2002). As AluyMICB is located near the HLA-B locus, which is within the beta

block, therefore is likely to have strong association with HLA-B alleles. Moreover,

many of different MIC gene polymorphisms have been strongly associated previously

with certain groups of HLA-B polymorphisms due to a decrease rate of recombination

between the genes (Dunn et al. 2003). Nevertheless, the HLA-B alleles of Javanese

were found to have association with AluyHJ (located in the alpha block of MHC).

4.3 SIX POINTS HAPLOTYPES AND PHYLOGENETIC TREE OF Alu

INSERTIONS

The haplotype analysis of six loci produced 205 possible haplotypes. There was no

haplotypes observed to have more than 15% frequency in the Javanese. The highest

frequency of six points haplotypes occurred only 13% in the population, while the other

frequencies occurred 6%. Two of the most frequent haplotypes in Javanese consist of

HLA-A*24:07 and the insertion of AluyHJ. Comparison to other populations, however,

cannot be performed as previous data of six points haplotypes is unavailable at present.

Phylogenetic tree was constructed to perform a better view of interrelationship of the

POALINs frequencies in different populations. A neighbor-joining phylogenetic tree

showed the relationship of ten populations, including the Arab Bedouin and the

Javanese, based on four Alu insertions in MHC class I region alone. Due to the human

specificity of the four Alu insertions, the tree was rooted with hypothetical ancestor

population which was known to be the absence of the insertion (Batzer & Deininger

2002; Deininger & Batzer 1999).

The phylogenetic tree of POALINs showed all African populations clustered closely to

the hypothetical ancestral population (root). The Southeast Asia populations, including

69

Javanese (Indonesia), and East Asia population were grouped into separate cluster

which formed a series of continuous clusters. The Arab Bedouin, on the other hand,

was distinctively clustered with Caucasian Australia, apart from the Javanese

(Indonesia).

A previous study of human populations phylogenetic which was determined by Alu

insertions supports the Out of Africa model (Antunez-de-Mayolo et al. 2002). The

model is most widely accepted and supported by previous molecular data of various

genetic markers including Y-chromosome and mitochondrial DNA (Bowcock et al.

1994; Antunez-de-Mayolo et al. 2002; Maca-meyer et al. 2001; Karafet et al. 2010).

The hypothesis, also known as the two-waves hypothesis, suggested that the first

expansion was through southern coastal route to south and southeast Asia between 60

and 40 thousand years ago. The second was a central route through the Middle East to

central Asia, and occurred in all directions to Europe, east and northeast Asia (Cavalli-

Sforza & Feldman 2003).

The findings by HUGO Pan-Asia SNP Consortium (2009), which provided evidence

from autosomal data of various ethnicities in Asia were inconsistent with the two-waves

hypothesis. The results showed decreasing haplotype diversity from southern Asia to

northern Asia. It was also observed that 50% of East Asia (EA) haplotypes were in

Southeast Asia (SEA) only, and 5% were in Central-South Asia (CSA) only which

indicated major source of EA populations were SEA populations. Therefore, it was

concluded that the settlement of early humans to the Asia continent was via single

primary wave of entry.

Based on the phylogenetic tree of POALINs, Javanese (SEA population) and Japanese

(EA population) were clustered together. Hence, the two populations were considered

sharing similar genetic relationship. It was also observed that Arab Bedouin was

clustered distinctively with Caucasian Australia, and separated from the cluster of

Southeast and East Asia populations. It can be suggested that the early human

migrations out of Africa occurred through southern coastal route, and central route to

Europe and Middle East. Therefore, the present study more likely corroborates previous

theory of two-waves hypothesis. However, limited number of populations used in the

study might not comprehensively represent world populations, thus complexity and

detail history of early human migrations to the Asia continent remains a challenge and

inconclusive towards the HUGO Pan-Asia SNP Consortium findings.

70

Further analysis of POALINs phylogenetic tree showed that there was no linkage

observed between the two distinct Islamic populations as Javanese and Arab Bedouin

were clustered differently. Therefore, the migration process which led to one of the

most dramatic cultural and social changes, the “Islamization”, of Indonesia resulted in

the smallest amount of genetic change.

In a study of human Y chromosome of Indonesia populations conducted previously

(Karafet et al. 2010), it was suggested that the final phase of settlement in Indonesia

(especially in western region) involved several incursions such as the spread of

Hinduism and the spread of Islam ultimately from the Arabia. However, similar to the

present study, the “Islamization” and “Indianization” occurred in Indonesia only

account for a small percentage of the Y-chromosome. The conclusion drawn from the

human Y chromosome study, however, was based on the paternal lineages.

Nevertheless, limited number of populations and loci in chromosome 6 (4 Alu elements)

analyzed in the present study was insufficient to establish direct genetic relationship

between Javanese and Arab Bedouin as well as history of human migrations. Hence, to

provide a better view of genetic linkages, more populations need to be analyzed using

the Alu elements and other loci such as HLA-DQ and HLA-DR located in chromosome

6 to obtain comprehensive coverage.

In conclusion, typing of HLA-A showed the A*24:07 allele to be the most frequent

allele in Javanese (Indonesia) which was also commonly observed at high frequency in

Sundanese (Indonesia), yet rarely found in eastern part of Indonesia and other Asian

populations. The most frequent allele of HLA-B in Javanese (B*15:02) was commonly

observed in other Asian populations, including the Sundanese (Indonesia).

Furthermore, phylogenetic tree of POALINs clustered the Javanese (Indonesia) with

other Southeastern to Eastern Asians, yet differently clustered with the Arab Bedouin.

Therefore, the present study shows that the six loci in MHC class I region have greater

similarity to the Southeast and East Asia ethnic groups rather than the Arab Bedouin. It

is then suggested that the Arab Bedouin had left very least genetic footprint in Indonesia

during the ancient trading era. Hence, no relationship observed between these two

distinct Moslem populations.

As suggestion for future works, further research and observation of the six loci in more

populations in Indonesia need to be performed, as well as the addition of AluyHG locus.

71

Thus, comprehensive comparison between Javanese (Indonesia) and other populations

can be obtained. Moreover, ambiguity problems have to be resolved by sequencing the

exon 4 of HLA-A and HLA-B. Finally, with respect to the possibility of new alleles of

HLA-A and HLA-B, research to meet the requirements of new allele has to be carried

out, as the result shows more than five alleles has the possibility of containing a new

allele. The use of new sequencing technologies such as next generation sequencing may

provide better identity and resolve HLA allele.

72

BIBLIOGRAPHY

Abdurashitov, MA, Tomilov, VN, Chernukhin, VA & Degtyarev, SK 2008, 'A physical

map of human Alu repeats cleavage by restriction endonucleses', BMC

Genomics, vol. 9, no. 305, pp. 1-11.

Al-Safar, HS 2009, Characterisation of MHC polymorphic Alu insertions (POALIN) in

Arab Bedouins population, thesis, University of Western Australia.

Antunez-de-Mayolo, G, Antunez-de-Mayolo, A, Antunez-de-Mayolo, P, Papiha, SS,

Hammer, M, Yunis, JJ, Yunis, EJ, Damodaran, C, Pancorbo, MMd, et al. 2002,

'Phylogenetics of worldwide human populations as determined by polymorphic

Alu insertions', Electrophoresis, vol. 23, no. 19, pp. 3346-3356.

Apple, RJ & Erlich, HA 1996, 'HLA class II genes: structure and diversity', in HLA and

MHC: genes, molecules and function, eds M Browning & A McMichael, BIOS

Scientific Publishers Limited, Oxford, pp. 97 - 112.

Applied-Biosystems 2009, DNA sequencing by capillary electrophoresis: Applied

Biosystems chemistry guide, Applied Biosystems, Foster City, CA.

Balding, DJ 2006, 'A tutorial on statistical methods for population association studies',

Nature Reviews Genetics, vol. 7, no. 10, pp. 781-791. Available from: aph.

Bathia, K, Easteal, S & Kirk, RL 1995, 'A study of genetic distance and the

Austronesian/Non-Austronesian dichotomy', in The Austronesians: historical

and comparative perspectives, eds P Bellwood, JJ Fox & D Tyron, Australian

National University, Canberra, Australia, pp. 181 - 191.

Batzer, MA & Deininger, PL 2002, 'Alu repeats and human genomic diversity', Nature,

vol. 3, pp. 370 - 379.

Batzer, MA, Kilroy, GE, Richard, PE, Shaikh, TH, Desselle, TD, Hoppens, CL &

Deininger, PL 1990, 'Structure and variability of recently inserted Alu family

members', Nucleic Acid Research, vol. 18, no. 23, pp. 6793 - 6798.

Beck, S & Trowsdale, J 2000, 'The human major histocompatibility complex: lessons

from the DNA sequence', Annual Review of Genomics and Human Genetics,

vol. 1, pp. 117-137.

Bellwood, P 2007, Prehistory of the Indo-Malaysian archipelago. 3rd ed, The

Australian National University Press, Canberra.

Bellwood, P, Fox, JJ & Tyron, D 1995, 'The Austronesians in history: common origins

and diverse transformations', in The Austronesians: historical and comparative

73

perspectives, eds P Bellwood, JJ Fox & D Tyron, Australia National

University, Canberra, Australia, pp. 1 - 16.

Bennett, EA, Keller, H, Mills, RE, Schmidt, S, Moran, JV, Weichenrieder, O & Devine,

SE 2008, 'Active Alu retrotransposons in the human genome', Genome

Research, vol. 18, no. 1875-1883.

Berkey, JP 2003, The formation of Islam: religion and society in the near east, 600-

1800, Cambridge University Press, Cambridge.

Bhoosreddy, GL & Wadher, BJ 2010, Basic immunology, Himalaya Publishing House,

New Delhi.

Bontrop, RE 2000, 'The evolution of the Major Histocompatibility Complex: insights

from phylogeny', in HLA in health and disease, eds R Lechler & A Warrens,

Academin Press Limited, London, pp. 163 - 170.

Bowcock, AM, Ruiz-Linares, A, Tomfohrde, J, Misch, E, Kidd, JR & Cavalli-Sforza,

LL 1994, 'High resolution of human evolutionary trees with polymorphic

microsatellites', Nature, vol. 368, pp. 455 - 457.

Bugawan, TL, Mack, SJ, Stoneking, M, Saha, M, Beck, HP & Erlich, HA 1999, 'HLA

class I allele distributions in six Pacific/ Asian populations: evidence of

selection at the HLA-A locus', Tissue Antigens, vol. 53, no. 4, p. 311. Available

from: aph.

Cavalli-Sforza, LL & Feldman, MW 2003, 'The application of molecular genetic

approaches to the study of human evolution', Nature Genetics, vol. 33, pp. 266-

275.

Chandanayingyong, D, Stephens, HAF, Klaythong, R, Sirikong, M, Udee, S, Longta, P,

Chantangpol, R, Bejrachandra, S & Rungruang, E 1997, 'HLA-A, -B, -DRB1, -

DQA1, and -DQB1 polymorphism in Thais', Human Immunology, vol. 53, no.

2, pp. 174-182.

Chung, W, Hung, S, Hong, H, Hsih, M, Yang, L, Ho, H, Wu, J & Chen, Y 2004,

'Medical genetics: a marker for Stevens-Johnson syndrome', Nature, vol. 428,

no. 6982, pp. 486-486. Available from: pbh.

Church, DL 2013, 'Principles of capillary-based sequencing for clinical microbiologist',

Clinical Microbiology Newsletter, vol. 35, no. 2, pp. 11-18.

Cole, DP 2003, 'Where Have the Bedouin Gone?', Anthropological Quarterly, vol. 76,

no. 2, pp. 235-267. Available from: pbh.

Comas, D, Calafell, F, Mateu, E, Perez-Lezaun, A, Bosch, E, Martinez-Arias, R,

Clarimon, J, Facchini, F, Fiori, G, et al. 1998, 'Trading genes along the silk

74

road: mtDNA sequences and the origin of central asian populations', The

American Journal of Human Genetics, vol. 63, pp. 1824-1838.

Consortium, THP-AS 2009, 'Mapping Human Genetic Diversity in Asia', Science, vol.

326, pp. 1541-1545.

Consortium, TMS 1999, 'Complete sequence and gene map of a human major

histocompatibility complex', Nature, vol. 401.

Cordaux, R & Batzer, MA 2009, 'The impact of retrotransposons on human genome

evolution', Nature Reviews Genetics, vol. 10, no. 10, pp. 691-703.

Cox, MP 2008, 'Accuracy of molecular dating with the Rho statistic: deviations from

coalescent expectations under a range of demographic models', Human

Biology, vol. 80, no. 4, pp. 335-357.

Dazzi, F 2010, 'Molecular Basis of Transplantation', in Molecular Hematology, Wiley-

Blackwell, pp. 380-389.

Deininger, PL & Batzer, MA 1999, 'Alu repeats and human disease', Molecular

Genetics and Metabolism, vol. 67, no. 3, pp. 183-193.

Dhaliwal, JS, Shahnaz, M, Too, CL, Azrena, A, Maiselamah, L, Lee, YY, Irda, YA &

Salawati, M 2007, 'HLA-A, -B and -DR allele and haplotype frequencies in

Malays', Asian Pacific Journal of Allergy and Immunology, vol. 25, pp. 47-51.

Drakeley, S 2005, The history of Indonesia, ABC-CLIO, Santa Barbara.

Dunn, DS, Choy, MK, Phipps, ME & Kulski, JK 2007, 'The distribution of major

histocompatibility complex class I polymorphic Alu insertions and their

associations with HLA alleles in a Chinese population from Malaysia', Tissue

Antigens, vol. 70, pp. 136-143.

Dunn, DS, Inoko, H & Kulski, JK 2003, 'Dimorphic Alu element located between the

TFIIH and CDSN genes within the major histocompatibility complex',

Electrophoresis, vol. 24, pp. 2740-2748.

Dunn, DS, Inoko, H & Kulski, JK 2006, 'The association between non-melanoma skin

cancer and a young dimorphic Alu element within the major histocompatibility

complex class I genomic region', Tissue Antigens, vol. 68, no. 2, pp. 127-134.

Dunn, DS, Naruse, T, Inoko, H & Kulski, JK 2002, 'The association between HLA-A

alleles and young Alu dimorphisms near the HLA-J, -H and -F genes ini

workshop cell lines and Japanese and Australian populations', Journal of

Molecular Evolution, vol. 55, pp. 718-726.

75

Dunn, DS, Ota, M, Inoko, H & Kulski, JK 2003, 'Association of MHC dimorphic Alu

insertions with HLA class I and MIC genes in Japanese HLA-B48 haplotypes',

Tissue Antigens, vol. 62, no. 3, pp. 259-262.

Dunn, DS, Romphruk, AV, Leelayuwat, C, Bellgard, M & Kulski, JK 2005,

'Polymorphic Alu insertions and their associations with MHC class I alleles

and haplotypes in the Northern Thais', Annals of Human Genetics, vol. 69, pp.

364-372.

Dunn, PPJ 2011, 'Human leucocyte antigen typing: techniques and technology, a critical

appraisal', International Journal of Immunology, vol. 38, pp. 463-473.

Dyer, PA, Martin, S & Stanford, RE 2000, 'Serological method in HLA typing', in HLA

in health and disease, eds R Lechler & A Warrens, Academic Press Limited,

London, pp. 429 - 440.

Ehlers, A, Beck, S, Forbes, SA, Trowsdale, J, Volz, A, Younger, R & Ziegler, A 2000,

'MHC-linked olfactory receptor loci exhibit polymorphism and contribute to

extend HLA/OR-haplotypes', Genome Research, vol. 10, pp. 1968-1978.

Eren, E & Travers, P 2000, 'The structure of Major Histocompatibility Complex and its

molecular interactions', in HLA in health and disease, eds R Lechler & A

Warrens, Academic Press Limited, London, pp. 23 - 34.

Erlich, H 2012, 'HLA DNA typing: past, present, and future', Tissue Antigens, vol. 80,

no. 1, pp. 1-11.

Erlich, HA 2000, 'Polymerase Chain Reaction-based methods of HLA typing', in HLA

in health and disease, eds R Lechler & A Warrens, Academic Press Limited,

London, pp. 451 - 462.

Excoffier, L, Laval, G & Schneider, S 2005, 'Arlequin (version 3.0): An integrated

software package for population genetic data analysis', Evolutionary

Bioinformatics Online, vol. I, pp. 47-50.

Excoffier, L & Lischer, EL 2010, 'Arlequin suite ver 3.5: a new series of programs to

perform population genetic analyses under Linux and Windows', Molecular

Ecology Resources, vol. 10, pp. 564-567.

Gablinger, N 2005, Arabs in Encyclopedia of world trade from ancient times to the

present, M.E. Sharpe, Inc.

Grover, D, Mukerji, M, Bhatnagar, P, Kannan, K & Brahmachari, SK 2004, 'Alu repeat

analysis in the complete human genome: trends and variations with respect to

genomic composition', Bioinformatics, vol. 20, no. 6, pp. 813-817.

76

Hagelberg, E, Keyser, M, Nagy, M, Roewer, L, Zimadahl, H, Krawezak, M, Lio, P,

Schiefenhovel, W, Bradman, N, et al. 1999, 'Molecular evidence for the human

settlement of the Pacific: analysis of mithocondrial DNA, Y chromosomes and

HLA markers', Philosophical Transactions: Biological Sciences, vol. 354, no.

1379, pp. 141-152.

Hedges, DJ, Walker, JA, Callinan, PA, Shewale, JG, Sinha, SK & Batzer, MA 2003,

'Mobile element-based assay for human gender determination', Analytical

Biochemistry, vol. 312, no. 1, pp. 77-79.

Hoa, BK, Hang, NTL, Kashiwase, K, Ohashi, J, Lien, LT, Horie, T, Shojima, J,

Hijikata, M, Sakurada, S, et al. 2008, 'HLA-A, -B, -C, -DRB1 and -DQB1

alleles and haplotypes in the Kinh population in Vietnam', Tissue Antigens, vol.

71, no. 2, pp. 127-134.

Horton, R, Wilming, L, Rand, V, Lovering, RC, Bruford, EA, Khodiar, VK, Lush, MJ,

Povey, S, Jr., CLT, et al. 2004, 'Gene map of the extended human MHC',

Genetics, vol. 3, pp. 889-899.

Hung, SI, Chung, WH, Jee, SH, Chen, WC, Chang, YT, Lee, WR, Hu, SL, Wu, MT,

Chen, GS, et al. 2006, 'Genetic susceptibility to carbamazepine-induced

cutaneous adverse drug reactions', Pharmacogenetics and Genomics, vol. 16,

pp. 297 - 306.

Hunter-Zinck, H, Musharoff, S, Salit, J, Al-Ali, KA, Chouchane, L, Gohar, A,

Matthews, Butler, MW, Fuller, J, et al. 2010, 'Population structure of the

people of Qatar', The American Journal of Human Genetics, vol. 87, pp. 17-25.

Inoko, KNaH 2006, 'Combination of HLA-A24, -DQA1*03, and -DR9 contributes to

acute-onset and early complete β-cell destruction in type 1 diabetes', Diabetes,

vol. 55, no. 6, pp. 1862 - 1868.

Itoh, Y, Mizuki, N, Shimada, T, Azuma, F, Itakura, M, Kashiwase, K, Kikkawa, E,

Kulski, JK, Satake, M, et al. 2005, 'High-throughput DNA typing of HLA-A, -

B, -C and -DRB1 loci by a PCR-SSOP-Luminex method in the Japanese

population', Immunogenetics, vol. 57, pp. 717-729.

Jayaram, S 2005, Srivijaya empire in Encyclopedia of world trade from ancient times to

the present, M.E. Sharpe, Inc.

Karafet, TM, Hallmark, B, Cox, MP, Sudoyo, H, Downey, S, Lansing, JS & Hammer,

MF 2010, 'Major east-west division underlies Y chromosome stratification

across Indonesia', Molecular Biology and Evolution, vol. 27, no. 8, pp. 1833-

1844.

77

Kark, R & Frantzman, SJ 2012, 'Empire, state and the Bedouin of the Middle East, Past

and Present: a comparative study of land and settlement policies', Middle

Eastern Studies, vol. 48, no. 4, pp. 487-510.

Kaufman, J 1996, 'Evolution of the major histocompatibility complex and MHC-like

molecules', in HLA and MHC: genes, molecules and function, eds M Browning

& A McMichael, Bios Scientific Publishers Limited, Oxford, pp. 1-22.

Keyser, M, Brauer, S, Cordaux, R, Casto, A, Lao, O, Zhivotovsky, LA, Moyse-Faurie,

C, Rutledge, RB, Schiefenhoevel, W, et al. 2006, 'Melanesian and Asian

origins of Polynesians: mtDNA and Y chromosome gradients across the

Pacific', Molecular Biology and Evolution, vol. 23, no. 11, pp. 2234-2244.

Khitrinskaya, IY, Stepanov, VA & Puzyrev, VP 2003, 'Alu repeats in the human

genome', Molecular Biology, vol. 37, no. 3, pp. 325-333.

Konkel, MK, Walker, JA & Batzer, MA 2010, 'LINEs and SINEs of primate evolution',

Evolutionary Anthropology: Issues, News, and Reviews, vol. 19, no. 6, pp. 236-

249.

Krausa, P & Browning, M 1996, 'Detection of HLA gene polymorphism', in HLA and

MHC: genes, molecules and function, eds M Browning & A McMichael, BIOS

Scientific Publishers Limited, Oxford, pp. 113 - 138.

Kulski, JK & Dunn, DS 2005, 'Polymorphic Alu insertions within the Major

Histocompatibility Complex class I genomic region: a brief review',

Cytogenetic and Genome Research, vol. 110, pp. 193-202.

Kulski, JK, Dunn, DS, Hui, J, Martinez, P, Romphurk, AV, Leelayuwat, C, Tay, GK,

Oka, A & Inoko, H 2002a, 'Alu polymorphism within the MICB gene and

association with HLA-B alleles', Immunogenetics, vol. 53, pp. 975-979.

Kulski, JK, Martinez, P, Longman-Jacobsen, N, Wang, W, Williamson, J, Dawkins, RL,

Shiina, T, Naruse, T & Inoko, H 2001, 'The association between HLA-A alleles

and an Alu dimorphism near HLA-G', Journal of Molecular Evolution, vol.

2001, no. 53, pp. 114-123.

Kulski, JK, Shigenari, A & Inoko, H 2011, 'Genetic variation and hitchhiking between

structurally polymorphic Alu insertions and HLA-A, -B, and -C alleles and

other retroelements within the MHC class I region', Tissue Antigens, vol. 78,

no. 5, pp. 359-377.

Kulski, JK, Shina, T, Anzai, T, Sakae, K & Inoko, H 2002b, 'Comparative genomic

analysis of the MHC: the evolution of class I duplication blocks, diversity and

complexity from shark to man', Immunological Reviews, vol. 190, pp. 95-122.

78

Kumar, S, Nei, M, Dudley, J & Tamura, K 2008, 'MEGA: A biologist-centric software

for evolutionary analysis of DNA and protein sequences', Briefings in

Bioinformatics, vol. 9, no. 4, pp. 299-306.

Kumar, S, Tamura, K & Nei, M 1994, 'MEGA: Molecular Evolutionary Genetics

Analysis software for microcomputers', CABIOS, vol. 10, no. 2, pp. 189-191.

Kurz, B, Steiret, I, Heuchert, G & Muller, CA 1999, 'New high resolution typing

strategy for HLA-A locus alleles based on dye terminator sequencing of

haplotypic group-specific PCR-amplicons of exon 2 and exon 3', Tissue

Antigens, vol. 53, pp. 81-96.

Lai, M-J, Wen, S-H, Lin, Y-H, Shyr, M-H, Lin, P-Y & Yang, K-L 2010, 'Distributions

of human leukocyte antigen–A, –B, and –DRB1 alleles and haplotypes based

on 46,915 Taiwanese donors', Human Immunology, vol. 71, no. 8, pp. 777-782.

Leffell, MS 2002, 'MHC polymorphism: Coping with the allele explosion', Clinical and

Applied Immunology Reviews, vol. 3, pp. 35-46.

Lewis, B 1993, The Arabs in history, 6th edition, Oxford University Press, Oxford, UK.

Lewis, MP 2009, Ethnologue: languages of the world. 16th ed., SIL International,

Dallas.

Lewis, MP, Simon, GF & Fennig, CD 2013, Ethnologue: languages of the world. 17th

ed., SIL International, Dallas.

Little, A-M, Marsh, SGE & Madrigal, JA 2007, 'Histocompatibility', in Postgraduate

Haematology, Blackwell Publishing Ltd, pp. 395-418.

Maca-meyer, N, Gonzalez, AM, Larruga, JM, Flores, C & Cabrera, VM 2001, 'Major

genomic mitochondrial lineages delineate early human expansions', BMC

Genetics, vol. 2, p. 13.

Makalowski, W 2000, 'Genomic scrap yard: how genomes utilize all that junk', Gene,

vol. 256, no. 61-67.

Male, D, Brostoff, J, Roth, DB & Roitt, I 2006, Immunology: Seventh edition, Mosby

Elsevier, Canada.

Man, CBL, Kwan, P, Baum, L, Yu, E, Lau, KM, Cheng, ASH & Ng, MHL 2007,

'Association between HLA-B*1502 allele and antiepileptic drug-induced

cutaneous reactions in Han Chinese', Epilepsia, vol. 48, no. 5, pp. 1015-1018.

Marsh, SGE, Albert, ED, Bodmer, WF, Bontrop, RE, Dupont, B, Erlich, HA,

Fernandez-Vifia, M, Gerathy, DE, Holdsworth, R, et al. 2010, 'An update to

HLA nomenclature, 2010', Bone Marrow Transplantation, vol. 45, pp. 846-

848.

79

McCormack, M, Alfirevic, A, Bourgeois, S, Farell, JJ & Kasperaviciute, D 2011, 'HLA-

A3101 and carbamazepine-induced hypersensitivity reactions in Europeans',

The New England Journal of Medicine, vol. 364, no. 12, pp. 1134 - 1143.

Meyer, D & Thomson, G 2001, 'How selection shapes variation of the human major

histocompatibility complex: a review', Annals of Human Genetics, vol. 65, pp.

1-26.

Miao, KR, Pan, QQ, Tang, RC, Zhou, XP, Fan, S, Wang, XY, X, XZ, Xue, M, Zhou,

XY, et al. 2007, 'The polymorphism and haplotype analysis of HLA-A, -B and

-DRB1 genes of population in Jiangsu province of China', International

Journal of Immunogenetics, vol. 34, pp. 419-424.

Milner, CM, Campbell, RD & Trowsdale, J 2000, 'Molecular genetics of the human

Major Histocompatibility Complex', in HLA in health and disease, eds R

Lechler & A Warrens, Academin Press Limited, London, pp. 35 - 50.

Mona, S, Grunz, KE, Brauer, S, Pakendorf, B, Castri, L, Sudoyo, H, Marzuki, S,

Barnes, RH, Schmidtke, J, et al. 2009, 'Genetic admixture history of eastern

Indonesia as revealed by Y-chromosome and mitochondrial analysis',

Molecular Biology and Evolution, vol. 26, no. 8, pp. 1865-1877.

Moribe, T, Kaneshige, T & Inoko, H 1997, 'Complete HLA-A DNA typing using the

PCR-RFLP method combined with allele group- and sequence- specific

amplification', Tissue Antigens, vol. 30, pp. 535-545.

Murphy, K, Travers, P, Walport, M & Janeway, C 2012, Janeway's immunobiology,

Garland Science, New York.

Ogata, S, Shi, L, Matsushita, M, Yu, L, Huang, XQ, Sun, H, Ohashi, J, Muramatsu, M,

Tokunaga, K, et al. 2007, 'Polymorphisms of human leucocyte antigen genes in

Maonan people in China', Tissue Antigens, vol. 69, no. 2, pp. 154-160.

Oppenheimer, S & Richards, M 2001, 'Fast trains, slow boats, and the ancestry of the

Polynesian islanders', Science Progress, vol. 84, no. 3, p. 157.

Owens, K & King, MC 1999, 'Genomic views of human history', Science, vol. 286, pp.

451-453.

Parham, P & Ohta, T 1996, 'Population biology of antigen presentation by MHC class I

molecules', Science, vol. 2272, pp. 67-73.

Patterson, M 2000, 'Linkange disequilibrium: highs and lows', Nature Reviews Genetics,

vol. 1, no. 2, p. 83. Available from: aph.

Pozzi, S, Longo, A & Ferrara, GB 1999, 'HLA-B locus sequence-based typing', Tissue

Antigens, vol. 53, pp. 275-281.

80

Pyo, CW, Choi, HB, Han, H, Hong, YS & Kim, TG 2001, 'Identification of HLA-A

variant (A*1107) in the Korean population', Tissue Antigens, vol. 58, pp. 190 -

192.

R-Development-Core-Team, R: A language and environment for statistical computing

R Foundation for Statistical Computing. Available at:http://www.R-project.org.

Ray, DA, Walker, JA & Batzer, MA 2007, 'Mobile element-based forensic genomics',

Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis,

vol. 616, no. 1–2, pp. 24-33.

Reid, A 1995, 'Continuity and change in the Austronesian transition to Islam and

Christianity', in The Austronesians: historical and comparative perspectives,

eds P Bellwood, JJ Fox & D Tyron, Autralian National University, Canberra,

Australia, pp. 314 - 331.

Romphruk, AV, Romphruk, A, Kongmaroeng, C, Klumkrathok, K, Paupairoj, C &

Leelayuwat, C 2010, 'HLA class I and II alleles and haplotype in ethnic

Northern Thais', Tissue Antigens, vol. 75, pp. 701-711.

Salibi, KS 1980, A history of Arabia, Caravan Books, New York.

Sanchez-Mazas, A, Poloni, ES, Jaques, G & Laurent, S 2005, 'HLA genetic diversity

and linguistic variation in East Asia', in Peopling of East Asia: putting together

archaelogy, linguistics and genetics, eds L Sagart, R Blench & A Sanchez-

Mazas, Routledge Curzon, London.

Santori, G, Andorno, E, Morelli, N, Casaccia, M, Bottino, G, Domenico, SD & Valente,

U 2009, 'A 20-Year Period of Orthotopic Liver Transplantation Activity in a

Single Center: A Time Series Analysis Performed Using the R Statistical

Software', Transplantation Proceedings, vol. 41, no. 4, pp. 1286-1289.

Shankarkumar, U 2004, 'The human leukocyte antigen (HLA) system', International

Journal of Human Genetics, vol. 4, no. 2, pp. 91-103.

Shen, C, B, BZ, Liu, M & Li, S 2008, 'Genetic polymorphisms at HLA-A, -B, and -

DRB1 loci in Han population of Xi'an City in China', Croatian Medical

Journal, vol. 49, pp. 478-482.

Shen, C, Zhu, B, Deng, Y, Ye, S, Yan, J, Yang, G, Wang, H, Qin, H, Huang, Q, et al.

2010a, 'Allele polymoprhism and haplotype diversity of HLA-A, -B and -

DRB1 loci in sequence-based typing for Chinese Uyghur ethnic group', Plos

One, vol. 5, no. 11, pp. 1-9.

Shen, C, Zhu, B, Ye, S, Liu, M, Yang, G, Liu, S, Qin, H, Zhang, H, Lucas, R, et al.

2010b, 'Allelic diversity and haplotype structure of HLA loci in the Chinese

81

Han population living in the Guanzhong region of the Shaanxi province',

Human Immunology, vol. 7, pp. 627-633.

Solberg, OD, Mack, SJ, Lancaster, AK, Single, RM, Tsai, Y, Sanchez-Mazas, A &

Thomson, G 2008, 'Balancing selection and heterogeneity across the classical

human leukocyte antigen loci: a meta-analytical review of 497 population

studies', Human Immunology, vol. 69, no. 443-464.

Steinlechner, M, Berger, B, Niederstatter, H & Parson, W 2002, 'Rare failures in the

amelogenin sex test', International Journal of Legal Medicine, vol. 116, pp.

117-120.

Suryadinata, L, Arifin, EN & Ananta, A 2003, Indonesian's population: ethnicity and

religion in a changing political landscape, Institute of Southeast Asian Studies,

Singapore.

Tabbada, KA, Trejaut, J, Loo, J, Chen, Y, Lin, M, Mirazon-Lahr, M, Kivisild, T &

Ungria, MCD 2010, 'Philippine mitochondrial DNA diversity: a populated

viaduct between Taiwan and Indonesia?', Molecular Biology and Evolution,

vol. 27, no. 1, pp. 21-31.

Teebi, AS 2010, 'Genetic diversity among Arabs', in Genetic disorders among Arab

populations, ed. AS Teebi, Springer Berlin Heidelberg, Berlin.

Thangaraj, K, Reddy, AG & Singh, L 2002, 'Is the amelogenin gene reliable for gender

identification in forensic casework and prenatal diagnosis?', International

Jornal of Legal Medicine, vol. 116, pp. 121-123.

Thorsby, E 1999, 'MHC structure and function', Transplantation Proceedings, vol. 31,

pp. 713-716.

Thorsby, E 2009, 'A short history of HLA', Tissue Antigens, vol. 74, pp. 101-116.

Tian, W, Wang, F, Cai, JH & Li, LX 2008, 'Polymorphic insertions in 5 Alu loci within

the major histocompatibility complex class I region and their linkage

disequilibria with HLA alleles in four distinct populations in mainland China',

Tissue Antigens, vol. 72, pp. 559-567.

Trowsdale, J 1996, 'Molecular genetics of HLA class I and class II regions', in HLA and

MHC: genes, molecules and function, ed. MBaA McMichael, BIOS Scientific

Publishers Limited, Oxford, pp. 23-38.

Tumonggor, MK, Karafet, TM, Hallmark, B, Lansing, JS, Sudoyo, H, Hammer, MF &

Cox, MP 2013, 'The Indonesian archipelago: an ancient genetic gighway

linking Asia and the Pacific', Journal of Human Genetics, vol. 58, pp. 165-173.

82

Tyron, D 1995, 'Proto-Autronesian and the major Autronesian subgroups', in The

Austronesians: historical and comparative perspectives, eds P Bellwood, JJ

Fox & D Tyron, Australian National University, Canberra, Australia, pp. 17 -

38.

Voris, HK 2000, 'Maps of Pleistocene sea levels in Southeast Asia: shorelines, river

systems and time durations', Journal of Biogeography, vol. 27, no. 5, pp. 1153-

1167.

Wallace, AR 1869, The Malay archipelago: the landof the orangutan, and the bird of

paradise, Macmillan and Co., London.

Worrall, S, Baptista, FG, Mason, VW & Ritter, LR 2009, 'Tang shipwreck', National

Geographic, pp. 1 - 5.

Yao, Y, Shi, L, Kulski, JK, Chen, J, Liu, S, Yu, L, Lin, K, Huang, X, Tao, Y, et al.

2010, 'The association and differentiation of MHC class I polymorphic Alu

insertions and HLA-B/Cw alleles in seven Chinese populations', Tissue

Antigens, vol. 76, no. 3, pp. 194-207.

Yao, Y, Shi, L, Shi, L, Lin, K, Tao, Y, Yu, L, Sun, H, Huang, X, Li, Y, et al. 2009,

'Polymorphic Alu insertions and their associations with MHC class I alleles

and haplotypes in Han and Jinuo populations in Yunnan Province, southwest of

China', Journal of Genetics and Genomics, vol. 36, pp. 51-58.

Yuliwulandari, R, Kashiwase, K, Nakajima, H, Uddin, J, Susmiarsih, TP, Sofro, ASM

& Tokunaga, K 2008, 'Polymorphisms of HLA genes in Western Javanese

(Indonesia): close affinities to Southeast Asian populations', Tissue Antigens,

vol. 73, pp. 46-53.

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APPENDICES

Appendix 1. Covering letter and list of thesis corrections

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Appendix 2. An example of HLA-A electropherogram

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Appendix 3. An example of HLA-B electropherogram