Genetic Characterization of ChrX MiniSTRs in Pakistani Population

MUHAMMAD ISRAR

National Centre of Excellence in Molecular Biology

University of the Punjab, Lahore Pakistan (2012)

Genetic Characterization of ChrX MiniSTRs in Pakistani Population

A THESIS SUBMITTED TO

UNIVERSITY OF THE PUNJAB

IN FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

MOLECULAR BIOLOGY

BY

MUHAMMAD ISRAR

SUPERVISORS: DR. AHMAD ALI SHAHID DR. ZIAUR RAHMAN

NATIONAL CENTRE OF EXCELLENCE IN MOLECULAR BIOLOGY UNIVERSITY OF THE PUNJAB LAHORE

PAKISTAN

(October, 2012)

IN THE NAME OF ALLAH, THE MOST BENEFICENT, THE MOST MERCIFUL

AL QURAN

O mankind, indeed We have created you from male and female and made you peoples and tribes that you may know one another. Indeed, the most noble of you in the sight of Allah is the most righteous of you. Indeed, Allah is Knowing and Acquainted.

(Quran 49:13)

DEDICATED

To

My Mother

A strong and gentle soul who taught me to trust in

Allah, believe in hard work and that so much

could be done with little

My Grandmother

For being my first teacher

My Father

For earning an honest living for us and for

supporting and encouraging me to believe in

myself

My Uncle

For being my guardian during my educational

career

CERTIFICATE

It is certified that the research work described in this thesis is the original work of the

author Mr. Muhammad Israr and has been carried out under our direct supervision. We

have personally gone through all the data reported in the manuscript and certify their

correctness/authenticity. It is further certified that the material included in this thesis has

not been used in part or full in a manuscript already submitted or in the process of

submission in partial/complete fulfillment of the award of any other degree from any

other institution. It is also certified that the thesis has been prepared under our supervision

according to the prescribed format and we endorse its evaluation for the award of Ph.D.

degree through the official procedures of the University.

In accordance with the rules of the Centre, data books # M157, M178, 942 and 998 are

declared as unexpendable document that will be kept in the registry of the Centre for a

minimum of three years from the date of the thesis defense examination.

Signature of the Supervisor: __________________

Name: Dr. Ahmad Ali Shahid

Assistant Professor

Signature of the Supervisor: ___________________

Name: Dr. Ziaur Rahman

Assistant Professor

iii

Summary

Short tandem repeats (STRs) are genetic loci containing tandemly repeated

sequences of 2–6 base pairs in length. High polymorphism as well as the ease in

genotyping has made STR DNA Profiling the most popular method of human

identification. As compared with minisatellites, the small amplified fragment length of

STRs facilitates its utility in the analysis of degraded DNA samples.

However, in situations where DNA is highly degraded, poor amplification of the

larger sized loci (300–500 base pairs) in standard multiplex typing kits is common. DNA

template can become highly fragmented as the sample decomposes, and the yield of

complete target fragments is greatly reduced. In this case, the larger amplicons often have

lower sensitivity and fall below the detection threshold which can result in a partial

genetic profile. To solve this problem, the primers were developed to positions as close as

possible to the ends of the repeat reducing the amplified product size. Amplification of

compromised DNA using these reduced sized primer sets in a single reaction was called

Miniplex system.

X-chromosomal short tandem repeats (X-STRs) have been proved to be the best

complement of autosomal markers in complex kinship cases such as deficiency paternity

testing in which disputed child is a female when mother and any paternal relative is

available for testing. X-STRs can also be used to test true sisterhood without father’s

DNA and to identify the female DNA in mixed stains. The experience of using autosomal

short-length amplicon STRs or miniSTRs in profiling of degraded DNA and mass disaster

victims is extended into the realm of X-chromosomal (ChrX) STR miniaturization. About

half of the total X-STRs are now short length amplicons and the focus is shifting to

completely use the mini versions of all of them. Multiplexing of these can be used for

iv

solving complex paternity cases and association of mass disaster victims with their

families. This technology may herald a new dimension into research of population

genetics and evolution.

This study presents development and optimization of a new ChrX-miniSTR

multiplex system providing short-amplicon (<200bp) fragments. This multiplex includes

11 ChrX miniSTRs (DXS101, DXS6789, DXS6793, DXS7132, DXS7423, DXS7424,

DXS8378, DXS9902, GATA31E08, GATA172D05 and HPRTB) as well as gender

determining locus Amelogenin. Developmental validation studies included sensitivity and

specificity studies, cycle number, annealing temperature, primer concentration, peak

balance and degraded DNA studies. Allele frequency distribution of 11 X-STRs was

determined in a large group of individuals including males and females from 4 provinces

(Baluchistan =100, KP = 120, Punjab = 250, Sindh = 100) of Pakistan. A total of 5–11

alleles were observed for each locus and altogether 79 alleles for all 11 X-STR loci.

Heterozygosity in females ranged from 0.5351 to 0.8332. No significant deviation was

observed from Hardy–Weinberg equilibrium for all 11 microsatellites. The power of

discrimination using all the 11 X-STRs was 1.28 x 10-11 in females and 2.64 x 10-7 in

males, which fulfills demands for paternity testing.

This study also includes haplotype analysis of DXS7424-DXS101 in a sample of

149 males from Baluchi, KP and Sindhi population. In this cluster, 9 alleles for marker

DXS101 were identified while 8 alleles were identified for DXS7424 and total of 46

different haplotypes were identified through genotyping.

The data can be used as reference database for Pakistani Population along with the

current battery of autosomal STR for forensic case work to increase the discrimination

capacity and strengthen the existing system.

v

Acknowledgements

All acclamations and appreciations are for ALIMIGHTY ALLAH, the Omnipotent, the

Omnipresent, the Compassionate, the Beneficent and the source of all knowledge and wisdom, who

bestowed upon me the intellectual ability, courage and strength to complete this humble contribution

towards knowledge. I am proud of being a follower of the Holy Prophet Hazrat Muhammad

(PBUH), the most perfect and exalted among and of ever born on the surface of earth, who declared it

to be an obligatory duty of every man and woman to seek and acquire knowledge.

I feel highly privileged to take this opportunity to wish my profound gratitude with a deep

sense of obligation to my doctoral research supervisors, Dr. Ahmad Ali Shahid and Dr. Ziaur

Rahman, Assistant Professors, National Centre of Excellence in Molecular Biology, for their personal

interest, inspiring guidance, helping attitude, and above all for providing necessary laboratory facilities

during the whole span of this research work. Thank you for your skillful guidance, invaluable

suggestions and sincere attitude throughout the course of my research. Thank you for teaching me the

noble arts of science, project management, technical writing, manuscripts preparation and

publications.

My wholehearted thanks go to the worthy Director, Dr. Tayyab Husnain for providing all the

necessary facilities for my research at the CEMB. He has been helpful in every facet of my studies and

research. He was an excellent advisor in many of the meetings that we had. Furthermore, I am greatly

indebted to Dr. S. Riazuddin, National Professor and founding director of CEMB and a researcher par

excellence, for his valuable suggestion, sympathetic attitude and cordial co-operation throughout the

progress of this research. I would like to thank Dr. Shaheen N. Khan for facilitating my research

work by extending her cordial support and guidance during my stay at the Centre.

Nothing happens all of a sudden but is the outcome of something preceding and this thesis is

no exception. I would like to thank Dr. Farhat Zaheer, Dr. G.A. Niazi, Dr. Idrees Nasir, Dr.

Muhammad Idrees, Dr. Sajida Hassan and Mr. Khalid Masood, all my teachers who taught me

during my stay at the Centre. My cordial thanks go to Dr. M. Saqib Shahzad and Dr. Obaid Ullah

for their help and immense support during every stage of my research and the write up. I wish to

extend my thanks to members of Forensic Research Lab., Genetic Diseases Lab., Genotyping Lab. and

Stem Cell Research Lab., especially, Dr. Shahid Yar Khan, Mr. Akram Tariq, Dr. Mohsin

Shehzad, Dr. Faizan Cheema, Dr. Farooq Sabir, Ms. Farheena Iqbal and Ms. Samra Kausar for

their guidance and cooperation whenever needed. I thank my Lab fellows, Mr. Ghulam Murtaza,

Ms. Shahla Nargis Mir, Dr. Usman Ali Ashfaq, Maida, Sana, Tariq Javed, Sidra Rehman, Sana,

Ms. Farah Naz, Rahat, Dr. Muhammad Ansar and Rabia Faridi for their help and support.

vi

Moreover I would like to thank Dr. Bushra Rashid, Dr. Sadia Mohsin, Dr. Azra, Dr. A. Qayyum

Roa, Mr. Kamran Bajwa, Mr. Fazal-ur-Rehman, Ms. Bushra Ijaz, Mr. Waqar, Mr. Sajid Iqbal,

Mr. Imran, Mr. Atif Anwar and Mr. Zulfiqar. Like a Movie where hundreds and thousands of

people contribute but we only see the on-screen characters, similarly a lot of people contributed and I

would like to thank all the Scientific, Para scientific and Administrative staff of CEMB who had been

directly or indirectly instrumental in my research work.

I thank my primary and high school teachers at Govt. Centennial Model School, Daggar

Buner. I thank my college lecturers at Excelsior College Swat as well as my university lecturers at

Centre of Biotechnology, University of Peshawar for their untiring devotion and dedication to

mould us into better humans and achieve something which I would not be able to do without their

enthusiasm and teachings.

I would like to thank my friends Sulaiman, Ilyas, Haji Akbar and Mian Sahib Zar for being

such wonderful friends during my stay at CEMB. Special thanks to Adnan for his help, thanks to all

the TPs and to all BBTians. Also thanks to Ibrahim and his cousin Ali, who hosted me in Baluchistan

for sampling, and to all the people who participated in this study by giving their blood samples. I

would also like to thank, Shakeel, Hayat, Fawad, Khitab, Niaz, Faidad, Masaud, Irshad, Tahir,

Liaqat, Ali, Abrar, Islam and Kazim, just a few of all the fantastic “CEMBIANS” at the CEMB,

thanks for your friendship and providing such a great research environment. I thank Asma, Sadie,

Fatima and Mona for their help and support in my research and write up. I am grateful to you all for

helping me get through the difficult times, and for all the emotional support, camaraderie,

entertainment, and caring you provided.

No words can express and no deeds can return the love, affection, amiable attitude, sacrifices,

advices, unceasing prayers, support, and inspiration that my mother, my sister Aapa Begum and

brothers Shehriyar and Bashar infused in me during my whole academic career. I thank my uncles

especially Omar and Ali and all my aunts and cousins particularly Dr. Khizar, Dr. Omar, Zeeshan

and Asif Ali, for their utmost support during my career.

Last but not the least; I would like to acknowledge Higher Education Commission (HEC) of

Pakistan for awarding me Indigenous Ph.D. fellowship and supporting a part of this study.

Muhammad Israr

vii

Abbreviations and Symbols

°C Degrees Celsius

ABI Applied Biosystems Incorporated

BLAT BLAST Like Alignment Tool

bp Basepair

CE Capillary Electrophoresis

cM CentiMorgan

CODIS Combined DNA Index System

dH2O Distilled water

DNA Deoxy Ribonucleic Acid

dNTPs DeoxyNucleoside TriPhosphates

EDNAP European DNA Profiling Group

EDTA Ethylene Diamine Tetra Acetic acid

HCl Hydrochloric Acid

ISFG International Society of Forensic Genetics

Kb Kilobases

KP Khyber Pakhtunkhwa

Mb Megabases

MgCl2 Magnesium Chloride

min Minute

mL Milliliter

mM Millimolar

miniSTRs Mini-Short Tandem Repeats

NaCl Sodium Chloride

NCBI National Center for Biotechnology Information

ng Nanogram

p Short Arm of Chromosome

PCI Phenol-Chloroform-Isoamylalcohol

PCR Polymerase Chain Reaction

pg Picogram

pmole Picomole

viii

q Long Arm of Chromosome

RFLP Restriction Fragment Length Polymorphism

RFU Relative Fluorescence Unit

RNA RiboNucleic Acid

rpm Revolution per minute

SDS Sodium Dodecyl Sulphate

s or sec Second

SNP Single Nucleotide Polymorphism

STR Short Tandem Repeat

Taq Thermus aquaticus

TE Tris-EDTA

UV Ultraviolet

Tm Melting Temperature

VNTR Variable Number of Tandem Repeats

X-STR X-chromosome Short Tandem Repeat

Y-STR Y-chromosome Short Tandem Repeat

μg Microgram

μL Microlitre

μM Micromole

ix

Table of Contents

Summary....................................................................................................... iii Acknowledgements....................................................................................... v Abbreviations and Symbols......................................................................... vii List of Tables................................................................................................ xi List of Figures............................................................................................... xii

Chapter 1 1. Introduction............................................................................................. 1

Chapter 2 2. Review of Literature............................................................................... 6

2.1 DNA Polymorphisms and Short Tandem Repeats (STRs).................................... 6 2.2 Techniques Used for Typing of STR Markers.......................................................

2.2.1 Polymerase Chain Reaction (PCR) and Multiplexing.......................................... 2.2.2 Capillary Electrophoresis (CE).............................................................................

9 9 11

2.3 X-Chromosomal STRs: Identity Testing and Beyond.......................................... 12

2.4 Evolution of MiniSTRs.......................................................................................... 17 2.5 Evidence Items Benefitting from MiniSTR Analysis............................................ 21 2.6 Miniaturization of ChrX STRs: Problems and Prospects....................................... 21

Chapter 3 3. Materials and Methods.......................................................................... 26

3.1 Blood Samples Collection..................................................................................... 26 3.2 DNA Extraction from Blood Samples................................................................... 27 3.3 DNA Quantitation.................................................................................................

3.3.1 Spectrophotometery.............................................................................................. 3.3.2 Gel Electrophoresis...............................................................................................

29 29 30

3.4 Multiplex Design Strategy..................................................................................... 3.4.1 Selection of Loci, Primer Design and Labelling................................................... 3.4.2 Size Determination................................................................................................ 3.4.3 Multiplex Schematics............................................................................................ 3.4.4 Primer Concentration............................................................................................

30 30 32 33 34

3.5 Polymerase Chain Reaction (PCR)........................................................................ 3.5.1 PCR Setup............................................................................................................. 3.5.2 Thermal Cycling Parameters................................................................................. 3.5.3 Cycle Number....................................................................................................... 3.5.4 Peak Balance......................................................................................................... 3.5.5 Sensitivity Studies................................................................................................. 3.5.6 Nonhuman DNA and Specificity Studies............................................................. 3.5.7 Degraded DNA Studies.........................................................................................

3.5.7.1 Enzymatic Degradation............................................................................ 3.5.7.2 Mechanical Degradation..........................................................................

3.5.8 Gel Extraction Protocol......................................................................................... 3.5.8.1 Gel Dissociation...................................................................................... 3.5.8.2 DNA Binding.......................................................................................... 3.5.8.3 Wash......................................................................................................... 3.5.8.4 DNA Elution............................................................................................

34 35 36 37 37 37 38 38 38 38 39 39 39 40 40

3.6 Detection and DNA Analysis................................................................................. 3.6.1 Sample Preparation for ABI 3130 Genetic Analyzer............................................ 3.6.2 Sample Electrophoresis.........................................................................................

41 41 41

x

3.6.3 Absorption/Emission Spectra of Fluorescent Dyes Used for Labelling............... 3.6.4 Software Programs for Data Analysis................................................................... 3.6.5 Statistical Analysis and Formulae.........................................................................

42 42 44

Chapter 4 4. Results...................................................................................................... 46

4.1 Development of Multiplexes.................................................................................. 4.1.1 Screening of Primers............................................................................................. 4.1.2 Development of 7-Plex......................................................................................... 4.1.3 Development of 12-Plex.......................................................................................

4.1.3.1 Sensitivity Studies.................................................................................... 4.1.3.2 Primer Concentration............................................................................... 4.1.3.3 Colour Balance......................................................................................... 4.1.3.4 Cycle Number.......................................................................................... 4.1.3.5 Annealing Temperature. .......................................................................... 4.1.3.6 MgCl2 Titration....................................................................................... 4.1.3.7 Specificity................................................................................................ 4.1.3.8 Efficiency of Multiplex Assay.................................................................

46 46 47 49 49 50 50 51 52 53 54 55

4.2 Degraded DNA Studies.......................................................................................... 4.2.1 Enzymatic Degradation......................................................................................... 4.2.2 Mechanical Degradation.......................................................................................

57 57 58

4.3 Population Genetics................................................................................................ 4.3.1 Baluchistan............................................................................................................ 4.3.2 Khyber Pakhtunkhwa (KP)................................................................................... 4.3.3 The Punjab............................................................................................................ 4.3.4 Sindh.....................................................................................................................

60 62 66 70 74

4.4 Linkage Disequilibrium.......................................................................................... 78 4.5 Haplotype Analysis................................................................................................

4.5.1 Allele Frequency for DXS7133............................................................................ 83 87

4.6 Allelic Ladder......................................................................................................... 88 4.7 Phylogenetic Analysis............................................................................................ 91

Chapter 5 5. Discussion................................................................................................. 94

5.1 Comparison of Multiplex PCR Systems................................................................ 94 5.2 Allele Distribution and Population Comparisons.................................................. 95 5.3 Linkage Disequilibrium and Haplotype Analysis.................................................. 99 5.4 Phylogenetic Studies.............................................................................................. 100 5.5 Conclusions............................................................................................................ 102

Chapter 6 6. References................................................................................................ 104

Publication............................................................................................... 127

xi

List of Tables

Table No. Page Table 2.1 X-Chromosomal miniSTRs……………………………………………… 23 Table 3.1 Markers along with their linkage groups and mapping information…….. 31 Table 3.2 Genotyping Primers Sequences and Dye Labelling................................... 32 Table 3.3a Primers concentration and ingredients for 7-plex master mix………… 35 Table 3.3b Primers concentration and ingredients for 12-plex master mix…………. 35 Table 3.3c Primers concentration and ingredients for 4-plex master mix………… 36 Table 3.4 Fluorescence Spectral Parameters……………………………………….. 42 Table 4.1 Primers interactions of 12 primer pairs with alignment score 6 or greater 47 Table 4.2 Population Specific F Statistics…………………………………………. 60 Table 4.3 Allele Frequency Distribution in Baluchi Population…………………… 64 Table 4.4 Forensic Efficiency Parameters for Baluchi Population………………… 65 Table 4.5 Allele Frequency Distribution in KP Population………………………... 67 Table 4.6 Forensic Efficiency Parameters for KP Population……………………... 68 Table 4.7 Allele Frequency Distribution in Punjabi Population…………………… 71 Table 4.8 Forensic Efficiency Parameters for Punjabi Population………………… 72 Table 4.9 Allele Frequency Distribution in Sindhi Population…………………….. 75 Table 4.10 Forensic Efficiency Parameters for Sindhi Population………………….. 76 Table 4.11 Linkage Disequilibrium Matrix Baluchistan…………………………….. 79 Table 4.12 Linkage Disequilibrium Matrix KP...…………………………………… 80 Table 4.13 Linkage Disequilibrium Matrix Punjab…………………………………. 81 Table 4.14 Linkage Disequilibrium Matrix Sindh…………………………………... 82 Table 4.15 Haplotype analysis of DXS7424-DXS101 in Baluchi Population………. 84 Table 4.16 Haplotype analysis of DXS7424-DXS101 in KP Population……...……. 85 Table 4.17 Haplotype analysis of DXS7424-DXS101 in Sindhi Population………... 86 Table 4.18 Allele Frequency for DXS7133…………………………………………. 87 Table 4.19 Observed alleles, fragment lengths and repeat size of 12-X miniSTRs…. 88 Table 4.20 Alleles of 12 X-STRs for allelic ladder…………………………………. 89

xii

List of Figures

Figure No. Page Figure 2.1 Polymorphic Regions in the Human Genome……………………………. 7 Figure 2.2 Localization of STRs on X-Chromosome………………………………... 14 Figure 2.3 MiniSTRs vs. conventional STRs………………………………………... 18 Figure 2.4 Discordance Between miniSTRs and Conventional STRs………………. 18 Figure 2.5 Evolution of miniSTRs – a Timeline…………………………………….. 20 Figure 3.1 Sampling Area and Number of Samples collected………………………. 26 Figure 3.2a Multiplex Schematics for 7-Plex…………………………………………. 33 Figure 3.2b Multiplex Schematics for 4-Plex…………………………………………. 33 Figure 3.2c Multiplex Schematics for 12-Plex………………………………………... 34 Figure 3.3a PCR Profile for 7-Plex…………………………………………………… 36 Figure 3.3b PCR Profile for 12-Plex………………………………………………….. 37 Figure 3.4 Emission Spectra…………………………………………………………. 42 Figure 4.1 Female DNA Profile of 7 X-STRs……………………………………….. 48 Figure 4.2 Sensitivity of 12-Plex…………………………………………………….. 49 Figure 4.3 Average amplification in each dye lane………………………………….. 50 Figure 4.4 Amplification at different number of PCR cycles ………………………. 51 Figure 4.5 Amplification at different annealing temperatures………………………. 52 Figure 4.6 MgCl2 Titration for 12-Plex……………………………………………… 53 Figure 4.7a Bovine DNA amplified through 12-Plex………………………………… 54 Figure 4.7b Equine DNA amplified through 12-Plex…………………………………. 55 Figure 4.8 Male DNA Profile of 11 X-STRs plus sex determining locus Amelogenin……. 56 Figure 4.9a DNA Degraded with DNase I……………………………………………. 57 Figure 4.9b DNA Degraded with ultrasonication……………………………………... 58 Figure 4.10 Degraded DNA amplified through 12-Plex……………………………… 59 Figure 4.11 Matrix of pair wise Fst between populations…………………………….. 61 Figure 4.12 Population Assignment Test for Baluchistan against other populations…. 62 Figure 4.13 Number of Alleles at each locus in Baluchi Population………………….. 63 Figure 4.14 Number of Alleles at each locus in KP Population………………………. 66 Figure 4.15 Population Assignment Test for KP against other populations………….. 69 Figure 4.16 Number of Alleles at each locus in Punjabi Population………………….. 70 Figure 4.17 Population Assignment Test for Punjab against other populations………. 73 Figure 4.18 Number of Alleles at each locus in Sindhi Population…………………… 74 Figure 4.19 Population Assignment Test for Sindhi against other populations………. 77 Figure 4.20 Tetraplex Profile of Male DNA………………………………………….. 83 Figure 4.21 Allelic Ladder for 12 X-STRs……………………………………………. 90 Figure 4.22 Phylogenetic Tree of 61 populations around the world …………………. 92

Chapter 1

The world is full of obvious things which nobody by any chance ever

observes.

Sherlock Holmes - The Hound of the Baskervilles

1

1. Introduction

Sherlock Holmes said, “I had come to an entirely erroneous conclusion which

shows how dangerous it always is to reason from insufficient data.” A century later,

forensic science has moved forward a long way and instead of crime psychology, it is the

empirical evidence that is of interest to a forensic scientist. But now it is even more

necessary to have sufficient data for an accurate analysis in a forensic laboratory lest the

faulty evidence be responsible for conviction of an innocent citizen (Caskey and

Hammond, 1992, Coovadia, 2008). As the forensic techniques advance, so do the

techniques of the perpetrators of the crimes. Moreover, the industrialization of our world

further confounds the evidence found at a crime scene or the samples left on a mass

disaster site. Hence, there is a constant pressure to develop novel and precise methods to

arrest the guilty but to exonerate the innocent or to accurately identify a missing relative.

From fingerprinting to DNA profiling, many techniques were standardized and discarded

until the data basing of core Short Tandem Repeats (STR) loci. Since then, millions of

profiles are generated and it is very likely that STRs will be the workhorses for the

foreseeable future (Gill et al., 2004, Muller et al., 2007).

Forensic science and the ability to solve crimes are revolutionized by DNA

typing. The invisible DNA molecule is the silent biological witness at crime scene. In

forensic casework we often have to face with samples which are not in the best of

conditions and can have highly degraded DNA. This includes burnt items, bones and

teeth. If exposed to heat and humidity, DNA molecules start breaking down to smaller

fragments. Nucleases from within the cell attack the DNA as soon as the cell dies, leading

to its degradation (El-Harouny et al., 2009). Biochemical, microbial or oxidative process

can also lead to DNA degradation. In favourable environment, microbes feed on the

2

hydrocarbons of DNA and render it highly fragmented. Bacteria are the main agents on

land while fungi are responsible for oceanic degradation (Leahy and Colwell, 1990,

Butler, 2006b).

The human genome consists of about 3 billion base pairs as estimated during the

human genome project (Lander et al., 2001, Venter et al., 2001). This includes coding

and non-coding regions. Around 2% of this DNA is responsible for coding genes for

polypeptides or RNA and is called coding region. However, the bulk of DNA, that is,

98% is called non-coding region. Moreover, it is estimated that 99.5% of human DNA is

similar between all individuals and only 0.5% varies from individual to individual. These

variations in DNA provide the base for human identification purposes (Feuk et al., 2006,

Levy et al., 2007).

The length polymorphisms include mini-satellites or Variable Number Tandem

Repeats (VNTR) and microsatellites or Short Tandem Repeats (STRs). VNTRs are the

markers containing repeat unit of 10 to 100 base pairs long that are repeated in tandem

over and over again (Butler, 2005). STRs, which are also called as Simple Sequence

Repeats (SSRs), as defined by Butler, “are accordion-like stretches of DNA containing

core repeat unit of between two and seven nucleotides in length that are tandemly

repeated from approximately a half dozen to several dozen times” (Butler, 2007).

Thousands of STRs have been identified in human DNA. According to an estimate, STR

marker occur every 10,000 nucleotides in the human genome (Butler, 2007). For human

identification in forensic science, STRs are the method of choice for DNA profiling

(Crow et al., 2000, Butler et al., 2007).

The DNA typing has played a pivotal role to establish the paternity of child which

is utmost priority for support, inheritance right and other social benefits of a child. Short

Tandem Repeats (STRs) located on X chromosome are powerful marker for complex

3

kinship testing such as deficiency paternity testing when the disputed child is a female

(Asamura et al., 2006, Gomes et al., 2007c, Butler, 2011). Now more than 40 X-STRs

have been characterized and validated for forensic use (Szibor et al., 2006). Although,

some of X-STRs like HPRTB (Hearne and Todd, 1991, Edwards et al., 1992), HumARA

(Edwards et al., 1992, Desmarais et al., 1998) and DXS981 (Mahtani and Willard, 1993),

were established early but the use of X-STRs in paternity testing was limited due to the

lack of their genetic characterization (Gomes et al., 2007c). Since X-Chromosome

recombines in females only, therefore, X-STRs show stronger Linkage Disequilibrium

compared with autosomal STRs (Hering et al., 2006) and the mutation rates of X-STRs

are not different to autosomal STRs. Therefore, it is worth-while including X-STRs with

autosomal markers in paternity testing (Szibor et al., 2006, Szibor, 2007, Machado and

Medina-Acosta, 2009, Jedrzejczyk et al., 2010).

X-STRs are routinely used in parentage analysis and relationship investigations

such as avuncular and first cousin relationships now a day. In addition to X-STRs, stable

haplotype of closely associated X-chromosome markers have proven to be a powerful

tool in kinship analysis (Szibor et al., 2005a) especially for cases when father/daughter

relationships are to be tested. It has been reported that a set of 12 X-STRs provides more

information than a set of 38 highly informative biallelic autosomal markers for cases

involving father–daughter duos (Gomes et al., 2012). X-STRs have also advantage over

autosomal STRs for paternity cases involving close blood relatives as alternative putative

fathers and in deficiency paternity cases, i.e. when the DNA sample from putative father

is not available and DNA from paternal relative has to be analyzed instead (Barbaro and

Cormaci, 2006). Further, X-linked STRs can be used to solve sibling ship status, without

using father’s DNA, of two females having the same biological father (Toni et al., 2003,

Toni et al., 2006). X-STRs can determine the relationship of grandmother/granddaughter

4

as granddaughter theoretically has to carry at least one allele in common with the

grandmother (Edelmann et al., 2004). In forensic analysis of mixed stains, X-STRs are

helpful to identify the female DNA (Szibor et al., 2000, Shin et al., 2005). X-STR

markers are low size markers and can efficiently be used for degraded DNA analysis.

Slightly degraded samples can be typed by traditional STRs, but may yield

negative results as the fragmentation increases (Holland et al., 2003, Grubwieser et al.,

2006, Parsons et al., 2007). Conventional STRs have a size range of 100-400bp most of

which consists of flanking sequences on both sides of the repeat region. To alleviate the

problems associated with analyzing DNA from degraded samples a new set of STR

primers known as Miniplexes were designed by moving the primers closer to the repeat

region leaving the extra sequences out (Butler et al., 2003, Coble and Butler, 2005).

Using shorter amplicons in polymerase chain reaction (PCR), improvement has been

reported in obtaining results from forensic evidence or a mass disaster site having

degraded specimens (Drabek et al., 2004).

Although, STRs are highly polymorphic and are used in combination to obtain a

DNA profile of an individual or a sample, yet the possibility that, any other person in the

population can have the exact same DNA profile cannot be ruled out. Therefore, it is of

paramount importance to determine the frequency and other statistical parameters for a

particular DNA profile in a particular population and that is presented in the court of law.

For this purpose, population databases are essential in forensic casework which provide

the information about what is the statistical probability of a particular profile to occur in a

population at random. Random match probability is the estimated frequency at which a

particular STR profile would be expected to occur in a population (Butler, 2005, Koehler

et al., 2009).

5

A sample size of about 100 individuals is reported to be sufficient for studying

genetic loci in a population provided that any allele with a frequency less than 1% should

not be included in forensic calculations (Chakraborty, 1992). However, it is customary to

assign a minimum allele frequency value to any such alleles, instead of exclusion. This

minimum value is usually very conservative, higher than the probable frequency of the

alleles in the actual population (Butler, 2011).

This study reports the successful optimization and testing of a multiplex system,

miniplex, which is capable of parallel amplification of 11 ChrX miniSTRs. The markers

included in multiplex have been proved as highly polymorphic. The markers included are:

DXS101, DXS6789, DXS6793, DXS7132, DXS7423, DXS7424, DXS8378, DXS9902,

GATA172D05, GATA31E08 and HPRTB as well as the gender determining locus,

Amelogenin. For forensic evaluation of a multiplex population data are crucial, therefore,

we analyzed Baluchi, KP, Punjabi and Sindhi population, using an ABI PRISM 3130

Genetic Analyzer (Applied Biosystems) with the miniplex system. Sampling was done

from all the 4 provinces of Pakistan totalling 570 individuals. A total of 5–11 alleles were

observed for each locus and altogether 79 alleles for all 11 X-STR loci. Heterozygosity in

females ranged from 0.5351 to 0.8332. No significant deviation was observed from

Hardy–Weinberg equilibrium for all 11 microsatellites. The power of discrimination

using all the 11 X-STRs was 1.28 x 10-11 in females and 2.64 x 10-7 in males.

This study also includes haplotype analysis of DXS7424-DXS101 in a sample of

149 males from Baluchi, KP and Sindhi population. In this cluster, 9 alleles for marker

DXS101 were identified while 8 alleles were identified for DXS7424. Therefore, 72

different combinations are possible but we identified only 46 different haplotypes through

genotyping.

Chapter 2

It has long been an axiom of mine that the little things are infinitely

the most important.

Sherlock Holmes - A Case of Identity

6

2. Review of Literature

2.1 DNA Polymorphisms and Short Tandem Repeats (STRs)

Human Genome consists of 23 pairs of chromosomes having 3 billion base pairs

of DNA. However, only about 5% of the total genome is coding sequences, the remaining

being non-coding as shown in figure 2.1. The non-coding region is highly repetitive and

although not very significant in expression studies but variations in this region are used

for identification purposes (Kashyap et al., 2004). There are mainly two types of DNA

polymorphisms in the human genome: Length polymorphism, which is the variation in

length of DNA at a particular locus in different individuals and sequence polymorphism

which is due to different recognition sequences in the DNA. Variation in fragment length

of DNA as a result of digestion by restriction enzyme is called RFLP (Restriction

Fragment Length Polymorphisms). The first polymorphic locus was discovered in 1980

(Wyman and White, 1980). RFLP, the method of choice in the 1980s is no longer used in

forensic casework due to its requirement for high quality DNA (Schneider, 2003).

The first breakthrough came in with the discovery of multi-locus probes (Jeffreys

et al., 1985a). The study found many dispersed, highly polymorphic, tandem-repetitive

'mini-satellite' regions. A probe based on a tandem-repeat of the core sequence could

detect many highly variable loci simultaneously. The multi-locus probe analysis has been

used to produce individual specific fingerprints of human DNA (Jeffreys et al., 1985c,

Jeffreys et al., 1985b) but they may also cross react with genomes from other species

(Jeffreys et al., 1987). Variable Number Tandem Repeats (VNTRs) typing became the

first generally accepted forensic DNA technology (Nakamura et al., 1987).

7

Figure 2.1: Polymorhpic Regions in the Human Genome (adapted from: Kashyap et al., 2004)

Human Genome

Mitochondrial Genome (16.5

kbp)

Nuclear Genome (3billion bp)

Coding Sequences (~5% of total genome)

Non-Coding Sequences (90-

95% of total genome)

Non-Repetitive Sequences (70-

75% of genome)

Flanking, leader, trailer

sequences, introns,

enhancers

Pseudogenes

Repetitive DNA (25-30% of

genome)

Intersperesed Repetitive DNA

(10-15%)

Tandem Repetitive DNA

(10%)

Tandem Repeat Genes (Coding

Sequences)

Satellite (~5%) Repeat Unit = 1-

1000s

Macrosatellite (Repeat Unit

=1000s) Cryptic, Midi

satellite

Microsatellite (Simple

Sequences/STR) 2-6bp, 50, 000

loci

Minisatellite (Repeat Unit = 100bp, 1500

loci)

8

VNTRs are also called single-locus probes. The locus D1S80 is one of the best-

known polymorphic loci, showing a variable number of tandem repeats (Kasai et al.,

1990). As interpretation of VNTRs was complicated by problems related to fragment

match criteria and population genetics, search for new markers of human identity testing

continued (Schneider, 2003).

Short Tandem Repeats (STR) is another class of length polymorphisms, which

occurs when a pattern of two or more nucleotides are repeated and the repeated sequences

are directly adjacent to each other. The position of STR on a chromosome is called an

STR locus. STR analysis was the real breakthrough in the field of forensic DNA testing.

The first reported STR markers were di-repeats (Litt and Luty, 1989, Weber and May,

1989) which can be typed using the polymerase chain reaction. Later on, tri-meric and

tetra-meric STRs markers were found to be more polymorphic (Edwards et al., 1991).

Compared to tri- tetra- and penta-repeats, the di-repeats produce more stutter bands due to

strand slippage during amplification (Hauge and Litt, 1993). Therefore, tri, tetra and

penta-repeat markers are more suitable for forensic purposes (Edwards et al., 1991).

For personal identification purposes like parentage testing, forensic identification

and medical applications, STRs are widely used. The presence of micro-variant further

increases the power of discrimination of STR (Butler et al., 1999). Discrimination value

is directly proportional to the increase in number of STRs used. The more STRs used,

lesser are the chances that an individual profile will match with any other in the

population at random (Panneerchelvam and Norazmi, 2003).

9

2.2 Techniques Used for Typing of STR Markers

The widespread use of STR as forensic markers was made possible through the

use of PCR. Later on with the advent of Capillary Electrophoresis (CE), the concept of

multiplexing was introduced. Till date, many multiplexes have been reported which are

capable of amplifying as many as 20 different set of primers in a single reaction.

Following is brief account of both PCR and CE.

2.2.1 Polymerase Chain Reaction (PCR) and Multiplexing

PCR has revolutionized molecular biology since its introduction and has become

the ideal choice for the analysis of STR (Saiki et al., 1985, Mullis et al., 1986). During

PCR, primers which are pair of synthetic oligonucleotides complementary to flanking

sequence around the region of interest, along with thermostable polymerase enzyme and

other reagents amplify particular segment of DNA into a large number of copies. The

process broadly consists of 3 main steps: denaturation of double-stranded DNA,

annealing of primers to their target and extension of the new sequence (Maniatis et al.,

1982).

Each cycle results in doubling the number of fragments and hence, exponential

increase is observed. Due to thermostable properties of Taq polymerase, isolated from

Thermus aquaticus (Chien et al., 1976, Lawyer et al., 1989), target amplification at

higher temperature has been made possible which results in high specificity, yield and

sensitivity (Saiki et al., 1988). There are many different types in which PCR can be set up

to achieve this specific goal. The hot start PCR is introduced by chemically inactivating

or physically separating the PCR components (Kebelmann-Betzing et al., 1998, Kaboev

et al., 2000, Kong et al., 2004), to suppress mis-priming artifacts and increase the specific

yield (D'Aquila et al., 1991, Chou et al., 1992). Other types of PCR include nested PCR

10

to increase the specificity (Haqqi et al., 1988), quantitative PCR to assess the number of

starting molecules, long range PCR to amplify the long DNA fragments (McDonald et

al., 2002), allele specific PCR to investigate SNPs and Asymmetric PCR to get single

strand amplification (Innis et al., 1988).

The ideal technique for forensic DNA typing is multiplex PCR because as the

number of polymorphic loci examined increases, the probability of identical alleles being

present in two different individuals decreases (Edwards and Gibbs, 1994). Multiplex PCR

is defined as parallel amplification of multiple loci of DNA in a single tube by adding two

or more than two primer pairs. The concept of multiplexing was initially reported in 1988

(Chamberlain et al., 1988). One of the most critical parameter for the Multiplex PCR is

the careful designing of primers as multiple primers have to anneal at similar temperature

without interfering with each other and no non specific binding. Primers designed should

have similar melting temperatures so that annealing events should occur at similar

temperature. The stringent initial primer selection reduces the costly process of

optimization of multiplex PCR (Schoske et al., 2003). There are some drawbacks to

multiplex PCR. For instance, multiplex PCR results in biased amplification due to

different length of PCR product as the shortest fragments show preferential amplification

in comparison to the longest ones. However, this effect can be reduced, either by

initiating PCR with the long amplicon primers and by adding the primer for the shorter

amplicon some cycles later (Bourque et al., 1993), or by using a low concentration of the

short amplicon primer. One of the critical issues of multiplexing is increase in number of

spurious fragments by increasing the number of loci to be amplified simultaneously

which is due to interaction of primers with each other or with non specific sequences.

Therefore, multiplexing capability is restricted to approximately 20 fragments by

11

traditional PCR. However, 1000-plex PCR is developed successfully by the careful

designing of primers (Wang et al., 2005).

The other ideas have been introducing to circumvent the affect of primer

interaction and to increase the multiplexing capability by, using chimeric primers (Shuber

et al., 1995), Molecular Inversion Probe (Hardenbol et al., 2003) and Golden Gate assay

(Fan et al., 2003). The approach of simultaneous analysis of STRs has been proved to be

worthwhile in minimizing labour, material and analysis time for forensic case work in

DNA testing laboratories. The traditional way of multiplexing is common for STR

analysis (Krenke et al., 2002, Schoske et al., 2003, Vallone et al., 2008).

2.2.2 Capillary Electrophoresis (CE)

The concept of capillary electrophoresis was introduced much earlier (Hjerten,

1967), but Jorgenson and Lukacs were the first to demonstrate the power of capillary

zone electrophoresis (Jorgenson and Lukacs, 1981, Jorgenson and Lukacs, 1983). Further

developments in the CE technique rendered it the method of choice for DNA typing

(Grossman and Soane, 1991, Williams et al., 1994, Butler et al., 1995).

Currently, STR measurement is based on electrophoretic technique, which

requires dye labelled primers and very careful analysis of results because of technology

artefacts (Butler et al., 2004). The system is composed of buffer solution containing a

water soluble polymer instead of rigid physical gel. Polyethylene oxide, hydroxyethyl

cellulose or linear polyacrylamide is used as polymers (Budowle et al., 1990) and is

pumped in to narrow 50-70µm diameter capillary, and replaced at the end of each sample

analysis. This polymer act as sieving medium and separation of DNA fragments occurs

due to retardation of molecules in polymer (Barron and Blanch, 1995). To reduce analysis

time, high voltage up to 15,000 volts can be used. The end of capillary has window for

12

purpose of detection by UV or laser-induced fluorescence. To detect the DNA,

fluorescent dye is attached to 5’end of PCR primer which becomes part of PCR product

during amplification. Different dyes have been used to label the PCR product in the case

of STR analysis (Butler, 2005). The length polymorphisms amplified by PCR can be

efficiently analyzed by capillary electrophoresis (Williams et al., 1994). The two

instruments have been calibrated for STR analysis which includes the ABI PRISM® 3730

Genetic Analyzer and ABI PRISM® 3100 Genetic Analyzer 16 Capillary System

(Applied Biosystems). These instruments provide a semi-automated and accurate

analytical system for STR typing (Lazaruk et al., 1998, Moretti et al., 2001).

2.3 X-Chromosomal STRs: Identity Testing and Beyond

The DNA typing has played a pivotal role to establish the paternity of child which

is utmost priority for support, inheritance right and other social benefits of a child.

Normal males possess one X chromosome and one Y chromosome, whereas females

possess two X chromosomes (Creighton, 1999, Baker et al., 2008, Butler, 2011). Due to

its unique inheritance pattern, the X chromosome is a potential candidate for forensic and

human identity testing applications (Butler, 2011). Short Tandem Repeats (STRs) located

on X chromosome are powerful markers for complex kinship testing such as deficiency

paternity testing when the disputed child is a female (Asamura et al., 2006, Gomes et al.,

2007b, Butler, 2011). The use of X-STRs for paternity testing was limited due to lacking

of genetically characterized markers (Gomes et al., 2007b). Although, some of X-STRs

like HPRTB (Hearne and Todd, 1991, Edwards et al., 1992), ARA (Edwards et al., 1992,

Desmarais et al., 1998) and DXS981 (Mahtani and Willard, 1993), were established early

but intentions to use these markers for forensic purposes developed late. As shown in

Figure 2.2, more than 40 X-STRs have been established as forensic markers. (Szibor et

13

al., 2006, Szibor, 2007, Machado and Medina-Acosta, 2009, Jedrzejczyk et al., 2010).

For investigation of avuncular and first cousin relationship as well as parentage, X-STRs

are routinely used.

Stable haplotyping of closely linked X-STRs are powerful tools in kinship

analysis (Hering et al., 2006). Positions of STR on X-Chromosome are given in Figure

2.2. X-STR show stronger Linkage Disequilibrium (LD) compared with autosomal STR

since X-chromosome recombines only in females and the mutation rates of X-STRs are

not different to that of autosomal STRs (Hering et al., 2006). Majority of X-STRs can be

used routinely and there are no peculiarities in term of their usage except HumARA, one

of the established STR markers. It is recommended recently that HumARA should not be

used as forensic marker (Szibor et al., 2005b) due to its association with bulbar muscular

atrophy (La Spada et al., 1991).

Inclusion of X-STRs with autosomal markers in cases when father/daughter

relationships are to be tested is worthwhile. X-STR haplotyping can be of particular help

in cases where a DNA sample from one of the parents is not available for testing. For

example, if a father/daughter parentage relationship is in question, X-STRs may be

helpful (Silveira et al., 2007, Reid et al., 2008, Aquino et al., 2009, Butler, 2009). X-

STRs have advantage over autosomal STRs for paternity cases involving close blood

relatives as alternative putative fathers.

The major advantage of X-STRs is proven in cases when the DNA sample from

putative father is not available and DNA from paternal relative has to be analyzed instead

(Barbaro and Cormaci, 2006). Further, X-linked STRs can be used to solve sibling ship

status, without father’s DNA, of two females having the same biological father (Toni et

al., 2003, Toni et al., 2006).

14

Figure 2.2: Localization of Short Tandem Repeats (STRs) on X Chromosome (from: Szibor et al., 2006)

15

X-STRs can determine the relationship of grandmother/granddaughter as

granddaughter theoretically has to carry at least one allele in common with the

grandmother (Edelmann et al., 2004).

Usually ChrX markers are less powerful in stain analyses than autosomal markers

and are not suitable for use in testing male traces where there is female contamination.

But these are more powerful than autosomal markers in identification of female traces in

male contamination (Szibor et al., 2003). In forensic analysis of mixed stains, X-STRs are

helpful to identify the female DNA (Szibor et al., 2000, Shin et al., 2005).

There are several measures like variation in allele size and numbers,

heterozygosity, genetic variability and discontinuous allele distribution, which are useful

in population genetics. These X-STRs may be powerful in the study of population

genetics and can be used to detect changes due to mutations or genetic drift among

populations (Calafell et al., 1998). Being lineage markers, these can also be used as

ancestry informative markers to deduce individual ancestry information from admixture

populations (Schaffner, 2004). The X chromosome, along with Y-chromosome and

mitochondrial DNA, has tremendous potential for the reconstruction of phylogenetic trees

to unravel the complexities of the history of populations (Santos-Lopes et al., 2007, Pinto

et al., 2011).

Similar to autosomal STRs, different multiplex systems have been developed to

use the X-STRs efficiently for paternity testing. The first multiplex study with respect to

X-STRs analysed nine loci in three different multiplexes which include duplex PCR

(DXS6789 and DXS6795), Triplex PCR (DXS7133, DXS9895 and DXS9898) and

Quadruplex PCR (DXS6803, DXS8378, GATA164A09 and DXS7132) (Son et al.,

2002). In very short period of time, other studies came up with parallel amplification of

16

three (Wiegand et al., 2003), four (Son et al., 2002, Lee et al., 2004), five (Zarrabeitia et

al., 2002, Poetsch et al., 2005), six (Robino et al., 2006), seven (Bini et al., 2005), eight

(Nakamura and Minaguchi, 2010), ten (Gomes et al., 2007b), eleven (Ribeiro Rodrigues

et al., 2008), twelve (Turrina et al., 2007), thirteen (Tariq et al., 2008, Hwa et al., 2009)

and fifteen (Liu et al., 2012) X-STR markers. Multiplex with greater number of markers

are being developed to obtain a high degree of discrimination.

Commercially, there has been little focus on developing multiplex kits for ChrX

applications. The first such effort is Mentype Argus X-UL PCR amplification kit by

Biotype AG. Four ChrX markers, HPRTB, DXS7132, DXS7423 and DXS8378 as well as

gender determining loucs Amelogenin, are included in this kit. One marker per linkage

group can be analyzed using this kit (Castella et al., 2006, Gehrig and Teyssier, 2006).

In April 2005, Biotype introduced another kit called Mentype® Argus X-8 PCR

Amplification Kit. This kit can analyze eight markers DXS8378, DXS10135, DXS7132,

DXS10134, HPRTB, DXS10101, DXS7423 and DXS10074 as well as Amelogenin for

gender determination. Two markers per linkage group have been chosen [group 1 (Xp22):

DXS8378/DXS10135, group 2 (Xq11): DXS7132/DXS10134, group 3 (Xq26):

HPRTB/DXS10101, group 4 (Xq28): DXS7423/DXS10074]. Thus, two markers of each

group have to be handled as haplotype for genotyping. The primers are labelled with 6-

FAM or HEX (Branicki et al., 2008).

Qiagen (Qiagen, Hilden, Germany), in collaboration with Biotype, developed

Investigator Argus X-12 PCR Amplification Kit which amplifies simultaneously the 12

markers DXS8378, DXS10135, DXS10148, DXS7132, DXS10134, DXS10079, HPRTB,

DXS10101, DXS10103, DXS7423, DXS10074 and DXS10146 as well as Amelogenin

for gender determination. Three markers per linkage group

17

have been chosen:

Group 1 (Xp22): DXS8378/DXS10135/DXS10148,

Group 2 (Xq11): DXS7132/DXS10134/DXS10079,

Group 3 (Xq26): HPRTB/DXS10101/SXS10103,

Group 4 (Xq28): DXS7423/DXS100749/DXS10146].

Thus, three markers of each group have to be handled as haplotype for genotyping

(Edelmann et al., 2012).

2.4 Evolution of MiniSTRs

In forensic casework we often have to face with DNA which is not in ideal

condition (El-Harouny et al., 2009). When DNA molecules are exposed to water and/or

heat, they start breaking down into smaller fragments. Different biochemical, microbial

and oxidative processes are responsible for this degradation (Leahy and Colwell, 1990,

Butler, 2006b). The commercial kits based on STR do not fare well with these

compromised samples. Hence, to improve the results obtained from such samples, we

need to look beyond the commercial kits (Butler, 2011).

One way is to use smaller PCR products, the so called miniSTRs (Wiegand and

Kleiber, 2001, Graham, 2005). As shown in Figure2.3, this is done by moving the primers

closer to the repeat region of the STR. It has been proved that short amplicons perfrom

better with degraded samples and can recover information when larger STR amplicons

produce partial or no profiles (Butler et al., 2003).

18

Figure 2.3 (from Butler, 2009): (a) MiniSTRs created by designing PCR primers (Butler et al., 2003) that anneal closer to the repat region than conventional STR kit primers. (b) PCR product sizes, such as demonstrated here with D16S539, can be reduced by 150bp relative to conventional tests.

As miniSTRs and commercial STRs have primers in different locations, there is a

possibility of mutation in the flanking sequences on both sides of the repeat region. And

as a result the PCR products generated could be of different sizes due to allele dropout

and size shifts. As shown in Figure 2.4, an apparent discordance would result due to this

(Drabek et al., 2004).

Figure 2.4: Discordance between PCR Product of MiniSTR primers and commercial Kit primers (as illustrated by: Butler, 2012)

19

In the criminal justice system, it is not uncommon that due to faulty evidence, a

person is indicted and sent to jail but years later proved to be innocent by using DNA

typing (Coovadia, 2008). Since, evidence is not always kept in a very ‘DNA-friendly’

environment; degradation is one of the most common hindrances in such cases.

MiniSTRs have the ability to extract reliable profiles from old samples (Evison et al.,

1997) and hence may be used to set the innocent free. But we need many loci for this kind

of work. Miniaturization of X-STRs used with autosomal STRs can increase the chances

of getting a complete profile from these kind of samples (Jobling and Gill, 2004).

Another avenue where miniSTRs are potential candidates for use is the study of

ancient samples (Eliasova et al., 2010) such as those found inside and around the

Pyramids of Giza, Egypt or the leftovers of Mayan civilization in Latin America. Since

the DNA found in sites like these is expected to be highly degraded, only short templates

DNA of up to 300bp is present (Haack et al., 2000) making it impossible for any

meaningful analysis with traditional STRs. These ‘minis’ can prove essential where other

anthropological methods cannot be applied for sex typing, phylogenies (Eliasova et al.,

2010) and to explore the gender differences in the past populations (Faerman et al., 1995,

Cipollaro et al., 1998). Although SNPs are potential candidates for use with degraded

samples, problems like low polymorphism and difficulty in mixture interpretation renders

their widespread use, outside the research setting, impractical (Butler et al., 2007).

The timeline below (Figure 2.5) shows the progress of forensic markers VNTRs,

STRs, and ultimately miniSTRs of autosomal and X-Chromosomes over the years.

20

Figure 2.5: Evolution of miniSTRs – a Timeline

PCR = Polymerase Chain Reaction, VNTR = Variable Number Tandem Repeats, STR = Short Tandem Repeats, WTC KADAP = World Trade Centre Kinship and DNA Analysis Panel, CODIS = COmbined DNA Indexing System, EDNAP = European DNA Profiling group, ChrX = X-Chromosome

DNA Sex Test (Sullivan

et al., 1993)

First mass disaster

case (Waco,

Texas) (Lygo et

al., 1994)

(Whitaker et al.,

1995) (Clayton et

al., 1995)

Fluorescent STR

work reported

(Edwards et al.,

1991) (Caskey and

Hammond, 1992)

Alec Jeffery discovers

VNTR (Jeffreys et

al., 1985a, Jeffreys et

al., 1985b)

1ST PCR (Saiki et al., 1985)

1985 1991 1993 1995 2001 2003 2004 2005 2006 2009 2010

DNA Databases

starts (McEwen,

1995)

New STR

primers for

CODIS loci

(Butler et al.,

2003) "MiniSTR"

coined

(Holland et

al., 2003)

Typing of

Telogen Hair

(Hellmann et

al., 2001)

WTC

KADAP

(Marchi,

2004)

“Genderplex” (Esteve

Codina et al., 2009)

8-plex and 10-plex

for ChrX miniSTR

(Diegoli and Coble,

2011)

EDNAP finds

miniSTR better

than STR and SNP

on degraded DNA

(Dixon et al., 2006)

ChrX

miniplexes

developed

(Asamura et al.,

2006)

Concordance studies

(Drabek et al., 2004)

STR miniplex for

degraded template

(Chung et al.,

2004) (Muller et

al., 2007)

Chromosome

X Sequence

(Ross et al.,

2005)

21

2.5 Evidence Items Benefitting from MiniSTR Analysis

DNA fragment sizes for the products amplified thorugh miniSTRs are much

smaller as compared with those amplified with traditional STRs. Degraded or inhibited

samples can benefit greatly from miniSTRs analysis. Degraded samples typically include

teeth, bones, items exposed to humidity or heat, burnt items, etc. The DNA in these

samples can be broken down and highly fragmented, therefore, traditional STR analysis

works poorly with these and sometimes even yield negative results (Holland et al., 2003,

Hill et al., 2007, Parsons et al., 2007).

On the other hand samples containing DNA, but also having some substance that

inactivates or slows down the PCR, are called inhibited samples. Inhibitors can be of

different types including but not limited to: heme from blood samples, humic acid present

in soil, melanin in hair or skin samples, certain dyes (such as the indigo dye in denim

fabrics) and tannins from leather (Butler, 2006a, Wiegand et al., 2006).

MiniSTR kits are very sensitive and work best with DNA concentration of 0.25-

0.5ng. These STRs have been shown to work successfully even with even less than this

amount of DNA. This sensitivity allows for obtaining results samples like the items that

have been handled by an individual called “touch” DNA items, and hairs which have very

little root tissue left (Andrade et al., 2008).

2.6 Miniaturization of ChrX STRs: Problems and Prospects

Research on ChrX miniSTRs is very slow as compared to autosomal miniSTRs.

As reported by some groups, amplicon size of miniSTR is below 200bp, above which

chances of getting complete profile from a degraded sample diminish significantly

(Muller et al., 2007). Others have suggested the optimum product size as below 150bp

(Dixon et al., 2006). Only a handful of studies are available on ChrX miniSTRs and most

22

of these are in combination with other larger amplicon size markers hence making it

unsuitable for use in degraded sample studies and casework.

The first mini-only multiplex for ChrX STRs was reported by Asamura and

colleagues. Designing new primers, they devised two 4-plexes consisting of X-STR loci,

DXS7424, DXS101, DXS7423, DXS6789, DXS8378, DXS7133, GATA165B12 and

GATA31E08. The miniplex strategy was shown to be effective for the analysis of

degraded DNA based on the results of tests with these multiplexes. It was concluded that

for personal identification, these multiplex systems offered high effectiveness with

degraded DNA samples (Asamura et al., 2006). But since samples from a mass disaster

site or from an environmentally exposed crime scene are not only highly degraded but

also in very scarce quantity, thus making it difficult for the scientist to perform multiple

PCR analyses (Butler, 2009). In 2009, a “Genderplex” was reported. This included a 4-

plex for ChrX miniSTRs along with 2 gender determining amelogenin loci and an SRY

locus (Esteve Codina et al., 2009). In 2011, two new miniplexes, an 8-plex and 10-plex

were reported which can amplify a total of 15 ChrX-STRs, DXS7132, DXS6803,

HPRTB, DXS6789, DXS7130, DXS9902, DXS101, DXS6795, DXS10147, DXS8378,

DXS7424, DXS7423, GATA165B12, GATA172D05 and GATA31E08 (Diegoli and

Coble, 2011).

As shown in Table 2.1, there has been substantial progress with shortening the

length of many ChrX STRs amplicons during the last decade. But there are significant

problems which hinders the process of miniaturization. Foremost among these is that due

to size limitation, not many STRs can be fit together in a single multiplex and most of the

time only one marker per dye is run to avoid overlap. This approach makes a maximum

of 4 miniSTRs in a single multiplex using the 5-dye chemistry (Butler, 2005).

23

Table 2.1: X Chromosomal miniSTRs Marker Size Range (in basepairs) Reference

DXS101 142–169 (Asamura et al., 2006)

DXS6789 122–162 (Asamura et al., 2006)

DXS7423 99–115 (Asamura et al., 2006)

GATA31E08 101–133 (Asamura et al., 2006)

DXS8378 95–111 (Asamura et al., 2006)

DXS7424 79–100 (Asamura et al., 2006)

GATA165B12 90–110 (Asamura et al., 2006)

DXS6795 90–111 (Son et al., 2002)

DXS10147 165–185 (Edelmann et al., 2008)

DXS7424 147-174 (Edelmann et al., 2001)

DXS7133 106-130 (Edelmann et al., 2002b)

DXS9895 139-161 (Edelmann et al., 2002b)

GATA172D05 108-136 (Edelmann et al., 2002b)

DXS10160 135 – 193 (Hering et al., 2008)

DXS10159 154–190 (Edelmann et al., 2010)

DXS10162 150–186 (Edelmann et al., 2010)

DXS10163 121–171 (Edelmann et al., 2010)

DXS10165 145–173 (Edelmann et al., 2010)

DXS9902 162-186 (Diegoli and Coble, 2011)

DXS7132 131–155 (Edelmann et al., 2002b)

DXS6803 109-128 (Edelmann and Szibor, 2003)

DXS7130 109-128 (Edelmann and Szibor, 2003)

HPRTB 144-176 (Szibor et al., 2000)

DXS6801 113-137 (Edelmann and Szibor, 2005)

DXS10103 160-200 (Bekada et al., 2010)

Amelogenin X=80, Y=83 (Haas-Rochholz and Weiler, 1997)

Amelogenin X=106, Y=112 (Haas-Rochholz and Weiler, 1997)

Amelogenin X=55, Y=58 (Esteve Codina et al., 2009)

24

Secondly, most human identity testing applications requires a high degree of

polymorphism and hence, a higher allele frequency distributions. In multiplexes, these

markers consume precious electrophoretic space. However, in some parentage testing

situations, less polymorphic loci having lower mutation rates are more useful. For

example, mutation rates are not significant when evidence is compared directly with a

suspect (Butler, 2006a). But in parentage and kinship testing where comparison between

relatives is done, as for identification of mass disaster victims, mutational events become

important (Leclair et al., 2004). STRs having long homogenous repeat structures are

prone to instability such as DXS8377 and DXS10011 (Szibor, 2007) and are, therefore,

not suitable for use (Hering et al., 2004). Hence, STR loci possessing a smaller size range

and sufficiently polymorphic are being characterized (Coble and Butler, 2005).

Thirdly, the locations of miniSTR primers and conventional STR primers are in

different locations, the flanking regions on both sides of the repeat regions may have

insertion or deletion. Discordant results may be produced in amplicons generated form

one of the primer sets due to size shifts and allele drop out (Boutrand et al., 2001, Rolf et

al., 2011).

Fourthly, there are analytical issues attached to the miniaturization approach.

When the size is reduced to less than 150bp, the biggest problem comes from dye blobs.

These may be left over from the oligonucleotide synthesis process or may result from

dyes falling off the primer during the heating and cooling steps of PCR (Edelmann et al.,

2010).

Lastly, smaller PCR products have a problem in the manner in which sizing is

performed using an internal lane standard. Local Southern, the default method used by the

GeneScan software, which requires two peaks from the internal size standard to be

25

present on either side of a peak being defined. Hence, both 35 and 50bp peaks from the

GS500 Liz size standard must be defined for measuring any allele below 75 bp (Butler et

al., 2003). Due to increased concentration of primer dimers, sizing peaks may become

ambiguous. Therefore, it is recommended that Global Southern, where a regression line

generated from all sizing peaks is used to fit the STR alleles, is the method of choice for

small size amplicons (Hartzell et al., 2003).

Chapter 3

You know my method. It is founded upon the observation of trifles.

Sherlock Holmes - The Bascombe Valley Mystery

26

3. Materials and Methods

3.1 Blood Samples Collection

Blood samples were collected from 570 unrelated informed male and female

volunteers from the four provinces of Pakistan (Figure 3.1). According to Population

Census Organization of Pakistan (www.census.gov.pk), the Punjab has the largest

population with 56.2 percent, followed by Khyber-Pakhtunkhwa (KP) with 25.4 percent,

Sindh 13.4 percent and Baluchistan with 5.0 percent being the least populated province

(according to Population Census of 1998) . From each individual, 3mL of blood was

drawn. The drawn blood was mixed with 70µL of 0.5 M EDTA on the spot in a 15 mL

culture tube and shifted to CEMB Forensic DNA Laboratory. Of the collected blood,

700µl –800µL aliquots were made in 1.5 mL centrifuge tubes and properly labelled.

These 2 to 3 aliquots per sample were then frozen at -70°C as backup and to use in future.

Figure 3.1: Sampling Area and Number of samples collected

27

3.2 DNA Extraction from Blood Samples

Phenol chloroform DNA extraction was performed on each of the blood

sample as follows (Maniatis et al., 1982, Signer et al., 1988).

1. From the frozen blood, 300µL was taken out and mixed with 800µL of 20mM

Tris-HCl in a 1.5mL centrifuge tube. Centrifuge tube was tilt-mixed and left for

10 minutes. After this, the aliquot was centrifuged at 14,000 rpm for 1 min. About

1mL of supernatant was pipetted out into another tube and saved as a

precautionary measure, leaving behind a small pallet (400µL -450µL).

2. Pellet was resuspended in 1mL of 20mM Tris-HCl and repeated the same

protocol. The supernatant (about 1200µL) was removed and taken into another

tube, without disturbing the pellet. In this way three washings were made.

3. After the third washing, the pellet was mixed with following:

a) Pelleted WBCs = 25-50μL

b) 0.2 M sodium acetate (pH =5.2) = 375μL

c) 10% SDS = 50μL

d) Proteinase-K (1U/μl; 40 mg/ml) = 10μL

4. That mixture was incubated at 37°C overnight.

5. The next day, the digested cell suspension was treated as:

a) Digested cell suspension = 400-450μL

b) Buffered phenol (pH =8) = 120μL

6. Mixture was gently mixed for 30 seconds and centrifuged at 14000 rpm for 5 min.

7. Aqueous phase was carefully pipetted out with wide mouth tip into a separate

centrifuge tube.

28

8. The separated supernatant was treated as:

a) Aqueous phase from the last step = 400-450μL

b) PCI (Phenol:Chloroform:Iso-amylalcohol

25:24:1)

= 120μL

9. Mixture was gently mixed for 30 seconds and centrifuged at 14000 rpm for 5min.

10. Again the aqueous phase was pipetted out into a separate centrifuge tube.

11. Supernatant was treated as:

a) Upper separated supernatant = 400μL

b) Ice-chilled 95% Ethanol = 1000μL

12. Solution was gently tilt-mixed for 10min and centrifuged at 14000rpm for 10 min.

13. The supernatant was discarded carefully without disturbing the pellet.

14. To the pellet 70% ethanol solution was added, tilt mixed and centrifuged at

14000rpm for 1min.

15. The supernatant was discarded without disturbing the pellet.

16. Then pellet was dissolved in 180μL of TE (pH-8.0) and incubated at 56°C until

DNA pellet was completely dissolved.

17. Following was then added to it:

a) 2M sodium acetate = 20μL

b) Chilled 90% ethanol = 500μL

18. The solution was tilt mixed for 30s and left at room temperature for 15min.

19. Solution was centrifuged at 14,000rpm for 10min and supernatant was decanted

carefully.

29

20. To the pellet, 500µL of 70% ethanol solution was added and centrifuged at 14,000

rpm for 1 min. Supernatant was again decanted.

21. Rim of the centrifuge tube was blot dried and then air dried for 2-3 min.

22. To the dried pellet was added 100μL TE (pH 8.0) and incubated at 56°C for 1hr.

23. Then the prepared DNA solution was stored at 4°C.

3.3 DNA Quantitation

DNA concentration was estimated through spectrophotometery using NanoDrop

and the integrity of DNA was confirmed by gel electrophoresis with 25ng/μL standard

DNA.

3.3.1 Spectrophotometery

NanoDrop ND-1000 (NanoDrop Technologies, Wilmington, DE) was used to

quantify DNA (Desjardins and Conklin, 2001). The following values were measured:

a) 260/280

This value is used to assess purity of DNA. A ratio of ~1.8 was accepted as pure.

b) 260/230

This value is for assessment of DNA purity. A ratio of ~1.8 is considered as pure.

c) ng/μL

This is the final concentration of the sample which is based on absorbance at

260nm and is calculated using Beer’s Law.

30

3.3.2 Gel Electrophoresis

1. For quantification of genomic DNA, 0.8% agrose gel was prepared while for

degraded DNA and PCD product, 2% agarose gel was used.

2. Agarose gel was run in the tank (Hoefer Scientific Inc.) and was completed at

6V/cm and 47mA after 1hour.

3. Gel was visualized and photographed in UV Transilluminator (UMP).

4. The gel image was analyzed using Image J software (Abràmoff et al., 2004).

3.4 Multiplex Design Strategy

The multiplex design strategy was followed as recommended in previous literature

(Schoske et al., 2003). The steps taken are explained briefly.

3.4.1 Selection of Loci, Primer Design and Labelling

The X-STRs characterized in major world populations and highly polymorphic

markers were selected. The markers are given along their mapping information in table

3.1 (Szibor et al., 2006).

There should not be complementary sequences between primers to be used in

multiplex as it is important to prevent the primer dimer formation. All the primer pairs

were compared to one another using Autodimer program to assess the possibility of

primer dimerization in multiplex PCR (Vallone and Butler, 2004).

31

Table 3.1: Markers along with their linkage groups and mapping information

Cytogenetic localization

Marker Linkage group

Physical localisation Rutgers Map v.2 [Mb] [cM Kosambi]

NCBI 36 p 22.31 DXS8378 X1 9.330 20.21 p 22.2 DXS9902 15.234 32.32 Centromere DXS7132 X2 64.572 90.75 q 21.1 DXS6793 80.638 q 21.33 DXS6789 95.336 108.47 q 22.1 DXS7424 100.505 115.25 q 22.1 DXS101 101.300 116.15 q 23 GATA172D05 113.061 124.36 q 26.2 HPRTB X3 133.443 149.66 q 27.1 GATA31E08 140.062 160.54 q 28 DXS7423 X4 149.460 184.19 Amelogenin

Primer for DXS7132 was designed, by using Primer 3 (Rozen and Skaletsky,

2000). Primer sequences for DXS8378, DXS6789, DXS7424, DXS101, GATA31E08 and

DXS7423 as well as DXS7133 which was used in the haplotyping 4-plex were the same

as previously reported by Asamura and colleagues (Asamura et al., 2006) while primer

sequences of GATA172D05, DXS9902 were taken from Ensemble Genome Database

(Edelmann et al., 2001, Edelmann et al., 2002b, Flicek et al., 2012). Primer sequences for

HPRTB and DXS6793 were the same as used by Tariq and colleagues (Tariq et al.,

2008). Primer sequences for the gender determining locus, amelogenin, were used as

reported previously (Haas-Rochholz and Weiler, 1997). The arrangement and labelling of

primers with different dyes was done in a way to ensure maximum number of markers in

the multiplex as well as providing space to each marker in the eletrophoretic system. The

sequences of primers along with their dye labelling and references are shown in table 3.2.

32

Table 3.2: Genotyping Primers Sequences and Dye Labelling Marker Dye Sequence Reference

Amelogenin FAM F CCCTTTGAAGTGGTACCAGAGCA (Haas-Rochholz and Weiler, 1997)

R GCATGCCTAATATTTTCAGGGAATA

DXS101 NED F TCTCCCTTCAAAAACAAAGATAA (Asamura et al., 2006) R GTGCATATTCTGCGCATGT

DXS6789 VIC F CCTCGTGATCATGTAAGTTGG (Asamura et al., 2006) R GCAGAACCAATAGGAGATAGATGGT

DXS6793 VIC F ACACACGTGGTTTAGACCGT (Tariq et al., 2008) R CCAGAGCTACGGGAATATGA

DXS7132 FAM F ATAAATCCCCTCTCATCTATCTGAC This Study R ACTCCTGGTGCCAAACTCTA

DXS7423 FAM F AGATTTCCTCCCCATCCATC (Asamura et al., 2006) R GTTGTCACACAAATAAATGAATGAGT

DXS7424 NED F AAAACAGGAAGACCCCATC (Asamura et al., 2006) R GGCTAAGAAGAATCCCGCACA

DXS8378 VIC F GCTCCTGGCAGGTCACTATC (Asamura et al., 2006) R GCGACAAGAGCGAAACTCCA

DXS9902 PET F TGGAGTCTCTGGGTGAAGAG (Edelmann et al., 2001) R CAGGAGTATGGGATCACCAG

GATA172D05 NED F TAGTGGTGATGGTTGCACAG (Edelmann et al., 2002b) R ATAATTGAAAGCCCGGATTC

GATA31E08 PET F CAGAGCTGGTGATGATAGATGA (Asamura et al., 2006) R GCTCACTTTTATGTGTGTATGTATCTCC

HPRTB FAM F GTCTCTATTTCCATCTCTGTCTCC (Tariq et al., 2008) R TTCTTTCTCTCACCCCTGTCT

3.4.2 Size Determination

PCR fragment size range was determined for each marker. All the alleles found

for each particular marker were considered. For example, in-silico PCR was used to know

fragment size of newly designed primer set for marker DXS7132 (Fujita et al., 2011).

The fragment size for DXS7132 was 140bp for allele 14 with repeat motif (TCTA)x. The

allele range for this marker was 11-17 including all major populations. So, size range of

PCR product determined was 128-152bp. All primer sets specificity were checked by

using Human BLAT (Kent, 2002) ensuring that these only bind to the unique given

location on the human genome.

33

3.4.3 Multiplex Schematics

Multiplex layout schematics were prepared for both 7-plex and 12-plex as shown

in figure 3.2a and 3.2c, respectively and also for the 4-plex which was used for haplotype

analysis of cluster DXS7424-DXS101 and is shown in figure 3.2b. The size ranges of

markers were represented in schematic to check the possible areas of overlap. All the

fragment sizes of markers were set apart in same fluorescent dye to reduce the difficulty

of assigning the observed allele to its correct locus in any case of discovery of new allele.

The primers for some markers as mentioned above were selected or designed by keeping

the fragment length fix as required in multiplex Schematic.

Figure 3.2a: Schematic of PCR product sizes produced with known allele size ranges for the loci in 7-

plex. Yellow colour represents NED, blue for FAM, green for VIC, red for PET while LIZ Size

Standard is shown in orange.

Figure 3.2b: Schematics of PCR product sizes produced with known allele size ranges for the loci in 4-plex. Yellow colour represents NED, blue for FAM, green for VIC and red for PET dye. A= Amelogenin

34

Figure 3.2c: Schematics of PCR product sizes produced with known allele size ranges for the loci in 12-plex. Yellow colour represents NED, blue for FAM, green for VIC and red for PET dye. A= Amelogenin

3.4.4 Primer Concentration

Primer binding and amplification ability in a multiplex reaction is governed by

different factors and a slight variation in concentration of one primer set can affect the

amplification of one or more other primers. The difference in the DNA sequence for each

locus is also responsible for the variable efficiency of primer binding. Thus, for obtaining

the best ratio that would yield good signal intensities and balanced peak height for each

multiplex set, adjusting primer concentrations become necessary. The primer

concentrations were optimized to efficiently amplify DNA samples with low quantities.

3.5 Polymerase Chain Reaction (PCR)

PCR was carried out in ABI2700 Thermal Cycler (Applied Biosystems (ABI),

Foster City, CA), with AmpliTaq Gold polymerase (ABI), quantified genomic DNA and

labelled primers.

35

3.5.1 PCR Setup

PCR master mixes were made for 7-plex, 12-plex and 4-plex as shown in tables

3.3a, 3.3b and 3.3c, respectively.

Table 3.3a: Primers concentration and ingredients for 7-plex master mix Marker (10pm/μL) Forward (μL) Reverse (μL) x50 (μL) DXS101 0.3 0.3 15.0 DXS6789 0.12 0.12 6.0 DXS7132 0.1 0.1 5.0 DXS7423 0.09 0.09 4.5 DXS8378 0.05 0.05 2.5 GATA172D05 0.07 0.07 3.5 GATA31E08 0.09 0.09 4.5 Taq. Polymerase (2U/μL) 0.5 25.0 10X Buffer (NH4SO2) 1.0 50.0 MgCl2 (25mM) 0.6 30.0 dNTPs (25mM) 0.08 4.0 H2O (Injection Water) 10.18 509.0 Total 14.0μL 700.0

Table 3.3b: Primers concentration and ingredients for 12-plex master mix

Marker (10pm/μL) Forward (μL) Reverse (μL) x50 (μL) Amelogenin 0.05 0.05 2.5 DXS101 0.35 0.35 17.5 DXS6789 0.09 0.09 4.5 DXS6793 0.12 0.12 6.0 DXS7132 0.09 0.09 4.5 DXS7423 0.09 0.09 4.5 DXS7424 0.15 0.15 7.5 DXS8378 0.09 0.09 4.5 DXS9902 0.14 0.14 7.0 GATA172D05 0.35 0.35 17.5 GATA31E08 0.13 0.13 6.5 HPRTB 0.20 0.20 10.0 Taq. Polymerase (2U/μL) 0.5 25.0 10X Buffer (NH4SO2) 1.0 50.0 MgCl2 (25mM) 0.8 40.0 dNTPs (25mM) 0.08 4.0 H2O (Injection Water) 2.92 146.0 Total 9.0μL 450.0

36

Table 3.3c: Primers concentration and ingredients for 4-plex master mix Marker (10pm/μL) Forward (μL) Reverse (μL) x50 (μL) Amelogenin 0.05 0.05 2.5 DXS101 0.2 0.2 10.0 DXS7133 0.12 0.12 6.0 DXS7424 0.1 0.1 5.0 Taq. Polymerase (2U/μL) 0.5 25.0 10X Buffer (NH4SO2) 1.0 50.0 MgCl2 (25mM) 0.8 40.0 dNTPs (25mM) 0.08 4.0 H2O (Injection Water) 5.68 284.0 Total 9.0μL 450.0

3.5.2 Thermal Cycling Parameters

The Microsatellite markers (STRs) were amplified by Polymerase chain reaction

(PCR) on ABI GeneAmp® PCR System 2700. The PCR cycling conditions for both 7-

and 12-plex are given in figure 3.3a and 3.3b respectively. For 4-plex, the cycling

conditions used were the same as shown in 3.3b with a slight modification that is 30

cycles were done instead of 33 cycles.

Fig 3.3a: PCR cycle for 7-plex

30 Cycles

72°C

1min

60°C

45min

4°C

∞

95°C

10min

95°C

1min

56°C

1min

2holds 1hold

37

Fig 3.3b: PCR cycle for 12-plex

3.5.3 Cycle Number

One way to improve the sensitivity of any PCR based method is to increase the

number of amplification cycles especially when amount of DNA template is very low. To

optimize the best cycle number for the multiplex, studies were carried out with different

cycle number.

3.5.4 Peak Balance

When the amount of template added to the PCR reaction is extremely low, there

may be stochastic effects which results in the imbalance of heterozygote alleles. Different

concentrations were used to obtain balanced peaks.

3.5.5 Sensitivity Studies

Sensitivity studies of the multiplex were performed on DNA sample using serial

dilution i.e. 5ng, 2.5ng, 1.0ng, 0.5ng and 0.250ng to evaluate minimum quantity required

to obtain the full DNA profile. The GeneScan Analysis threshold for these studies was set

at 100 RFU (relative fluorescence units).

33 Cycles

72°C

1min

66°C

60min

4°C

∞

95°C

11min

94°C

1min

59°C

1min

2holds 1hold

38

3.5.6 Nonhuman DNA and Specificity Studies

Different variety of equine and cattle DNA was amplified to ensure that the

multiplex demonstrates specificity for humans.

3.5.7 Degraded DNA Studies

Studies were performed on artificially degraded DNA. As previously reported

(Asamura et al., 2006), miniSTRs are effective profiling degraded samples which are

otherwise difficult to genotype correctly through conventional STR kits.

3.5.7.1 Enzymatic Degradation

DNase I enzyme was used for digestion of genomic DNA. 2U of DNase (Turbo -

Ambion, Austin) was used to digest 1.2μg of human genomic DNA (100 ng/μL) at 37°C

in 120μL final volume. Aliquots of 10μL were removed predefined intervals (0, 0.5, 1, 2,

5, 10, 30 and 60 minutes). The enzyme was quenched by adding the aliquots to 490μL

preheated (TLE + 20 μg/ml glycogen) buffer at 95°C. DNase I was inactivated by heating

the aliquot at 95°C for 10 min (Grubwieser et al., 2006). The degraded DNA was run on

2% agarose gel stained with ethidium bromide along DNA ladder to assess the extent of

degradation.

3.5.7.2 Mechanical Degradation

DNA was degraded mechanically through an ultrasonic homogenizer Model

300VT (Biologics Inc., VA, USA). 1 mL of DNA solution (0.5 mg/mL) was sonicated on

ice at 180W in intervals of 30 seconds for up to 5 min. The degradation status was

determined empirically as ultrasound cannot be defined in physical units. Aliquots of 4µL

were taken from the sonicated solution after every 30s. Agarose gel of 2%, stained with

ethidium bromide was used for DNA electrophoresis (Bender et al., 2004).

39

3.5.8 Gel Extraction Protocol

To denature enzymes and dissolve agarose gel, chaotropic salt is used. The spin

column has glass fibre matrix to which the DNA fragments in the chaotropic salt bind

(Vogelstein and Gillespie, 1979). The degraded DNA fragments were extracted and

purified from the gel using Gel DNA Fragments Extraction Kit (Geneaid Biotech, Bade

City, Taiwan), as described below. Absolute ethanol was added to the Wash Buffer before

use.

3.5.8.1 Gel Dissociation

The agarose gel slice with the required DNA fragment was excised. To minimize

the gel size, any extra agarose was removed.

1. Gel slice of about 300 mg was transferred to an Eppendorf tube.

2. To this sample, DF Buffer 500 µL was added and mixed by vortex.

3. To ensure that gel slice has been completely dissolved, it was incubated at 55-

60ºC for 10-15 minutes. Every 2-3 minutes, the tube was inverted to ensure

uniform heating. After this, the sample was cooled at room temperature.

3.5.8.2 DNA Binding

1. In a 2mL Collection Tube the DF Column was placed.

2. From the previous step, 800 µL sample mixture was added to the DF Column and

centrifuged for 30 seconds at 14,000 rpm.

3. In the Collection Tube, The DF Column was placed back after discarding the

flow-through.

40

3.5.8.3 Wash

1. W1 Buffer (400 µL) was added to the DF Column and centrifuged for 30 seconds

at 14,000 rpm.

2. After discarding the flow-through, The DF Column was placed back in the

Collection Tube.

3. Eathonol-added Wash Buffer (600 µL) was added into the DF Column and after 1

minute, centrifuged it for 30 seconds at 14,000 rpm.

4. In the Collection Tube, the DF Column was placed back after discarding the flow-

through.

5. To dry the column matrix, it was centrifuged for 3 minutes at 14,000 rpm.

3.5.8.4 DNA Elution

1. DF Column was transferred after drying to a new 1.5mL Eppendorf tube.

2. Into the centre of the column matrix, 20µL was added.

3. It was let stand to ensure the Elution Buffer absorbed by the matrix for 2 minutes

and then centrifuged at 14,000 rpm for 2 minutes for the elution of the purified

DNA.

41

3.6 Detection and DNA Analysis

3.6.1 Sample Preparation for ABI 3130 Genetic Analyzer

For genotyping of samples on ABI Prism 3130, the samples were pooled into the

genotyping plate according to the following protocol (Butler, 2001).

1. Using a 96-well genotyper plate, 1μL of each amplified sample was carefully

pipetted into each well.

2. Mixed 0.2μL GeneScan® 500-LIZ™ size standard with 13.80μL Hi-Di™

Formamide (ABI) (Schumm, 1997).

3. To each well, 14μL of this cocktail mix was added and a clean plate septum was

placed over the 96-well plate and gently pushed septum into wells.

4. The plate was centrifuged for at least 10 seconds at more than 500 rpm to mix the

sample, remove bubbles, and spin the liquid to the bottom of each well.

5. The plate was put in a thermal cycler at 95°C for about 5 minutes to denature the

double stranded DNA into single stranded.

6. The plate was immediately put on ice for 5 minutes to stop DNA from renaturing.

7. The plate was again centrifuged for at least 10 seconds at 500rpm to mix the

sample, remove bubbles, and spin the liquid to the bottom of each well before

placing it in the genotyping machine.

3.6.2 Sample Electrophoresis

Using a 3130xl Genetic Analyzer (ABI), at 3kV the samples were injected for 10

seconds. In a Performance Optimized Polymer (POP-4), electrophoresis was carried out

for 25minutes at 15 kV and run temperature of 60°C. ABI 3130xl Data Collection

Software application v3.0 was used for collection of data.

42

3.6.3 Absorption/Emission Spectra of Fluorescent Dyes used

for Labelling

The absorption and emission wavelengths of the 5-dye system of ABI which was

used for multiplexing (Butler, 2005) is given below in table 3.4 as well as graphically

shown in figure 3.4.

Table 3.4: Fluorescence Spectral Parameters

Fluorescein FAM VIC NED PET LIZ

Absorption Wavelength (nm) 493 538 546 558 638

Emission Wavelength (nm) 522 554 575 595 655

Figure 3.4: Emission Spectra

3.6.4 Software Programs for Data Analysis

The results from electrophoresis were then analyzed by using GeneMapper

Software v4.0. A threshold of 100 relative fluorescence units (RFUs) was kept to interpret

allele peaks. Alleles were assigned as recommended by ISFG (International Society of

Forensic Genetics) through comparison with standard DNA 9947A (Butler et al., 2004,

Szibor et al., 2006).

43

Once STR genotypes had been generated from population samples the data were

typically evaluated through statistical tests to ensure that the data would be a useful one

when applied to human identity testing (Butler, 2005). A set of inter-population

comparisons of allele frequencies from this study was performed. Statistical tests on

genetic data have been greatly aided by the availability of computer processing power and

a number of available computer programs were used to perform various tests.

RFU values of all the peaks in a dye colour were averaged to calculate the tetra-

colour peak balance. For homozygous peaks, the heights are divided by two and averaged

with the values for heterozygous peaks for each marker in a dye.

After allele scoring, statistical analyses were carried out like Polymorphic

information content (PIC) expected heterozygosity (Het) (Shete et al., 2000), power of

discrimination (PD) in females and males and power of exclusion (PE). Fisher’s Exact

Test was used to check Hardy Weinberg Equilibrium at all selected STR loci.

Allele frequencies were calculated by simple counting. The minimum allele

frequency was set at 5/2N which is in accordance with the recommendations of National

Research Council for reliable statistical calculations (Bär et al., 1997) . For Hardy-

Weinberg Exact Test (Guo and Thompson, 1992), AMOVA, and Linkage Disequilibrium,

Arlequin v3.1 was used (Excoffier et al., 2005). PowerMarker v3.1 (Liu and Muse, 2005)

was used for Exact Test of population differentiation, Gene Diversity, Polymorphic

Information Content (PIC), and Heterozygosity. The ChrX-STR website was used for

calculation of Paternity Index, Power of Exclusion, Power of Discrimination in males

(PDm) and females (PDf), and Mean Exclusion Chance (MEC) (Szibor et al., 2006).

44

Poptree was used to estimate Nei’s distances based on the frequency distribution

(Takezaki et al., 2010). Unrooted tree based on Neighbour-Joining method was

constructed. MEGA5 was used to visualize the tree (Tamura et al., 2011).

3.6.5 Statistical Analysis and Formulae

Proportion of heterozygous individuals in the population is called Heterozygosity

(H). It is calculated by dividing the number of samples containing heterozygous alleles

into the total number of samples. Higher the heterozygosity, more will be the diversity of

alleles and lesser chance of random sample matching. Gene diversity or expected

heterozygosity, is the probability that two alleles chosen at random from the population

are different. Power of discrimination is equal to 1 minus the sum of the square of the

genotype frequencies. Formulae for all these are given below (Butler, 2005).

Polymorphism Information Content (Botstein et al., 1980)

PIC = 1 − (∑ ������� ) −∑ ∑ 2����

����������� ���.

Mean Exclusion Chance by Kruger (Krüger et al., 1968) for autosomal markers in

trios. This is usable for ChrX markers in deficiency cases in which the paternal

grandmother is investigated instead of the alleged father.

MECKRU = ∑ ���(1 − ��)�� +∑ ��(1 − ��)�� +∑ ����(�� + ��)(1 − �� − ��)����

Mean Exclusion Chance by Kishida (Kishida et al., 1997) for ChrX markers in trios

involving daughters.

MECKISH = ∑ ���(1 − ��)� + ∑ ��(1 − ��)�� + ∑ ����(�� + ��)(1 − �� − ��)���

45

Mean Exclusion Chance by Desmarais (Desmarais et al., 1998) for ChrX markers in

trios involving daughters.

MECDES = 1 −∑ ��� +∑ ��� −�� �∑ ���)��� ��

For ChrX markers in father/daughter duos

MECDES =1 − 2∑ ��� +∑ �����

Power of Discrimination in female (Desmarais et al., 1998)

PDf = 1 − 2(∑ ���� )� +∑ ����

Power of Discrimination in male (Desmarais et al., 1998)

PDm= 1 −∑ ����

Homozygosity (Liu and Muse, 2005)

h = ∑ ��������

Heterozygosity

H = 1 Homozygosity

Gene Diversity (Liu and Muse, 2005)

GD = ���∑ �

������� �

������

��

It is a capital mistake to theorize before you have all the evidence. It

biases the judgment.

Sherlock Holmes - A Study in Scarlet

Chapter 4

46

4. Results

4.1. Development of Multiplexes

4.1.1 Screening of Primers

There were 11 X-STR markers (DXS101, DXS6789, DXS6793, DXS7132,

DXS7423, DXS7424, DXS8378, DXS9902, GATA172D05, GATA31E08 and HPRTB)

included in the multiplex along with sex determining locus amelogenin. Moreover, there

was one other marker namely DXS7133 which was used with DXS7424 and DXS101 in

the haplotyping of male sample. The primer designing is critical parameter for parallel

amplification of STRs (Schoske et al., 2003). Alignment score of 8 or greater in

Autodimer program for any marker has been proved to result in significant primer dimer

formation during PCR amplification (Innis et al., 1999). Therefore, the primers which

showed the alignment score 8 or greater than 8, were eliminated from the study. It is

observed that primer interactions showing alignment score 6 or 7 were not problematic

during the multiplex amplification. Results of all primer interactions of 12 primer pairs

with alignment score 6 or greater are shown in table 4.1.

The similar primer Tm value of all primers is also another important parameter to

amplify multiple primers in a single reaction. Therefore, utmost care was taken to include

primers with similar Tm values. An important factor in multiplex optimization is the

specificity of primers to its target region in the human genome. BLAT was used to

confirm that the selected primers were specific only to a single location on the X

chromosome of humans.

47

Table 4.1: Primer interactions of 12 primer pairs with alignment score 6 or greater

No. of Sequences = 24 No. of Hits = 4

Threshold Score = 6

DXS7423-F AGATTTCCTCCCCATCCATC

versus DXS6789-R GCAGAACCAATAGGAGATAGATGGT

Matches = 8 Score = 6 Match Sequence = NCCATCNATC

3'-TGGTAGATAGAGGATAACCAAGACG-5' X| | || | x||| 5'-AGATTTCCTCCCCATCCATC-3'

DXS7132-R ACTCCTGGTGCCAAACTCTA

versus DXS9902-R CAGGAGTATGGGATCACCAG

Matches = 7 Score = 7 Match Sequence = CAGGAGT

5'-CAGGAGTATGGGATCACCAG-3' | | | || | | 3'-ATCTCAAACCGT G GT C CT C A-5'

Amelogenin-F CCCTTTGAAGTGGTACCAGAGCA

versus Amelogenin-F CCCTTTGAAGTGGTACCAGAGCA

Matches = 12 Score = 6 Match Sequence = TGNNNTGGTACCANNNCA

3'-ACGAGACCATGGTGAAGTTTCCC-5' | | xx x| | ||| | || x xx|| 5'-CCCTTTGAAGTGGTA CCAGAGCA-3'

Amelogenin-R GCATGCCTAATATTTTCAGGGAATA

versus Amelogenin-R GCATGCCTAATATTTTCAGGGAATA

Matches = 6 Score = 6 Match Sequence = GCATGC

5'-GC ATGCCTAATATTTTCAGGGAATA-3' || || | | 3'-ATAAGGGACTTTTATA ATC CGTA CG-5'

4.1.2 Development of 7-Plex

First 7 X-STRs were selected and combined into a multiplex. This system was

based on the primers reported by Asamura and colleagues (Asamura et al., 2006) along

with one newly designed primer set for DXS7132. This multiplex was optimized for

sensitivity, specificity, MgCl2 concentration, peak balance, cycle number, annealing

temperatures and was used for the population studies of Punjabi population (Israr et al.,

2012). A female DNA profile female typed with this multiplex is shown in figure 4.1.

48

Figure 4.1: Female DNA profile of 7 X-STRs

49

4.1.3 Development of 12-Plex

After the successful optimization of the 7-plex system, 4 more X-STRs were added

to the system along with the sex determining locus, amelogenin. The following different

parameters were optimized for this multiplex:

4.1.3.1 Sensitivity Studies

Studies were preformed to evaluate minimum quantity required to obtain the full

DNA profile. The GeneScan Analysis threshold for these studies was set at 100 RFU

(relative fluorescence units) (Hanson and Ballantyne, 2004). As shown in figure 4.2, the

optimal quantity of the template DNA for 12-plex PCR system ranged from 1 to 2 ng.

Figure 4.2: Sensitivity of 12-plex

0

2000

4000

6000

8000

10000

12000

14000

16000Sensitivity

5 ng

2.5 ng

1.0 ng

500 pg

250 pg

50

Each amplification at 1-2 ng DNA produced full 11-locus profile with a >100 RFU

threshold. Full profile was obtained with DNA concentration as low as 250pg. On the

other hand, split peaks were observed with higher concentration (<100ng) of DNA

template.

4.1.3.2 Primer Concentration

The primer concentrations were optimized to efficiently amplify DNA samples

with lower concentrations. Many PCRs were performed with primer concentration ranging

from 0.01 to 0.5µM to reach the optimal quantity and concentration for each individual

primer of the 11 X-STRs as well as Amelogenin.

4.1.3.3 Colour Balance

Different labelling dyes affect the amplification of template. Tetracolor balance

was calculated by averaging amplification peak values in each dye lane. As shown in

figure 4.3, in this multiplex system, the blue dye or FAM-6 labelled markers showed the

highest peaks while the yellow dye or NED showed the lowest peaks.

Figure 4.3: Average amplification in each dye lane

0

1000

2000

3000

4000

5000

6000

RFU

Colours

FAM-6

NED

VIC

PET

51

4.1.3.4 Cycle Number

Studies were carried out to optimize best cycle number for the multiplex. Different

cycle numbers from 27 to 35 were tested for the multiplex system. As shown in figure 4.4,

the multiplex exhibited optimal amplification with 33 cycles.

Figure 4.4: Amplification at different numbers of PCR cycles

0

2000

4000

6000

8000

10000

12000

14000

Cycle Number

30 Cycles

33 Cycles

35 Cycles

52

4.1.3.5 Annealing Temperature

Annealing temperature plays critical role in optimization of a multiplex and it is

essential in achieving the optimum annealing temperature at which all the primers in the

multiplex specifically bind to their respective targets. A range of temperatures (57, 59, 60

and 61°C) were tested. As shown in figure 4.5, it was found that 59°C is the optimal

temperature for this multiplex.

Figure 4.5: Amplification at different annealing temperatures

0

2000

4000

6000

8000

10000

12000

14000

Annealing Temperature

57°C

59°C

60°C

53

4.1.3.6 MgCl2 Titration

MgCl2 is essential component of PCR. DNA polymerase needs magnesium as a

cofactor to function. Too little of it affects the ability of polymerase to perform. While too

much of it results in non-specific amplification. Therefore, it is important to add the

optimum amount of MgCl2. Figure 4.6 shows that the 12-plex performed better with a

MgCl2 concentration of 2.0mM.

Figure 4.6: MgCl2 Titration of 12-plex

0

1000

2000

3000

4000

5000

6000MgCl2 Titration

1.0 mM

1.25 mM

1.5 mM

1.75 mM

2.0 mM

54

4.1.3.7 Specificity

The 12-plex was used to amplify a variety of equine and cattle DNA to

demonstrate its specificity for humans. The results showed that this multiplex is highly

specific to amplify only the human DNA since no amplification was observed in the

equine or cattle DNA and only primer dimers are visible in the eletropherograms in figure

4.7a and 4.7b respectively.

Figure 4.7a: Bovine DNA amplified through 12-plex

55

Figure 4.7b: Equine DNA amplified through 12-plex

4.1.3.8 Efficiency of Multiplex Assay

The multiplex PCR system which uses 11 X-STRs and sex determining locus

amelogenin was developed as shown in figure 4.8. All the markers of multiplex showed

high discriminatory powers ranging from 0.745 to 0.953 in females and 0.551 to 0.835 in

males. We selected 11 X-STR markers on the basis of their genetic localization and high

polymorphism and small size. X-STRs included in this study span the whole X-

chromosome and represent all the 4-linkage groups categorized previously by Szibor and

colleagues (Szibor et al., 2003).

56

Figure 4.8: Male DNA profile showing 11 X-STRs plus sex determining locus amelogenin

57

All the markers of this study are well spaced from each other on the X

chromosome to reduce the chance of LD and no evidence of LD was detected in all pair of

markers. Therefore, all the markers of this study are recommended to be considered as

independent markers for forensic practice in Pakistani population. The total length of

amplified fragments of the multiplex was limited to a maximum of 190 basepairs because

small sized markers have a great advantage over large sized markers during the analysis of

degraded DNA (Grubwieser et al., 2003).

4.2. Degraded DNA Studies

Studies were performed on artificially degraded DNA.

4.2.1. Enzymatic Degradation

DNase I enzyme was used for digestion of genomic DNA. The degraded DNA was

run on 2.0% agarose gel stained with ethidium bromide and a DNA ladder to assess the

extent of degradation as shown in figure 4.9a. DNA was extracted from gel and divided

into two categories: lesser than 500bp and fragments that were larger than 500bp.

Figure 4.9a: DNA degraded with DNase I. Lanes 1= 50ng standard, lane 2 and 14 = 100bp ladder, lane 3 = blank, lanes 4-12 = degraded DNA, lane 13 = 25ng standard, lane 15 = non-degraded DNA. The line shows the 500bp mark.

58

4.2.2. Mechanical Degradation

Ultrasonic homogenizer was used for the mechanical degradation of DNA. The

degradation status was confirmed empirically by running the different aliquots on

2%agarose gel. A 20bp DNA ladder was run with the samples to determine the extent of

degradation as shown in figure 4.9b.

Figure 4.9b: Sonicated fragments on gel. Lane 1 and 15 = control, Lane 2 = 25ng DNA, Lane 3-11= Sonicated DNA from 30s up to 5 minutes of ultrasonication at 30s interval, Lane 12 = 20bp DNA Ladder, Lane 13= blank, Lane 14 = 50ng DNA.

DNA was extracted from gel in the range of 200-300bp and also fragments of

lesser than 200bp. This DNA was then amplified with the multiplex. Full profile was

observed in the fragments of 300bp while there were partial profiles in the less than 200bp

DNA. The smaller size amplicons were observed but the larger amplicons (>150bp)

dropped out with this highly degraded DNA (Bender et al., 2004). Figure 4.10 is

graphical representation of degraded DNA at different stages amplified with the 12-plex

system.

59

Figure 4.10: Degraded DNA amplified with 12-plex.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Am

el

DX

S101

DX

S67

89

DX

S67

93

DX

S71

32

DX

S74

23

DX

S74

24

DX

S83

78

DX

S99

02

GA

TA1

72D

05

GA

TA3

1E08

HP

RTB

Degraded DNA Studies

Control >1000bp >700bp >300bp

60

4.3. Population Genetics

Once the multiplex was optimized, it was used to study the population living in the

four provinces of Pakistan, i.e. Baluchistan, Khyber-Pakhtunkhwa (KP), Punjab and

Sindh. Different parameters were determined for each of these populations. Global test of

differentiation was done amongst the 4 population and the exact test found no significant

difference in the allele frequency distribution of these populations (Exact P value =

0.19405 +- 0.07987 (100000 Markov steps done)). Table 4.2 shows the population specific

F statistics (Fst) amongst all the 4 populations while figure 4.11 is the graphical

representation of pair-wise F statistics between the populations.

Table 4.2: Population Specific F.Statistics

Locus Overall Punjab Baluchistan KP Sindh

DXS101 0.0175 0.0076 0.0045 0.0438 0.0278

DXS6789 0.0591 0.1181 0.0170 0.0077 -0.0093

DXS7132 0.0652 0.0431 0.0573 0.1216 0.0571

DXS7423 0.1102 0.1424 0.0873 0.0856 0.0587

DXS7424 0.1309 1.0000 0.1352 0.1185 0.1543

DXS8378 0.1350 0.1147 0.1997 0.1400 0.0778

GATA172D05 0.0199 0.0263 -0.0258 0.0534 0.0091

GATA31E08 0.0062 0.0046 0.0069 0.0003 0.0271

DXS7133 0.3045 1.0000 0.2899 0.3096 0.3115

DXS6793 0.5783 0.5778 1.0000 1.0000 0.5811

DXS9902 0.3154 0.3144 0.3581 1.0000 0.2962

HPRTB 0.0426 0.0468 0.0417 0.0478 0.0083

Overall 0.0999 0.1801 0.1094 0.1489 0.0803

61

Figure 4.11: Matrix of pair wise Fst between populations. PKA = Punjab, PKB = Baluchistan, PKPT = KP, PKS = Sindh

62

4.3.1. Baluchistan

DNA was isolated from the blood of 100 individuals (41 males, 59 females) after

informed consent was taken. Allele frequencies of males and females were pooled together

for all the 150 chromosomes studied. Figure 4.12 shows population assignment test of

Baluchi population against KP, Punjabi and Sindhi populations.

Figure 4.12: Population Assignment Test for Baluchistan against other populations.

PKA = Punjab, PKB = Baluchistan, PKPT = KP, PKS = Sindh

63

A total of 59 alleles were found at all the 9 loci tested. Maximum of 10 alleles

were observed at locus DXS6789 while minimum of 5 alleles were found at both

DXS7423 and DXS8378 as shown in figure 4.13.

Figure 4.13: Number of alleles at each locus

Table 4.3 shows allele frequency distribution while table 4.4 shows forensic

efficiency parameters for the 10 X-STRs in Baluchi population. Minimum gene diversity

of 0.651 was observed at locus DXS8378 while maximum of 0.8272 was observed at

locus DXS101. Locus GATA31E08 was the most heterozygous with 0.8261 while the

least heterozygous was DXS7424. Polymorphic Information Content (PIC) ranged from

0.5884 at locus DXS7423 to 0.8084 at locus DXS101. The combined power of

discrimination in females is 1.16 x 10-10 while in male it is 9.71 x 10-7. The population

deviates from Hardy-Weinberg at locus DXS101, DXS6789 and DXS7424 even after

Bonferroni’s correction for multiple testing.

64

Table 4.3: Allele frequency distribution of 10 X-STRs in Baluchi population (150 chromosomes)

Alleles DXS101 DXS6789 DXS7132 DXS7423 DXS7424 DXS8378 GATA172D05 GATA31E08 HPRTB

6 0.1838

7 0.2482

8 0.1397 0.0146

9 0.0444 0.1029 0.1314

10 0.3333 0.2426 0.2774

11 0.5037 0.2353 0.2628 0.0076

12 0.1022 0.0292 0.0917 0.0741 0.0956 0.0657 0.1212

13 0.2847 0.4161 0.0734 0.0444 0.3485

14 0.1022 0.3869 0.3869 0.1743 0.3182

15 0.0584 0.1825 0.0949 0.2569 0.1364

16 0.0219 0.0365 0.0730 0.2661 0.0682

17 0.0073 0.0826

18 0.0292 0.0550

19 0.2628

20 0.1898

21 0.1261 0.2409

22 0.0420 0.0730

23 0.1933 0.0146

24 0.2857 0.0073

25 0.1008

26 0.1261

27 0.0924

28 0.0336

65

Table 4.4: Different Forensic Efficiency Parameters for Baluchi Population

Marker DXS101 DXS6789 DXS7132 DXS7423 DXS7424 DXS8378 GATA172D05 GATA31E08 HPRTB Mean

GeneDiversity 0.8272 0.798 0.7079 0.6524 0.8223 0.651 0.8169 0.7647 0.734 0.7399

Heterozygosity 0.5 0.6304 0.6304 0.587 0.2121 0.5652 0.6957 0.8261 0.75 0.6397

PIC 0.8084 0.771 0.6592 0.5884 0.8023 0.5892 0.7908 0.7254 0.6896 0.6979

Power of Exclusion (PE):

0.651259 0.629348 0.466675 0.372047 0.616166 0.322944 0.62292 0.545737 0.491744

Paternity Index (PI): 0.086192 0.091926 0.137902 0.168968 0.095416 0.187118 0.093624 0.114679 0.130319 aPD female: 0.949313 0.941976 0.878038 0.824066 0.937615 0.796644 0.938124 0.909584 0.889706 bPD male 0.827617 0.816149 0.724195 0.662065 0.809168 0.625764 0.812753 0.770642 0.739363

MEC Krüger: 0.661871 0.639941 0.485447 0.40141 0.626118 0.366295 0.625898 0.550794 0.509617

MEC Kishida: 0.806646 0.791926 0.678301 0.600331 0.7832 0.56234 0.785824 0.732831 0.697

MEC Desmarais: 0.806646 0.791926 0.678301 0.600331 0.7832 0.562461 0.785938 0.732831 0.697

MEC Desmarais Duo: 0.691712 0.672939 0.536532 0.455305 0.661326 0.416931 0.66371 0.598602 0.557869

Hardy Weinberg 0 0.0008 0.163 0.032 0 0.138 0.192 0.295 0.027 aOnly female’s data is used for the analysis bOnly male data is used for the analysis. MEC Krueger, Mean Exclusion Chance for Autosomal markers typed in trios involving mother, child and putative father MEC Kishida, Mean Exclusion Chance for ChrX markers which covers trios including a daughter MEC Desmarais, Mean Exclusion Chance for ChrX markers in trios involving daughters MEC Desmarais, Mean Exclusion Chance for father/daughter duos lacking maternal genotype information

66

4.3.2. Khyber-Pakhtunkhwa (KP)

DNA samples of a total of 120 individuals (81 males and 39 females) were studied.

Allele frequency distribution and other forensic efficiency parameters for 9 X-STRs were

observed in the KP population. At all of the 9 loci, 61 alleles were found. Maximum of 10

alleles were observed at locus DXS6789 while minimum of 5 alleles were observed at

DXS7423 as shown in figure 4.14.

Figure 4.14: Number of alleles at each locus

Table 4.5 shows allele frequency distribution while table 4.6 shows other forensic

efficiency parameters for KP population. Minimum gene diversity of 0.56 was observed at

locus DXS8378 while maximum of 0.8075 was observed at locus DXS6789. Locus

GATA31E08 was the most heterozygous with 0.80 while the least heterozygous was

DXS7424 with 0.3684. Polymorphic Information Content (PIC) ranged from 0.5202 at

locus DXS8378 to 0.7781 at locus DXS6789. The combined power of discrimination in

females is 9.4 x 10-10 while in male it is 4.02 x 10-6. The population is in perfect Hardy-

Weinberg equilibrium at all loci except DXS7424 and GATA31E08 but after Bonferroni’s

correction, all loci are in equilibrium.

0

2

4

6

8

10

12KP

6

7

Table 4.5: Allele frequency distribution of KP population (159 Chromosomes)

Alleles DXS101 DXS6789 DXS7132 DXS7423 DXS7424 DXS8378 GATA172D05 GATA31E08 HPRTB

6 0.0413

7 0.2586

8 0.1736 0.0172

9 0.0661 0.0826 0.1724

10 0.1983 0.4132 0.2586

11 0.5372 0.1405 0.2414

12 0.0796 0.0833 0.0932 0.1570 0.1488 0.0517 0.0769

13 0.3274 0.4083 0.0678 0.0165 0.2479

14 0.1525 0.4071 0.3917 0.1102 0.0248 0.3846

15 0.0678 0.1239 0.0833 0.2034 0.2308

16 0.0169 0.0531 0.0333 0.3390 0.0342

17 0.1356 0.0256

18 0.0169 0.0088 0.0508

19 0.1864

20 0.0932

21 0.0661 0.2797

22 0.0496 0.1356

23 0.3471 0.0339

24 0.1818 0.0169

25 0.1818

26 0.0826

27 0.0496

28 0.0331

29 0.0083

6

8

Table 4.6: Different forensic efficiency parameters for KP population

Marker DXS101 DXS6789 DXS7132 DXS7423 DXS7424 DXS8378 GATA172D05 GATA31E08 HPRTB Mean

GeneDiversity 0.7738 0.8075 0.7701 0.635 0.6524 0.56 0.7413 0.7788 0.6731 0.6767

Heterozygosity 0.6 0.7895 0.6842 0.7 0.3684 0.6 0.8 0.75 0.5789 0.6371

PIC 0.738 0.7781 0.7349 0.5679 0.5921 0.5202 0.7042 0.7425 0.6101 0.6293

Power of Exclusion (PE):

0.581663 0.665802 0.432129 0.379722 0.579909 0.33363 0.510126 0.554009 0.472357

Paternity Index (PI):

0.104713 0.082429 0.148753 0.166276 0.105193 0.183019 0.124895 0.112356 0.136164

aPD female: 0.931056 0.952826 0.862221 0.82967 0.929146 0.823624 0.905475 0.912239 0.881189 bPD male 0.790573 0.835143 0.702494 0.667449 0.789615 0.633961 0.75021 0.775288 0.727673

MEC Krüger: 0.607703 0.67432 0.46035 0.409694 0.601916 0.402438 0.542572 0.556349 0.491739

MEC Kishida: 0.765374 0.815147 0.653106 0.607709 0.763023 0.59145 0.718081 0.737906 0.683024

MEC Desmarais: 0.765489 0.815147 0.653225 0.607709 0.763023 0.59157 0.718081 0.738023 0.683024

MEC Desmarais Duo:

0.64003 0.703094 0.510109 0.46279 0.636412 0.443062 0.581725 0.604497 0.541901

Hardy-Weinberg 0.183 0.701 0.158 0.802 0.005 1 0.745 0.007 0.368 aOnly female’s data is used for the analysis bOnly male data is used for the analysis. MEC Krueger, Mean Exclusion Chance for Autosomal markers typed in trios involving mother, child and putative father MEC Kishida, Mean Exclusion Chance for ChrX markers which covers trios including a daughter MEC Desmarais, Mean Exclusion Chance for ChrX markers in trios involving daughters MEC Desmarais, Mean Exclusion Chance for father/daughter duos lacking maternal genotype information

69

Figure 4.15 shows population assignment test of KP population against Baluchi,

Punjabi and Sindhi populations.

Figure 4.15: Population Assignment Test for KP against other populations. PKA = Punjab, PKB = Baluchistan, PKPT = KP, PKS = Sindh

70

4.3.3. The Punjab

Total of 250 individuals (116 females and 134 males) were genotyped. Different

forensic efficiency parameters along with allele frequency distribution were determined.

At all of the 10 loci, 67 alleles were found. Maximum of 9 alleles were found each at

locus DXS6789 and DXS101 while minimum of 5 alleles were found each at DXS8378

and DXS9902 as shown in figure 4.16.

Figure 4.16: Number of alleles at different loci

Table 4.7 shows allele frequency distribution while table 4.8 shows other forensic

efficiency parameters for Punjabi population. Minimum gene diversity of 0.5351 was

observed at locus DXS6793 while maximum of 0.8332 was observed at locus DXS101.

Locus DXS6789 was the most heterozygous with 0.8022 while the least heterozygous

was DXS7423 with 0.5604. Polymorphic Information Content (PIC) ranged from 0.4869

at locus DXS6793 to 0.8116 at locus DXS101. The combined power of discrimination in

females is 4.69 x 10-10 while in male it is 2.27 x 10-6. After Bonferroni’s correction, the

population is in perfect Hardy-Weinberg equilibrium at all loci.

0123456789

10The Punjab

71

Table 4.7: Allele Frequency Distribution in Punjabi Population (366 Chromosomes)

Allele DXS101 DXS6789 DXS6793 DXS7132 DXS7423 DXS8378 DXS9902 GATA172D05 GATA31E08 HPRTB

6 0.0898

7 0.064 0.2647

8 0.1575 0.0490

9 0.0073 0.1099 0.0769 0.1176

10 0.6277 0.0138 0.2711 0.3773 0.2157

11 0.0219 0.2630 0.3919 0.1832 0.3039 0.1136

12 0.0036 0.0034 0.0034 0.4291 0.2198 0.0513 0.0392 0.2601

13 0.1861 0.1065 0.0206 0.2630 0.0073 0.0098 0.3700

14 0.1387 0.2784 0.4777 0.0311 0.2051

15 0.1821 0.0146 0.3402 0.3677 0.0476

16 0.0584 0.2096 0.1065 0.0037

17 0.0103 0.0550 0.0241

18 0.0069

19 0.0241

20 0.4055

21 0.0756 0.1924

22 0.1684 0.0790

23 0.2509 0.0447

24 0.2096 0.0034

25 0.1375

26 0.0653

27 0.0653

28 0.0137

29 0.0137

72

Table 4.8: Forensic Efficiency Parameters for Punjabi Population

Marker DXS101 DXS6789 DXS7132 DXS7423 DXS8378 GATA31E08 DXS6793 DXS9902 GATA172D05 HPRTB Mean

GeneDiversity 0.8332 0.7802 0.753 0.613 0.673 0.7763 0.5351 0.6924 0.7591 0.737 0.7152

Heterozygosity 0.7033 0.8022 0.6813 0.5604 0.5824 0.7143 0.5862 0.6552 0.7126 0.6782 0.6676

PIC 0.8116 0.7519 0.7117 0.5367 0.6123 0.7402 0.4869 0.6379 0.7278 0.6939 0.6711

Power of Exclusion (PE):

0.658232 0.514987 0.50703 0.320984 0.392672 0.550264 0.236527 0.447823 0.539432 0.489879

Paternity Index (PI): 0.084383 0.123478 0.125801 0.18788 0.161811 0.113406 0.224319 0.143762 0.116462 0.130875 aPD female: 0.950159 0.907587 0.894864 0.788093 0.834009 0.912916 0.754261 0.86613 0.914101 0.888226 bPD male 0.831233 0.753043 0.748399 0.62424 0.676378 0.773189 0.551363 0.712476 0.767076 0.73825

MEC Krüger: 0.665253 0.549203 0.518715 0.352492 0.40653 0.560068 0.322129 0.460309 0.562254 0.504482

MEC Kishida: 0.809875 0.721501 0.706566 0.553528 0.615118 0.737431 0.50678 0.661276 0.735431 0.694989

MEC Desmarais: 0.809875 0.721618 0.706566 0.553528 0.615118 0.737548 0.506899 0.661276 0.735431 0.694989

MEC Desmarais Duo: 0.695838 0.586721 0.568332 0.408436 0.468179 0.604832 0.359172 0.517013 0.602147 0.55493

Hardy-Weinberg 0.0118 0.05 0.3389 0.0455 0.3352 0.3736 0.234 0.5535 0.6089 0.438 aOnly female’s data is used for the analysis bOnly male data is used for the analysis. MEC Krueger, Mean Exclusion Chance for Autosomal markers typed in trios involving mother, child and putative father MEC Kishida, Mean Exclusion Chance for ChrX markers which covers trios including a daughter MEC Desmarais, Mean Exclusion Chance for ChrX markers in trios involving daughters MEC Desmarais, Mean Exclusion Chance for father/daughter duos lacking maternal genotype information

73

Figure 4.17 shows population assignment test of Punjabi population against

Baluchi, KP and Sindhi populations.

Figure 4.17: Population Assignment Test for Punjab against other populations.

PKA = Punjab, PKB = Baluchistan, PKPT = KP, PKS = Sindh

74

4.3.4. Sindh

Total of 100 individuals (73 females and 27 males) were genotyped. Different

forensic efficiency parameters along with allele frequency distribution were determined.At

all of the 10 loci, 72 alleles were found. Maximum of 10 alleles were found at locus

DXS6789 while minimum of 5 alleles were found each at DXS8378 and DXS9902 and

HPRTB as shown in figure 4.18.

Figure 4.18: Number of alleles at different loci

Table 4.9 shows allele frequency distribution while table 4.10 shows other forensic

efficiency parameters for Sindhi population. Minimum gene diversity of 0.6 was observed

at locus DXS6793 while maximum of 0.8022 was observed at locus DXS6789. Locus

DXS7132 was the most heterozygous with 0.7857 while the least heterozygous was

DXS7424 with 0.4286. Polymorphic Information Content (PIC) ranged from 0.5482 at

locus DXS6793 to 0.7956 at locus DXS7424. The combined power of discrimination in

females is 1.28 x 10-11 while in male it is 2.64 x 10-7. After Bonferroni’s correction, the

population deviates from Hardy-Weinberg equilibrium only at DXS7424. This deviation

observed could potentially be due to population sampling effects.

0

2

4

6

8

10

12Sindh

75

Table 4.9: Allele Frequency Distribution in Sindhi Population (173 Chromosomes)

Alleles DXS101 DXS6789 DXS6793 DXS7132 DXS7423 DXS7424 DXS8378 DXS9902 GATA172D05 GATA31E08 HPRTB

6 0.0536

7 0.0179 0.3455

8 0.0714 0.0727

9 0.0196 0.0370 0.1000 0.1071 0.1273

10 0.5882 0.2593 0.2600 0.3393 0.2545

11 0.0196 0.2778 0.2600 0.1786 0.1818 0.0784

12 0.0196 0.1481 0.0980 0.0566 0.3889 0.3400 0.2321 0.0182 0.2941

13 0.2941 0.1852 0.3725 0.1132 0.0370 0.0400 0.2941

14 0.1579 0.0588 0.4259 0.3922 0.0943 0.2157

15 0.0175 0.2037 0.0980 0.2075 0.1176

16 0.0175 0.0185 0.0196 0.3585

17 0.0185 0.0196 0.0943

18 0.0175 0.0755

19 0.2632

20 0.1930

21 0.0385 0.1579

22 0.0385 0.0351

23 0.2692 0.0877

24 0.2885 0.0526

25 0.1538

26 0.0769

27 0.0769

28 0.0385

29 0.0192

76

Table 4.10: Forensic Efficiency Parameters for Sindhi Population

Marker DXS101 DXS6789 DXS7132 DXS7423 DXS7424 DXS8378 GATA172D05 GATA31E08 DXS6793 DXS9902 HPRTB Mean

GeneDiversity 0.7806 0.8022 0.6837 0.6607 0.8189 0.7044 0.7778 0.78 0.6 0.74 0.7533 0.7277

Heterozygosity 0.5714 0.4667 0.7857 0.6429 0.4286 0.7333 0.5333 0.6 0.6667 0.7333 0.7333 0.6195

PIC 0.7529 0.7753 0.6477 0.6124 0.7956 0.6524 0.7484 0.745 0.5482 0.6937 0.7108 0.6886

Power of Exclusion (PE):

0.606489 0.657787 0.460195 0.409109 0.578637 0.430754 0.561285 0.52875 0.248721 0.488793 0.527914

Paternity Index (PI):

0.097999 0.084498 0.139901 0.156277 0.105541 0.149195 0.110327 0.119507 0.218542 0.1312 0.119746

aPD female: 0.935985 0.950378 0.879704 0.84772 0.929626 0.855281 0.919962 0.905573 0.745111 0.884898 0.902643 bPD male 0.804002 0.831003 0.720198 0.687447 0.788918 0.701609 0.779345 0.760986 0.562916 0.7376 0.760508

MEC Krüger: 0.622908 0.666075 0.487643 0.436914 0.603011 0.44202 0.577966 0.542346 0.309866 0.496883 0.534522

MEC Kishida: 0.778402 0.809828 0.678073 0.632736 0.762985 0.645927 0.747996 0.723686 0.498948 0.691352 0.72039 MEC Desmarais:

0.778402 0.809941 0.678191 0.632856 0.7631 0.645927 0.747996 0.723686 0.499069 0.691352 0.720507

MEC Desmarais Duo:

0.656251 0.696181 0.535717 0.488806 0.636586 0.501012 0.617705 0.588161 0.355001 0.55072 0.584035

Hardy Weinberg

0.038 0.004 0.301 0.073 0.002 0.387 0.112 0.308 0.506 0.662 0.256

aOnly female’s data is used for the analysis bOnly male data is used for the analysis. MEC Krueger, Mean Exclusion Chance for Autosomal markers typed in trios involving mother, child and putative father MEC Kishida, Mean Exclusion Chance for ChrX markers which covers trios including a daughter MEC Desmarais, Mean Exclusion Chance for ChrX markers in trios involving daughters MEC Desmarais, Mean Exclusion Chance for father/daughter duos lacking maternal genotype information

77

Figure 4.19 shows population assignment test of Sindhi population against Baluchi,

KP and Punjabi populations.

Figure 4.19: Population Assignment Test for KP against other populations. PKA = Punjab, PKB = Baluchistan, PKPT = KP, PKS = Sindh

78

4.4. Linkage Disequilibrium (LD)

In female germ line, recombination rate of X-chromosome is lower which render X-

linked markers to have higher LD as compared to Autosomal markers (Pereira et al., 2007).

For all pair of loci, the exact test for LD was performed in Baluchi, KP, Punjabi and Sindhi

populations for all pairs of markers in this study. To reduce the chance of LD, All the

markers of this study are well spaced from each other and no evidence of LD was detected in

all pair of markers. Linkage disequilibrium matrices for Baluchi, KP, Punjabi and Sindhi

populations are given in tables 4.11, 4.12, 4.13 and 4.14, respectively. Any significant

linkage disequilibrium is given in bold font. The differences between populations regarding

LD is due to the fact that LD is not only monotonic function of distance between two

markers (Lonjou et al., 2003) but there are some other factors like selection, mutation,

random drift, population admixture, founder effect and stratification (Chakravarti, 1999). All

markers of this study are to be considered as independent and are recommended for forensic

practice in Baluchi, KP, Punjabi and Sindhi populations. The statistical parameters showed

that these markers are appropriate for individual identification, paternity testing involving a

female child.

79

Table 4.11: Linkage Disequilibrium Matrix Baluchistan

Marker DX

S1

01

DX

S6

789

DX

S7

132

DX

S7

423

DX

S7

424

DX

S8

378

GA

TA

172D05

GA

TA

31E0

8

HP

RT

B

DXS101 0.2279 0.2051 0.1685 0.2988 0.1701 0.2275 0.1701 0.2175

DXS6789 0.2028 0.1713 0.1974 0.1576 0.2399 0.2499 0.2165

DXS7132 0.1446 0.1225 0.1431 0.1817 0.1928 0.1781

DXS7423 0.1621 0.0721 0.1337 0.1840 0.1409

DXS7424 0.2044 0.2814 0.2391 0.1813

DXS8378 0.1365 0.1598 0.1346

GATA172D05 0.2359 0.1766

GATA31E08 0.7020

HPRTB

80

Table 4.12: Linkage Disequilibrium Matrix KP

Marker DX

S101

DX

S678

9

DX

S713

2

DX

S742

3

DX

S742

4

DX

S837

8

GA

TA

172D

05

GA

TA

31E08

HP

RT

B

DXS101 0.3415 0.3514 0.2188 0.2421 0.2237 0.3407 0.2741 0.2222

DXS6789 0.1989 0.2664 0.2431 0.2297 0.2804 0.4680 0.2846

DXS7132 0.2046 0.1829 0.2129 0.3200 0.3193 0.2114

DXS7423 0.0739 0.4439 0.2529 0.2024 0.1998

DXS7424 0.1722 0.1820 0.2554 0.2712

DXS8378 0.2209 0.1986 0.2488

GATA172D05 0.3482 0.2814

GATA31E08 0.1723

HPRTB

81

Table 4.13: Linkage Disequilibrium Matrix the Punjab

Marker DX

S1

01

DX

S6

789

DX

S7

132

DX

S7

423

DX

S8

378

GA

TA

31E0

8

DX

S6

793

DX

S9

902

GA

TA

172D05

HP

RT

B

DXS101 0.2005 0.1163 0.0739 0.1336 0.0959 0.1128 0.1264 0.1554 0.1001

DXS6789 0.1445 0.0884 0.1122 0.1172 0.1217 0.1384 0.1384 0.0949

DXS7132 0.0561 0.0971 0.0884 0.1284 0.0639 0.079 0.0967

DXS7423 0.0307 0.0557 0.0471 0.0898 0.0649 0.0581

DXS8378 0.0684 0.038 0.0732 0.0863 0.0707

GATA31E08 0.1036 0.1012 0.0771 0.1064

DXS6793

0.1114 0.1165 0.0772

DXS9902 0.0699 0.0268

GATA172D05 0.0694

HPRTB

82

Table 4.14: Linkage Disequilibrium Matrix Sindh

Marker DX

S1

01

DX

S6

789

DX

S7

132

DX

S7

423

DX

S7

424

DX

S8

378

GA

TA

172D05

GA

TA

31E0

8

DX

S6

793

DX

S9

902

HP

RT

B

DXS101

0.3961 0.4544 0.2853 0.3741 0.4007 0.4127 0.4655 0.3432 0.3532 0.3327

DXS6789

0.3990 0.1761 0.4138 0.3972 0.4326 0.3282 0.3335 0.3472 0.2547

DXS7132

0.3250 0.2787 0.3455 0.3224 0.3939 0.4542 0.3606 0.3890

DXS7423

0.2125 0.3214 0.3280 0.3888 0.4263 0.3418 0.4634

DXS7424

0.2571 0.4942 0.3971 0.2986 0.3093 0.2980

DXS8378

0.2355 0.2697 0.2535 0.2686 0.1795

GATA172D05

0.4340 0.3381 0.3470 0.2256

GATA31E08

0.3408 0.3148 0.3646

DXS6793

0.2835 0.3148

DXS9902

0.2775

HPRTB

83

4.5. Haplotype Analysis

The haplotypes of X-chromosome cluster (DXS7424-DXS101) was studied in the

Pakistani population. A total of 149 DNA samples from unrelated Pakistani males (KP =

81, Baluchi = 41 and Sindhi = 27) were analyzed in a triplex PCR reaction as described in

materials and methods section. The optimal quantity of the template DNA for triplex PCR

system ranged from 1 to 2 ng using 30 cycles of PCR. The tetraplex profile of male DNA

is shown in figure 4.20.

Figure 4.20: Tetraplex profile of male DNA

84

In 41 males from Baluchi population, we found 23 different haplotypes for the

cluster DXS7424-DXS101. As shown in table 4.15, haplotype 16-23 was the most

prevalent in the studied sample with frequency of 14%.

Table 4.15: Haplotype analysis in 41 Baluchi males of DXS7424-DXS101 cluster

S. No. Haplotype

Frequency S.D. Upper frequency DXS7424 DXS101

1 10 25 0.02439 0.02439 0.04878

2 10 26 0.02439 0.02439 0.04878

3 13 21 0.02439 0.02439 0.04878

4 13 26 0.02439 0.02439 0.04878

5 14 21 0.02439 0.02439 0.04878

6 14 23 0.02439 0.02439 0.04878

7 14 24 0.097561 0.046916 0.144477

8 14 25 0.02439 0.02439 0.04878

9 14 26 0.02439 0.02439 0.04878

10 15 21 0.02439 0.02439 0.04878

11 15 22 0.02439 0.02439 0.04878

12 15 23 0.073171 0.041175 0.114346

13 15 25 0.02439 0.02439 0.04878

14 15 28 0.02439 0.02439 0.04878

15 16 21 0.04878 0.034059 0.082839

16 16 23 0.146341 0.055885 0.202226

17 16 24 0.097561 0.046916 0.144477

18 16 25 0.02439 0.02439 0.04878

19 16 26 0.097561 0.046916 0.144477

20 16 27 0.04878 0.034059 0.082839

21 17 23 0.02439 0.02439 0.04878

22 17 24 0.02439 0.02439 0.04878

23 17 25 0.02439 0.02439 0.04878

85

In 81 males studied from KP population, we found 36 different haplotypes for the

cluster DXS7424-DXS101. As shown in table 4.16, haplotype 16-23 was the most

prevalent with frequency of 11% in the population sample.

Table 4.16: Haplotype analysis in 81 KP males of DXS7424-DXS101 cluster

S. No. Haplotype

Frequency S.D. Upper frequency DXS7424 DXS101

1 11 21 0.012346 0.012346 0.024692

2 11 22 0.012346 0.012346 0.024692

3 11 26 0.012346 0.012346 0.024692

4 12 23 0.037037 0.021114 0.058151

5 12 24 0.012346 0.012346 0.024692

6 12 26 0.012346 0.012346 0.024692

7 13 22 0.012346 0.012346 0.024692

8 13 23 0.037037 0.021114 0.058151

9 13 25 0.012346 0.012346 0.024692

10 13 26 0.024691 0.01735 0.042041

11 13 28 0.024691 0.01735 0.042041

12 14 22 0.012346 0.012346 0.024692

13 14 23 0.024691 0.01735 0.042041

14 14 24 0.049383 0.024224 0.073607

15 14 25 0.037037 0.021114 0.058151

16 14 26 0.024691 0.01735 0.042041

17 14 27 0.012346 0.012346 0.024692

18 15 22 0.037037 0.021114 0.058151

19 15 23 0.098765 0.033356 0.132121

20 15 24 0.037037 0.021114 0.058151

21 15 25 0.024691 0.01735 0.042041

22 15 26 0.024691 0.01735 0.042041

23 15 27 0.012346 0.012346 0.024692

24 16 21 0.037037 0.021114 0.058151

25 16 23 0.111111 0.035136 0.146247

26 16 24 0.012346 0.012346 0.024692

27 16 25 0.061728 0.026907 0.088635

28 16 26 0.012346 0.012346 0.024692

29 17 23 0.037037 0.021114 0.058151

30 17 24 0.012346 0.012346 0.024692

31 18 23 0.037037 0.021114 0.058151

32 18 24 0.012346 0.012346 0.024692

33 18 25 0.012346 0.012346 0.024692

34 18 28 0.012346 0.012346 0.024692

86

In 27 males studied from the Sindhi population, 19 different haplotypes were

found for the cluster DXS7424-DXS101.As shown in table 4.17, haplotype 16-24 was the

most prevalent with frequency of 11% in the population sample.

Table 4.17: Haplotype analysis in 27 Sindhi males of DXS7424-DXS101 cluster

No. Haplotype

Frequency S.D. Upper frequency DXS7424 DXS101

1 12 26 0.037037 0.037037 0.074074

2 13 24 0.074074 0.051361 0.125435

3 13 29 0.037037 0.037037 0.074074

4 13 28 0.037037 0.037037 0.074074

5 14 26 0.037037 0.037037 0.074074

6 14 27 0.037037 0.037037 0.074074

7 15 23 0.074074 0.051361 0.125435

8 15 24 0.074074 0.051361 0.125435

9 15 25 0.037037 0.037037 0.074074

10 16 23 0.074074 0.051361 0.125435

11 16 24 0.111111 0.061633 0.172744

12 16 25 0.074074 0.051361 0.125435

13 16 26 0.037037 0.037037 0.074074

14 16 27 0.074074 0.051361 0.125435

15 16 29 0.037037 0.037037 0.074074

16 17 21 0.037037 0.037037 0.074074

17 18 25 0.037037 0.037037 0.074074

18 18 24 0.037037 0.037037 0.074074

19 17 25 0.037037 0.037037 0.074074

The locus by locus amova result showed average F-Statistics at all loci as FST =

0.01160.

In this cluster, 9 alleles for marker DXS101 were identified while 8 alleles were

identified for DXS7424. Therefore, 72 different combinations are possible but we

identified only 46 different haplotypes through genotyping.

87

4.5.1. Allele Frequency for DXS7133

In the 149 samples analysed for haplotype cluster DXS7424-DXS101. Table 4.18

shows the allele frequencies that were observed for DXS7133 in the 3 populations.

Table 4.18: Allele Frequency of DXS7133 in male sample of 3 populations

Allele Baluchistan (n = 41) KP (n = 81) Sindh (n =27) 7 0.0154 8 0.5538 9 0.1538 0.0377 10 0.5610 0.2769 0.5660 11 0.2195 0.2075 12 0.2195 0.1887 PIC 0.5236 0.5274 0.5769 PD (male) 0.588919 0.59274 0.625123

88

4.6. Allelic Ladder

Overall, 85 different alleles were found for all the 12 loci in the 4 populations

studied. Maximum of 11 alleles were found for locus DXS6789 while minimum of 5

alleles were found for locus DXS9902. Table 4.19 shows alleles found for each locus

along with the range of amplicon length observed in the populations studied. These alleles

were used to construct allelic ladder.

Table 4.19: Observed alleles, fragment lengths and repeat size of 12 X-linked STRs in Baluchi, KP, Sindhi and Punjabi Populations

Locus No. of Alleles Fragment length Repeat

Size Genotypes (9947A)

DXS101 9 142-166 3 24, 26

DXS6789 11 122-162 4 21,22

DXS6793 7 178-196 3 10,10

DXS7132 7 128-152 4 12,12

DXS7133 6 80-100 4 9, 10

DXS7423 6 99-119 4 14,15

DXS7424 7 82-100 3 14, 16

DXS8378 6 95-115 4 10,11

DXS9902 5 169-185 4 11,11

GATA172D05 7 108-132 4 10,10

GATA31E08 7 101-125 4 11,11

HPRTB 7 161-185 4 14,14

Allelic ladder is artificial mixture of all common alleles of particular STR

present in human population (Sajantila et al., 1992). The allelic ladder has been used to

take in to account the run to run size variations of markers due to varying environmental

parameters such as temperature fluctuations (Butler, 2005). Therefore, allelic ladder was

constructed including all the common alleles of 12 X-STRs in this population (Butler,

2005). The exact allele sizes were determined by comparing PCR products with standard

DNA 9947A.

89

Altogether, ladder marker consisted of 81 alleles for 12 X-STRs. The alleles

included in ladder are shown in table 4.20. The resulting position of all alleles of ladder

was obtained in such way that there was no overlapping between two adjacent markers in

the ladder. The minimum difference of 4 base pairs (bp) between two adjacent markers

was maintained. Therefore, overlapping between the markers of multiplex was not

observed in the whole population. Allelic ladder was used for genotyping of few samples

as initial proof-of-concept experiment. The allelic ladder is given in figure 4.21.

Table 4.20: alleles of 12 X-STRs for allelic ladder

Locus Alleles included in allelic ladder

DXS101 21,22,23,24,25,26,27,28,29

DXS6789 14,15,16,17,18,19,20,21,22,23,24

DXS6793 9,10,11,12,13,14,15

DXS7132 12,13,14,15,16,17,18

DXS7133 9,10,11,12

DXS7423 12,13,14,15,16

DXS7424 12,13,14,15,16,17,18

DXS8378 9,10,11,12,13,14

DXS9902 9,10,11,12,13

GATA172D05 6,7,8,9,10,11,12

GATA31E08 7,8,9,10,11,12,13

HPRTB 11,12,13,14,15,16

Ladder was constructed by mixing all the hemizygous male samples having all the

different alleles for a particular locus. This mixture was then amplified in thermocyler

with Polymerase, buffer and the specific primers for that locus.

90

Figure 4.21: Allelic ladder for 12 X-STRs

91

4.7. Phylogenetic Analysis

As STR loci have high mutation rates, and high level of length polymorphism

within and among populations, making it ideal markers for intra-species phylogenetic and

forensic analysis (Rowold and Herrera, 2003). With the appropriate statistical approaches,

even lesser number of forensic STR loci are powerful enough to reconstruct the recent

human phylogenies despite their relatively high mutation rates (Agrawal and Khan,

2005). Therefore, the four populations studied were compared with 57 other populations

around the world on the basis of their X-STR frequency. Only those populations were

selected which had at least frequency reported at 6 of the 9 loci (DXS101, DXS6789,

DXS7132, DXS7424, DXS8378, DXS9902, GATA172D05, GATA31E08 and HPRTB).

The populations were from Morocco and Madagascar (Poetsch et al., 2011), Polish

(Łuczak et al., 2011), Ghana (Poetsch et al., 2009), Columbia (Pico et al., 2008), Angola,

Mozambique and Uganda (Gomes et al., 2007b), Algeria (Bekada et al., 2010), Taiwan,

Vietnamese, Chinese, Filipinos, Thais and Taos (Hwa et al., 2011), Asian-American,

African-American, Caucasian-American and Hispanic-American (Diegoli and Coble,

2011), Ethnic Chinese (Sun et al., 2011), Brazil, Argentina, Costa Rica, Portugal and

Spain (Gusmão et al., 2009), Germany (Edelmann et al., 2001), India (Mukerjee et al.,

2010), Korea (Shin et al., 2005), Japan (Asamura et al., 2006), Italy (Turrina et al., 2007),

Mongol (Liu and Li, 2006), Native Argentina (Toscanini et al., 2009) and Nu-China (Gao

et al., 2007). Poptree was used to estimate Nei’s distances based on the frequency

distribution (Takezaki et al., 2010). Unrooted tree based on Neighbour-Joining method

was constructed. MEGA5 was used to visualize the tree (Tamura et al., 2011). As shown

in figure 4.22, Punjabi population is closer to Sindhi while Baluchi population is closer to

KP. Overall these four populations formed a cluster which is nearer to the populations

from India.

92

93

Figure 4.22: Phylogenetic Tree of 61 populations around the world. Ethnic Chinese (Dongxiang, Tu,

Salar, Bonan), Iberian (Paran-Brazil, BuenosAires-Argentina, Misiones-Argentina, RioNegro-Argentina,

Costa Rica, Cordoba-Argentina, EntresRios-Argentina, SaoPaolo-Brazil, RioJenerio-Brazil, MatoGross-

Brazil, Antioqia-Columbia, Northern Portugal, Central Portugal, Galacia-Spain, Cantabria-Spain), Native

Argentina (Colla and Toba), Pakistan (Punjab, Sindh, Balochistan and KP), India (Balmiki, Sakaldwip

Brahimin, Konkanastha Brahimin, Mahadev Koli, Kurumans, Munda, Tripuri, Riang).

Chapter 5

It was easier to know it than to explain why I know it.

Sherlock Holmes - A Study in Scarlet

94

5. Discussion

5.1. Comparison of Multiplex PCR Systems

A multiplex PCR system has been developed consisting primers for the

amplification of sub-200bp amplicons of 11 X-STRs, DXS101, DXS6789, DXS6793,

DXS7132, DXS7423, DXS7424, DXS8378, DXS9902, GATA172D05, GATA31E08 and

HPRTB along with the gender determining locus, Amelogenin. A lot of PCR multiplex

systems for X chromosomal STRs including from 3 to 15 X-STRs have been optimized

for paternity testing by the forensic community (Zarrabeitia et al., 2002, Athanasiadou et

al., 2003, Bini et al., 2005, Poetsch et al., 2005, Robino et al., 2006, Gomes et al., 2007a,

Pereira et al., 2007, Turrina et al., 2007, Tariq et al., 2008, Hwa et al., 2009, Liu et al.,

2012). Moreover, multiplex PCR assay with 12 X chromosomal STRs is developed for

forensic identification (Turrina et al., 2007). Out of 12 markers of that study, 9 markers

can be typed by using our multiplex with reduced size amplicons. While the 2 markers,

DXS6809 and DXS6807 were replaced in this study by DXS9902 and DXS6793 having

almost similar power of discriminations with markers replaced. The marker DXS7133

was not included in our 12-plex but it was included in the 4-plex used for haplotype

analysis of DXS7424-DXS101.

However, there is very little data available on multiplexing of X-linked miniSTRs.

After 2006, when the first 2 multiplexes consisting of ChrX miniSTRs were published

(Asamura et al., 2006), recently another work was reported about the development of 2

new multiplexes consisting of 8 and 10 miniSTRs (Diegoli and Coble, 2011). The 8-plex

shares 5 markers (DXS6789, GATA31E08, DXS101, DXS7424 and DXS9902) with our

system while the 10-plex shares 6 markers (GATA172D05, DXS8378, DXS7132,

95

HPRTB, DXS7423 and DXS9902) with our system. Owing to reasonable combined

power of discrimination of markers (female = 1.28 x 10-11, male = 2.64 x 10-7) and

number of markers in the multiplex (11 plus amelogenin) may prove it to be worthwhile

tool for deficiency paternity testing.

5.2. Allele Distribution and Population Comparisons

The allele distribution across the populations was evaluated to determine the

presence of population specific pattern to aid the forensic case work as recommended in

previous literature (Kashyap et al., 2006). As reported in the literature, all non-Africans

descended from a single group of humans that left Africa by a coastal route across the

mouth of the Red Sea to South Asia (Macaulay et al., 2005). One migrant group of no

more than a few hundred souls was forced out of its homeland by increasing salinity in

the Red Sea, some 85,000 years ago. All non-Africans today can trace their mitochondrial

DNA to one woman from this group (Oppenheimer, 2004). They rapidly spread around

the Indian Ocean towards the Antipodes, long before a small branch left a South Asian

colony, earlier on the trail, through Turkey, Bulgaria and Hungary to populate Europe

(Oppenheimer, 2003, Oppenheimer, 2012). It is therefore, believed on the basis of genetic

evidence, that all human beings in existence now descended from Africa (Wells and

Read, 2002). Among human populations, diversity of microsatellites is highest in Africa,

which supports the hypothesis of an African origin for humans (Bowcock et al., 1994).

Allelic range of 21-28 for marker DXS101 was observed in Baluchi population

while 21-29 was observed in KP, Punjabi and Sindhi populations. Full range of alleles 14-

34 for marker DXS101 is found only in populations of African descent (Bekada et al.,

2009, Diegoli and Coble, 2011). Populations from Europe lacks allele 14 except Germans

(Edelmann et al., 2001) and Portuguese (Gusmão et al., 2009). Asian populations have

96

allele distribution starting from allele 19 (Shin et al., 2005, Hwa et al., 2009, Diegoli and

Coble, 2011). Interestingly allele 14 is reported in Mongol population (Liu and Li, 2006)

while the rare allele 35 is reported only in the Iyenger population of India. Allele 15

which is absent in Asians but interestingly, Balmiki population which is the lowest caste

in the Indian caste system, has this allele (Mukerjee et al., 2010).

Allele distribution of Marker DXS6789 ranged from 14 to 24. African-American

(Diegoli and Coble, 2011), German (Edelmann et al., 2001), Taiwanese (Hwa et al.,

2009) and Hungarian (Zalan et al., 2007) are the only populations having 14-25 allele

distribution. Allele 18 is missing in populations from African and Latin America

(Gusmão et al., 2009, Martins et al., 2009, Bobillo et al., 2011, Diegoli and Coble, 2011),

European (Aler et al., 2007, Gusmão et al., 2009) and Indian (Mukerjee et al., 2010)

populations. German (Edelmann et al., 2001), Italian (Turrina et al., 2007), Mongolian

(Liu and Li, 2006) and East Asian (Shin et al., 2005, Hwa et al., 2009, Hwa et al., 2011)

populations have this allele.

The marker DXS6793 is only studied in Chinese population in which alleles 14

and 15 are absent (Jia et al., 2004). However, allele 14 and 15 were observed in Punjabi

population which had allele distribution of 9-15. Similarly, allele 14 was present in Sindhi

population which had allele distribution of 9-14.

In the case of DXS7132, allele range 12-18 was found in Punjabi and KP

populations while 12-17 was observed in Sindhi and Baluchi populations. Allele range

10-18 is present in African populations (Poetsch et al., 2009, Diegoli and Coble, 2011).

Apart from African populations, allele 10 is present in Koreans (Shin et al., 2005) and

Asian-Americans (Diegoli and Coble, 2011). Europeans lack allele 10 and 18 (Edelmann

et al., 2001, Aler et al., 2007, Turrina et al., 2007, Zalan et al., 2007, Łuczak et al., 2011).

97

The rare allele 6 have been reported in Hungarians (Zalan et al., 2007), allele 8 in

Taiwanese (Hwa et al., 2009) and US population (Gomes et al., 2007a), allele 9 in

Spanish (Zarrabeitia et al., 2006) and Portuguese populations (Gusmão et al., 2009).

Similarly, microvariant alleles (15.3, 16.3, 17.3 and 18.3) have been observed only in

individuals from Argentina, Brazil and Columbia; therefore, a Native American origin is

postulated for these alleles (Gomes et al., 2009). The rare allele 19 was reported in

Hungarian (Zalan et al., 2007), Taiwanese (Hwa et al., 2009), Spanish (Zarrabeitia et al.,

2006) and Colombian populations (Pico et al., 2008). Allele 13 and 14 are the most

frequent alleles in major populations.

In the case of DXS7423, the allele range 12-16 was observed in KP and Baluchi

populations while allele range 12-17 observed in Punjabi and Sindhi populations. Allele

10 and 11 is only found in Portuguese population (Pereira et al., 2007) and rare allele 19

is reported in Hungarian population only (Zalan et al., 2007). Allele 13, 14 and 15 were

the frequent alleles in major populations. The rare allele 8 at marker DXS7423 has been

observed only in populations with African descent suggesting an African specificity

(Diegoli and Coble, 2011). Allele 9 is observed in Germans (Edelmann et al., 2001) while

allele 18 is only observed in Moroccan population (Poetsch et al., 2011).

For marker DXS7424, allele range 12-18 was observed in Baluchi, KP and Sindhi

populations. African-American and Caucasian-American (Diegoli and Coble, 2011) as

well as German (Edelmann et al., 2001) populations have allele 9. The alleles 15 and 16

were most frequent alleles of this marker in all populations.

For marker DXS8378, allelic range 9-14 was observed in KP and Baluchi

populations, 9-13 for Sindhi population and 10-14 for Punjabi population. This

distribution is similar to Indian (Mukerjee et al., 2010), African (Gomes et al., 2007b)

98

and Costa Rican (Gusmão et al., 2009) populations. Rare allele 7 is present in Spanish

population (Aler et al., 2007). Allele 14 is absent in Asian populations except Taiwanese

(Hwa et al., 2009). Alleles 10, 11 and 12 of DXS8378 were most abundant in all major

populations including Pakistani populations except Korean (Shin et al., 2005) and ethnic

Chinese (Sun et al., 2011) which showed allele 9 as a most frequent allele.

For DXS9902, the allele distribution in Sindhi and Punjabi populations was 9-13

which was similar to that of US Caucasian (Diegoli and Coble, 2011), Germany

(Edelmann et al., 2001), Vietnamese and Taos (Hwa et al., 2011). Allele 14 is present in

Asians (Diegoli and Coble, 2011, Hwa et al., 2011) as well as Brazilian (Martins et al.,

2009) populations. Allele 7 is only present in Korean (Shin et al., 2005) and Taiwanese

(Hwa et al., 2009).

The allele distribution of marker GATA172D05 in Baluchi, KP populations was

similar to Italian (Turrina et al., 2007), Taos and Filipinos (Hwa et al., 2011) and some

Indian (Mukerjee et al., 2010) populations, where allele 7 of this marker is absent. But

the same allele was found in Punjabi and Sindhi along with all other populations like US

(Diegoli and Coble, 2011), Iberian and Latin American (Gusmão et al., 2009), African

(Gomes et al., 2007b), Korean (Shin et al., 2005), Portuguese (Pereira et al., 2007),

Spanish (Zarrabeitia et al., 2006), German (Edelmann et al., 2001) and Colombian

Population (Pico et al., 2008). Allele 5 of GATA172D05 was only reported for German

population (Edelmann et al., 2001). Except Germans (Edelmann et al., 2001) and

Caucasian-Americans (Diegoli and Coble, 2011), allele 13 is present only in Latin

American populations (Gusmão et al., 2009) while allele 14 is reported only in

Columbians (Pico et al., 2008). The allele 10 of GATA172D05 was the most frequent

allele in all the above populations.

99

Allele distribution of GATA31E08 ranged from allele 7 to 12 in Sindhi, KP and

Baluchi populations which is similar to Italy (Turrina et al., 2007) and Korean (Shin et

al., 2005) while it was 7-13 in Punjabi population. Allele 6 is only reported in Koreans

(Shin et al., 2005) while allele 16 is reported only in Asian-Americans and African-

Americans (Diegoli and Coble, 2011).

In the case of marker HPRTB, the allele range 11-16 was observed in Baluchi and

Sindhi populations, which was similar to Asian-American (Diegoli and Coble, 2011),

African (Gomes et al., 2007b) and some Indian populations (Mukerjee et al., 2010).

Allele 6 is observed only in population from Madagascar (Poetsch et al., 2011) while

allele 7 is reported in Ugandans (Gomes et al., 2007b). Allele 18 is observed only in

Columbians (Pico et al., 2008) and Ghanaians (Poetsch et al., 2009). The allele range 12-

17 was observed in KP population while 11-15 was observed in Sindhi population. The

rare allele 18 is found in Colombian population (Pico et al., 2008). Alleles 12 and 13

were the most frequent alleles reported in all studied populations along with Punjabi and

Sindhi populations but alleles 13 and 14 were most frequent in KP and Baluchi

populations.

5.3. Linkage Disequilibrium and Haplotype Analysis

The exact test for LD was performed for all pairs of the 11 markers in female

samples from the Pakistani population. In Baluchi, KP and Sindhi populations, no pair of

markers showed any linkage disequilibrium. But in the Punjabi population, DXS6793

showed linkage with DXS8378 and DXS7423 while DXS7423 showed linkage with

DXS8378. Also DXS9902 showed linkage with HPRTB but after Bonferroni’s

correction, the LD values were not significant. Since these pairs of markers are not

located in close proximity or within the same linkage group on the X chromosome,

100

therefore, it is more likely that it is populations sampling effects or substructure and do

not indicate true linkage.

Linkage between pair of markers DXS7424 and DXS101 in any of the 4

populations that we studied could not be observed which is consistent with Korean

population (Lee et al., 2004) but significant LD was reported in Germans for same pair of

markers (Edelmann et al., 2002a). However, a previous study on generic Pakistani

population did not report any such linkage (Tariq et al., 2009). The linkage between

DXS8378 and DXS7423 has been reported in Waorani population of Ecuador which is an

extremely small and highly isolated population of about 3000 individuals (Baeta et al.,

2012). The differences among populations in terms of LD are due to factors such as

mutation, selection, random drift, the founder effect and population admixture

(Chakravarti, 1999).

In this cluster, 9 alleles for marker DXS101 were identified while 8 alleles were

identified for DXS7424. Therefore, 72 different combinations are possible but we

identified only 46 different haplotypes through genotyping. Haplotype 16-23 was the

most frequent in Baluchi and KP populations. In Sindhi population, haplotype 16-24 was

the most frequent. However, haplotype 15-24 is reported to be the most frequent in

German population (Edelmann et al., 2002a).

5.4. Phylogenetic Studies

Human genetic diversity is shaped by both demographic and biological factors

which is consistent with the hypothesis of a serial founder effect with a single origin in

sub-Saharan Africa. A pattern of ancestral allele frequency distributions that reflects

variation in population dynamics among geographic regions has been reported (Li et al.,

101

2008). In recent years, microsatellites based population genetics studies are on the rise

and these markers may be considered as good neutral Mendelian markers, at least for

intra-specific population studies (Jarne and Lagoda, 1996).

Baluchi, KP, Punjabi and Sindhi populations were compared with 57 other

populations around the world on the basis of their X-STR frequencies. It has been

reported in the literature that polymorphic microsatellites allow trees of human

individuals to be constructed that reflect their geographic origin with remarkable accuracy

(Bowcock et al., 1994). However, the irregular mutation pattern and upper limit of repeat

numbers are some of the problems in using microsatellite loci. Moreover, the long-term

stability of microsatellite loci is affected by the fact that loci that are highly polymorphic

in some species or populations may be monomorphic or less polymorphic in others. The

reason for this polymorphism between different populations is yet unknown. For these

reasons, caution is necessary in extrapolating results from distantly related populations.

Locus to locus mutation rates of microsatellites are variable and may increase the

variance of distance values. However, simultaneous examination of many polymorphic

loci is relatively easy. Therefore, these loci are very useful, at least for the study of

closely related populations (Takezaki and Nei, 1996).

The Baluchi populations clustered together with KP while the Punjabi population

with Sindhi. Together these 4 populations formed a cluster which was closer to the

populations from India. The results concur with a previous pyhlogenetic study of some

Pakistani ethnic populations on the basis of Y-Chromosomal STRs (Qamar et al., 2002).

Another study showed that Pakistani populations tend to be closer to Indian populations

on the basis of Y-Chromosomal (Underhill et al., 2000) and Autosomal STRs (Rakha et

al., 2009).

102

The Indian and Pakistani populations cluster seems closer to Western populations

especially from Latin American origin than to the Eastern populations. The Y-STR based

study also showed similar results (Qamar et al., 2002). The East Asian populations

clustered together with Asian-Americans and then with the other US populations. Similar

results have been reported previously where populations from US were shown closer to

population from Asia (Takezaki and Nei, 2008). This closeness is surprising as Genetic

relationships, as ascertained at least by Y-STRs, are dictated primarily by geographic

proximity rather than by remote linguistic origin (Ayub et al., 2003). Similarly

populations from Brazil, Argentina, Columbia and Costa Rica showed proximity with

populations from Spain and Portugal which are related by history and language but are

situated geographically on two different continents. However, there is no reason to

believe that actual molecular phylogenies would be convergent between different

molecules and would therefore represent populations’ history. In any case, our present

knowledge is obviously insufficient to reconstruct our genetic past, especially on the long

term (Langaney et al., 1992). As more and more data about phylogenetics and distances

of different populations become available, it will be helpful in ascertaining the behaviour

of X-STRs and their role in evolution of humans.

5.5. Conclusions

The proposed 12-plex system is the first in the field of ChrX miniSTRs. There

exist two other publications describing the use of mini-X chromosomal STRs. Indeed, we

adapted some of the markers in our miniplex from this work. However, the first

miniplexes published lacked a marker to represent linkage group 3, while we have

included markers to represent all the 4 linkage groups of X-chromosome. Moreover, the

second set of 2 published miniplexes contained 8 and 10 markers while our system

103

includes 11 highly polymorphic X-STRs. In conclusion, the parallel amplification of 11

informative X-STRs and sex determining locus amelogenin proved this multiplex PCR

assay as reliable tool for paternity testing. This multiplex can be a powerful tool in

forensic case work and can be used in parentage analysis especially in the identification

of female DNA profile in a mixture. It would be cost effective, less laborious and less

time consuming. Population characteristics of human at the genetic level are integral to

both population genetics and forensic biology and to the best of our knowledge, the

multiplex PCR system, consisting of 11 X-Chromosomal miniSTR loci as well as

amelogenin, developed in this study has not been previously reported anywhere in the

world. This multiplex system is highly recommended to be used along with the current

battery of forensic markers especially on compromised samples as this system high

specificity and low DNA requirement to give high quality DNA profiles. Hence, this

system can be used for forensic casework involving the Punjabi, Sindhi, Baluchi and KP

populations.

The present study did not include a comparison of miniSTRs with their larger size

counterparts. In the future, concordance between these miniSTRs and conventional STRs

present in commercial kits, like Argus X12, will be studied. This study demonstrates the

potential of this multiplex for routine forensic casework and inquiries into human

evolutionary history.

Chapter 6

There is nothing new under the sun. It has all been done before.

Sherlock Holmes - A Study in Scarlet

104

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Publication

Muhammad Israr, Ahmad Ali Shahid, Ziaur Rahman, Muhammad Saqib Shahzad, Obaid

Ullah and Tayyab Husnain (2012). Punjabi population data for seven X-chromosome

short tandem repeat (X-STR) loci using a new miniplex system. African Journal of

Biotechnology, 11: 10513-10516.