Identification of Biomarkers Associated with Ascites ...

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Theses and Dissertations

12-2013

Identification of Biomarkers Associated withAscites Incidence in BroilersKaylee RowlandUniversity of Arkansas, Fayetteville

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Identification of Biomarkers Associated with Ascites Incidence in Broilers

Identification of Biomarkers Associated with Ascites Incidence in Broilers

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Poultry Science

By

Kaylee Rowland University of Arkansas

Bachelor of Science in Poultry, 2012

December 2013

University of Arkansas

This thesis is approved for recommendation to the Graduate Council

___________________________________ Dr. Byung-Whi Kong Thesis Director

___________________________________ Dr. Nicholas Anthony Committee Member

___________________________________ Dr. Douglas Rhoads Committee Member

ABSTRACT

Poultry is key in genetic research due to breeding feasibility, relatively short generation interval,

and distinct phenotypes. It is estimated that 8% of broiler deaths annually can be attributed to ascites, an

economically important disease that has been challenging the industry for the past 2 decades. Genetically

selected ascites resistant (RES) and susceptible (SUS) chicken lines have been established and

maintained by the University of Arkansas, Fayetteville. Intensive research efforts have been made to

reveal physiological and biochemical characteristics for the incidence of ascites. Since the whole genome

of the major ancestral chicken, the Red Jungle Fowl, has been sequenced, genome-wide genetic study is

now possible in chickens to identify genetic variations throughout the entire genome. To identify genetic

biomarkers responsible for ascites resistance or susceptibility, whole genome sequences of genetically

selected chicken lines (RES and SUS) in addition to the unselected parental Relaxed line (REL) were

analyzed using the Illumina platform of next generation sequencing techniques and bioinformatics tools.

Over 4 million SNPs were identified in each line and over 95% of SNPs were found in the intergenic

regions. In the protein coding regions (CDS), SNPs that generated synonymous, non-synonymous,

frameshift, non-sense, no start, and no stop mutations were 69.3%, 29.3%, 1%, 0.3%, 0.07% and 0.03%,

respectively. Eight SNPs were chosen by the following parameters: over 75% SNP rate, over 10 depth

(read counts of contig), and verified in larger numbers (96) of birds for each line using PCR and Sanger

sequencing. A fast and accurate method of genotyping was developed (AS-PCR) in order to investigate

genotype/disease association in unrelated (RMQ) and unselected (REL) populations. No statistically

significant correlations were found between the 8 SNPs and ascites incidence in the REL or RMQ lines

due to sample size. However, CHTF18 continually associated with ascites phenotypic data. The

homozygous RJF genotype more often appeared in resistant-type birds while heterozygotes were mostly

susceptible.

AKNOWLEDGEMENTS

I owe many thanks to both my advisors, Dr. Byung-Whi Kong and Dr. Nicholas B. Anthony, for

supporting me throughout my study. They have provided friendship and encouragement all those times I

thought I would never see the end. Dr. Kong provided me with knowledge of many lab techniques and

computer programs that will be invaluable to my future. I admire Dr. Anthony’s ‘lead by example’ attitude.

I thank him for allowing me the use of the ascites lines. Working with his team at the farm has made me

both physically and mentally stronger! The guidance of Dr. Douglas Rhoads made this research possible.

He was there at the very beginning when I was certain it was impossible.

Of course, I wouldn’t be here without my family. My parents and sister have always supported

me in life.

My colleagues, Alex, Grant, Audrianna, Katy, and Ashley, have continually given up their time to

help me complete trials and data collection. I would especially like to thank Alex Gilley for training me on

the use of the hypobaric chamber and for always offering his help and friendship, no matter the

circumstances.

The farm crew (Lori Silva, Cory Burbidge, and various undergraduate students) does an excellent

job maintaining the farm and caring for the birds. They were always there to help me collect blood and

data and hatch my chicks.

I thank God for allowing me this opportunity.

Every person mentioned above has not only helped me to complete this thesis but has also

offered their friendship, which is just as valuable to me.

DEDICATION

I would like to dedicate this thesis to my father. He is the strongest, hardest working, most

determined person I know. His attitude towards life has been the single most influential factor in my life.

TABLE OF CONTENTS 1 INTRODUCTION 1 2 LITERATURE REVIEW 2

INTRODUCTION 3 GENETIC SELECTION IN POULTRY 4 GENETIC MARKERS IN POULTRY 5 MARKER ASSISTED SELECTION (MAS) 6 PATHOPHYSIOLOGY OF ASCITES 6 MANAGEMENT AND TRIGGERS OF ASCITES 8 Environmental Temperature 8 Ventilation 8 Altitude 9 Lighting 9 Feed 9 Diet 10 EXPERIMENTAL METHODS OF INDUCING ASCITES 10 Physiological Manipulation 10 Clamping of one pulmonary artery 10 Intravenous injection of microparticles 11 Environmental Manipulation 11 Cold stress 11 Dietary supplementation 11 Altitudes 11 DEVELOPMENT OF RES AND SUS LINES 12 MARKERS ASSOCIATED WITH ASCITES 14 NEXT GENERATION SEQUENCING AND SNPs 14 SUMMARY 15 REFERENCES 17 3 GENOME SEQUENCING AND DATA ANALYSIS 24

INTRODUCTION 25 MATERIALS AND METHODS 25 Genetic Lines 25 DNA purification and Next Gen Sequencing 25 Genome Sequence Assembly and Data Analysis 26 RESULTS AND DISCUSSION 26 Genome Sequence Assembly 26 SNP Filtering 27 Genome Resequencing 28 REFERENCES 29 LEGEND OF TABLES AND FIGURES 30 4 SNP VALIDATION IN LARGER POPULATION 35

INTRODUCTION 36 MATERIALS AND METHODS 36 DNA 36 Primers 37 PCR 37 Gel electrophoresis 37 PCR product purification 38 Sample Pooling and Sanger Sequencing 38 RESULTS AND DISCUSSION 39 SNP validation 39 Potential Biomarkers 40 REFERENCES 42 LEGEND OF TABLES AND FIGURES 44

5 DEVELOPMENT OF GENOTYPING METHOD USING ALLELE-SPECIFIC PCR (AS-PCR) 49 INTRODUCTION 50 MATERIALS AND METHODS 51 SNPs 51 Primers for AS-PCR 51 PCR 51 RESULTS AND DISCUSSION 52 Primer design 52 REFERENCES 64 LEGEND OF TABLES AND FIGURES 55 6 TESTING OF POTENTIAL BIOMARKERS IN UNSELECTED AND UNRELATED POPULATIONS 60

INTRODUCTION 61 MATERIALS AND METHODS 61 Genetic Lines 61 Hypobaric chamber 62 Husbandry 62 DNA 63 PCR 63 TaqMan quantitative PCR (qPCR) assay 63 Data analysis 63 RESULTS AND DISCUSSION 64 Trial #1. REL- 8,000ft Altitude 64 Trial #2 and #3. RMQ (12,000ft and 8,000ft Altitude) 65 CHTF18 66 REFERENCES 67 LEGEND OF TABLES AND FIGURES 68 7 CONCLUSION 78

1

INTRODUCTION

Poultry is key in genetic research due to breeding feasibility, relatively short generation interval,

and distinct phenotypes. Therefore, chickens are often used to study diseases such as pulmonary

arterial hypertension (PAH) in parallel to human research. It is estimated that 8% of broiler deaths

annually can be attributed to PAH or ascites syndrome, a disease that has been challenging the industry

for the past 2 decades. Genetically selected ascites resistant (RES) and susceptible (SUS) chicken lines

have been established and maintained by the University of Arkansas, Fayetteville. Since the whole

genome of the major ancestral chicken, the Red Jungle Fowl, has been sequenced, genetic study is now

possible in chickens to discover genetic variations throughout the entire genome. In this thesis, genomes

of RES, SUS, and REL (parental/unselected line) were compared with the ancestral RJF in an attempt to

identify genetic variations contributing to the incidence of ascites syndrome in these genetically selected

populations. Overall, the goal was to discover biomarkers associated with ascites syndrome that could

be applied to commercial populations through marker assisted selection and possibly relate to human

disease research.

2

LITERATURE REVIEW

The poultry industry continue

risen from 30 to 80 lbs over the last 50 years. Over the same time period, beef consumption has fallen

slightly with pork remaining fairly steady (Figure 1) (Davis and Lin, 2005; Meyer and Steiner, 2011).

Processing over 8 billion chickens and 250 million turkeys annually, the combined value of poultry

production was about $40 billion for the 2012 year (US Poultry and Egg Association, 2013).

Figure 1. U.S. meat and poultry consumption per capita, by species (Meye

Trends of per capita consumption for beef, pork, poultry, and turkey from 1955

Starting around 1950, the poultry industry began the evolutionary process of large

commercialization which is evident today (Ewart,

used as dual-purpose animals: egg and meat production. People began to realize that meat production

and reproduction were not complementary traits. Thus, the modern poultry industry was founded on

separation of egg and meat-type birds.

Since then, primary broiler breeding companies have been employing quantitative genetics to

improve growth rate, feed conversion, and meat yield. It was reported that body weight at 42 days of age

has improved significantly over time. A typical broiler line from 1957 weighs 539g at 42 days while a

3

INTRODUCTION

The poultry industry continues to grow rapidly. In the US per capita consumption of chicken has



over 8 billion chickens and 250 million turkeys annually, the combined value of poultry


Figure 1. U.S. meat and poultry consumption per capita, by species (Meyer and Steiner, 2011)

Trends of per capita consumption for beef, pork, poultry, and turkey from 1955-2012 are shown.

Starting around 1950, the poultry industry began the evolutionary process of large

commercialization which is evident today (Ewart, 1993). Until that time, chickens had traditionally been

purpose animals: egg and meat production. People began to realize that meat production

and reproduction were not complementary traits. Thus, the modern poultry industry was founded on

type birds.



ignificantly over time. A typical broiler line from 1957 weighs 539g at 42 days while a

per capita consumption of chicken has



over 8 billion chickens and 250 million turkeys annually, the combined value of poultry


r and Steiner, 2011).

2012 are shown.

Starting around 1950, the poultry industry began the evolutionary process of large-scale

1993). Until that time, chickens had traditionally been

purpose animals: egg and meat production. People began to realize that meat production

and reproduction were not complementary traits. Thus, the modern poultry industry was founded on the



ignificantly over time. A typical broiler line from 1957 weighs 539g at 42 days while a

modern commercial broiler will reach 2,672g in the same number of days (Havenstein et al., 2003).

Havenstein et al. (1994a) reported that 85

with the remaining 10 – 15% accredited to nutritional advances. All of these great advances have not

come without cost however. Several correlated responses to increased growth rate and muscle yield

have also negatively impacted the industry over the last 20 years. Increased carcass fat deposition

(Soller and Eitan, 1984; Chambers, 1990), physiological leg problems (Nestor, 1984), reproductive

inefficiency (Siegel and Dunnington, 1985), and increased ascites incidence

examples.

GENETIC SELECTION IN POULTRY

Traits of economic importance are polygenic and most genetic progress has been accomplished

through phenotypic selection (Dekkers, 2005). In the poultry industry, genetic progress is made

the use of a pyramidal breeding structure (Figure 2) in addition to quantitative genetics. The most elite,

pedigree lines are located at the top of the pyramid and genetic progress is made at this level.

Unselected pedigree birds are moved into t

multiply bird numbers and produce grandparent (G

sold to integrators to produce consumer products (broilers or table eggs).

4


Havenstein et al. (1994a) reported that 85 – 90% of this increase can be attributed to genetic selection

15% accredited to nutritional advances. All of these great advances have not


ly impacted the industry over the last 20 years. Increased carcass fat deposition


inefficiency (Siegel and Dunnington, 1985), and increased ascites incidence (Anthony, 1998) are a few

GENETIC SELECTION IN POULTRY


through phenotypic selection (Dekkers, 2005). In the poultry industry, genetic progress is made



Unselected pedigree birds are moved into the great grandparents (GGP) category where they are used to

multiply bird numbers and produce grandparent (GP) stock. The GP’s multiply into parent birds which are

sold to integrators to produce consumer products (broilers or table eggs).

Figure 2. Poultry Breeding

Structure (Stanley, 2009)

pyramidal breeding structure

employed by poultry breeding

companies starts with pedigree elite

lines, where all of the permanent

genetic progress is made, and is

multiplied through great

grandparents, grandpa

and broilers (consumer products).


n be attributed to genetic selection

15% accredited to nutritional advances. All of these great advances have not


ly impacted the industry over the last 20 years. Increased carcass fat deposition


(Anthony, 1998) are a few


through phenotypic selection (Dekkers, 2005). In the poultry industry, genetic progress is made through



he great grandparents (GGP) category where they are used to

to parent birds which are

Poultry Breeding

Structure (Stanley, 2009). The

pyramidal breeding structure

employed by poultry breeding

companies starts with pedigree elite

lines, where all of the permanent

genetic progress is made, and is

multiplied through great-

grandparents, grandparents, parents,

and broilers (consumer products).

5

Geneticists utilize highly heritable traits, maximize selection intensity, and minimize generation

interval in order to achieve the highest level of genetic progress (Emmerson, 1997). Traits possessing

high heritabilities will experience more rapid progress due to selection pressure. Genotypes have

heritabilities of 1 assuming no genotyping errors (Dekkers, 2005). By associating phenotypic traits to

specific genotypes, the heritability of the trait can become close to 1. Linking phenotype and genotype

has challenged the poultry industry since the notion of molecular genetics arose.

GENETIC MARKERS IN POULTRY

Tremendous efforts to link genotype and phenotype have been made for several different traits of

economic importance (Bumstead et al., 1994; Smith et al., 1997). Lamont et al. (1996) identified 20

genetic markers using DNA fingerprinting method as being linked with traits including growth,

reproduction, and egg quality. Li et al. (2005) reported an association between the very low density

apolipoprotein-II (apoVLDL-II) gene and traits including body weight, breast muscle weight, drumstick

weight, and tibia length. Traits concerning health and welfare have also been investigated. Toll-like

receptor 4 (TLR4) has been linked to resistance of Salmonella enterica serovar Typhimurium infection

(Leveque et al., 2003). A number of loci were implicated in Marek’s disease resistance in Leghorn

chickens (Yonash et al., 1999).

Most recently, single nucleotide polymorphism (SNP) discovery has come to the forefront of

genomic research. SNPs are single base positions in genomic DNA in which one or more sequence

alternatives exists at that particular location (Brookes, 1999). SNPs have been successfully linked to

specific phenotypes and have therefore been identified as biomarkers for traits of interest (Wang et al.,

2006; Ober et al., 2012; Rajasekaran et al., 2013; Sheng et al., 2013; Subedi et al., 2013). A SNP in the

leptin receptor (OBR) gene was found to be associated with abdominal fat deposition (Wang et al. 2006).

Cathepsin D (CTSD) was revealed as a key component in yolk formation by SNP analysis. Two SNPs in

the CTSD gene have highly significant effects on egg quality traits (Sheng et al., 2013). Neither SNP was

a protein coding change, thus both SNPs were speculated to cause a disruption of CTSD expression. The

highly significant effects linked to these SNPs show their usefulness as molecular markers in assisted

breeding programs.

6

MARKER ASSISTED SELECTION (MAS)

Marker assisted selection (MAS) will be a part of future genetic selection in the animal breeding

industry. A marker can be a particular variation in the DNA sequence that can be associated with a trait

of interest. Restriction fragment length polymorphisms (RFLPs), microsatellites, and SNPs are examples

of easily detectable DNA sequence variations. By linking a phenotype of economic importance to an

easily detectable genotype, selection can be aided by testing for the genotypic marker. Since genotypes

have heritabilities of 1 assuming no genotyping errors (Dekkers, 2005), associating genotype and

phenotype will provide a heritability close to 1 for the important trait. Selection will become much more

efficient and rapid progress will be made. The marker used for selection must be associated with the

gene or trait at high frequency in order to be effective.

MAS is particularly helpful for selecting traits difficult or expensive to measure, have low

heritability, or are expressed late in the development of the individual. Disease resistance poses several

challenges when under selection. The disease must be induced in order to determine

resistance/susceptibility of individuals and subsequent breeding values. Also, working with diseases can

be dangerous to humans and other animals. Traits such as reproductive efficiency cannot be observed

until reproductive maturity is reached (21 weeks in chickens). In general, the cost of growing,

maintaining, and caring for animals (phenotype collection) continues to rise as the cost of genotyping is

steadily decreasing. Though a variety of potentially important genetic biomarkers have been identified to

represent economically important traits in poultry, the usability of candidate markers are still limited in field

application for marker assisted selections since the benefit and side effects on the use of candidate

markers have not been proved yet.

PATHOPHYSIOLOGY OF ASCITES

Increased ascites incidence is one of several correlated responses to a high level of selection

pressure on economically important traits. The rapid growth results from higher metabolic rates and

therefore requires a greater supply of O2 to the body. Selection for increased muscle mass has not

resulted in a proportionate increase in cardiopulmonary organs (Decuypere et al., 2000). As a result, the

heart and lungs are required to work harder to provide the necessary amount of O2 to a greater mass of

muscle tissues. The constant strain causes the development of p

subsequently ascites.

Ascites has been a problem in the poultry industry since the early 1990s affecting both live

production and processing. Mortality due to ascites can reach 30% in broiler flocks. A significant portion

of processing plant condemnations is due to ascites. The 8% of annual broiler mortalities

ascites is estimated to cost 26 million dollars per year (Anthony and Balog, 2003). Severity of losses is

compounded because mortalities are typically obser

Affected birds can be identified through the presence of fluid in the abdominal cavity, an enlarged

flaccid heart, and occasional liver changes (Figure 3) (Ridde

2000). Ascites is a progressive disease which begins with pulmonary hypertension and ends with death

by right ventricular failure. Pulmonary hypertension is initiated by the inability to meet the high O

requirements of elevated metabolic rates (Anthony and Balog, 2003). The need for O

output which increases vascular pressure in the lung, placing higher pressure on the right ventricular wall.

The muscular ventricle copes with the heightened p

1998). The stronger right ventricle wall is now able to increase the pressure on the pulmonary arteries

and lung further. The cycle continues as the increased pressure in the right ventricle persists;

7

muscle tissues. The constant strain causes the development of pulmonary hypertension and



cessing plant condemnations is due to ascites. The 8% of annual broiler mortalities

is estimated to cost 26 million dollars per year (Anthony and Balog, 2003). Severity of losses is

compounded because mortalities are typically observed late in the grow-out period (Shapiro, 1993).


flaccid heart, and occasional liver changes (Figure 3) (Riddell, 1991; Julian, 1993; Decuypere et al.,


by right ventricular failure. Pulmonary hypertension is initiated by the inability to meet the high O

ents of elevated metabolic rates (Anthony and Balog, 2003). The need for O2 stimulates cardiac


The muscular ventricle copes with the heightened pressure by thickening the wall (hypertrophy) (Julian,


and lung further. The cycle continues as the increased pressure in the right ventricle persists;

Figure 3. Accumulation of

fluid in the abdominal cavity

of an ascites affected bird

typical bird affected by ascites

syndrome. Serous fluid is being

removed from the abdominal

cavity. (Rowland, K. “Ascites

Blood Serum in the Abdominal

Cavity.” University of Arkansas,

2013)

lmonary hypertension and



cessing plant condemnations is due to ascites. The 8% of annual broiler mortalities attributed to

is estimated to cost 26 million dollars per year (Anthony and Balog, 2003). Severity of losses is

out period (Shapiro, 1993).


ll, 1991; Julian, 1993; Decuypere et al.,


by right ventricular failure. Pulmonary hypertension is initiated by the inability to meet the high O2

stimulates cardiac


ressure by thickening the wall (hypertrophy) (Julian,


and lung further. The cycle continues as the increased pressure in the right ventricle persists; the wall

Figure 3. Accumulation of

fluid in the abdominal cavity

of an ascites affected bird. A

typical bird affected by ascites

syndrome. Serous fluid is being

removed from the abdominal

(Rowland, K. “Ascites –

Blood Serum in the Abdominal

Cavity.” University of Arkansas,

8

keeps thickening and adding pressure to the lung capillaries. The muscular right atrio-ventricular (AV)

valve also experiences hypertrophy (Chapman and Wideman, 2001). This added thickness and the

increasing back pressure from pulmonary arteries causes the valve to be insufficient. Right AV valve

failure adds volume to the already high pressure of the right ventricle; it continues to dilate. The failing

AV valve causes cardiac output and pulmonary hypertension to decline, but at the same time resulting in

right atrial, sinus venosus, vena cava, and portal vein pressure to rise considerably (Wideman et al.,

1999; Chapman and Wideman, 2001). The increased pressure in liver capillaries causes leakage of

plasma into the hepato-peritoneal space (Wideman, 2000b). Tissue hypoxia results from decreased

cardiac output: The natural response is production of erythropoietin. Erythrocytes are produced causing

blood volume and viscosity to increase, worsening the pressure overload (Lubritz and McPherson, 1994;

Julian, 2000). Cardiac muscles are in a state of hypoxia causing extreme damage and finally right

ventricular failure (Huchzermeyer and De Ruyck, 1986; Peacock et al., 1989; Mirsalimi et al., 1993a;

Wideman and French, 2000a; Balog, 2003).

MANAGEMENT AND TRIGGERS OF ASCITES

Environmental Temperature

Chickens do not have a wide ranging thermo-neutral zone. When challenged with cold

temperature, chickens will increase metabolic activity to maintain body temperature (Olson et al., 1972).

It has been reported that cold temperatures can increase a bird’s oxygen requirements up to 32%

(Huchzermeyer et al., 1989). The subsequent increase in oxygen demand has been linked to an increase

in ascites incidence (Huchzermeyer and De Ruyck, 1986; Wideman, 1988; Bendheim et al., 1992;

Shlosberg et al., 1992; Lubritz and McPherson, 1994; Julian, 2000; Druyan et al., 2007a; Ozkan et al.,

2010).

Ventilation

Ventilation is a major focus in broiler house management. Inappropriate environments such as

extreme weather conditions, overcrowding, insufficient or broken equipment can lead to poor ventilation in

a poultry house. Poor ventilation can result in litter and health problems such as ascites. Lower

environmental oxygen and higher toxic fumes have been discussed as two mechanisms of inducing

9

ascites syndrome (Wideman, 1988; Albers and Frankenhuis, 1990; Dale, 1990; Julian, 1993; Wideman,

1998).

Altitude

Ascites syndrome was first reported in flocks raised at high altitudes (>800m) (Smith et al., 1954;

Smith et al., 1956). The partial pressure of O2 decreases as altitude increases. Lower availability of

oxygen (acute hypoxia) causes blood vessel constriction and increased vascular resistance. Continuous

pressure increase results in ventricular hypertrophy (Peacock et al., 1989; Owen et al., 1995; Wideman,

1998; Julian, 2000; Anthony and Balog, 2003; Balog, 2003).

Lighting

Proper management of lighting can be helpful in controlling ascites. Lighting schedule is related

to feed consumption in birds. Broilers are grown under a continuous light schedule in order to maximize

food consumption and growth rate. Maximizing growth rate inevitably results in higher metabolic rates.

Ascites incidence can be increased consequently when birds are grown on a continuous lighting program.

Intermittent lighting will reduce ascites by limiting feed intake and therefore slowing the growth curve

(Mirsalimi et al., 1993a; Buyse et al., 1996; Decuypere et al., 2000; Julian, 2000).

Feed

Feed is often restricted since a greater volume consumed will increase the metabolic rate which

can trigger ascites. Temporary feed restriction during the early grow-out period can reduce ascites

incidence while maintaining overall performance (Reeves et al., 1991; Acar et al., 1995; Decuypere et al.,

2000).

Mash rations are nutritionally the same as pelleted feeds but the physical form is a crumble or

powder like texture. A mash diet is useful in controlling metabolic rates since the birds eat less than when

provided a dense, easily consumed, pelleted ration (Wideman, 1988; Acar et al., 1995; Balog et al., 2000;

Decuypere et al., 2000; Julian, 2000). Mash diets have been shown to reduce ascites incidence at high

altitudes (Bendheim et al., 1992; Shlosberg et al., 1992), low altitude (Lamas da Silva et al., 1988), and in

cold environments (Shlosberg et al., 1992).

10

Diet

Diet can be a causative factor inducing ascites or be used to prevent ascites through the control

of metabolic rate (Julian, 2000). Diets with high protein content can induce ascites due to the high O2

demand for protein metabolism (Mirsalimi et al., 1993a; Summers, 1994; Julian, 2000). Other

researchers have found results contrary to this however (Julian et al., 1989; Dale, 1990; Buys et al.,

1998). Julian et al. (1989) did not find a significant association between diet protein level and ascites

incidence in two strains of broilers grown in cold environments. Dale (1990) observed an effect on

ascites when a high energy diet was fed but the energy level from protein did not correlate with ascites

incidence.

Feed and water additives can be used in various ways to alter ascites incidence. Toxic levels of

cobalt (Diaz et al., 1994) and sodium (Julian, 1987; Mirsalimi et al., 1993b) have been known to induce

ascites syndrome. Several reports have shown a decrease in ascites incidence with the addition of

vitamin C to the diet (Al-Taweil and Kassab, 1990; Hassanzadeh Ladmakhi et al., 1997). Vitamin C is

known for its antioxidant properties and free radical formation has been linked to the development of

ascites (Bottje and Wideman, 1995). Furosemide has been shown to decrease ascites incidences due to

its diuretic (pressure decreasing) properties (Wideman et al., 1995). Supplementing water with

metaproterenol (bronchodilator) is also known to lower disease levels (Vanhooser et al., 1995).

EXPERIMENTAL METHODS OF INDUCING ASCITES

To study ascites syndrome, models had to be created to reproduce the disease consistently.

Resistance/susceptibility status of individual birds cannot be determined unless the birds are challenged

with the disease. This information is needed to select breeders that can create offspring resistant to

ascites incidence. Susceptible offspring are also needed in research models used to study the disease.

Physiological Manipulation

Clamping of one pulmonary artery results in immediate increases in pulmonary pressure since all

blood flow and gas exchange is forced through one lung (Wideman et al., 1995; Wideman et al., 1997;

Wideman and French, 2000a). Hypoxia induces ascites instantly.

11

Invasive manipulation techniques such as pulmonary artery clamping require time and skill to

perform. Although highly effective, a simpler method was needed for application to selection programs

evaluating large numbers of birds. Wideman and Erf (2002) developed a less invasive method involving

intravenous injection of micro-particles. The particles were appropriately sized to become trapped in the

pulmonary arterioles and block subsequent blood flow. The proceeding physiological responses are very

similar to that of pulmonary artery clamping. This technique was successful for inducing ascites in

chickens.

Environmental Manipulation

Cold stress has been positively linked to ascites syndrome in a number of cases, thus

environmental temperature has been adopted as a method for inducing the disease (Julian et al., 1989;

Wideman et al., 1995; Deaton et al., 1996; Shlosberg et al., 1996). Several variations of the cold-stress

model have been reported: constant cold temperature (Julian et al., 1989; Wideman et al., 1995), gradual

decline in temperature (Deaton et al., 1996; Buys et al., 1999), and intermittent cold temperatures

(Shlosberg et al., 1996). All three methods were shown to be effective at inducing ascites under

experimental conditions.

A few methods of dietary supplementation have been experimentally used to induce ascites

syndrome. Thyroid hormone, triiodothyronine (T3), was found to increase right ventricular hypertrophy

and ascites in two lines of broilers in a dose dependent manner through decreasing GH-pulsatility

(Decuypere et al., 1994). Hassanzadeh (1997) also used T3 supplementation to increase ascites

incidence. These studies indicate that the development of ascites could be linked with thyroid function.

Ascites incidence was decreased in birds fed an alkalinized ration – the mechanism is suspected to be

lowered pulmonary arterial pressure (Owen et al., 1994).

Ascites was first noticed as a problem in flocks grown at high altitudes (Smith et al., 1954; Smith

et al., 1956). Researchers found ways to simulate high altitude environments effective for inducing

ascites disease. Early practice involved supplementing environmental oxygen with other gases such as

nitrogen (Burton and Smith, 1969; Maxwell and Mbugua, 1990; Jones, 1995). As technology advanced,

the use of hypobaric chambers was developed by operating under partial vacuum to lower the partial

pressure of oxygen, a similar condition to natural high altitude environments (Mirsalimi et al., 1993a;

12

Owen et al., 1995; Balog et al., 2000; Anthony et al., 2001; Anthony and Balog, 2003; Pavlidis et al.,

2007). The hypobaric chamber method was used for creating an ascites challenge environment for birds

in this thesis.

DEVELOPMENT OF RES AND SUS LINES

Divergent selection for ascites incidence has been very successful. Dr. Anthony at the University

of Arkansas began a selection program in 1994 for ascites incidence (Figure 4). Three genetic lines are

currently maintained and have undergone 19 generations of selection. The resistant (RES) line typically

shows ~25% mortality from ascites grown at 12,000ft, while susceptible (SUS) showed ~98% grown at

8,000ft, and relaxed (REL) showed ~69% (Pavlidis et al., 2007). The parental line was obtained from a

primary breeding company and was representative of a pedigree elite line. The REL line has not been

subjected to genetic selection since the formation of the lines and is therefore indicative of the original

parental population (Anthony et al., 2001; Anthony and Balog, 2003).

Figure 4. Cumulative % mortality due to ascites experienced by RES, SUS, and REL lines (Pavlidis

et al., 2007). Cumulative mortality (%) due to ascites syndrome experienced by the three lines (RES,

SUS, REL) in an ascites inducing environment as a function of time is shown.

REL Relaxed; parental line RES Resistant SUS Susceptible

13

A hypobaric chamber model was used for inducing the disease and applying selection pressure

(Figure 5). The chamber measures 2.4m x 3.7m x 2.4m and is capable of housing 480 birds to three

weeks and 240 birds to 6 weeks of age. Custom stainless steel batteries, holding 60 birds each, are

equipped with trough feeders and nipple waterers. Ventilation rate, temperature, and simulated altitude

can be set by the experimenter and monitored on a daily basis. Chicks were placed in the chamber at

day of hatch and warm-room brooded. Selection of RES and SUS lines required a simulated altitude of

2,900m above sea level. Interestingly, birds from the SUS line succumb to ascites during exposure to

cool temperature or injection with microparticles while RES line birds are resistant to these methods

(Wideman et al., 2002; Chapman and Wideman, 2006). Heritabilities for ascites in the SUS and RES

lines were estimated to be 0.30 ± 0.05 and 0.55 ± 0.05 respectively (Anthony and Balog, 2003), while

estimates from other selection studies have been between 0.1 and 0.7 (Lubritz et al., 1995; Moghadam et

al., 2001; Druyan et al., 2007a; Druyan et al., 2007c). Further selection studies have been successful in

divergence but heritabilities were not calculated (Wideman and French, 2000a). Anthony et al (2001) and

Druyan et al. (2007b) suggested 2 unlinked genes with complementary interaction between them, and

dominant in nature are responsible for differences in ascites incidence.

Figure 5. Hypobaric chamber. Hypobaric chamber diagram including, transfer chamber, batteries, air

valves, doors, and dimensions, is shown.

14

MARKERS ASSOCIATED WITH ASCITES

The probability of ascites incidence being due to the action of 1 or 2 major genes (Lubritz and

McPherson, 1994; Wideman and French, 2000a; Anthony and Balog, 2003; Navarro et al., 2006; Druyan

et al., 2007a; Druyan and Cahaner, 2007b) give reasonable motive to attempt finding the underlying

genetic basis of the disease. Several molecular studies have attempted to link phenotypic and genotypic

data for the RES and SUS lines. A genome-wide panel of 3,072 SNPs (Muir et al., 2008) indentified 7

regions as significantly associated with the ascites phenotype. At least 3 of these regions show

associations in several different unselected/unrelated lines (Wideman et al., 2013). Locations based on

the Gallus gallus v2.1 assembly are 9:13.5-14.8 Mbp, 9:15.5-16.3 Mbp, and 27:2.0-2.3 Mbp.

From the previously identified regions of significance, the candidate genes affecting ascites

incidence are AGTR1 (angiotensin II type 1 receptor), UTS2D (urotensin receptor 2 D), 5HT2B (serotonin

receptor/transporter type 2B), and ACE (angiotensinogen cleaving enzyme). All four of these genes have

been implicated in hypertension in humans (Simonneau et al., 2004; Watanabe et al., 2006; Djordjevic

and Görlach, 2007; MacLean, 2007; Chung et al., 2009). Two microsatellite markers were identified as

significantly correlated with ascites incidence in the Chr9:13 region of the chicken genome

(Krishnamoorthy, 2012). The AGTR1 (previously implicated in hypertension) gene is also located in this

region. This result indicated that the microsatellites are in linkage disequilibrium with a DNA variation of

the AGTR1 gene responsible in part for ascites susceptibility. These markers would indeed be indicative

of disease level however, linkage disequilibrium is not stable over long periods of time (Brookes, 1999).

The end result would be dissociation of the marker and actual trait of interest. By considering DNA

polymorphisms known to cause non-synonymous protein coding changes, a marker significantly

associated with disease incidence could become an underlying cause of susceptibility and/or resistance.

NEXT GENERATION SEQUENCING AND SNPs

Next-Generation Sequencing (NGS) provides high-throughput and low-cost technologies for

massive nucleotide sequencing. Several different methods of NGS have been developed, differing

mainly by read length, reads per run, and time required per run. Along with a massive increase in data

output has come an immense decrease in cost of whole-genome sequencing. In 1995 sequencing 2 Mb

would cost approximately 1 million dollars and take a year to complete. By 2012, 5 Mb of sequence could

15

be obtained for about 100 dollars in one day. The ease of sequencing whole-genomes using NGS

technologies makes it an attractive avenue for genetics research (Illumina. Inc., 2013). In 2004 the

Gallus gallus draft genome was released (v2.1). The most recent version (v4.0) was published in 2011.

Molecular genetics in the poultry industry changed drastically with the completion of the draft genome

(International Chicken Genome Sequencing Consortium, 2004). The reference genome originates from

one female Red Jungle Fowl from the UCD001 line. The Red Jungle Fowl is the closest living ancestor to

the modern chicken and can be considered as a ‘wild type’ in poultry genomics. The genome is

approximately 1,000 Mb in length and is estimated to contain ~20,000 genes across 38 autosomes and

one pair of sex chromosomes. The chicken genome is about one third of the size of the human genome,

thus 1cM is about 300kb in chickens. The most recent assembly of Homo sapiens is approximately 3,200

Mb and 39,900 genes (Gregory et al., 2006). Mus musculus is of similar size being 2,800 Mb in length

and containing 37,700 genes (Church et al., 2011).

Next generation sequencing can be applied to agricultural populations to identify SNPs

associated with traits of economic importance (Illumina. Inc. 2013). Association of a particular SNP or

group of SNPs to important phenotypes allows geneticists to incorporate these genetic tools into selection

programs. For instance, if a SNP is found to be associated with disease susceptibility, diseased

individuals are expected to have the SNP genotype in higher frequencies than resistant individuals.

SNPs are particularly useful as biomarkers since they can be obtained early in life, from both sexes, and

results are typically clear like yes/no. Certain traits such as growth or egg production are limited in these

factors when quantitative data is collected. SNP markers can be incorporated into breeding programs,

improving efficiency, or used for predicting disease, lowering time required for diagnosis and treatment.

The future of poultry genetics is certain to include molecular approaches. Traditional and molecular

selection techniques will work in concert (Emmerson, 1997).

SUMMARY

In this thesis, the genomes of ascites resistant (RES), susceptible (SUS), and relaxed (REL) lines

were sequenced and annotated with the reference red jungle fowl genome (International Chicken

Genome Sequencing Consortium, 2004). Through next generation sequencing, SNPs accounting for

valuable phenotypic traits can be identified. SNPs differing between the three lines and the RJF were

16

identified and filtered based on location and reliability. The remaining most reliable SNPs contributing to

protein coding changes were compared between the SUS and RES lines. Those having the highest

difference in frequency of occurrence between resistant and susceptible birds were identified as possible

biomarkers. The SNPs were then tested in the REL (unselected) line and another unselected/unrelated

line. It was found that none of the tested SNPs were significantly associated with ascites incidence in

these populations and therefore no candidates for marker assisted selection with respect to ascites

syndrome in broiler breeders were identified.

17

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Smith, A. H., W. O. Wilson, and N. Pace. 1956. Growth and Reproduction in domestic birds at high altitudes. Poult. Sci. 35:1129-1181.

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Smith, E. J., L. A. Lyons, H. H. Cheng, and S. P. Suchyta. 1997. Comparative mapping of the chicken genome using the East Lansing reference population. Poult. Sci. 76:743-747.

Soller, P. B., and Y. Eitan. 1984. Why does selection for live weight gain increase fat deposition? A model. Worlds Poult. Sci. J. 40:5-10.

Stanley, B. 2009. Biosecurity - A foundational principle to poultry primary breeders.

Subedi, S., Z. Feng, R. Deardon, and F. S. Schenkel. 2013. SNP Selection for Predicting a Quantitative Trait. J. App. Statistics 40:600-613. doi:http://www.tandf.co.uk/journals/titles/02664763.asp.

Summers, J. D. 1994. Ascites and related metabolic diseases. Recent advances in animal nutrition - 1994:83-94.

US Poultry and Egg Association. 2013. Economic Data. US Poultry and Egg Association. http://www.uspoultry.org/economic_data/ Accessed September/4, 2013.

Vanhooser, S. L., A. Beker, and R. G. Teeter. 1995. Bronchodilator, oxygen level, and temperature effects on ascites incidence in broiler chickens. Poult. Sci. 74:1586-1590.

Wang, Y., Z. L. Gu, Q. G. Wang, H. Li, and Y. D. Zhang. 2006. Analysis on Association of a SNP in the Chicken OBR Gene with Growth and Body Composition Traits. Asian-Aust. J. Anim. Sci. 19:1706-1710.

Watanabe, T., T. Kanome, A. Miyazaki, and T. Katagiri. 2006. Human urotensin II as a link between hypertension and coronary artery disease. Hypertens. Res. 29:375-387.

Wideman, R. F. 1988. Ascites in poultry. Monsanto Nutr. Update 6:1-7.

Wideman, R. F., Jr., M. Ismail, Y. K. Kirby, W. G. Bottje, R. W. Moore, and R. C. Vardeman. 1995. Furosemide reduces the incidence of pulmonary hypertension syndrome (ascites) in broilers exposed to cool environmental temperatures. Poult. Sci. 74:314-322.

Wideman, R. F., Jr., Y. K. Kirby, R. L. Owen, and H. French. 1997. Chronic unilateral occlusion of an extrapulmonary primary bronchus induces pulmonary hypertension syndrome (ascites) in male and female broilers. Poult. Sci. 76:400-404.

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Wideman, R. F., Jr. 1998. Causes and control of ascites in broilers. 33rd National Meeting on Poultry Health & Processing, Sheraton Ocean City, Ocean City, Maryland, USA, October 14-16, 1998.:56-85.

Wideman, R. F., Jr., P. Maynard, and W. G. Bottje. 1999. Venous blood pressure in broilers during acute inhalation of five percent carbon dioxide or unilateral pulmonary artery occlusion. Poult. Sci. 78:1443-1451.

Wideman, R. F., J., and H. French. 2000a. Ascites resistance of progeny from broiler breeders selected for two generations using chronic unilateral pulmonary artery occlusion. Poult. Sci. 79:396-401.

Wideman, R. F. 2000b. Cardio-pulmonary hemodynamics and ascites in broiler chickens. Avian Poult. Bio. Rev. 11:21-43.

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24

GENOME SEQUENCING AND DATA ANALYSIS

25

INTRODUCTION

Next-generation sequencing can allow the identification of SNPs on a genome-wide scale

accounting for valuable phenotypic traits. In this study, resistance and/or susceptibility to ascites

syndrome in broiler chickens was investigated for genetic effects. The genomes of chicken lines

genetically selected for ascites were sequenced and compared in an effort to identify SNPs correlated

with disease incidence.

The RES (resistant) and SUS (susceptible) lines have undergone 19 generations of selection

pressure (Anthony, 2013) based on the level of disease caused by pulmonary hypertension syndrome.

RES and SUS were derived from a commercial pedigreed line which is represented by REL (relaxed).

The REL line has not been subjected to selection and mating has remained random since the formation

of the lines. Initial line formation and selection methods were described in the previous chapter (pages

13-15) and earlier report (Pavlidis et al., 2007).

The objectives of this study were to characterize the nucleotide sequences of the RES, SUS, and

REL genomes, compare with the major ancestor RJF (red jungle fowl), and to identify genetic variations

among RES, SUS, and REL. This is the first genome re-sequencing study to use the RES and SUS lines

to identify genetic variations on a genome-wide scale. In the RES line, 10 SNPs were found to be

candidates for biomarkers based on requirements met and 22 in SUS.

MATERIALS AND METHODS

Genetic Lines

Three genetically selected lines of chickens, RES, SUS, and REL, which have been maintained

by N. B. Anthony at the University of Arkansas, were used in this study. Selection methods are described

in Chapter 1.

DNA purification and Next Gen Sequencing

Blood was collected (5-10 mL) from 12 birds per line by wing vein puncture (Ison et al., 2005).

Genomic DNA was isolated from each whole blood sample using QiaAmp DNA mini kit (Qiagen,

Valencia, CA) following manufacturer’s instructions. DNA quality was determined by agarose gel

electrophoresis and equal amounts of DNA from the 10 samples having the highest qualities per line were

26

pooled to represent their respective line. Library preparation and Illumina genome sequencing for the

pooled DNA samples were performed at the National Center for Genome Resources (NCGR; Santa Fe,

New Mexico). Illumina HiSeq system 2x100 bp paired end read technology was used for genome

sequencing.

Genome Sequence Assembly and Data Analysis

Next generation sequencing data was aligned to the chicken reference genome sequence – Red

Jungle Fowl – retrieved from NCBI (GenBank accession number: GCA_000000185.2) (International

Chicken Genome Sequencing Consortium, 2004). For the reference based genome assembly, the NGen

program of the Lasergene software package (DNAStar, Madison, WI) was used. Assembly parameters

were as follows: file format, BAM; mer size, 21; mer skip query, 2; minimum match percentage, 93;

maximum gap size, 6; minimum aligned length. 35; match score, 10; mismatch penalty, 20; gap penalty,

30; SNP calculation method, diploid Bayesian; minimum SNP percentage, 5; SNP confidence threshold,

10; minimum SNP count, 2; minimum base quality score, 5. The SeqMan Pro program of the Lasergene

package was used to identify single nucleotide polymorphism (SNP) positions throughout all

chromosomes from genome assembly.

SeqMan Pro SNP data was analyzed, sorted, and filtered using JMP genomics (SAS Institute

Inc., Cary, NC). SNPs occurring in more than one line were filtered out to find unique SNPs. Next, SNPs

showing over 75 SNP% and read depth of at least 10 were considered reliable.

RESULTS AND DISCUSSION

Genome Sequence Assembly

Average coverage of NGS for ascites Resistant (RES), Susceptible (SUS), and Relaxed (REL)

was 7.4, 7.7, and 6.1 respectively (Table 1). Over 40 million read counts were achieved by next

generation sequencing for every line. SeqMan Pro identified more than 4 million SNPs in each line. The

majority of SNPs were identified as being homo variants, meaning that only one other base was called

different from the reference. In the case of a hetero variant, two or three other bases are called different

from the reference. The number of indels was 10 fold less reaching about 400,000 per line and most

(~90%) were deletion mutations. Numbers for the SUS line were highest in all categories listed in Table 1.

27

Chromosome (Chr) 1 had the largest number of possible SNPs. Likewise, larger chromosomes such as

2, 3, 4, and Z contain more SNPs than the smaller ones (Figure 1). Chromosome Z despite having a

lower gene density is one of the larger chromosomes, slightly smaller than Chr 4.

In the case of feature type, a majority of SNPs were found in the intergenic regions (data not

shown). A much smaller number of SNPs were found in Coding DNA Sequences (CDS), genes,

miscellaneous RNA, messenger RNA, and non-coding RNA feature types (Table 2). In the protein coding

(CDS) regions, number of SNPs generating non synonymous, frameshift, nonsense, no start, and no stop

mutations in addition to synonymous mutations were also listed in Table 3.

SNP Filtering

Using JMP genomics, SNP reports derived from SeqMan Pro were filtered and sorted. After

considering only unique SNPs, 3.8 million SNPs remained in total. Approximately 1.3 million SNPs were

in RES and 1.5 million in SUS. Of those, about 500,000 SNPs for RES and SUS showed at least 75

SNP%, which means 75% of reads aligning to that location contained the SNP base. Only SNPs having

protein coding effects (not synonymous mutations) and a depth of at least 10 sequence read counts were

considered as genetic marker candidates. In the RES line, 10 SNPs met all above requirements and 22

SNPs remained for SUS (Tables 4, 5, and 6). All SNPs showing over 75 SNP% found in the CDS regions

causing protein coding changes, but depth not considered were listed in Additional file 1 of the attached

material. For RES, 1781 SNPs were listed and 1810 for SUS.

It is known that SNPs are not required to be located within coding DNA sequences to be effective

as biomarkers for selection assistance. However, all SNPs in this initial data could not be considered

since analyzing thousands of SNPs is time-consuming. If a SNP located outside of protein coding

regions was identified as being significantly associated with disease incidence, it would be highly

probable that the association was due to linkage disequilibrium. This SNP would indeed be indicative of

disease level however, linkage disequilibrium is not stable over long periods of time (Brookes, 1999). The

end result would be dissociation of the SNP and actual trait of interest. By considering SNPs known to

cause non-synonymous protein coding changes, a SNP significantly associated with disease incidence

could be an underlying cause of susceptibility and/or resistance. Therefore, SNPs inducing non-

28

synonymous protein coding changes identified in this chapter not only can serve as genetic markers for

selection, but also may provide important insight into the etiology of ascites incidence.

Genome Resequencing

Chicken genome resequencing using massively parallel next generation sequencing technology

was first reported in an effort to identify loci under intense genetic selection during chicken domestication

in eight chicken populations (Rubin et al., 2010). Whole-genome resequencing for chickens has been

performed in a number of studies. Dorshorst et al. (2011) used pooled DNA samples to represent the

genetic characteristics for the Silkie breed and the SOLiD (Life Technologies, Carlsbad, CA) platform

provided 30 X read depths over the reference genome (galGal3). Kerstens et al. (2011) resequenced

broiler and layer strains to identify structural variations with 13X and 20X coverages obtained,

respectively. Imsland et al. (2012) also used pooled samples representing 3 different breeds including Le

Mans, White Leghorn, and Silkie using SOLiD technology to obtain 1X, 10X, and 20X read depths for the

three lines. These three studies investigated structural variations relevant to their trait(s) of interest. The

current study focused on SNPs found in CDS regions for the ascites SUS and RES lines. Structural

variations (SVs) include large deletions, translocations, inversions, and duplications. SVs can

encompass millions of bases including whole genes and regulatory regions (Feuk et al., 2006). More

often, SVs are linked to phenotypic variations when identified, however SNPs are much easier to detect

and occur in higher frequency than SVs.

Previously, a genome wide association study (GWAS) of RES and SUS lines exposed 1,763 SNPs

that are informative for resistance or susceptibility to ascites (Smith, 2009). Seven regions across the

genome showed significant association to ascites phenotype and were regarded as possible QTL

locations. Microsatellite markers in two of these locations were later found to be descriptive of ascites

incidence in several different lines. In addition to the previous findings, 32 potentially important SNPs

were identified in this study. Next, these 32 SNPs will be subjected to validation in a larger sample size of

96 birds to overcome a limitation generated by original sequencing data based on 10 pooled samples.

29

REFERENCES

Anthony, N. B. 2013, February 14.


Dorshorst, B., A. Molin, C. Rubin, A. M. Johansson, L. Strömstedt, M. Pham, C. Chen, F. Hallböök, C. Ashwell, and L. Andersson. 2011. A complex genomic rearrangement involving the endothelin 3 locus causes dermal hyperpigmentation in the chicken. PLoS Genet 7:e1002412-e1002412. doi:10.1371/journal.pgen.1002412.

Feuk, L., A. R. Carson, and S. W. Scherer. 2006. Structural variation in the human genome. Nat. Rev. Genet. 7:85-97.

Illumina. Inc. 2013. An introduction to Illumina next-generation sequencing technology for agriculture. http://res.illumina.com/documents/products/appspotlights/app_spotlight_ngs_ag.pdf Accessed September 19, 2013.

Imsland, F., C. Feng, H. Boije, B. Bed'hom, V. Fillon, B. Dorshorst, C. Rubin, R. Liu, Y. Gao, X. Gu, Y. Wang, D. Gourichon, M. C. Zody, W. Zecchin, A. Vieaud, M. Tixier-Boichard, X. Hu, F. Hallböök, N. Li, and L. Andersson. 2012. The Rose-comb mutation in chickens constitutes a structural rearrangement causing both altered comb morphology and defective sperm motility. PLoS Genet 8:e1002775-e1002775. doi:10.1371/journal.pgen.1002775.


Ison, A. J., S. J. Spiegle, and T. Y. Morishita. 2005. Poultry Blood Collection. Ohio State University Extension Fact Sheet.

Kerstens, H. H., R. P. Crooijmans, B. W. Dibbits, A. Vereijken, R. Okimoto, and M. A. Groenen. 2011. Structural variation in the chicken genome identified by paired-end next-generation DNA sequencing of reduced representation libraries. BMC Genomics 12:94-94. doi:10.1186/1471-2164-12-94.


Rubin, C., M. C. Zody, J. Eriksson, J. R. S. Meadows, E. Sherwood, M. T. Webster, L. Jiang, M. Ingman, T. Sharpe, S. Ka, F. Hallböök, F. Besnier, O. Carlborg, B. Bed'hom, M. Tixier-Boichard, P. Jensen, P. Siegel, K. Lindblad-Toh, and L. Andersson. 2010. Whole-genome resequencing reveals loci under selection during chicken domestication. Nature 464:587-591. doi:10.1038/nature08832.

Smith, C. D. 2009. Applications of variable number tandem repeat genotyping in the validation of an animal medical model and gene flow studies in threatened populations of reptiles. Ph.D. ed. University of Arkansas, United States -- Arkansas.

30

LEGEND OF TABLES AND FIGURES

Table 1: Summary of NGS results for REL, RES, and SUS

Columns of total read counts, average coverage, number of SNPs, number of indels, number of

deletions, and SNP type for REL, RES, and SUS are shown.

Table 2: Summary of feature type

The numbers are cumulative totals across all chromosomes. Number of SNPs represents unique

showing a SNP% of at least 0.75 in the columns of Coding DNA sequences (CDS), gene,

miscellaneous RNA (misc_RNA), messenger RNA (mRNA), non-coding RNA (ncRNA), and total

number.

Table 3: Summary of SNP impact

Numbers of unique SNPs showing at least 75 SNP% in the columns of mutation type (impact)

including frameshift, no start, no stop, non-synonymous (non syn), synonymous and total are

shown.

Table 4: Number of SNPs at each filtering point

Total number of SNPs identified in RES and SUS being reduced as filtering points were shown.

Table 5: List of 32 potentially significant SNPs

The 32 SNPs after the series of filtering parameters from RES and SUS lines are listed. Line

(MID), Chromosome (Chr), Reference position, Reference base, Called base, Genotype, Impact,

SNP%, Q call, Feature type, Feature name, DNA change, Protein change, and depth are detailed

for all SNPs. Information was provided by SeqMan Pro.

Table 6: Gene Names of 32 potentially significant SNPs

Figure 1: Total number of SNPs per chromosome in lines RES, SUS, and REL.

The X-axis showed chromosome numbers. LGE22 and LGE64 represent 2 linkage groups that

were not assigned to a chromosome during the reference genome assembly, MT indicates

mitochondrial chromosome, and W and Z depict sex chromosome.

31

Table 1: Summary of NGS results for REL, RES, and SUS

total read counts

average coverage

possible # SNPs

possible # indels

possible # deletions

SNPs

homo hetero

REL 41775591 6.1 4080441 372990 325794 2895314 1557395

RES 45074942 7.4 4322376 407153 352992 3218754 1510190

SUS 49715434 7.7 4644354 436383 392032 3141742 1938280 Table 2: Summary of feature type

FEATURE CDS gene misc_RNA mRNA ncRNA total

RES 6630 229056 131 3896 3 239716

SUS 6526 222521 162 4001 11 233221 Table 3: Summary of SNP impact

IMPACT frameshift no start no stop non syn nonsense synonymous total

RES 56 0 1 1704 20 4073 5854

SUS 63 6 2 1717 22 3956 5766 Table 4: Number of SNPs at each filtering point

Line All Unique SNP% 75 Not syn Depth ≥ 10

RES 4643938 1278960 513242 1781 10 SUS 4977224 1458108 492752 1810 22

32

Table 5: The 32 SNPs as potential genetic markers

MID Chr Ref Pos Ref

Base Called Base

Genotype Impact SNP

% Q call

Feature Type

Feature Name DNA Change Protein Change Depth

RES 10 12553367 C T Homo. Var Nsyn 1 60 CDS ACAN c.5327C>T G1776E 10 RES 5 931071 A C Homo. Var Nsyn 0.9 27.02 CDS CASC5 c.770A>C N257T 10 RES 10 19419537 G A Homo. Var Nsyn 0.8 8.05 CDS CTSH c.743G>A T248M 10 RES 4 41449610 C T Homo. Var Nsyn 1 60 CDS LOC100858992 c.121C>T G41R 11 RES 3 8987759 G A Homo. Var Nsyn 0.9 27.02 CDS LOC421285 c.1435G>A V479I 10 RES 6 30858767 A C Homo. Var Nsyn 0.8 0.53 CDS LOC423943 c.703A>C T235P 10 RES 3 98354421 T C Homo. Var Nsyn 0.9 27.02 CDS NBAS c.6829T>C M2277V 10 RES 12 6303318 C T Homo. Var Nsyn 0.9 40.79 CDS PHF2 c.1420C>T V474I 10 RES 3 41697998 A T Homo. Var Nsyn 1 60 CDS UNC93A c.524A>T E175V 10 RES 2 85644290 T C Homo. Var Nsyn 0.8 0.53 CDS ZNF830 c.322T>C T108A 10 SUS 1 138528917 C T|C Hetero. Ref. Nsyn 0.79 11.09 CDS CARS2 c.[565C>T]+[565C>C] G189R, G189G 14 SUS 18 4858738 A C|A Hetero. Ref. Nsyn 0.75 16.76 CDS CCDC57 c.[153A>C]+[153A>A] E51E, E51D 12 SUS 1 45663501 C G Homo. Var Nsyn 1 60 CDS CDK17 [1] c.1445C>G S482T 10 SUS 14 5980646 G A Homo. Var Nsyn 0.92 33.03 CDS CHTF18 c.686G>A A229V 12 SUS 8 15489432 A T|A Hetero. Ref. Nsyn 0.75 16.76 CDS CTBS c.[326A>T]+[326A>A] Q109Q, Q109L 12 SUS 6 17137207 C T|C Hetero. Ref. Nsyn 0.75 16.76 CDS CYP2H1 c.[71C>T]+[71C>C] R24K, R24R 12 SUS 2 137760814 T C Homo. Var Nsyn 0.8 0.53 CDS FAM91A1 c.2351T>C M784T 10 SUS 4 33407817 G C Homo. Var Nsyn 1 60 CDS FHDC1 c.3282G>C E1094D 10 SUS 26 4992058 T C Homo. Var Nsyn 0.9 27.02 CDS FRS3 c.599T>C N200S 10 SUS 10 18382794 T G Homo. Var Nsyn 1 60 CDS IQCH c.599T>G I200S 11 SUS 14 3318144 C T Homo. Var Nsyn 0.82 3.46 CDS LOC416472 c.817C>T V273M 11 SUS 1 62239907 C A Homo. Var Nsyn 0.82 4.29 CDS LOC418168 c.793C>A H265N 11 SUS 5 15963733 C T Homo. Var Nsyn 0.82 3.46 CDS MTL5 c.272C>T S91N 11 SUS 13 13755767 A G Homo. Var Nsyn 0.83 6.4 CDS MYOT c.575A>G V192A 12 SUS 7 34737075 C T Homo. Var Nsyn 0.8 0.53 CDS NEB c.10895C>T R3632H 10 SUS 4 49844314 T T|G Hetero. Ref. Nsyn 0.75 16.76 CDS NPFFR2 c.[67T>T]+[67T>G] Y23D, Y23Y 12 SUS 13 17294379 A G Homo. Var Nsyn 1 60 CDS RBM27 c.1926A>G I642M 10 SUS 1 168140662 C T Homo. Var Nsyn 1 60 CDS RCBTB2 c.979C>T V327I 10 SUS 2 67794489 C A Homo. Var Nsyn 1 60 CDS SERPINB14B c.16C>A V6L 12 SUS 2 128206257 G A Homo. Var Nsyn 0.91 39.53 CDS SPAG1 c.2053G>A G685S 11 SUS 15 5878997 C T Homo. Var Nsyn 0.8 0.53 CDS ZCCHC8 c.1595C>T C532Y 10 SUS 5 23517579 G C Homo. Var Nsyn 0.85 9.35 CDS ZFYVE19 c.526G>C A176P 13

32

33

Table 6: Gene Names of 32 potentially significant SNPs

FEATURE NAME ENTEREZ GENE NAME

ACAN aggrecan

CASC5 cancer susceptibility candidate 5

CTSH cathepsin H

LOC100858992 uncharacterized

LOC421285 similar to Hypothetical protein MGC66455

LOC423943 deleted in malignant brain tumors 1-like

NBAS neuroblastoma amplified sequence

PHF2 PHD finger protein 2

UNC93A unc-93 homolog A

ZNF830 zinc finger protein 830

CARS2 cysteinyl-tRNA synthetase 2, mitochondrial (putative)

CCDC57 coiled-coil domain containing 57

CDK17 [1] cyclin-dependent kinase 17, transcript variant 1

CHTF18 CTF18, chromosome transmission fidelity factor 18 homolog

CTBS chitobiase, di-N-acetyl-

CYP2H1 cytochrome P450 2H1

FAM91A1 family with sequence similarity 91, member A1

FHDC1 FH2 domain containing 1

FRS3 fibroblast growth factor receptor substrate 3

IQCH IQ motif containing H

LOC416472 IQ domain-containing protein E-like

LOC418168 aldo-keto reductase

MTL5 metallothionein-like 5, testis-specific (tesmin)

MYOT myotilin

NEB nebulin

NPFFR2 neuropeptide FF receptor 2

RBM27 RNA binding motif protein 27 RCBTB2 regulator of chromosome condensation and BTB domain containing protein 2

SERPINB14B serpin peptidase inhibitor, clade B (ovalbumin), member 3

SPAG1 sperm associated antigen 1

ZCCHC8 zinc finger, CCHC domain containing 8

ZFYVE19 zinc finger, FYVE domain containing 19

34

Figure 1: Total number of SNPs per chromosome in lines RES, SUS, and REL

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

32

LG

E2

2

LG

E6

4

MT

W

Z

# o

f S

NP

s (x

10

5)

Chromosome

RES

SUS

REL

34

35

SNP VALIDATION IN LARGER POPULATION

36

INTRODUCTION

Genome wide SNPs were identified by whole-genome resequencing of ascites selected lines –

RES, SUS, and their parental line REL. After a series of filtering processes, a list of 32 significant SNPs

remained. Eighteen of these SNPs were randomly chosen for further investigation. Since the whole-

genome sequences were based on 10 pooled DNA samples for each line, it was necessary for the

chosen SNPs to be validated in a larger sample size (96 birds per line). The efficiency of pooling DNA

samples for genome sequencing depends on coverage expected and number of reads required for allele

calling. When coverage is small, pooling is less efficient but with higher expected coverage, individual

sequencing becomes the more inefficient option (Cutler and Jensen, 2010). In this chapter, frequencies

of the 18 chosen SNPs were determined in larger populations of SUS, RES, and REL birds to find

dependable SNP markers. All nucleotide sequence results at SNP positions were analyzed to determine

genotypic frequencies. Frequencies of SNP occurrence were determined and compared between the

three lines and the critical comparison was performed between the RES and SUS lines. SNPs showing a

higher frequency, which means relatively fixed variations, were chosen as potential genetic biomarkers

for selection of disease resistant birds at the commercial level. Eight out of the 18 SNPs tested were

determined to be highly divergent between the RES and SUS lines. These were chosen as biomarker

candidates for marker assisted selection (MAS).


DNA

Approximately 100uL of blood was collected from 96 birds per each line (RES, SUS, and REL)

using tubes containing sodium citrate (anticoagulant) by wing vein puncture. Genomic DNA was isolated

from whole blood using the Wizard SV 96 Genomic DNA Purification System (Promega; Madison, WI)

following manufacturer’s instructions with modifications. Briefly, whole blood (10µL) was mixed with

proteinase K (6.7µg/µL in mixture) and incubated at room temperature for 10 minutes. Lysis buffer

(300µL) mixed with triton X-100 (33 µL) and RNase A (55µL) was then added and clots were dissociated

by pipetting. A vacuum manifold system was used to create pressure and force lysate through the filters

of the binding plate. Three rounds of ethanol wash were performed, and then DNA was eluted with

200µL nuclease-free water. After isolation, DNA was quantified using a Nanodrop 1000

37

spectrophotometer (Thermo Fisher Scientific Inc.; Waltham, MA) and a dilution of 1ng/uL was prepared in

96 well plates for further PCR assay.

Primers

All primers in this study were designed based on the RJF genome sequence (GenBank

accession number: GCA_000002315.2) using Primer 3 online software (http://primer3plus.com/cgi-

bin/dev/primer3plus.cgi;) (Untergasser et al., 2007). Three primers including forward (F), reverse (R), and

sequencing (seq) primers (Table 1) were designed to verify SNP position identified by whole genome

sequencing discussed in chapter 2. All primers were 20-22 bases long and had GC content between 40-

60%. Seq primers were designed to anneal at least 50bp upstream of the SNP position. Forward and

reverse primers were chosen at the flanking regions of the seq primer and the SNP position. Product size

(between F and R primers) ranged from 220-557bp. Seq primers were designed separately from PCR

primers to ensure product specificity. In a few cases, seq primers could not be located between the F

primer and SNP position mostly due to GC content, these seq primers were designed in the reverse

orientation, located between SNP position and reverse primer. All working primers were prepared at

20ng/µL. All primers were commercially synthesized by Integrated DNA Technologies (Ames, IA).

PCR

DNA from 96 birds per line was used for 96 well format PCR reactions. PCR was carried out as

25µL reaction volumes: 5ng of DNA (1ng/µL), 1x buffer (NEB; Ipswich, MA), 2.5mM dNTP mix (NEB;

Ipswich, MA), F and R primers 20ng/µL, 0.5 units TaqPolymerase (NEB; Ipswich, MA). Cycle conditions

were as follows: denaturation at 95°C for 1min, 40 cycles of amplification (95°C for 30 sec, 55°-63°C for

1min, 72°C for 1 min), final extension at 72°C for 10 mins. Annealing temp varied between samples as

shown in Table 1. All PCR reactions were performed on Applied Biosystems 2720 Thermal Cycler (Life

Technologies, Carlsbad, CA). Verification of PCR reaction was done by 1% agarose gel electrophoresis.

Gel electrophoresis

PCR product (5µL) was mixed with a 6x loading dye containing bromophenol blue and xylene

cyanol dyes. Mixture was loaded into wells of a 1% agarose gel prepared with 1X TAE buffer. The all

purpose Hi-Lo DNA molecular weight marker (Bionexus, Inc., Oakland, CA) was loaded into the gel along

38

with the PCR products to confirm size of products. Gel imaging was done with a Bio-Rad Gel Doc XR

imaging system (Bio-Rad Laboratories, Hercules, CA).

PCR product purification

PCR products were purified using the Wizard SV 96 PCR Clean-Up System (Promega; Madison,

WI) following manufacturer’s protocol. Briefly, four plates (four different PCR products) from the same

line were pooled into one plate and were subjected to PCR clean-up (Figure 1). Cross-specificity of seq-

primers to the four pooled PCR products was examined using the BLAST function (NCBI) and PCR

products that were not cross-specific with other seq primers were pooled. Membrane binding solution

was added in equal volume with PCR product (i.e. 60µL solution added to 60µL total PCR product).

Vacuum pressure was applied. Three 95% ethanol washes were performed and PCR products were

eluted with 100µL nuclease-free water. DNA concentration after clean-up was quantified in preparation

for the Sanger sequencing reaction using the Nanodrop 1000.

Sample Pooling and Sanger Sequencing

To determine frequency of SNPs in 96 bird samples per line, columns 1-12 of the plate, which

contained cleaned PCR products in 96 well format, were pooled into one column (i.e. A1-A12 would be

pooled into 1 tube, etc.). Likewise, samples from one 96 well plate would now be contained in eight

tubes, resulting in 32 total samples from 4 different PCR products (SNPs) in the same tube (Figure 1B).

Four separate aliquots for each seq primer of the 8 pooled samples were made for sequencing (Figure

1C). DNA concentration was 10-50ng/µL in each of the aliquots. Each seq primer, which corresponded

to the original PCR product pooled prior to clean-up, were added separately. Sanger sequencing

reactions were performed by the DNA Resources Center at the University of Arkansas (Fayetteville, AR).

Results were analyzed using ABI sequence scanner software (Life Technologies, Carlsbad, CA).

Estimated ratios of bases occurring at SNP locations were recorded based on the ratio of peak heights. If

a distinction between background noise and peak could not be made, then further testing of individual

samples followed.

39


SNP validation

Eighteen of the most reliable SNPs in CDS regions were chosen from the analysis of whole

genome sequencing (described in chapter 2) for the verification of segregation between RES and SUS.

PCR was run for all 18 SNPs in a larger population of 96 birds for each line. Amplified PCR products

were analyzed by Sanger sequencing. Sequence analysis provided that none of the SNPs were entirely

divergent between RES and SUS (Table 2). Seven SNPs found in fibroblast growth factor receptor

substrate 3 (FRS3), myotilin (MYOT), regulator of chromosome condensation and BTB domain containing

protein 2 (RCBTB2), plant homeodomain finger protein 2 (PHF2), LOC100858992, and unc-93 homolog

A (UNC93A) genes showed to be fixed in either the RES or SUS line, but not in both. In the RES line

FRS3 and MYOT were 100% RJF genotype at the SNP location; in contrast, LOC100858992 and

UNC93A were 100% SNP genotype. For SUS, PHF2 and ACAN were found to be 100% RJF genotype;

RCBTB2 was 100% SNP genotype. UNC93A was 100% RJF genotype in the lines of RES and REL,

while 86% RJF in SUS. SNP% for UNC3A from 10 pooled DNA samples analyzed by next generation

sequencing showed 100% SNP genotypes in RES, while low SNP presence was detected in larger 96

bird samples. The very low presence of the SNP genotype across all three lines for UNC93A may be due

to amplicon bias that can be generated by low cycle PCR steps from next generation sequencing

reaction. Genotype results were not obtained for ACAN in the RES or REL lines because PCR

amplifications were not sufficient for Sanger sequencing reactions.

Though the ratio of peak height is not an accurate indicator of nucleotide frequency in pooled

samples, it can still reflect the rough estimation of SNP frequencies in large (96) bird populations. The

ratio of SNP frequencies based on the comparison of peak heights was confirmed by test sequencing.

Individual sequencing reactions were performed, genotypes were recorded, samples were then pooled,

and peak heights were used to record genotype ratios (data not shown), suggesting that comparison

using ratio of peak heights were sufficient to estimate genotypic frequencies roughly in larger number of

individuals.

40

Potential Biomarkers

Eight of the eighteen SNPs, identified as being highly divergent between RES and SUS, were

chosen as potential biomarkers for selection assistance in broiler breeders (Table 2; first 8 rows). SNPs

with frequency differences over 50 were chosen (Table 2). FRS3, having a difference slightly less than

50, was chosen based on fixation in the RES line. This indicates that SNP in FRS3 has been driven to

fixation in the RES line as a possible result of selection pressure. Thus, this SNP may be under the

influence of selection for ascites incidence. Table 3 lists the 8 biomarker candidates (CHTF18, FRS3,

CDK17, RCBTB2, LOC100858992, ZFYVE19, PHF2, and MYOT) with previously associated functions.

The 8 SNPs identified in this chapter can be applied to unselected/unrelated populations to

determine the level of association with ascites incidence. If a significant association is found, marker

assisted selection can be achieved in broiler breeders. SNPs are particularly useful as biomarkers

because they can be obtained very early in life, from both sexes, and results are typically clear (yes/no).

Certain traits such as growth or egg production are limited in these factors when it comes to quantitative

data collection. Marker assisted selection (MAS) is beneficial to geneticist because genotypes have

heritabilities of 1 (assuming no genotyping errors) (Dekkers, 2005) meaning maximum genetic progress is

possible when an association is made between a genotype and phenotypic trait of interest. Achieving

maximum genetic progress allows response time of the trait to selection pressure to be much quicker

(Falconer and Mackay, 1996). MAS is particularly helpful for selecting traits difficult or expensive to

measure, have low heritability, or are expressed late in the development of the individual (Van

Eenennaam, 2006). Disease resistance poses several challenges when under selection. Traits such as

reproductive efficiency cannot be observed until reproductive maturity is reached (21 weeks in chickens).

In general, the cost of growing, maintaining, and caring for animals (phenotype collection) continues to

rise as the cost of genotyping is steadily decreasing.

Linking the phenotype to genotype has challenged the poultry industry since the notion of

molecular genetics arose. Various molecular methods have attempted to solve the puzzle. Genome

wide association studies (GWAS) using established SNP chips [e.g. recently developed 600K chip

(Kranis et al., 2013)] have been used widely to investigate genetic marker discovery. Closter et al. (2010)

reported a chicken GWAS, considering 19,314 SNPs from chromosomes 1 – 28, investigating association

41

with the ascites phenotype. 67 SNPs correlated with RV:TV ratio (defined in Chapter 1) were identified.

SNPs having the most significant effects were located on chromosomes 12, 18, and 22 (Closter et al.,

2010). Of the 18 SNPs validated in this chapter, one location (PHF2 at Chr12) correlates with data of the

ascites GWAS.

Differential gene expression studies have also been used to investigate the genotypic cause of

ascites incidence. Cisar et al. (2005) found two mitochondrial proteins, DLST (dihydrolipoamide

succinyltransferase component of the 2-oxoglutarate dehydrogenase complex) and HADHB (alpha-

subunit of mitochondrial trifunctional enzyme), being expressed at significantly different levels in resistant

and susceptible broilers. These results suggest an inappropriate response to hypoxia in mitochondria of

susceptible birds (Cisar et al., 2005). In a subsequent study, IL-8, K60, IL-1β, IL-6, IFN-γ, and IL-4 were

also found to be expressed differentially in lung cells of resistant and susceptible birds (Hamal et al.,

2010).

One of the differences among GWAS, differential gene expression, and resequencing studies,

such as research in this chapter, is the involvement of genome sequencing. By comparing genome

sequences to a reference, it is possible to determine locations and impacts of identified SNPs before

further investigation. In this study, 18 SNPs known to have non-synonymous protein coding implications

were selected as potentially associated biomarkers with ascites incidence, resulting in that 8 genes

containing SNPs were highly divergent between the SUS and RES populations.

42

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Bermudez, V. P., Y. Maniwa, I. Tappin, K. Ozato, K. Yokomori, and J. Hurwitz. 2003. The alternative Ctf18-Dcc1-Ctf8-replication factor C complex required for sister chromatid cohesion loads proliferating cell nuclear antigen onto DNA. Proc. Natl. Acad. Sci. U. S. A. 100:10237-10242.

Cisar, C. R., J. M. Balog, N. B. Anthony, and A. M. Donoghue. 2005. Differential expression of cardiac muscle mitochondrial matrix proteins in broilers from ascites-resistant and susceptible lines. Poult. Sci. 84:704-708.

Closter, A. M., M. G. Elferink, P. Van As, R. P. M. A. Crooijmans, M. A. M. Groenen, and H. Bovenhuis. 2010. Genome-Wide Association Analysis Identifies Loci That Influence Ascites In broilers. .

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Latil, A., L. Chêne, P. Mangin, G. Fournier, P. Berthon, and O. Cussenot. 2003. Extensive analysis of the 13q14 region in human prostate tumors: DNA analysis and quantitative expression of genes lying in the interval of deletion. Prostate 57:39-50.

Legartova, S., A. Harnicarova-Horakova, E. Bartova, R. Hajek, L. Pour, and S. Kozubek. 2010. Expression of RAN, ZHX-2, and CHC1L genes in multiple myeloma patients and in myeloma cell lines treated with HDAC and Dnmts inhibitors. Neoplasma 57:482-487.

Merkle, C. J., L. M. Karnitz, J. Henry-Sánchez T., and J. Chen. 2003. Cloning and characterization of hCTF18, hCTF8, and hDCC1. Human homologs of a Saccharomyces cerevisiae complex involved in sister chromatid cohesion establishment. J. Biol. Chem. 278:30051-30056.

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Naqvi, N., M. Li, E. Yahiro, R. M. Graham, and A. Husain. 2009. Insights into the Characteristics of Mammalian Cardiomyocyte Terminal Differentiation Shown Through the Study of Mice with a Dysfunctional c-Kit. Pediatr. Cardiol. 30:651-658. doi:10.1007/s00246-008-9366-1.

Okuno, Y., F. Ohtake, K. Igarashi, J. Kanno, T. Matsumoto, I. Takada, S. Kato, and Y. Imai. 2013. Epigenetic regulation of adipogenesis by PHF2 histone demethylase. Diabetes 62:1426-1434. doi:10.2337/db12-0628.

Olivé, M., Z. Odgerel, A. Martínez, J. J. Poza, F. G. Bragado, R. J. Zabalza, I. Jericó, L. Gonzalez-Mera, A. Shatunov, H. S. Lee, J. Armstrong, E. Maraví, M. R. Arroyo, J. Pascual-Calvet, C. Navarro, C. Paradas, M. Huerta, F. Marquez, E. G. Rivas, A. Pou, I. Ferrer, and L. G. Goldfarb. 2011. Clinical and myopathological evaluation of early- and late-onset subtypes of myofibrillar myopathy. Neuromuscul. Disord. 21:533-542. doi:10.1016/j.nmd.2011.05.002.

Ranzi, V., S. O. Meakin, C. Miranda, P. Mondellini, M. A. Pierotti, and A. Greco. 2003. The signaling adapters fibroblast growth factor receptor substrate 2 and 3 are activated by the thyroid TRK oncoproteins. Endocrinology 144:922-928.

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Sinha, S., R. K. Singh, N. Alam, A. Roy, S. Roychoudhury, and C. K. Panda. 2008. Alterations in candidate genes PHF2, FANCC, PTCH1 and XPA at chromosomal 9q22.3 region: pathological significance in early- and late-onset breast carcinoma. Mol. Cancer 7:84-84. doi:10.1186/1476-4598-7-84.

Teles, L. M. B., E. N. Aquino, A. C. D. Neves, C. H. S. Garcia, P. Roepstorff, B. Fontes, M. S. Castro, and W. Fontes. 2012. Comparison of the neutrophil proteome in trauma patients and normal controls. Protein Pept. Lett. 19:663-672.

Untergasser, A., H. Nijveen, X. Rao, T. Bisseling, R. Geurts, and J. A. M. Leunissen. 2007. Primer3Plus, an enhanced web interface to Primer3. http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi/ Accessed 9/5, 2013.

Van Eenennaam, A. 2006. Marker-assisted selection in beef cattle.

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Wen, H., J. Li, T. Song, M. Lu, P. Kan, M. G. Lee, B. Sha, and X. Shi. 2010. Recognition of histone H3K4 trimethylation by the plant homeodomain of PHF2 modulates histone demethylation. J. Biol. Chem. 285:9322-9326. doi:10.1074/jbc.C109.097667.

Zhou, L., K. McDougall, C. J. Kubu, J. M. Verdi, and S. O. Meakin. 2003. Genomic organization and comparative sequence analysis of the mouse and human FRS2, FRS3 genes. Mol. Biol. Rep. 30:15-25.

44


Table 1: List of primers

Names of genes containing SNP, chromosomal location, along with relevant primer information

[annealing temp, expected (exp) size of amplicon, primer sequence, GC% and melting

temperature (Tm, °C)] of forward (F), reverse (R), and sequencing (seq) primers were shown.

Table 2: Summary of sequencing results for 18 tested SNPs

*Numbers in the RES, SUS, and REL columns represent percentage of individuals containing the

reference (RJF) base at the SNP location. Subtracting this number from 100 will give the SNP

percent.

NR=no results

Table 3: Potential biomarkers and associations

Symbols of genes containing SNPs, Enterez gene name, and discovered functions are listed.

Figure 1: Example for pooling PCR products

A) Plate for pooling samples contained four different PCR products in each well and was ready for

PCR clean-up. Plate orientation was preserved throughout the entire process so that each well

contains four PCR products from the same individual bird.

B) After PCR clean-up, each well contains PCR product from SUS – ZFYVE19, MYOT, RCBTB2,

and CARS2 (4 products per well) and then 8 strip-tubes contained columns 1-12 for its

corresponding row, resulting in purified PCR product from SUS – ZFYVE19, MYOT, RCBTB2,

and CARS2 for 12 individual birds (48 products per tube).

C) Aliquots were prepared for Sanger sequencing. 8 strip-tubes containing purified four PCR

products for 12 individual birds (48 PCR products per tube) were prepared by four aliquots and

four corresponding seq primers are added separately.

45

Table 1: List of primers used

Gene Containing SNP

Chr:Mbp Exp Size (bp)

Primer Type

Primer Sequence (5'-3') GC (%)

Tm (°C) Annealing

Temp (°C)

CHTF18 14:06.0 500 60

F agcaaacttgtccacccaga 50 58 R ttcctcccacatagggatagag 50 59 Seq_F ctgcagcatctcacaactga 50 58

FRS3 26:05.0 280 60

F cgggacctaagacaaacttcac 50 60 R tctttcatctccagcttcatca 41 60 seq_F gagcagctgcgtttcctatg 55 61

CDK17 01:45.7 339 60

F gctggtgtgttatcagcaatgt 46 60 R agcaagttgaactcttccttcg 46 60 seq_F caacaccatacaagctgccg 55 61

RCBTB2 03:48.1 315 60

F tactgagagaagacgccacgta 50 60 R tgaagtgcatgtgtgcttgtag 46 60 seq_F agcaggggtagcaaagcaag 55 61

LOC100858992 04:41.4 412 60

F tctgtgatagcagcaaggtcat 46 60 R ccaaagaaacattttgcgtaca 36 60 seq_F tcacagtttctaactcatcccg 45 60

ZFYVE19 05:23.5 321 60

F tcagagcatgattccagaaaaa 36 60 R ctgaaggacgaagaaaggctaa 45 60 seq_F agccatctcgaagccatttg 50 58

PHF2 12:06.3 418 60

F cccttaacttttggtgttttgg 41 60 R ggggtaaatcagcagtgagaag 50 60 seq_F ttgggtggtttgggaggctt 55 61

MYOT 13:13.8 557 55

F tactttgccaatcaaacctgtg 51 60 R ttcactccaggttactgaagca 46 60 seq_F catgtttgacctgctagggc 55 61

CARS2 03:18.5 398 60

F gcaacagtgaatgcctcaataa 41 60 R tgctgctgtagcatttctgaat 41 60 seq_F catactgttgaacgaaggtt 40 54

SERPINB14B 03:07.8 491 60

F gtccactgtccactttacacca 50 60 R atgagcttctttcaaggcaagt 41 60 seq_F acctcttgcccccagataga 55 61

SPAG1 04:08.2 422 53

F actcatccttggtttccctttt 41 60 R aacccaaatgctttgctgttat 36 60 seq_F ccagtgttgatgatctcaag 45 56

UNC93A 03:41.7 488 60

F tctgtgccttcatgtaaccatc 45 60 R agcaaattgggttgaagtagga 41 60 seq_R gcagagtgtagaccaagga 53 58

NEB 07:34.7 315 60

F gtactgcgtgccactctttatg 50 60 R tgctgctaagtaattcccacaa 41 60 seq_F ggcaaaatctagaagaagacag 41 58

ACAN 10:12.6 499 60

F tttcccttccttggtgtgttag 45 60 R agacatccacaagtcaggaagc 50 61 seq_F ggcacagtaccttctgtcgt 55 61

IQCH 10:18.4 439 60

F aaatgggttttggtggtatgtc 41 60 R ccacttcctaaaccatctctgc 50 60 seq_F gagcaaactggaacgtgtca 50 58

RBM27 13:17.3 419 60

F actggcacagagaaagtgaaca 45 60 R cattaagacggtcagtttgcac 45 60 seq_F agtcaccctcacagtctctt 50 58

FHDC1 04:33.4 475 60

F cagctttgcaaggaatacagtg 45 60 R cttttccagtggtgttgtggta 45 60 seq_F tgtcccagaggctgcctaaa 55 61

LOC416472 14:03.3 405 60

F atatgaacgtgtgaatgcgtgt 41 60 R gccattagtgctgactgctatg 50 60 seq_F caagcacagagggaaatgag 50 58

46

Table 2: Summary of sequencing results for 18 tested SNPs

Gene Symbol *RJF SNP RES

(%RJF) SUS

(%RJF) REL

(%RJF) SUS%-RES%

CHTF18 G A 73 10 53 63

FRS3 T C 100 56 58 44

ZFYVE19 G C 80 13 86 67

MYOT A G 100 43 97 57

RCBTB2 C T 60 0 21 60

CDK17 C G 87 15 88 72

PHF2 C T 43 100 57 57

LOC100858992 C T 0 90 59 90

NEB C T 43 63 41 20

CARS2 C T 91 43 63 48

RBM27 A G 49 30 48 19

UNC93A A T 0 14 0 14

SPAG1 G A 63 28 70 35

SERPINB14B C A 21 41 46 20

LOC416472 C T 59 18 62 41

IQCH T G 47 9 53 38

FHDC1 G C 52 29 39 23

ACAN C T NR 100 NR NR

47

Table 3: Potential biomarkers and associations

GENE CONTAINING SNP

ENTEREZ GENE NAME FUNCTIONS

CHTF18 chromosome transmission fidelity factor 18

• required for sister chromatid cohesion (Bermudez et al., 2003; Merkle et al., 2003; Berkowitz et al., 2008)

• cardiomyocyte differentiation (Naqvi et al., 2009)

FRS3 fibroblast growth factor receptor substrate 3

• implicated in the transmission of extracellular signals from nerve growth factor (NGF) or fibroblast growth factor (FGF) receptors to the Ras/mitogen-activated protein kinase signaling cascade (Ranzi et al., 2003; Zhou et al., 2003)

CDK17 cyclin-dependent kinase 17 • associated with functions of aurora

kinases in cell division (Hochegger et al., 2013)

RCBTB2 (aka CHC1-L)

regulator of chromosome condensation (RCC1) and BTB (POZ) domain containing protein 2

• multiple myeloma (Harousseau et al., 2004; Legartova et al., 2010)

• prostate cancer (Latil et al., 2003)

LOC100858992 hypothetical protein • NA

ZFYVE19 zinc finger, FYVE domain containing 19

• involved in vesicular transport and exocytosis (Teles et al., 2012)

PHF2 PHD (plant homeodomain) finger protein 2

• novel histone H3K9 demethylase (Wen et al., 2010)

• regulation of adipogenesis in vivo (Okuno et al., 2013)

• candidate tumor suppressing gene in breast cancer (Sinha et al., 2008)

MYOT myotilin • correlated with myofibrillar myopathy

(Olivé et al., 2011; Reilich et al., 2011; Wang et al., 2011; Keduka et al., 2012)

48

Figure 1: Example for Pooling PCR products

49

DEVELOPMENT OF GENOTYPING METHOD USING ALLELE-SPECIFIC PCR (AS-PCR)

50

INTRODUCTION

Since the genome of the Red Jungle Fowl, the common ancestor to all domestic chickens, has

been sequenced (International Chicken Genome Sequencing Consortium, 2004), molecular genetics

research in poultry has been founded on this accomplishment. Recently, SNP studies have experienced

a rise in popularity but the large amounts of resulting data are struggling to find a way into commercially

applied fields (Dekkers, 2005).

In order to apply knowledge gained from SNP studies to large commercial populations, very

accurate, time and cost effective techniques of genotyping must be developed. Currently, the most

accurate experimental method for genotyping is nucleotide sequencing. Since nucleotide sequencing of

every sample is costly and time consuming, a variety of attempts have been made to develop a method of

individual genotyping at known SNP locations without the need for sequencing.

Current SNP genotyping technologies require the use of probes, fluorescence detection, or mass

spectrometry (e.g. MALDI-TOF) to reveal the SNP genotype (Kim and Misra, 2007). Probes are typically

used as a method of allele specific-PCR (AS-PCR) and require the use of real-time PCR technologies

with fluorescence detection. Probes can also be detected through the use of mass spectrometry (MALDI-

TOF). Although these methods are effective and accurate, either specialized equipment is required or

they are costly to perform. A simple gel electrophoresis detection method would be sufficient for

visualizing the amplification of a PCR reaction. It is both accurate and cost-effective.

A single non-complementary base between primer and template would not affect annealing and

extension in relation to genotype (data not shown). Liu et al. (2012) detailed the position and base

needed for adding an intentionally mismatched base into the AS-primer. This method provides that all

primer-template pairings would have at least one non-complementary base which is the intentional

mismatch. Templates containing the SNP would then have two non-complementary bases with the

primer, thus annealing and PCR amplification would be inhibited with two base mismatches.

Eight of the 18 SNPs described in Chapter 3 were used in the development and testing of the

allele-specific PCR (AS-PCR) method in this chapter. The objective of this study was to develop a time

and cost-effective method of genotyping samples at known SNP locations without the use of sequencing.

51


SNPs

Genetic lines and SNP selection for 8 SNPs were detailed in chapters 2 and 3 respectively.

Primers for AS-PCR

Reference (RJF) and called (SNP) bases for each of the 8 locations were shown in Table 1.

SNP-primers were designed in the forward orientation with the SNP positioned at the 3’ end based on the

RJF genome sequence. Primers were 20 bases in length and GC content was not considered. SNP

forward primers (SNP_M1 and SNP_M3) contained the SNP location but another base within the primer

was purposely mismatched from the template DNA (Table 2 and Figure 1). Liu et al. (2012) outlined

position and base required for an intentional mismatch in AS primers and was referenced in the design of

M1 and M3 primers. SNP_M3, unlike the M1 primers, was designed complimentary to the SNP, not to

the RJF sequence.

PCR

DNA samples from the RES and SUS lines were individually sequenced at each of the 8 SNP

locations to determine genotypes. Known genotypes were used to verify results of allele-specific PCR

reactions. Two separate allele-specific PCR reactions were performed with forward primer SNP_M1 or

SNP_M3 with a common reverse primer using Applied Biosystems 2720 Thermal Cycler (Life

Technologies, Carlsbad, CA). Using SNP-primers, PCR was carried out as 25µL reaction volumes: 5ng

of DNA (1ng/µL), 1x buffer (NEB; Ipswich, MA), 2.5mM dNTP mix (NEB; Ipswich, MA), SNP and R

primers 20ng/µL, 0.5 units TaqPolymerase (NEB; Ipswich, MA). Cycle conditions were as follows:

denaturation at 95°C for 1min, 33 cycles of amplifi cation (95°C for 30 sec, 55°-63°C for 1min, 72°C fo r 30

sec), final extension at 72°C for 10 min. Annealin g temperature varied between samples as shown in

Table 1. Verification and specificity of PCR reaction and SNP-primer were confirmed by 1% agarose gel

electrophoresis as described in chapter 3.

52


Primer design

SNP_M1 forward primer contains a second mismatching base within the primer that was

purposely changed (Table 2). Therefore, every PCR reaction carried out with this primer, regardless of

template genotype, would have this base mismatched at 3rd position from 3’ end of forward primer. If the

DNA also does not match the primer at the SNP location, two bases would be mismatched between the

primer and template DNA (Figure 1A). Annealing and extension were not affected by one base mismatch

(data not shown). Two mismatches between primer and template have been shown to disrupt annealing

and extension (Liu et al., 2012). Liu et al (2012) detailed the position and base needed for an intentional

mismatch to sufficiently disrupt primer annealing and PCR product formation.

M1 primers were designed to complement the RJF sequence and SNP_M3 were designed to

complement the SNP present at the respective site. By subjecting individual samples to PCR reactions

with SNP_M1 or SNP_M3 primers (separately), AS-PCR reactions can be genotyped at the SNP location

and distinguished between homozygous and heterozygous. The M1 primer will bind and amplify if the

DNA matches the RJF sequence. If the template contains the SNP, the M3 primer will bind. Both primers

will bind in the case of a heterozygote (Figure 1B). Genotype can be visualized on an agarose gel by

identifying which of the two PCR reactions amplify the template DNA as shown in Figure 2 with an

example of SNP detection in the ZFYVE19 gene. From this gel image, genotypes can be determined at

the SNP location. As shown in Figure 2, bird 1 is homozygous (GG), bird 2 is heterozygous (G/C), and

bird 3 is homozygous (CC). AS-PCR successfully differentiated between RJF and SNP genotypes at the

8 known SNP locations (Figure 3). Gel images were compared with nucleotide sequencing results to

confirm accurate genotyping by AS-PCR method.

All eight of the SNP cases considered here were homo-variant which means only one SNP

genotype (differing from the RJF genotype) was detected at each of these positions. Therefore, only one

primer was required to detect SNP genotypes (SNP_M3) and one for RJF genotypes (SNP_M1). A

hetero-variant SNP would have two or more SNP genotypes present at the same location. Although very

rare, if a hetero-variant SNP is identified as being of interest, this method could still be utilized. Additional

primers would be designed to complement the additional SNP genotypes and each sample would be

53

subjected to three or four PCR reactions (depending on if the SNP had two or three variants). One

reaction would detect RJF genotypes, one for the first SNP genotype, one for the second, and one for the

third. No more than two bands would be seen on the gel image because it is not possible to have more

than two alleles at any one location in the genome.

The AS-PCR assay described in this chapter is fast and accurate. There are other methods

available such as real-time PCR that use probes to identify specific alleles. Although real-time PCR does

not require gel electrophoresis and is therefore a faster method, it is costly due to fluorescent probes.

Due to the significant difference in cost between these methods, studies in certain cases may be better

suited for a slightly more time consuming technique. This AS-PCR method is very versatile and can be

utilized to determine genotype at all kinds of SNP locations in all kinds of studies.

54

REFERENCES



Kim, S., and A. Misra. 2007. SNP Genotyping: Technologies and Biomedical Applications. Annu. Rev. Biomed. Eng. 9:289-320. doi:10.1146/annurev.bioeng.9.060906.152037.

Liu, J., S. Huang, M. Sun, S. Liu, Y. Liu, W. Wang, X. Zhang, H. Wang, and W. Hua. 2012. An improved allele-specific PCR primer design method for SNP marker analysis and its application. Plant Methods 8:34-34. doi:10.1186/1746-4811-8-34.

55


Table 1: Reference and called bases at 8 SNP locations and annealing temperature of M1/M3 primers

Table 2: Sequences of SNP_M1, SNP_M3, and R primers

SNP base locations were capitalized. The 3rd bases from the 3’ end of M1 and M3 primers which

were purposely mismatched were underlined.

Figure 1: Diagram of SNP_M1 and SNP_M3 primers

A) The mechanism of allele discrimination for the SNP_M1 primer (SNP genotype vs RJF

genotype). Boxes indicate mismatched base pairs between primer and template. Capitalized

bases are located at the SNP position and underlined bases in primers are purposely

changed

B) The differentiation between homozygous and heterozygous template using SNP_M1 and

SNP_M3 primers. Boxes indicate mismatched base pairs between primer and template.

Capitalized bases are located at the SNP position and underlined bases in primers are

purposely changed.

Figure 2: Genotyping of ZFYVE19 SNP using SNP_M1 and SNP_M3

PCR was performed for three different birds having three known genotypes. GG (Bird 1), GC

(Bird2), and CC (Bird3) represent ZFYVE19 genotypes for homozygous RJF, heterozygote, and

homozygous SNP, respectively. M_1 and M_3 indicate the use of forward primer.

Figure 3: Application of M1 and M3 to 8 SNP locations

Optimized results of PCR reactions including M1 and M3 primers at all 8 SNP locations were

shown. Three different genotype examples (AA, Aa, and aa) are depicted for each location. In

the first column, all individuals are homozygous RJF genotype. The middle samples are

heterozygous. The third column represents homozygous SNP genotype individuals.

56

Table 1: Reference and Called Bases at 8 SNP Locations and Annealing Temperature of M1/M3 Primers

Gene containing SNP location

Reference base (RJF)

Called base (SNP)

Annealing temperature of M1/M3

CHTF18 G A 63°C

FRS3 T C 63°C

ZFYVE19 G C 64°C

MYOT A G 64°C

RCBTB2 C T 64°C

CDK17 C G 63°C

PHF2 C T 64°C

LOC100858992 C T 60°C

57

Table 2: Sequences of SNP_M1, SNP_M3, and R primers

Primer name Oligo sequence (5’-3’)

CHTF18_SNP_M1 ctccgtcaactgctgtgctG

CHTF18_SNP_M3 ctccgtcaactgctgtgctA

CHTF18_R ttcctcccacatagggatagag

CDK17_SNP_M1 cttcttactttcaggcacaC

CDK17_SNP_M3 cttcttactttcaggcacaG

CDK17_R agcaagttgaactcttccttcg

LOC100858992_SNP_M1 atagaggatctaggatcacC

LOC100858992_SNP_M3 atagaggatctaggatcacT

LOC100858992_R ccaaagaaacattttgcgtaca

PHF2_SNP_M1 gatggtggtgtcacttgtaC

PHF2_SNP_M3 gatggtggtgtcacttgtaT

PHF2_R ggggtaaatcagcagtgagaag

FRS3_SNP_M1 cctcagctcctgatcacaaT

FRS3_SNP_M3 cctcagctcctgatcacaaC

FRS3_R tctttcatctccagcttcatca

RCBTB2_SNP_M1 tggcaggctgcaatctcgaC

RCBTB2_SNP_M3 tggcaggctgcaatctcgaT

RCBTB2_R tgaagtgcatgtgtgcttgtag

ZFYVE19_SNP_M1 gagagatcctccttccctgG

ZFYVE19_SNP_M3 gagagatcctccttccccgC

ZFYVE19_R ctgaaggacgaagaaaggctaa

MYOT_SNP_M1 ctgaggtgagttctcatcaA

MYOT_SNP_M3 ctgaggtgagttctcatcaG

MYOT_R ttcactccaggttactgaagca

58

Figure 1: Diagram of SNP_M1 and SNP_M3 primers

B)

A)

Figure 2: Genotyping of ZFYVE19 SNP using SNP_M1 and SNP_M3

Figure 3: Application of M1 and M3 to 8 SNP locations

59

Genotyping of ZFYVE19 SNP using SNP_M1 and SNP_M3

Application of M1 and M3 to 8 SNP locations

60

TESTING OF POTENTIAL BIOMARKERS IN UNSELECTED AND UNRELATED POPULATIONS

61

INTRODUCTION

A genome-wide panel of 3,072 SNPs (Muir et al., 2008) indentified 7 regions as significantly

associated with the ascites phenotype and at least 3 of these regions show associations in several

different unselected/unrelated lines (Smith, C. D. 2009). Locations of these 3 regions based on the

Gallus gallus v2.1 assembly are 9:13.5-14.8 Mbp, 9:15.5-16.3 Mbp, and 27:2.0-2.3 Mbp. Two

microsatellite markers were identified as significantly correlated with ascites incidence in the Chr9:13

region of the chicken genome (Krishnamoorthy, 2012). The angiotensin II receptor type 1 (previously

implicated in hypertension) gene is located in this region. This result indicated that the microsatellites are

in linkage disequilibrium with a DNA variation of the AGTR1 gene responsible in part for ascites

susceptibility. These markers would indeed be indicative of disease level however, linkage disequilibrium

is not stable over long periods of time (Brookes, 1999). The end result would be dissociation of the

marker and actual trait of interest. By considering DNA polymorphisms known to cause non-synonymous

protein coding changes, a marker significantly associated with disease incidence could become an

underlying cause of susceptibility and/or resistance.

In previous chapters, eight SNPs causing non-synonymous protein coding changes were

identified as potential biomarkers for ascites selection and the AS-PCR method to determine SNP

genotype was developed. The high frequency change between the RES and SUS lines for the

prospective biomarkers could be attributed to genetic drift rather than selection for disease incidence.

Even though the difference in frequency between the RES and SUS lines for the 8 SNPs was significant,

the effect of genetic background on genotype-phenotype interaction had to be investigated. In order to

determine if the chosen SNPs are in fact segregating for ascites incidence, unselected and unrelated

populations of broilers were challenged with ascites and genotyped at the 8 SNP locations. This

objective was obtained by challenging two populations of broilers with the disease and analyzing the

correlation between disease level and genotype of the birds.


Genetic Lines

Two unrelated lines of broiler type chickens, REL and RMQ, maintained at the University of

Arkansas were utilized in this study. The REL line was derived from a commercial pedigree elite

62

population and has undergone 17 generations of random mating within the line, (Anthony, 2013, February

14). REL line has growth and yield characteristic of commercial broilers and is the parental/unselected

line for RES and SUS which were used to discover potential biomarkers in previous chapters. RMQ is a

random bred control line formed from a composite of commercial parent stock available in 1997. Seven

male and six female line sources from various primary breeding companies were included in the line

(Harford, 2010).

Hypobaric chamber

The hypobaric chamber used to simulate a high altitude environment measures 2.4 x 3.7 x 2.4m

(Pavlidis et al., 2007) (Figure 1). The chamber was equipped with four custom stainless steel batteries

fitted with trough feeders and nipple waterers. Altitude, ventilation rate, and temperature were under

control of the experimenters and monitored daily. For all experiments, ventilation was set at 17m3/min.

Initial temperature was set for warm-room brooding of chicks and decreased as the birds aged. The

altitude setting was constant within trials. Daily management tasks were conducted inside the chamber at

the specified altitude through the use of an air-lock system. The chamber was capable of housing 240

broilers to 6 weeks of age. Three trials in the hypobaric chamber were conducted: 1 – REL at 8000ft

altitude, 2 – RMQ at 12000ft altitude, 3 – RMQ at 8000ft altitude.

Husbandry

All matings of parent stock to produce REL and RMQ chicks used in this study were done by

artificial insemination of 72 females with pooled semen from 24 males per line. At hatch, chicks were

wing banded, vaccinated against Marek’s disease, and assigned randomly to pens in the hypobaric

chamber. Feed and water were provided ad libitum throughout the grow-out period. Mortalities were

collected from the chamber daily and necropsied to determine cause of death and evaluated for ascites

symptoms including enlarged, flaccid, or round heart, presence of fluid in the abdominal

cavity/pericardium, and liver lesions. Upon necropsy, body weight, sex, and day of death were recorded.

The length of each trial was six weeks. At the completion of each six week trial, all surviving birds were

euthanized and necropsied for determining ascites status.

63

DNA

Approximately 50µL of blood was collected from each bird in each trial at one week of age using

tubes containing sodium citrate (anticoagulant) by wing vein puncture. Genomic DNA was isolated from

whole blood using the Wizard SV 96 Genomic DNA Purification System (Promega; Madison, WI)

following manufacturer’s instructions with modifications as described in Materials and Methods of Chapter

3. After isolation, DNA was quantified using a Nanodrop 1000 spectrophotometer (Thermo Fisher

Scientific Inc.; Waltham, MA) and a dilution of 1ng/µL was prepared in 96 well PCR format.

PCR

For each trial, birds were divided into two groups – the 48 most susceptible birds to die from

ascites syndrome and the 48 most resistant birds surviving entire 6 week trial. PCR reactions were

performed using M1 or M3 primers detailed in Chapter 4 to genotype birds for 8 SNP locations discussed

in Chapter 3. Each bird was subjected to two PCR reactions with M1 or M3 primers along with a reverse

primer (Chapter 4). PCR reactions and gel electrophoresis were carried out following methods described

in the Materials and Methods section of Chapter 3 and 4.

TaqMan quantitative PCR (qPCR) assay

The TaqMan qPCR assay was used in the analysis of trial #3 RMQ and analysis of other

populations obtained from D. Rhoads’ collection. Two TaqMan probes were designed in order to detect

both the RJF and SNP bases in the forward orientation (Table 1) and synthesized by Integrated DNA

Technology Inc. (Coralville, IA). Fluorophores were located at the 5’ end and quenchers at the 3’ end.

HEX was the fluorophore used for detection of the RJF genotype and FAM was used for the SNP

genotype. The assay contained: DNA (2µL, variable concentration), 1X buffer (NEB; Ipswich, MA), 20mM

dNTP mix (NEB; Ipswich, MA), 50uM F/R primers, TaqMan probes, 2 units TaqPolymerase. Cycle

conditions were as follows: denaturation 90°C for 3 0 sec, 40 cycles of amplification (90°C for 15 sec,

65°C for 30 sec), and data was recorded during the last 30 cycles.

Data Analysis

Each individual was scored as 1, 2, or 3 based on genotype at each of the 8 SNP locations; 1

indicates homozygous RJF-type, 2 designates heterozygous type, and 3 specifies homozygous SNP-

64

type. Genotype and allele frequencies were calculated based on observed numbers of chickens within

each genotype/allele category. Chickens were grouped according to level of disease incidence.

Individuals dying from ascites early (first 48 to die) in the grow-out period were classified as ‘most

susceptible’ and those surviving six weeks in the chamber (randomly selected 48) were termed ‘most

resistant’. Genotype and allele frequencies were compared between the two groups by Chi-square

analysis. Allele frequencies were tested for Hardy-Weinberg equilibrium. This procedure was used in the

analysis of all three trials.


Trial #1. REL- 8,000ft Altitude

The relaxed line (generation 19) was used to determine if the SNPs of interest would segregate

differentially relative to ascites incidence. In this trial 146 chicks were placed in the hypobaric chamber at

hatch, 58 (40%) of which experienced mortality due to ascites syndrome and 78 (53%) survived the grow-

out period for 6 weeks (Table 2). To check for possible errors in AS-PCR, genotyping results were

confirmed by DNA sequencing and the average error rates were 16.1%. The majority of incorrect

genotypes resulting from the gel-based AS-PCR method were true homozygotes mistaken for

heterozygous individuals. Thus, genotypic and allelic frequencies and Chi-square analysis were

calculated based on DNA sequencing results (Tables 3 and 4, respectively). Results showed that

genotypic frequencies for CHTF18 differed between resistant and susceptible birds (Figure 3), but the

following Chi-square analysis revealed no significant differences at the individual SNP locations between

resistant and susceptible birds. Chi-square values for CHTF18 ranged from 0.24 to 0.98. MYOT was

fixed for the homozygous RJF genotype in all birds. Therefore, MYOT was not included in subsequent

statistical analyses.

After analysis of individual SNPs showed no association with ascites incidence, correlations

between SNPs were investigated. Two-, three- and four-way analysis were performed between SNPs

using the categorical function of JMP Genomics (SAS Institute Inc., Cary, NC). Interestingly, a four-way

association of SNPs showed that changing CHTF18 from 1 (homozygous RJF) to 2 (heterozygous)

changes the ascites status from R to S (Table 5). Eight out of nine chickens having homozygous RJF

genotypes for PHF2, ZFYVE19, CDK17, and CHTF18 SNPs were resistant. When the CHTF18 genotype

65

was changed to heterozygote (PHF2, ZFYVE19, CDK17 unchanged), 100% (9) of birds having this

combination of genotypes were susceptible. Similar circumstances can be seen in the

CHTF18/ZFYVE19/CDK17/LOC100858992 and CHTF18/ZFYVE19/CDK17/FRS3 genotype

combinations. However, statistical significance cannot be demonstrated due to low sample sizes. These

results suggest the involvement of CHTF18 with ascites syndrome. The homozygous RJF genotype is

repeatedly associated with resistance while the heterozygous form indicates susceptibility.

Trial #2 and 3. RMQ (12,000ft and 8,000ft Altitude)

The RMQ line of birds is completely unrelated to the SUS, RES, and REL lines. Even though the

difference in frequency between the RES and SUS lines for the 8 biomarkers was significant, the effect of

genetic background on genotype-phenotype interaction had to be investigated. RMQ was used to

determine if the possible biomarkers are reliable in other, unrelated populations. For trial #2, the ascites

inducing environment, simulated 12,000ft altitude, was excessively severe for the birds. In total, 109

chicks were placed in the hypobaric chamber at the beginning of the trial, 86 of which (79%) died from

ascites. Only 10 birds (9%) survived the grow-out for 6 weeks and Figure 2 showed the death curve of

cumulative mortality (%) due to ascites syndrome. These results did not provide a balanced sample size

of susceptible to resistant birds and therefore, chi-square analysis of this trial may not be reliable.

RMQ birds were subsequently challenged with lower altitude (8,000ft; Trial #3). This trial with the

RMQ line provided a more balanced sample size than the previous attempt (Table 2). At the start of trial

#3, 124 RMQ chicks were placed in the hypobaric chamber at day of hatch. 48 birds (39%) succumbed

to ascites disease while 61 birds (49%) survived the entire trial. It seems 8,000ft above sea level is a

better suited challenge environment for RMQ’s apparent predisposition to ascites. Previous findings

(REL#1) implicated CHTF18 as a possible biomarker showing differential genotype frequencies. Thus,

RMQ#3 was genotyped at this SNP location. Since only one potential biomarker needs to be analyzed,

genotypes of most resistant/susceptible individuals were analyzed using the TaqMan qPCR assay for

rapid detection process. Genotype and allele frequencies were shown in Tables 6 and 7, respectively.

No frequency differences between resistant and susceptible birds were detected in the CHTF18 SNP in

the ascites challenge for RMQ line.

66

In addition to the current ascites trials, REL birds previously challenged by N. B. Anthony

(Krishnamoorthy, 2012) were analyzed for CHTF18 genotypes by TaqMan qPCR method. Similar to

results found in the RMQ challenge trial, differential genotypic frequencies were not found, (data not

shown).

CHTF18

A SNP known to cause an amino acid coding change was found in the CHTF18 gene. Though

sample size limited the statistical significance of the association, the CHTF18 SNP is potentially a genetic

biomarker for ascites disease and also may be a functional physiological factor in ascites susceptibility.

CHTF18 (chromosome transmission fidelity factor 18) located on Chr14 at 6.0Mbp has been associated

with cardiomyocyte differentiation (Naqvi et al., 2009) through its involvement in sister chromatid cohesion

(Bermudez et al., 2003; Merkle et al., 2003; Berkowitz et al., 2008). Cell cycle reentry ceases soon after

birth in cardiomyocytes, therefore these cells can no longer undergo cell division. This explains why

mammalian hearts can only respond to increased pressure by hypertrophic growth – an increase in

individual cell size. A study of mice with dysfunctional c-Kit (affecting cellular differentiation) revealed that

heterozygote loss-of-function mice were capable of hyperplastic left ventricle cardiomyocyte growth when

challenged with a pressure overload. In the wild-type mice, increase of left ventricle size was due to

hypertrophy. The CHTF18 gene was found to be up-regulated (4.3 fold) in c-Kit dysfunctional mice due to

its role in sister chromatid cohesion during S phase cell division (Naqvi et al., 2009). Hyperplastic left

ventricle cardiomyocyte growth in heterozygote wild-type/loss-of-function mice may be associated with

the up-regulation of the CHTF18 gene. The current study suggests the implication of CHTF18 with

ascites syndrome in broiler chickens. Left-ventricle hypertrophy is a clinical sign of the disease.

In this chapter, 8 potential biomarkers were evaluated. Two lines of chickens (parental and

unrelated) were challenged with ascites and genotyped at these 8 SNP locations. No statistical

significance was found due to sample size. However, CHTF18 was repeatedly associated with

differences in ascites incidence. Homozygous RJF is shown to segregate in resistant birds while

heterozygous birds are often susceptible.

67

REFERENCES

Anthony, N. B. 2013, February 14.

Berkowitz, K. M., K. H. Kaestner, and T. A. Jongens. 2008. Germline expression of mammalian CTF18, an evolutionarily conserved protein required for germ cell proliferation in the fly and sister chromatid cohesion in yeast. Mol. Hum. Reprod. 14:143-150. doi:10.1093/molehr/gan005.

Bermudez, V. P., Y. Maniwa, I. Tappin, K. Ozato, K. Yokomori, and J. Hurwitz. 2003. The alternative Ctf18-Dcc1-Ctf8-replication factor C complex required for sister chromatid cohesion loads proliferating cell nuclear antigen onto DNA. Proc. Natl. Acad. Sci. U. S. A. 100:10237-10242.


Harford, I. D. 2010. Divergent selection for muscle color in broilers. M.S. ed. University of Arkansas, United States -- Arkansas.

Krishnamoorthy, S. 2012. Investigation of a Locus on Chromosome 9 for Contributions to Pulmonary Hypertension Syndrome in Broilers. Ph.D. ed. University of Arkansas, United States -- Arkansas.

Merkle, C. J., L. M. Karnitz, J. Henry-Sánchez T., and J. Chen. 2003. Cloning and characterization of hCTF18, hCTF8, and hDCC1. Human homologs of a Saccharomyces cerevisiae complex involved in sister chromatid cohesion establishment. J. Biol. Chem. 278:30051-30056.

Muir, W. M., G. K. Wong, Y. Zhang, J. Wang, M. A. M. Groenen, R. P. M. A. Crooijmans, H. Megens, H. Zhang, R. Okimoto, A. Vereijken, A. Jungerius, G. A. A. Albers, C. T. Lawley, M. E. Delany, S. MacEachern, and H. H. Cheng. 2008. Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds. Proc. Natl. Acad. Sci. U. S. A. 105:17312-17317. doi:10.1073/pnas.0806569105.

Naqvi, N., M. Li, E. Yahiro, R. M. Graham, and A. Husain. 2009. Insights into the Characteristics of Mammalian Cardiomyocyte Terminal Differentiation Shown Through the Study of Mice with a Dysfunctional c-Kit. Pediatr. Cardiol. 30:651-658. doi:10.1007/s00246-008-9366-1.


Smith, C. D. 2009. Applications of variable number tandem repeat genotyping in the validation of an animal medical model and gene flow studies in threatened populations of reptiles. Ph.D. ed. University of Arkansas, United States -- Arkansas.

68


Table 1: TaqMan probes used in RMQ trial #2

Probes used in TaqMan qPCR analysis of RMQ trial #3 were shown. Lowercase base indicates

SNP position.

Table 2: Summary of bird numbers and mortality for 3 trials

Number of chicks started at beginning of each trial, mortality due to ascites, non-ascitic

mortalities, and number of surviving birds were listed.

Table 3: Trial #1 REL 8,000ft; Genotype frequencies and Chi-square analysis

Frequencies of genotypes for 8 SNPs (1=homo RJF, 2=hetero, 3=homo SNP) obtained from REL

chickens raised at 8,000ft simulated altitude and subsequent chi-square analyses were shown.

*0.00 indicates <0.01

Table 4: Trial #1 REL 8,000ft; Allele frequencies and Chi-square analysis

Allelic frequencies for 8 SNPs from REL trial #2 and chi-square analysis were shown. P

represents the RJF allele; q represents the SNP allele.

*0.00 indicates <0.01

Table 5: Four-way association of genotypes

Some genotypes in association with other genotypes were showing segregation with ascites

incidence.

Table 6: Trial #3 RMQ 8,000ft; Genotype frequencies and Chi-square analysis

Frequencies of genotypes for CHTF18 SNP (1=homo RJF, 2=hetero, 3=homo SNP) obtained

from RMQ chickens raised at 8,000ft simulated altitude and subsequent chi-square analyses

were shown.

Table 7: Trial #3 RMQ 8,000ft; Allele frequencies and Chi-square analysis

Allelic frequencies for CHTF18 SNP from RMQ trial #3 and chi-square analysis were shown. P

represents the RJF allele; q represents the SNP allele.

69

Figure 1: Hypobaric chamber

Hypobaric chamber diagram including, transfer chamber, batteries, air valves, doors, and

dimensions, was shown.

Figure 2: Cumulative Percent Mortality due to Ascites Syndrome

Cumulative mortality due to ascites syndrome is shown as a percent of the total number of chicks

placed at the beginning of each trial.

Figure 3: Application of Biomarkers to REL (parental) line

Comparison of the ‘most susceptible’ and ‘most resistant’ groups based on genotype at the 8

SNP locations.

Figure 4: Application of Biomarkers to RMQ (unrelated) line

Comparison of the ‘most susceptible’ and ‘most resistant’ groups based on genotype at the

CHTF18 SNP location.

70

Table 1: TaqMan probes used in RMQ trial #2 Probe name Sequence Fluorophore

CHTF18tmA ATaCTTCCACGGCTCTCCTCCG FAM CHTF18tmG ATgCTTCCACGGCTCTCCTCC HEX

Table 2: Summary of bird numbers and mortality for 3 trials

Trial #1 REL 8,000ft Trial #2 RMQ 12,000ft Trial #3 RMQ 8,000ft

chicks started 146 109 124

surviving to end of trial 78 10 61

mortality due to ascites 58 86 48

mortality for other reasons 10 13 15

71

Table 3: Trial #1 REL 8,000ft; Genotype frequencies and Chi-square analysis

OBSERVED EXPECTED FREQUENCY CHI

ALL SUS RES SUS RES ALL SUS RES TEST

CHTF18 1 32 11 21 14.3 17.7 0.4 0.3 0.5 0.24

2 35 19 16 15.7 19.4 0.4 0.5 0.3 0.25

3 18 8 10 8.1 10.0 0.2 0.2 0.2 0.98

FRS3 1 24 9 15 10.7 13.3 0.3 0.2 0.3 0.48

2 48 21 27 21.5 26.5 0.6 0.6 0.6 0.89

3 13 8 5 5.8 7.2 0.2 0.2 0.1 0.22

CDK17 1 66 30 36 29.6 36.4 0.8 0.8 0.8 0.92

2 21 9 12 9.4 11.6 0.2 0.2 0.3 0.86

3 0 0 0 0.0 0.0 0.0 0.0 0.0 NA

LOC100858992 1 33 16 17 14.8 18.2 0.4 0.4 0.4 0.67

2 40 19 21 17.9 22.1 0.5 0.5 0.4 0.73

3 14 4 10 6.3 7.7 0.2 0.1 0.2 0.22

MYOT 1 86 39 47 39.0 47.0 1.0 1.0 1.0 1.00

2 0 0 0 0.0 0.0 0.0 0.0 0.0 NA

3 0 0 0 0.0 0.0 0.0 0.0 0.0 NA

PHF2 1 47 20 27 20.8 26.2 0.6 0.5 0.6 0.82

2 34 15 19 15.0 19.0 0.4 0.4 0.4 0.99

3 5 3 2 2.2 2.8 0.1 0.1 0.0 0.48

RCBTB2 1 4 2 2 1.8 2.2 0.1 0.1 0.0 0.87

2 24 12 12 11.0 13.0 0.3 0.3 0.3 0.69

3 59 26 33 27.1 31.9 0.7 0.7 0.7 0.77

ZFYVE19 1 70 32 38 31.4 38.6 0.8 0.8 0.8 0.88

2 16 7 9 7.2 8.8 0.2 0.2 0.2 0.93

3 1 0 1 0.5 0.6 0.0 0.0 0.0 0.37

72

Table 4: Trial #1 REL 8,000ft; Allele frequencies and Chi-square analysis

OBSERVED FREQUENCY EXPECTED CHI

TEST

CHTF18 p2 0.339 28.83

p 0.58 2pq 0.486 41.35

q 0.42 q2 0.174 14.83 0.3673

FRS3 p2 0.319 27.11

p 0.56 2pq 0.492 41.79

q 0.44 q2 0.189 16.11 0.391

CDK17 p2 0.773 67.27

p 0.88 2pq 0.212 18.47

q 0.12 q2 0.015 1.267 0.4407

LOC100858992 p2 0.371 32.29

p 0.61 2pq 0.476 41.43

q 0.39 q2 0.153 13.29 0.9498

MYOT p2 1 86

p 1 2pq 0 0

q 0 q2 0 0 NA

PHF2 p2 0.554 47.63

p 0.74 2pq 0.381 32.74

q 0.26 q2 0.065 5.628 0.9387

RCBTB2 p2 0.034 2.943

p 0.18 2pq 0.3 26.11

q 0.82 q2 0.666 57.94 0.7518

ZFYVE19 p2 0.804 69.93

p 0.9 2pq 0.185 16.14

q 0.1 q2 0.011 0.931 0.9968

73

Table 5: Four-way association of genotypes

CHTF18 PHF2 ZFYVE19 CDK17 fR nR fS nS N

1 1 1 1 0.89 8 0.11 1 9

2 1 1 1 0 0 1 9 9

CHTF18 ZFYVE19 CDK17 LOC100 fR nR fS nS N

1 1 1 1 0.75 6 0.25 2 8

2 1 1 1 0.33 2 0.67 4 6

CHTF18 ZFYVE19 CDK17 FRS3 fR nR fS nS N

1 1 1 1 0.83 5 0.17 1 6

2 1 1 1 0.4 2 0.6 3 5

Table 6: Trial #3 RMQ 8,000ft; Genotype frequencies and Chi-square analysis

OBSERVED EXPECTED FREQUENCY CHI

ALL SUS RES SUS RES ALL SUS RES TEST

CHTF18 1 74 36 38 36.2 37.8 0.8 0.8 0.8 0.95

2 19 10 9 9.3 9.7 0.2 0.2 0.2 0.75

3 3 1 2 1.5 1.5 0.0 0.0 0. 0.59

Table 7: Trial #3 RMQ 8,000ft; Allele frequencies and Chi-square analysis OBSERVED EXPECTED CHI TEST

CHTF18 p2 0.76 73

p 0.87 2pq 0.22 21

q 0.13 q2 0.02 2 0.703

74

Figure 1: Hypobaric chamber

75

Figure 2: Cumulative percent mortality due to ascites syndrome

0

10

20

30

40

50

60

70

80

90

9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57

% m

ort

alit

y

Days

Mortality Due to Ascites

RMQ#2

REL#1

RMQ#3

76

Figure 3: Application of Biomarkers to REL (parental) line

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

1 2 3

f(S

US

)-f(

RE

S)

genotype

REL

CHTF18

PHF2

ZFYVE19

CDK17

MYOT

LOC100

RCBTB2

FRS3

77

Figure 4: Application of Biomarkers to RMQ (unrelated) line

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

1 2 3

f(S

US

)-f(

RE

S)

genotype

RMQ

CHTF18

78

CONCLUSION

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The poultry industry continues to grow rapidly and in the US, per capita consumption of chicken

has risen from 30 to 80 lbs over the last 50 years. Primary broiler breeding companies have been

employing quantitative genetics to improve growth rate, feed conversion, and meat yield. Body weight at

42 days of age has improved significantly over time. It has been reported that 85 – 90% of this increase

can be attributed to genetic selection. Several correlated responses to increased growth rate and muscle

yield have also negatively impacted the industry over the last 20 years. Increased carcass fat deposition,

physiological leg problems, reproductive inefficiency, and increased ascites incidence are a few

examples.

By associating phenotypic traits to specific genotypes, the heritability of the trait can become

close to 1. Traits possessing high heritabilities will experience more rapid progress due to selection

pressure. Genotypic data can provide genetic biomarkers which will be used in future genetic selection

programs in the animal breeding industry. Single nucleotide polymorphisms (SNPs) are examples of

easily detectable genotypic markers. Next generation sequencing can be applied to agricultural

populations to identify SNPs associated with traits of economic importance such as ascites.

Genetic lines representing resistance (RES) and susceptibility (SUS) to ascites syndrome have

been developed by N. B. Anthony at the University of Arkansas. The genomes of the RES and SUS

lines, along with their parental (REL) line, were sequenced and aligned with the reference Red Jungle

Fowl genome. SNPs were identified in the three lines as being different from the RJF reference. Over

four million SNPs from each line were filtered based on location and reliability indicators. In the RES line,

10 SNPs causing protein coding changes were found to be most reliable. 22 were identified in the SUS

line.

Genome sequencing provided 32 reliable, non-synonymous coding change SNPs to be

considered as biomarker candidates. Sequences were based solely on 10 pooled DNA samples per line.

Therefore, these SNPs were validated in a larger sample size of 96 birds in each line. Frequencies in

which each SNP occurred in the RES and SUS lines were compared. Those having the largest

differences between RES and SUS were considered for the next step of the study. Eight SNPs were at

least 50% divergent between the RES and SUS birds. Larger numbers of chickens needed to be

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considered for the eight SNP locations identified. Traditional genotyping methods are costly and time-

consuming. An AS-PCR technique was developed that is accurate, time-effective, and economical.

In order to investigate the effect of genetic background on genotype-phenotype interaction, two

populations were challenged with ascites and genotyped at the 8 previously identified locations. The REL

(parental) line was used to represent unselected populations of birds and the RMQ (outside population)

line was used to consider the genotypes of unrelated birds. The CHTF18 SNP was continually

associated with ascites incidence in the REL line. The homozygous RJF genotype was more often seen

in resistant-type chickens while heterozygous individuals were usually susceptible. However, sample size

limited the statistical significance of this data. No associations were seen in the RMQ line.

CHTF18 is not likely a good choice as a biomarker for ascites incidence in broiler chickens due to

its lack of significance in the RMQ line. This SNP would not be of value in populations unrelated to the

ascites lines (RES, SUS, REL) at the University of Arkansas. In contrast, the AS-PCR techniques

developed in chapter 4 may be of use to future investigators. This method is very versatile and can be

utilized to determine genotype at all kinds of SNP locations in all kinds of studies.

Identification of Biomarkers Associated with Ascites ...

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