GENOME WIDE ASSOCIATION STUDY AND GENOMIC HERITABILITY OF ANTIMULLERIAN HORMONE IN DAIRY HOLSTEIN HEIFERS
By
Muhammad Yasir Nawaz

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Animal Science-Master of Science
2017

ABSTRACT
GENOME WIDE ASSOCIATION STUDY AND GENOMIC HERITABILITY OF ANTIMULLERIAN HORMONE IN DAIRY HOLSTEIN HEIFERS
By
Muhammad Yasir Nawaz
The objectives of this study were to estimate the genomic heritability of Anti Mullerian hormone
(AMH), identify candidate genes associated with AMH production, and establish phenotypic
correlations between serum AMH concentrations and parameters of reproductive performance in
Holstein heifers. AMH concentrations were determined in 2905 dairy Holstein heifers. Animals
were genotyped using the Zoetis 70K SNP Panel. Genotypes were imputed to standard USDA
60,671 bovine SNP set with 54,519 SNP markers remaining after standard editing procedures. A
linear mixed model was used to model the random effects of sampling day and genomics on the
logarithm of AMH. Results showed that the genomic heritability (ÂąSEM) of AMH was
0.36Âą0.03. We identified significant associations between AMH and 11 SNP markers on
chromosome 11 and one marker on chromosome 20 based on a 5% false discovery rate. Some of
the annotated genes in those regions have been previously identified as being important for AMH
expression and ovarian function. There was no strong evidence of any association between
conventional reproductive performance measures of dairy heifers and their serum AMH
concentration. It seems that these associations should be studied in later parities as these heifers
continue to mature. Nevertheless, the high heritability of AMH and a well-established
association of AMH with super ovulatory response may make AMH a biomarker to genetically
select cattle for larger gonads, more follicles and better response to superovulation.

This thesis is dedicated to my parents, their love and affection has always been a source of
strength for me.
I dedicate this thesis to my wife, my brothers and my sisters whose continued support motivated
me to accomplish everything I have achieved in my life.
I also dedicate this work to all my friends and teachers who believed in me and helped me sort
out the challenges of graduate school life.

iii

ACKNOWLEDGMENTS

I owe my deepest gratitude to my research adviser Dr. Robert J Tempelman. He encouraged and
supported me throughout my degree program. I will forever be thankful to him for being patient,
and allowing me to work on my pace while transitioning from the field of Veterinary Medicine
to Animal Genetics. It looked too hard for me in the beginning but his leadership, guidance and
support helped me to successfully cope up with studies.
I would like to thank other members of my research committee, Dr. Ireland always asked the
thought provoking questions which improved my ability to think in the realm of genomics in
animal reproduction. Dr. Steibel extended his support in developing my skills in R programming
and genomic analysis. Dr. Erskine was kind to me in dedicating some time to give inputs
regarding the application of my research in dairy industry. I could never have made this far
without their guidance.
I would like to extend my gratitude to my parents Malik Muhammad Nawaz and Mastooran
Nawaz for all their hard work and sacrifices for my education. My biggest motivation to pursue
graduate studies was to make them proud of me. Furthermore, I want to extend my deepest
appreciation to my wife who sacrificed a lot for my graduate studies. She was always with me
through all the stresses of graduate school to support me and ensured that I had the peace of mind
to focus on my studies. I am also thankful to my elder brothers Kashif and Wasif, and sister
Summayya for their exemplary hard work in the pursuit of education and success in life. They
always set high targets for me and created a sense of competition which helped me achieve more
in life. I am greatly thankful to my younger siblings Ayesha, Asif and Areej, for their

iv

unconditional love and support. Whenever I felt down and out, I would talk with them to
replenish my energy.
I want to extend my gratitude to my fellow graduate students Kaitlyn, Ryan, Scott, Deborah,
Yongfang, Sebastian and Chunyu for their help in studies and all the non-academic experiences
we shared. I am also thankful to my roommate Azam and close friends at MSU; Faizan, Ali,
Salman and Hassan who became my extended family in East Lansing. I am also thankful to my
friends in Pakistan Rashid, Usman, Saboor, Burhan, Akhtar, and Sajid who always kept in
contact with me,
Finally, I would like to thank Fulbright Program Pakistan for giving me the opportunity to pursue
graduate studies at MSU. I owe deep gratitude to Institute of International Education (IIE)
Program Officer; Stephanie Sasz and administrative staff at MSU; Barb Sweeny and Steve
Bursian for handling the administrative aspect of my studies.

v

TABLE OF CONTENTS

LIST OF TABLES……………………………………….…………………………….……… viii
LIST OF FIGURES……………………………………………………………………………... ix
CHAPTER ONE…………………………………………………….……………………..…….. 1
Introduction……………………………………………………….………………..…….. 2
LITERATURE CITED………………………………………………...…...................... 10
CHAPTER TWO…………………………………………………………………...……...…… 17
Genome wide association analysis and genomic heritability of anti-Mullerian hormone
in Holstein dairy heifers…………….………………………………..…………………. 18
ABSTRACT...……………………………………………………………………………18
INTRODUCTION……………………………………………………………………….19
MATERIALS AND METHODS………………………………………….…………......21
AMH assay……………………………………………………………….……....21
Genotyping.……...…………………………………………………………….... 22
Statistical analysis.…...…………………………………………………………..22
Number of QTL per peak………………………………………………………...24
Confidence interval of the QTL peak.………………………………………...…24
Proportion of genetic variance explained by the significant region……………..25
Identification of candidate genes………………………………………………...26
RESULTS………………………………………………………………………..............27
Heritability.………………………………………………………………………27
GWA Study.……………………………………………………………………...29
Number of QTLs.………………………………………………………………...32
Confidence interval of the peak association on Chromosome 11.……………….34
Confidence interval of the peak association on Chromosome 20.……………….35
LD between significant SNPs.…………………………………………………...36
Candidate genes.…………………………………………………………………37
DISCUSSION……………………………………………………………………….…...38
CONCLUSION………………………………………………………………………......42
APPENDIX.……………………………………………………………………………...44
LITERATURE CITED…………………………………………………………..…...….50
CHAPTER THREE…………………………………………………………………………...…60
Relationship of Anti-Mullerian Hormone with Reproductive Traits in Holstein
Heifers…………………………………………………………………………............... 61
ABSTRACT.……………………………………………………………………………..61
INTRODUCTION……………………………………………………………………….62
MATERIAL AND METHODS……………………………………………………...…..63
Statistical analysis……………………………………………………...………...64
RESULTS...…………………………………………………………………..……….....66
Probability of conception in embryo recipients.…………………………………66
vi

Probability off conception to first service in artificial insemination recipients …66
Overall probability of conception.……………………………………………….67
Probability of abortion.…………………………………………………………..67
Days open.………………………………………………………………………..68
Age at first calving ……………………………………………………………....68
Number of services per conception.……………………………………………...69
DISCUSSION.………………………………………………………………..…..……...70
CONCLUSION…………………………………………………………………..……....72
OVERALL CONCLUSIONS AND FUTURE DIRECTIONS.…………………………73
LITERATURE CITED.……...……………..…………………………………...…….....75

vii

LIST OF TABLES

Table 2.1: Significant associations from genome wide association analysis of logarithm of AMH
in dairy Holstein heifers.………………………………………………………………………....32
Table 2.2: List of genes identified with in the significant region on chromosome 11 and 20...…45
Table 3.1: Cut off values of quartiles for AMH concentration.………………………………….65
Table 3.2: Least square geometric mean AMH concentrations for different reproductive
events....………………………………………………………………………………………….67
Table 3.3: Relationship of AMH with probability of conception nested within quartiles.………67

viii

LIST OF FIGURES

Figure 2.1: Distribution of serum concentration of AMH in dairy Holstein heifers.……………27
Figure 2.2: Distribution of log transformed serum concentration of AMH in dairy Holstein
heifers.……………………………………………………………………………………………28
Figure 2.3: Distribution of accuracy of predicted genomic breeding values of AMH using 2905
animals and 54519 markers.……………………………………………………………………..29
Figure 2.4: Manhattan plot of –log10 P values verses genomic location for logarithm of serum
concentrations of AMH in dairy Holstein heifers.…...………………………………………..…30
Figure 2.5: Manhattan plot of P values of markers on chromosome 11 for logarithm of serum
concentrations of AMH in dairy Holstein heifers…………………………………………….….31
Figure 2.6: Manhattan plot of P values of markers on chromosome 20 for logarithm of serum
concentrations of AMH in dairy Holstein heifers………………………………………………..31
Figure 2.7: Manhattan plot after using the peak SNP as a fixed effect.….....………….………..33
Figure 2.8: Manhattan plot of markers on chromosome 11. Markers with in the 99% confidence
interval of the most significant SNP are shown in different color.…………………………........34
Figure 2.9: Manhattan plot of markers on chromosome 20. Markers with in the 99% confidence
interval of the most significant SNP are shown in different color……………………………….35
Figure 2.10: LD of all SNPs (n=68) within 99% confidence interval of peak SNP on
chromosome 11 arranged in increasing order of base pair position. The significant marker is
marked in the map………………………………………………………………………..………36
Figure 2.11: LD of all SNPs (n=25) within 99 % confidence interval of peak SNP on
chromosome 20 arranged in increasing order of base pair position. The significant marker is
marked in the map……………………………………………….……………………………….37
Figure 3.1: Plot of number of days open after first lactation and serum AMH concentration.….68
Figure 3.2: Plot of age at first calving and serum AMH concentration.………...…………...…..79

ix

CHAPTER ONE

1

Introduction
Dairy farming is a vital part of the global food system and it plays a key role in sustainability of
rural areas in particular. Efficiency of dairy farming is an important consideration not only for
global food security but also for profitability and hence, the sustainability of dairy farming. Milk
production of individual cows depends on their ability to reproduce as the lactation cycle is
initiated and renewed by pregnancy. Efficiency of reproduction is the primary determinant of
dairy farm profitability because reproductive losses translate into a number of economic losses
due to longer calving interval, shorter herd life, higher replacement cost and higher costs of
veterinary treatment and drugs. The total loss due to reproductive failure equates to about 2% of
the gross production value or 10% of an average dairy farmer's income (Dijkhuizen et al., 1984) .
Therefore, farmers emphasize the optimization of reproductive performance of cattle.
Reproductive efficiency in dairy cattle has been decreasing, at least until 2005 (GarcĂ­a-Ruiz et
al., 2016). It was observed that modern dairy cows have lower first service conception rates
(Butler, 1998; Lucy, 2001), longer intervals to first ovulation (Marion and Gier, 1967; de Vries
and Veerkamp, 2000), higher incidences of anestrus and abnormal luteal phases (de Vries and
Veerkamp, 2000), lower blood progesterone concentrations (Lucy et al., 1998), higher multiple
ovulation and twinning rates (Fricke and Wiltbank, 1999), and greater embryonic losses (Lucy,
2001). The root causes of poor reproductive performance were a variety of physiological and
management factors that had cumulative effects on reproductive efficiency. However, there is
some evidence that a genetic antagonism exists between reproductive traits and milk production
(Macmillan et al., 1996; Dematawewa and Berger, 1998; Hansen, 2000). The aggressive genetic
selection of dairy cattle for increased milk production has been an important contributing factor
to the decreased reproductive efficiency in modern cows. Realizing this situation, cattle breeding
2

schemes around the world have been broadened to include reproduction traits in dairy selection
indices (Miglior et al., 2005). National genetic evaluation of cows for fertility was started in
USA based on daughter pregnancy rates (DPR) (VanRaden et al., 2004). Consequently, an
increase in estimated breeding values (EBV) of cows has been observed for DPR (GarcĂ­a-Ruiz et
al., 2016). However, the rate of gain in EBV of DPR has been slow due to its low heritability
(0.04).
Animal breeding is defined as the selective breeding of animals for economically important
traits. Traditional genetic selection of animals involves extensive phenotype recording of animals
and their relatives to determine their EBV. Animals are selected as parents of next generation
based on their EBV. This typically requires a long generation interval in dairy due to a long
duration of pregnancy and higher age of puberty. There is also a high cost associated with
progeny testing programs to calculate EBVs. Genomic prediction (Meuwissen et al., 2001)
allows the determination of EBV of animals right after birth by using their genotype information.
Genomic prediction assumes that genetic markers or specifically single nucleotide
polymorphisms (SNP) are in linkage disequilibrium (LD) with underlying quantitative trait loci
(QTL), which have an additive effect in expression of the trait. It focuses on estimating the
cumulative effects of markers without testing for their significance. Therefore, we can predict the
breeding values of animals without having to wait for them to mature and exhibit the phenotype.
Rates of genetic gain have doubled by using genomic selection in cattle, in part because of a
decrease in generation interval. However, the rate of genetic gain and the accuracy of genomic
prediction also depends on the genomic heritability of the trait under selection. Heritability refers
to the proportion of phenotypic variance due to genetic differences in the population.
Unfortunately, the heritability of reproductive traits in dairy is very low (Berry et al., 2014).

3

Discovery of novel biomarker traits in genomic selection that have a high heritability and a
relatively high genetic correlation with reproductive traits would increase the rate of genetic gain
in reproductive potential.
While breeding programs use selection approaches based on genetic evaluations to identify
phenotypically superior animals, assisted reproductive technologies (ART) such as artificial
insemination, in vitro embryo production, and embryo transfer (Smidt and Niemann, 1999), are
used to breed cows considered to be genetically superior. Genomic selection and ART are
routinely being used by dairy farmers to improve cattle genetics and reproduction (Garcia et al.,
2013). However, the success of the reproductive technologies depends on the individual
characteristics of the animal. The high variability of animal responses to super-ovulatory
treatments coupled with the high cost of treatments, are limiting factors for widespread use of
these technologies. If some of this variation can be attributable to genetics, genomic selection
may allow an efficient use of ART. Super ovulatory traits (fertilization and blastocyst rate,
ovulation rate, total number of embryos per flush, the number of viable embryos per flush etc.)
are difficult to measure, are highly invasive, and require expertise in ultrasound scanning, ovum
pick up and in vitro fertilization procedure. Endocrine markers of super ovulatory response traits
provide an opportunity to select animals more efficiently for ART.
Anti Mullerian hormone (AMH) is a potentially useful endocrine indicator of reproductive
potential in dairy cattle. In recent studies, the circulating serum concentration of AMH has been
found to be associated with antral follicle count (AFC) (Ireland et al., 2008; Rico et al., 2009;
Batista et al., 2014). AFC in turn, is positively associated with size of ovary, total number of
follicles in ovary, number of oocytes and embryos in response to super ovulation, and higher
gonadotropin and progesterone concentration (Ireland et al., 2011). AMH has been identified as
4

an important marker of super ovulatory response (Monniaux et al., 2010; Souza et al., 2015),invitro embryo production (IVEP) in cattle (Guerreiro et al., 2014; Gamarra et al., 2015; Vernunft
et al., 2015), and dairy herd longevity (Jimenez-Krassel et al., 2015). There is some evidence that
AMH is correlated with fertility traits like maintenance of pregnancy between 30 and 65 days of
pregnancy, and pregnancy rate in cows bred following natural estrus (Ribeiro et al., 2014).
Furthermore, AMH is increasingly being viewed as an indicator of health. Mossa et al., (2013)
showed that calves born to feed restricted mothers had lower AMH concentrations, larger aortic
trunk size and high blood pressure as compared to calves born to control cows. Similarly, low
AMH levels in men have been associated with cardiovascular disease in men (Dennis et al.,
2013).
AMH is originally a growth factor, produced from the granulosa cells of pre-antral and small
antral follicles in ovary and sertoli cells of testes in males. It is a 140-kDa dimeric glycoprotein
hormone which belongs to the transforming growth factor-β (TGF-β) family (Cate et al., 1986).
AMH gene in cow (ENSBTAG00000014955) is located on chromosome 7 and ranges from
22,696,978 bp to 22,699,843 bp on the forward strand. The AMH mRNA and protein exist in the
granulosa cells of preantral and small antral follicles (Hirobe et al., 1992; Durlinger et al., 2001).
In cattle, Rico (2011) showed that AMH is highly expressed in the cumulus cells and the outer
layers of the granulosa cells close to the theca in healthy antral follicles while its expression is
strongly diminished in atretic follicles, except in the cumulus cells of atretic follicles surrounding
the oocyte. Studies involving circulating serum concentrations of AMH have been conducted by
various researchers in dairy animals. These concentrations have been reported to be between 60
to 570 pg/ml in Bos taurus heifers and from 60 to 780 pg/ml in Bos indicus heifers (Batista et al.,
2014; Guerreiro et al., 2014; Ribeiro et al., 2014). Another study has reported that plasma

5

concentration of AMH is higher in Bos taurus indicus (Zebu cattle) compared to Bos taurus
taurus (European cattle) and found that relationship between AMH and reproductive parameters
was found to be significantly greater in Zebu compared to European cattle (Stojsin-Carter et al.,
2016). These results suggest that genetic background of cattle may impact their AMH levels.
AMH was first discovered to play an important role in sex differentiation in the fetal life
(Munsterberg and Lovell-Badge, 1991; Lee et al., 2008). It inhibits formation of Mullerian but
not Wolffian ducts during embryonic development. Wolffian ducts in turn give rise to parts of
the male reproductive system like epididymis and vas deferens. AMH plays an important role in
regulation of ovarian follicle growth and development in adults (Tiftik et al., 2016). AMH also
prevents initial recruitment and the premature depletion of the follicular population in the ovary
(Durlinger et al., 1999) and prevents activation of primordial follicles into the growing follicle
pool (Durlinger et al., 2002). AMH may inhibit follicle stimulating hormone (FSH) induced
follicular growth because AMH reduces sensitivity of follicles to FSH treatments Durlinger
(2001). In the rat ovary, AMH was found to be expressed in pre antral and small antral follicles
while absent in large antral follicles (Baarends et al., 1995). Some follicles inherently show
lower AMH expression than others and these follicles may be more sensitive to FSH and more
prone to be selected by the secondary estrous FSH peak (Visser and Themmen, 2005). As there
is a certain FSH threshold level required for follicles to ovulate, different follicles might have a
different threshold level due to their inherent differences in AMH expression. Therefore, AMH
may play a role in determining which follicles undergo selection and grow and which follicles
undergo atresia.
In the ovaries of domestic animals, AMH remains constant throughout the estrous cycle. A single
blood sample taken on any day of the estrous cycle to measure serum AMH concentration is a
6

reliable phenotypic marker as it is independent of the stage of estrous cycle (Ireland et al., 2011).
The concentration of AMH in 4 to 9 years old Holstein dairy cows did not change over a threemonth period (Rico et al., 2009), and measurements of AMH taken before OPU protocols over a
period of one year were also significantly correlated with each other (Rico et al., 2012). Several
studies in humans also suggest that the concentration of AMH measured through the menstrual
cycle does not show significant fluctuations (La Marca et al., 2004; Tsepelidis et al., 2007).
Although concentrations may remain very constant within a female, female to female variability
does exist. For example, the concentration of AMH varied from 25 to 228 pg/ml (Ireland et al.,
2009) and 6 to 433 pg/ml (Ireland et al., 2011). As AMH appears to be greatly variable across
individuals, and it has been established as an indicator of reproductive potential in animals, it
may be useful for identification of cattle with superior reproductive potential.
Genomic prediction relies on the linkage disequilibrium between the SNP markers and the actual
causative alleles or “Quantitative Trait Loci” (QTL) which control the expression of a
phenotype. As different breeds of animals might have different LD between the markers and
QTL, the marker effects estimated in any breed cannot be used to accurately predict the breeding
values in another breed (Habier et al., 2007). One possible solution to this problem is to use the
causative “Quantitative Trait Nucleotide” (QTN) in selection indexes (Weller and Ron, 2011)
instead of relying on the linkage disequilibrium between markers and QTL. This can also
facilitate the selection for negatively correlated traits. Selection indexes can incorporate
information from markers that have positive QTN effects for one trait but, say, neutral effects for
the negatively correlated trait. Furthermore, identification of QTN can also have
biotechnological significance as well. Gene editing technologies like CRISPR (Clustered
Regularly Interspaced Short Palindromic Repeats) can be used in to alter the genotype of an

7

individual to change its phenotypic performance (Cong; et al., 2013). Finally, by extracting
functional information about the genes located near the QTL can possibly give rise to a handful
of interesting hypotheses on the biological pathways that underlie economically important traits.
Genome wide association (GWA) studies are performed as a first step to help locate QTN. GWA
studies are designed to detect the association between DNA markers and the traits of interest.
Hundreds of GWA analyses have been performed in the last decade on a wide variety of traits in
plants, animals and humans. Some of the putative QTLs identified can be validated by previously
identified QTNs. For example Cole (2009) showed that the largest marker effects for fat
percentage and protein yield were found on BTA14 flanking the diacylglycerol O-acyltransferase
1 (DGAT1) gene (Grisart et al., 2004) and BTA6 flanking the ATP-binding cassette, subfamily
G, member 2 gene (Cohen-Zinder et al., 2005) respectively. Other studies have identified novel
regions of the genome to search for candidate genes. It has led to new discoveries about genes
and pathways involved in common diseases and complex traits (Visscher et al., 2012). For
example, DENND1A was identified as a candidate gene for polycystic ovarian condition in
human females (Welt et al., 2012). Later functional genomic studies revealed increased
expression of DENND1A in theca cells (McAllister et al., 2015). It has been proposed as a
diagnostic criterion for polycystic ovarian disease. Therefore, GWA studies can facilitate basic
research in genetics and genomics by exploiting the functional meaning of the genes underlying
economically important traits.
Therefore, the objectives of the study were to:
1. Estimate the genomic heritability of serum AMH concentration in dairy Holstein heifers
2. Perform a genome wide association analysis to identify the regions of genome
significantly associated with serum AMH concentration
8

3. Study the correlations of serum AMH concentration with reproductive phenotypes in
dairy Holstein heifers

9

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10

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CHAPTER TWO

17

Genome wide association analysis and genomic heritability of anti-Mullerian hormone in
Holstein dairy heifers
ABSTRACT
Anti-Mullerian hormone (AMH) is a growth factor which has an important role in regulation of
ovarian follicle growth. Recent studies have shown a positive association between AMH
concentrations in serum with the number of follicles and oocytes in ovaries (ovarian reserve),
response to superovulation and embryo production, and herd longevity in dairy and beef cattle.
The objectives of this study were to estimate the genomic heritability of AMH and identify
candidate genes associated with AMH production. AMH concentrations were determined in
dairy Holstein heifers. Animals were genotyped using Zoetis 70K SNP Panel. Genotypes were
imputed to standard USDA 60,671 bovine SNP set with 54,519 SNP markers remaining after
standard editing procedures. A linear mixed model was used to model the random effects of
sampling day and genomics on the logarithm of AMH. Results showed that the genomic
heritability (ÂąSEM) of AMH was 0.36Âą0.03. We identified significant associations between
AMH and 11 SNP markers on chromosome 11 and one marker on chromosome 20 based on a
5% false discovery rate. Annotated genes in those regions were identified using the ENSEMBL
genes database v. 88. of the cow genome (v. UMD 3.1). Some of these genes have been
previously reported to be involved in maintenance of cell structure, cell signaling, cell viability,
embryonic development of cardiac, gonadal and brain tissues, embryonic survival, oocyte
competence, cancer suppression and immunity. The high heritability and a potentially positive
association of AMH with some reproductive traits implies that AMH may be used as a biomarker
to improve reproductive potential in dairy cattle.

18

INTRODUCTION
Reproductive efficiency is vitally important for the profitability of a dairy herd. Aggressive
genetic selection of dairy cattle on milk production over the years led to a decrease in genetic
merit for reproductive performance (Veerkamp et al., 2001). Reproductive performance was
included in the breeding goals around the world only a decade ago. (Miglior et al., 2005).
However, the rate of gain in genetic potential observed for reproduction in all these years is slow
(GarcĂ­a-Ruiz et al., 2016) as the heritability estimates of typical reproductive traits of dairy cattle
are low (Berry et al., 2014). Moreover, as reproductive traits are necessarily expressed after
puberty, selection decisions cannot be made early in the life of an animal thereby leading to a
long generation interval. Reliable biomarkers that are highly correlated with reproductive
performance, expressed early in life and are moderately to highly heritable have not been
discovered. Such a discovery will be instrumental to identify cattle with superior reproductive
potential and design breeding programs for faster genetic gain in reproductive efficiency and
hence the profitability of the dairy industry.
Anti-Mullerian hormone (AMH) is considered to be a potentially important biomarker of
reproductive potential of cattle. It is a growth factor, produced from the granulosa cells of ovary
and sertoli cells of testes, and was first discovered to play an important role in sex differentiation
in the fetal life (Lee et al., 2008). In adults, AMH regulates ovarian follicle growth (Tiftik et al.,
2016). Recent studies have shown a positive association between AMH and dairy herd longevity
(Jimenez-Krassel et al., 2015), maintenance of pregnancy, and pregnancy rate in cows bred on
estrus (Ribeiro et al., 2014). It is also positively related with in vivo (Monniaux et al., 2010) and
in vitro (Guerreiro et al., 2014) embryo production performance of dairy cattle. A single blood
sample taken on any day of the estrous cycle to measure serum AMH concentration is a reliable
19

phenotypic marker as it is highly repeatable within an animal and not affected by the stage of
estrous cycle. (Ireland et al., 2011). However, AMH appears to be highly variable across
individuals. Therefore, it can be useful for identification and subsequently genetic selection of
cattle with superior reproductive potential.
Genomic prediction allows one to predict the breeding values of animals using their genomic
information (Meuwissen et al., 2001). It makes use of the single nucleotide polymorphism (SNP)
which is a kind of genetic variation in which a mutation occurs at a single base pair in the deoxy
ribo nucleic acid (DNA). Genomic prediction models typically assume that the SNP markers are
roughly equally spaced and spread across the whole genome are in linkage disequilibrium (nonrandom association of markers, LD) with the underlying quantitative trait loci (QTL) that control
the traits. However, the accuracy of genomic predictions depends on size of the reference
population used to derive the prediction equation, the effective population size (which refers to
the number of animals contributing as parents of the next generation), the number of SNP
markers, the genetic architecture (distribution of the marker effects), and the heritability of the
trait (Daetwyler et al., 2008; Goddard, 2009; Meuwissen, 2009). For lowly heritable traits, large
number of animals with dense marker information are required for accurate genomic estimated
breeding values (GEBV). Genome-Wide Association (GWA) analyses are performed to explore
the genetic architecture of a trait i.e. the key genomic regions associated with a trait and the
distribution of their effects by identifying the single nucleotide polymorphisms (SNP)
significantly associated with the trait. Significant markers from a GWA study may not
necessarily be the causal mutations (QTL) themselves, but can be in high LD with the QTL.
GWA studies often serve as an important first step to identify the causative QTL. Such GWA
studies can suggest candidate genes and formulate new hypothesis for further genetic research.

20

They may also indicate potential drug targets to improve phenotypic performance or cure a
disease condition. Marker effects from a GWA study can also be included in genomic prediction
models that put additional weight on the certain genomic regions to improve prediction
accuracies. However, few GWA studies have been performed for traits associated with dairy
cattle fertility (Berry et al., 2014).
This study aims to estimate the genomic heritability of AMH and conduct a genome wide
association study using the 60k standard USDA SNP panel.
MATERIALS AND METHODS
All experiments involving cattle were approved by the IACUC at Michigan State University.
Holstein heifers (n = 3252, 11-15 months old, located on Green Meadow Farms Inc, Elsie, MI)
were subjected once to two intramuscular injections of PGF2Îą spaced 11 d apart to synchronize
estrous cycles. Heifers were synchronized in groups of 95 to 124 heifers once or twice per month
for a total number of 29 groups. At 96 h after the last PGF2Îą injection, a single tail vein blood
sample was taken from each heifer to measure serum AMH concentration. Blood samples were
taken beginning on 4/18/2014 and ending on 12/4/2015. Follicle hair samples were collected at
these times for genotypic analyses. Freemartins (n=144) were not included in the statistical
analyses.
AMH assay
The commercially available anti-MĂźllerian hormone ELISA serum sample test kit for bovine
(MiniTube of America) was used to measure serum AMH concentrations in duplicate 20 ul
serum samples in cattle per kit instructions. The two-site AMH assay was validated (Ireland et
al., 2008) for use in cattle and does not cross react with other members of the transforming
21

growth factor beta (TGFβ) superfamily including TGFβ, bone morphogenic factor-4 (BMP4),
inhibin or activin (Kevenaar et al., 2006). In addition, serum AMH concentrations for five heifers
in the study were 75, 55, 1117, 436, 208 pg/ml. When samples from these same individuals were
assayed ~12 mo after storage at -80°C, AMH concentrations were 59, 48, 1227, 435, 191 pg/ml,
respectively. These results implied that AMH values are relatively stable during long-term
storage.
Genotyping
A total of 2939 Holstein heifers were genotyped for single nucleotide polymorphisms (SNP)
using the Zoetis 70K SNP Panel. The genotypes were imputed to standard USDA 60,671 bovine
SNP set. SNPs were retained for analysis if they fulfilled the following criteria: missing values <
20%, minor allele frequency (MAF) > 0.05, and pairwise linkage disequilibrium (LD) value of r2
<0.95. The final genotype data set contained 54519 SNPs for 2905 cows.
Statistical analysis
A total of 2914 cows with both genotypes and phenotypes were used for GWA analysis. A linear
mixed model was used to model the random effects of sampling day and genomics on the
logarithm of AMH to estimate the genetic and environmental variance components. The model
was as follows:
𝑦𝑖𝑗 = 𝜇 + 𝑑𝑖 + 𝑎𝑗 + 𝑒𝑖𝑗

(1.1)

Where yij is the vector of phenotype (AMH), 𝜇 is the overall mean, 𝑑𝑖 is the random effect of
the date of sample collection, i= 1,2,…,29, 𝑎𝑗 is the genetic effect of animal j, j=1,2,…,2914,
such that 𝒂 = {𝑎𝑗 } ∼ 𝑁(𝟎, 𝐆𝜎𝑎2 ) and 𝐞 is the random error vector such that 𝐞 = {𝑒𝑖𝑗 } ∼ 𝑁(𝟎,

22

Iσ2). Here G=ZZ` is the genomic relationship matrix between animals with Z being the
standardized matrix of genotypes and obtained by standardizing the matrix of allelic dosages
(M). (VanRaden, 2008)

𝒁=

𝑀𝑖𝑗 − 2𝑝𝑗
√𝑚(2𝑝𝑗 (1 − 𝑝𝑗 )

(1.2)

Note that Mij, element ij of M, indicates the number of copies of the reference allele on SNP
marker j for animal i whereas 𝑝𝑗 is the allelic frequency of the reference allele, and 𝑚 is the
number of markers.
The best linear unbiased predictions were calculated by maximizing the log likelihood of the
model in E.q 1.1. The accuracy of estimated breeding values was calculated by using following
method (Van Vleck, 1993) :

𝑟=

𝜎𝑎2 − 𝑪𝒊𝒊
𝜎𝑎2

(1.3)

Here 𝑟 refers to the accuracy of prediction, 𝑪𝒊𝒊 refers to the ith diagonal element of 𝑪𝒂𝒂 which is
the prediction error variance-covariance matrix or the portion of the inverse of the mixed model
equations that corresponds to animal effects.
Breeding values were linearly transformed to estimate the marker effects as explained by
(GualdrĂłn Duarte et al., 2014)
̂ = 𝒁′ 𝑮−𝟏 𝒂
̂
𝒈

(1.4)

̂ is the estimate of marker effect, and 𝒂
̂ is the best
Here Z and G have been previously defined, 𝒈
linear unbiased prediction (BLUP) of a. The variances of genetic effects were estimated by the
following equations as explained by (GualdrĂłn Duarte et al., 2014):
23

̂ ) = 𝒁′𝑮−𝟏 𝒁𝜎𝑎2 − 𝒁′𝑮−𝟏 𝑪𝒂𝒂 𝑮−𝟏 𝒁
𝑉𝑎𝑟(𝒈

(1.5)

We divided SNP effect estimates by their corresponding standard errors to obtain test statistics as
follows:

𝑆𝑁𝑃𝑗 =

𝑔̂𝑗
√𝑉𝑎𝑟(𝑔̂𝑗 )

(1.6)

where the subscript 𝑗 refers to marker j. Here P values were obtained using the z score test as
follows:
𝑃 − 𝑣𝑎𝑙𝑢𝑒𝑗 = 2(1 − 𝛷 (|𝑆𝑁𝑃𝑗 |))

(1.7)

where 𝛷 denotes the standard normal cumulative distribution function. The false discovery rate
(FDR) (Storey and Tibshirani, 2003) of 5% was used as a significance criteria for multiple tests.
A linkage disequilibrium (LD) heat map was constructed for significant markers.
Number of QTL per peak
To determine the number of QTL (quantitative trait loci) per peak we ran GWA analysis again
by fixing the top most significant peak (CasirĂł et al., 2017). The model thus used was as follows
𝑦𝑖𝑗 = 𝜇 + 𝛽𝑝𝑒𝑎𝑘 + 𝑑𝑖 + 𝑎𝑗 + 𝑒𝑖𝑗

(1.8)

Here all the terms are as described in Eq. 1.1 except 𝛽𝑝𝑒𝑎𝑘 which is the fixed effect of peak
significant SNP.
Confidence interval of the QTL peak
We used cross validation approach to estimate the 99% confidence interval for the peak
significant QTL proposed by Hayes (2013). In this method, we firstly conducted the GWA as

24

described above to get the position of peak significant QTL (p=peak). Then we divided the
dataset into two parts to conduct GWA and get the position of the most significant SNP from
both datasets and calculate the difference between them (CasirĂł et al., 2017). We repeated the
process 300 times to get the standard error of half the difference between two peaks (h) as
follows:

𝑛

1
𝑠𝑒(ℎ̅) = √ ∑(𝑥1𝑖 − 𝑥2𝑖 )2
4𝑛

(1.9)

𝑖

Here 𝑠𝑒(ℎ̅)is the standard error of half the distance between peak significant SNPs of the two
split datasets, n is the number of data splits (in this case 300), x1i and x2i are the position of the
peak significant SNP of the first and second data set respectively in the ith data split. Assuming
symmetric distribution of the peak significant SNP after n splits of data, we calculated the 99%
confidence interval of the peak SNP as follows:
𝐶𝐼 = 𝑝 ± 𝑧99.5 𝑠𝑒(𝑥̅ )

(1.10)

Proportion of genetic variance explained by the significant region
The proportion of genetic variance explained by the significant region was calculated by fitting a
model that contains two genetic effects: the genetic effect of the 99% confidence interval of peak
significant SNP and the genetic effect of the rest of SNPs.
𝑦𝑖𝑗 = 𝜇 + 𝑑𝑖 + 𝑎𝑝𝑒𝑎𝑘,𝑗 + 𝑎−𝑝𝑒𝑎𝑘,𝑗 + 𝑒𝑖𝑗

(1.11)

Here 𝜇 is the overall mean 𝑑𝑖 is the effect of ith date of blood collection, 𝑒𝑖𝑗𝑘 is the residual,𝑎𝑝𝑒𝑎𝑘
and 𝑎−𝑝𝑒𝑎𝑘,𝑗 correspond to genetic effects of the 99% confidence interval of peak significant

25

SNP marker and the genetic effects of the rest of the SNP markers respectively. The proportion
of genetic variance can be calculated by:
𝑉𝑎𝑟(𝑎𝑝𝑒𝑎𝑘,𝑗 )
𝑉𝑎𝑟(𝑎𝑝𝑒𝑎𝑘,𝑗 ) + 𝑉𝑎𝑟(𝑎−𝑝𝑒𝑎𝑘,𝑗 )

(1.12)

Where 𝑉𝑎𝑟(𝑎𝑝𝑒𝑎𝑘,𝑗 ) is the estimated variance component associated with the significant
genomic region and 𝑉𝑎𝑟(𝑎−𝑝𝑒𝑎𝑘,𝑗 )is the estimated variance component associated with the rest
of genome. A likelihood ratio test was used to test the significance of identified region by
comparing the likelihood of model in Eq 1.11 to a null model that does not contain 𝑎𝑝𝑒𝑎𝑘 as
shown below:
𝑦𝑖𝑗𝑘 = 𝜇 + 𝑑𝑖 + 𝑎𝑝𝑒𝑎𝑘 + 𝑎𝑘 + 𝑒𝑖𝑗𝑘

(1.13)

The variance components were estimated by restricted maximum likelihood method using the R
package regress (https://cran.r-project.org/web/packages/regress/regress.pdf). GWA analysis was
done using gwaR (https://github.com/steibelj/gwaR) package in R. All analysis was done using R
version 3.2.0 (http://www.R-project.org/).
Identification of candidate genes
Firstly, a genomic region on chromosome 11 corresponding to 99% confidence interval of the
peak significant SNP was identified as described above. Annotated genes within that region were
identified from ENSEMBL genes database v. 88. of the cow genome (v. UMD 3.1) using
“GenomicRanges” package in R (Lawrence et al., 2013).

26

RESULTS
Heritability
Serum concentrations of AMH in the dairy heifers had a phenotypic mean of 438.50 pg/ml and a
standard deviation of 604.33 pg/ml. (Figure 1.1) The median AMH concentration was 333.3
pg/ml and it ranged from 2 pg/ml to 14350 pg/ml. The distribution of AMH concentration and
log transformed AMH concentrations is also given below which shows an approximate normal
distribution.

Figure 2.1: Distribution of serum concentration of AMH in dairy Holstein heifers

27

Figure 2.2: Distribution of log transformed serum concentration of AMH in dairy Holstein
heifers

The variance associated with random day of blood sample collection was 0.015 Âą 0.006. The
estimated genetic variance associated with AMH was 0.28 Âą 0.003 and the residual variance was
0.48 Âą 0.020. Finally, the heritability of AMH was 0.36 with a standard error of 0.03.
We calculated the accuracy of the breeding values in our study by inverse of the set of linear
model equations (the coefficient matrix). The average accuracy of prediction of breeding values
in our study using 2905 animals and 54519 markers was 55.7%. The distribution of prediction
accuracies is given below in figure 2.3.

28

Figure 2.3: Distribution of accuracy of predicted genomic breeding values of AMH using 2905
animals and 54519 markers

GWA Study
The genome wide association analysis determined 11 significant SNPs on chromosome 11 and 1
SNP on chromosome 20 (Figure 2.4) based on a 5% false discovery rate (FDR). SNP marker
“Hapmap41435-BTA-115556” on chromosome 11 (pos= 95026013 bp) turned out to be the peak
significant SNP.

29

Figure 2.4: Manhattan plot of –log10 P values versus genomic location for logarithm of serum
concentrations of AMH in dairy Holstein heifers

30

Figure 2.5: Manhattan plot of P values of markers on chromosome 11 for logarithm of serum
concentrations of AMH in dairy Holstein heifers

Figure 2.6: Manhattan plot of P values of markers on chromosome 20 for logarithm of serum
concentrations of AMH in dairy Holstein heifers

31

Table 2.1: Significant associations from genome wide association analysis of logarithm of AMH
in dairy Holstein heifers
SNP ID

Ch
r

Position
(Mbp)

P-value

q-value

Effect

BTA-17666-no-rs

11

92.05

1.59x10-6

0.007

-

Minor
Allele
Frequency
0.18

ARS-BFGL-NGS-118517

11

93.546

1.29 x10-6

0.007

+

0.47

Hapmap38572-BTA-115523

11

94.071

5.10 x10-7

0.003

-

0.22

BTA-115525-no-rs

11

94.101

7.77 x10-9

0.0001

-

0.20

BovineHD1100027436

11

94.202

2.00 x10-6

0.008

-

0.22

Hapmap46766-BTA-115526

11

94.446

7.92 x10-7

0.005

-

0.22

Hapmap41435-BTA-115556

11

95.026

5.94 x10-9

0.0001

-

0.18

ARS-BFGL-NGS-12334

11

95.516

4.65 x10-7

0.00386

+

0.37

Hapmap53866-rs29019867

11

95.568

1.40 x10-8

0.00018

-

0.14

Hapmap47514-BTA-115564

11

96.074

7.87 x10-9

0.00013

-

0.16

ARS-BFGL-NGS-114094

11

99.413

2.63 x10-7

0.00279

-

0.10

ARS-BFGL-NGS-110286

20

25.689

1.64 x10-6

0.00794

+

0.46

Number of QTLs
When the peak SNP (Hapmap41435-BTA-115556) was fixed in the model, none of the
remaining SNP markers reached the level of statistical significance (5% FDR) (Figure 2.7)
suggesting that all the significant SNP markers are in LD with a single QTL.

32

Figure 2.7: Manhattan plot after using the peak SNP as a fixed effect
We noticed that the only SNP on chr 20 (ARS-BFGL-NGS-110286) which was previously
significant (q=0.007) and had a low correlation with the peak, was no longer significant
(q=0.085) by fixing the peak SNP (Hapmap41435-BTA-115556). Although the P-value of that
SNP did not increase (p=1.56 x10-6-1.65x10-6), it did not reach the level of significance because
the new FDR was increased due to the loss of 11 highly significant SNPs on chr 11 having very
low (~10-9) P-values. This implies that ARS-BFGL-NGS-110286 on chr 20 is moderately
associated with AMH expression.

33

Confidence interval of the peak association on Chromosome 11
The 99% confidence interval of the peak significant SNP ranged from 92879023 bp to 97160473
bp on chromosome 11, a span of roughly 4.28MB. This region contained 68 SNPs and accounted
for 7.9% of the genetic variance. When a likelihood ratio test was used to test whether this region
accounted for a significant genetic variance, the test was highly significant (p=1.7x10-7). This
means that the 99% confidence interval of the peak significant SNP accounted for a significant
portion of the genetic variance.

Figure 2.8: Manhattan plot of markers on chromosome 11. Markers with in the 99% confidence
interval of the most significant SNP are shown in different color.

34

Confidence interval of the peak association on Chromosome 20
The 99% confidence interval of the peak significant marker on chromosome 20 (ARS-BFGLNGS-110286) ranged from 25002762 bp and 26362752 bp, a span of roughly 1.35 MB. This
region contained 25 SNPs and accounted for 5.25% of the genetic variance. When a likelihood
ratio test was used to test whether this region accounted for a significant genetic variance, the
test was significant (p=0.007). This means that the 99% confidence interval of the peak
significant SNP on chromosome 20 accounted for a significant portion of the genetic variance.
Therefore, the joint variance explained by both the regions simultaneously is 13.25 %.

Figure 2.9: Manhattan plot of markers on chromosome 20. Markers with in the 99% confidence
interval of the most significant SNP are shown in different color.

35

LD between significant SNPs
The LD heat map of all the SNPs with in the 99% confidence interval of the peak significant
SNP on chromosome 11 and 20 indicated that there is moderate linkage disequilibrium between
most of the SNP markers in that region. However, some SNPs were found to be highly correlated
with each other with a LD (r2) value as high as 0.99) The heat map is shown below

Figure 2.10: LD of all SNPs (n=68) within 99% confidence interval of peak SNP on
chromosome 11arranged in increasing order of base pair position. The significant markers are
marked in the map.

36

Figure 2.11: LD of all SNPs (n=25) within 99% confidence interval of peak SNP on
chromosome 20 arranged in increasing order of base pair position. The significant marker is
marked in the map.

Candidate genes
The significant regions on chromosome 11 (92879023 bp - 97160473 bp) and 20 (25002762 bp
and 26362752 bp) were searched for candidate genes associated with AMH. Gene annotation
information was retrieved from Ensembl genes database v. 88. of the cow genome (v. UMD 3.1).
The region identified on chromosome 11 contained 81 genes. The most significant SNP was
found within gene DENND1A. The region identified on chromosome 20 contained 9 genes.
These genes perform a wide range of functions including maintenance of cell structure, cell

37

signaling, cell viability, embryonic development of cardiac, gonadal and brain tissues, embryonic
survival, oocyte competence, cancer suppression and immunity. All the candidate genes and their
functions have been listed in the appendix
DISCUSSION
The genomic heritability of AMH has not been reported for any species. Moreover, the genomic
heritability of AMH reported in our study (0.36 Âą0.03) is the highest for any trait related to
reproduction in female cattle. Since endocrine reproductive traits directly reflect a cow’s
reproductive physiology and are less likely to be influenced by farm management (Bulman and
Lamming, 1978) , Darwash et al., 1999), the heritability of endocrine reproductive traits is
expected to be higher (Veerkamp et al., 2000) (Petersson et al., 2007) (Tenghe et al., 2015)
relative to direct fertility traits as defined from calving data and insemination records.
The moderate to high heritability of AMH along with a potentially positive association of AMH
with fertility in cattle indicates that AMH might be helpful in accurate predictions of breeding
values for genetic potential of reproduction. The accuracy of genomic predictions depends on a
number of factors including size of the reference population, density of markers, effective
population size and most importantly the heritability of the trait under selection. We calculated
the accuracy of the breeding values in our study by inverse of the set of linear model equations
(the coefficient matrix). The average accuracy of prediction of breeding values in our study using
2905 animals and 54519 markers was 55.7%. Interestingly, the mean accuracy matches what
would be expected based on a reference population of 3000 animals and a trait heritability of
0.35 (Hayes et al., 2009).

38

A number of genome wide association studies have been conducted in the past on a plethora of
reproductive traits in dairy and beef cattle. Most of the GWA studies on fertility traits suffer
from inadequate statistical power partly due to low heritabilities (Berry et al., 2014).
Nonetheless, a number of QTLs have been identified for fertility traits in cattle (Tenghe et al.,
2016) (Pryce et al., 2010) (HĂśglund et al., 2009) (Berry et al., 2012). However, when it comes to
AMH, no GWA study has been reported in any livestock species. Perry et al., (2016) found 3
SNP variants associated with log serum AMH in human males which surprisingly had a sex
interaction effect i.e. the same SNPs did not appear significant in females. As data in our study
consists of female heifers, there is every possibility that a sex interaction effect exists for serum
AMH levels in cattle as well. More research is needed in this regard to do a GWA study on a
population that includes bulls and cows.
A review of GWA studies in various species revealed that the proportion of genetic variation in
complex traits explained by major genes is usually <10% (Visscher et al., 2012). The proportion
of genetic variance explained by significant regions mentioned earlier in this study is 13.3%.
High density sequencing of the region on Chromosome 11 and 20 might help in identifying the
causative QTL. Genes identified from the cow reference genome with in the significant region
identified by the 99% confidence interval of the peak significant SNP display a wide range of
functions i.e. maintenance of cell structure, cell signaling, cell viability, embryonic development
of cardiac, gonadal and brain tissues, embryonic survival, tumor suppression, tumor
proliferation, chemo resistance and immunity. Other genes identified in this region belong to
olfactory signaling system or micro RNAs. Some of the important candidate genes identified are
mentioned below:

39

The most significant SNP (Hapmap41435-BTA-115556 at 95026013 bp on chr 11) for AMH
was located within the DENND1A gene (94526960 bp – 95055931 bp). Welt (2012) found that
the most significant SNP for polycystic ovary (PCOS) in European and Chinese human females
was also found within the same gene. Females suffering from this condition generally have
abnormally high AMH concentrations (Pigny et al., 2003). Durlinger (2001) showed that AMH
affects sensitivity of follicles to FSH treatments. Since there is a threshold level of FSH required
for follicular growth, different follicles might show different thresholds depending the amount of
AMH expressed in them. Therefore, AMH may play a role in the determining which follicles
undergo selection and which are removed through atresia.
The gene NR5A1 gene (95514365-95538847)) encodes SF-1 (Steroidogenic factor 1) protein
which binds to two different binding sites at the promoter region of AMH gene (Giuili et al.,
1997; Watanabe et al., 2000). SF-1 binding site to AMH promoter is essential for sex and cell
specific AMH promoter activity (Giuili et al., 1997) and transcription of key genes involved in
sexual development and reproduction. Therefore, our analysis can be validated by previously
published research findings. Similarly, the NR6A1 gene which is an oocyte specific transcription
repressor also belongs to the same family and plays an important role in embryo development
and folliculogenesis. NR6A1 is expressed in unfertilized eggs and preimplantation embryos. In
adults, NR6A1 is expressed in the oocytes of primary follicles and all subsequent stages of
folliculogenesis.
The PTGS1gene (93219287-93245045) has been identified as a candidate gene in a recent GWA
study for superovulatory traits (total number of embryos and the number of viable embryos)
(Jaton et al., 2015). This study showed that 81% of the significant SNP markers (46/57) for the
total number of embryos, were located on Chromosome 11. Jaton et al. (2015) also determined
40

that for the number of viable embryos, 46 out of 47 significant SNPs associated with that trait
were located on chromosome 11. All the significant SNP markers on chromosome 11 in that
study were located within the region identified in our study (92879023 bp to 97160473 bp on chr
11). The most significant SNP in that analysis (BovineHD1100027188; 93306002 bp) was
located nearby the (60,957 bases) prostaglandin-endoperoxide synthase 1 (PTGS1) gene. This
gene is responsible for the conversion of arachidonic acid into different form of prostaglandins
such as PGE2 and PGF2Îą (Arosh et al., 2002). Prostaglandins are well known to play an
important role in ovulation (Armstrong, 1981) and are routinely used by veterinarians to treat
ovarian cysts in dairy. As AMH is identified as an important marker of superovulation response
(Monniaux et al., 2010; Souza et al., 2015), these findings suggest that AMH might have a QTL
in common with super ovulatory traits.
The NADH dehydrogenase (ubiquinone) 1 alpha subcomplex (NDUFA8) (93011815-93029730)
transfers electrons with a high redox potential from NADH to ubiquinone. This gene is highly
expressed in human heart, skeletal muscle, and fetal heart (Triepels et al., 1998). A meta-analysis
of studies in humans indicates that patients with PCOS have a 2-fold higher risk of cardiac
disease than normal subjects (de Groot et al., 2011). More recently, AMH has been inversely
correlated with the ultrasonographic diameters of the distal and mid-infrarenal aorta and high
AMH levels have been associated with the absence of cardiovascular disease in men (Dennis et
al., 2013).
The Follistatin gene (FST) (25588642-25594057) encodes for Follistatin protein which binds to
members of the TGF-β superfamily particularly activin hormone. It was first discovered in the
follicular fluid and known to regulate the follicle stimulating hormone (FSH ) secretion from
pituitary gland (Ying, 1988). Follistatin and activin interaction is widely known as the
41

autocrine/paracrine regulator of various physiological processes in reproduction including
folliculogenesis and maturation of oocytes (Muttukrishna et al., 2004). Furthermore, increased
abundance of Follistatin mRNA transcript was found to be positively associated with oocyte
competence to fertilize and develop to blastocyst stage (Patel et al., 2007) and bovine early
embryogenesis (Lee et al., 2009). Association of AMH with SNP markers adjacent (95027 bp
apart) to FST gene indicates there might be a genetic relationship between the two hormones.
CONCLUSION
We have estimated the genomic heritability of AMH in cattle using an animal effects model
based on their genotype information using 60 k standard USDA SNP panel. The higher
heritability of AMH (0.36 Âą 0.02) compared to other reproductive traits and its positive
association with fertility can help accurately predict the breeding values of animal for fertility
traits. Therefore, it can possibly be used for genetic improvement of reproductive potential in
cattle. We also performed a GWA study to identify the QTL involved in AMH regulation. The
study resulted in identification of a ~4.28 MB window on chromosome 11 and 1.35 MB window
on chromosome 20 of bovine genome that might contain causative QTL of AMH regulation. We
also identified potential candidate genes that appeared to have a key role in AMH expression
based on previous research. Some of these genes have been previously reported to be involved in
maintenance of cell structure, cell signaling, cell viability, embryonic development of cardiac,
gonadal and brain tissues, embryonic survival, oocyte competence, cancer suppression and
immunity. This might give us an insight to the mechanisms involved in AMH regulation and
endocrine role of AMH in reproduction and development. Furthermore, this same region on
Chromosome 11 has been recently identified as being associated with super ovulatory response

42

in an independent population indirectly indicating that some of these candidate genes mediate a
role between AMH and super ovulatory response.

43

APPENDIX

44

Table 2.2: List of genes identified with in the significant region on chromosome 11 and 20
ENSEMBLE Gene ID

Gene Name

Start position

End position

ENSBTAG00000012827 TTLL11

92737269

92903570

ENSBTAG00000004295 NDUFA8

93011815

93029730

ENSBTAG00000004296 MORN5

93029874

93064357

ENSBTAG00000005525 LHX6

93069014

93090242

ENSBTAG00000016367 RBM18

93101473

93121906

ENSBTAG00000047226 MRRF

93121981

93138808

ENSBTAG00000018426 MRRF

93149379

93158576

ENSBTAG00000006716 PTGS1

93219287

93245045

ENSBTAG00000047610

93268704

93269738

ENSBTAG00000047693

93290982

93291917

ENSBTAG00000046018

93304997

93305947

ENSBTAG00000046137

93313595

93314530

ENSBTAG00000038309

93339271

93340212

ENSBTAG00000038796

93346999

93347931

ENSBTAG00000037739

93401560

93402516

ENSBTAG00000038278

93409030

93409962

ENSBTAG00000039225

93459896

93460798

ENSBTAG00000038726

93563425

93564411

ENSBTAG00000037542

93584334

93585269

ENSBTAG00000000726 OR1J1

93593230

93594171

45

Table 2.2 (cont’d)
ENSBTAG00000046126 OR1J2

93609988

93610929

ENSBTAG00000040047

93617052

93618040

ENSBTAG00000037767 OR1N1

93638723

93639658

ENSBTAG00000047728

93646064

93647008

ENSBTAG00000047112

93655028

93655969

ENSBTAG00000046536

93670180

93671118

ENSBTAG00000045527

93678033

93678974

ENSBTAG00000045545

93691882

93692865

ENSBTAG00000020660

93702914

93703858

ENSBTAG00000038551 OR1N2

93731042

93731977

ENSBTAG00000046221 OR1Q1

93742434

93743378

ENSBTAG00000045528

93765538

93766494

ENSBTAG00000001549 OR1L1

93797496

93798488

ENSBTAG00000048218 OR1L3

93808260

93809237

ENSBTAG00000038822

93835432

93836470

ENSBTAG00000038941

93853095

93854133

ENSBTAG00000037822

93866953

93868392

ENSBTAG00000038665

93885139

93886080

ENSBTAG00000004667

93900519

93901442

ENSBTAG00000030678

93927186

93927978

ENSBTAG00000030677

93935690

93936631

ENSBTAG00000038249 OR5C1

93960722

93961672

46

Table 2.2 (cont’d)
ENSBTAG00000030676 OR1K1

93969256

93970206

ENSBTAG00000030675 PDCL

93984123

93991685

ENSBTAG00000023867 RC3H2

94010161

94058169

ENSBTAG00000042329 SNORD90

94037106

94037216

ENSBTAG00000039815 ZBTB6

94072877

94074965

ENSBTAG00000038610 ZBTB26

94081312

94082670

ENSBTAG00000039172 RABGAP1

94103009

94264248

ENSBTAG00000040296 GPR21

94188119

94190302

ENSBTAG00000024604

94221939

94319233

ENSBTAG00000021921 STRBP

94281217

94418835

ENSBTAG00000042085 U6

94384741

94384847

ENSBTAG00000014391 CRB2

94501988

94523379

ENSBTAG00000003610 DENND1A

94526960

95055931

ENSBTAG00000044885 SNORA25

94574530

94574657

ENSBTAG00000023860

94931694

94932632

ENSBTAG00000010990 LHX2

95125308

95145048

ENSBTAG00000019470 NEK6

95314593

95400813

ENSBTAG00000003067 PSMB7

95401692

95458914

ENSBTAG00000017576 ADGRD2

95484105

95508348

ENSBTAG00000009017 NR5A1

95514365

95538847

ENSBTAG00000040585 NR6A1

95551663

95586813

ENSBTAG00000029841 bta-mir-181a-2

95709411

95709520

47

Table 2.2 (cont’d)
ENSBTAG00000029896 bta-mir-181b-2

95710626

95710714

ENSBTAG00000004848 OLFML2A

95780659

95805530

ENSBTAG00000044811 U6

95816939

95817043

ENSBTAG00000039223 WDR38

95846030

95850595

ENSBTAG00000003205 RPL35

95850116

95854734

ENSBTAG00000015278 ARPC5L

95859894

95867086

ENSBTAG00000015286 GOLGA1

95869270

95911957

ENSBTAG00000015553 SCAI

95925113

95996116

ENSBTAG00000045071 SNORA64

95982801

95982910

ENSBTAG00000008033 PPP6C

96044189

96085315

ENSBTAG00000015098 RABEPK

96092486

96113995

ENSBTAG00000007662 GRP78

96115572

96119306

ENSBTAG00000011544 GAPVD1

96144147

96208779

ENSBTAG00000043246 U6

96242761

96242864

ENSBTAG00000048297 U5

96264484

96264596

ENSBTAG00000010271 MAPKAP1

96278654

96511560

ENSBTAG00000013314 PBX3

96719400

96772227

ENSBTAG00000019834 ARL15

24955390

25026808

ENSBTAG00000003728 NDUFS4

25407689

25523382

ENSBTAG00000003329 FST

25588642

25594057

ENSBTAG00000046997 NA

25631428

25632999

ENSBTAG00000005380 MOCS2

25965175

25975522

48

Table 2.2 (cont’d)
ENSBTAG00000019289 ITGA2

25984555

26089593

ENSBTAG00000016525 ITGA1

26116747

26227530

ENSBTAG00000046575 NA

26174067

26174830

ENSBTAG00000003268 PELO

26278617

26280571

49

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follicle-stimulating hormone. Endocr. Rev. 9:267–293. doi:10.1210/edrv-9-2-267.

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CHAPTER THREE

60

Relationship of Anti MÂľllerian Hormone with Reproductive Traits in Holstein Heifers
ABSTRACT
AMH is an endocrine marker of reproductive potential in various species of animals.
Superovulation response and some traits of reproductive performance in dairy cows like
conception rate and herd longevity are positively correlated with AMH concentration. The
objective of this study was to look at the relationship of dairy heifer fertility with AMH
concentrations using 3071 Holstein dairy heifers. Measures of reproductive performance
analyzed were: first service conception rate of animals subjected to artificial insemination via
regular or sexed semen, conception rate following embryo transfer, overall probability of
conception, probability of abortion, number of services per conception, age at first calving and
number of days open at first lactation. Results indicated that the probability of conception has a
moderately significant (P=0.03) linear association with AMH concentration in animals within the
first quartile of AMH, suggesting a possible threshold effect of AMH. However, no phenotypic
relationship was found between AMH and the other measures of reproductive performance in
heifers. Future studies will evaluate whether AMH concentration is correlated with measures of
reproductive performance in later stages of productive life when animals are more prone to
reproductive problems.

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INTRODUCTION
Efficiency of reproduction is one of the primary determinants of dairy farm profitability.
Reproductive losses on a dairy farm translate into a number of short term and long term
economic losses such as longer calving intervals, shorter herd life, higher replacement costs,
higher costs of veterinary treatment and drugs, etc. Total losses due to reproductive failure equals
about 2% of the gross production value or 10% of an average farmer's income (Dijkhuizen et al.,
1984). Therefore, in addition to optimizing reproductive management of animals on the farm,
identification and selection of animals with higher genetic potential for reproduction and
successful application of assisted reproductive technologies (ART) are equally important to
maximize fertility. However, the rate of genetic gain depends on the heritability of reproductive
traits while the success of ART depends on successful identification of animals with superior
genetic potential for super ovulatory traits.
AMH is a novel endocrine indicator of reproductive potential in animals. We have established
AMH concentration in Holstein heifers (n = 2905) is highly heritable compared with other
reproductive traits (h=0.36Âą0.02). AMH has already been reported to be positively associated
with antral follicle count (AFC) (Ireland et al., 2008; Rico et al., 2009; Batista et al., 2014),
superovulation response (Monniaux et al., 2010; Souza et al., 2015), in-vitro embryo production
(IVEP) (Guerreiro et al., 2014; Gamarra et al., 2015; Vernunft et al., 2015), dairy herd longevity
(Jimenez-Krassel et al., 2015), maintenance of pregnancy, and pregnancy rate in cows bred on
estrus (Ribeiro et al., 2014). Moreover, AFC which is highly positively correlated with AMH
concentration, is also positively correlated with number of morphologically healthy oocytes,
progesterone and testosterone production during estrous cycles, responsiveness of granulosa and
thecal cells to gonadotropins in vitro and responsiveness to superovulation. Taken together,
62

circulating AMH concentration has the potential to be a unique phenotypic marker that may be
useful to genetically improve reproduction in dairy cattle. The present study used a large number
of Holstein heifers (n = 3259) to test the hypothesis that AMH concentration is positively
correlated with fertility. To test this hypothesis, we examined the association of AMH
concentration with the following reproductive traits:
•

Probability of conception to first service

•

Probability of conception in embryo recipients

•

Probability of conception after multiple services

•

Number of services per conception

•

Probability of abortion

•

Days open in first lactation animals

•

Age at first calving

•

Differences in AMH concentrations between pregnant and non-pregnant animals
MATERIALS AND METHODS
All experiments involving cattle were approved by the IACUC at Michigan State

University. Adult Holstein heifers (n = 3259, 11-15 months old, located on Green Meadow
Farms Inc, Elsie, MI) were subjected once to two intramuscular injections of PGF2Îą spaced 11 d
apart to synchronize estrous cycles. Heifers were synchronized in groups of 95 to 124 heifers
once or twice per month for a total number of 29 groups. At 96 h after the last PGF2Îą injection, a
single tail vein blood sample was taken from each heifer to measure serum AMH concentration
and establish relative size of the ovarian reserve. Blood samples were taken beginning on
4/18/2014 and ending on 12/4/2015. Follicle hair samples were collected at this times for

63

genotypic analyses. Animals that were considered freemartins (n=144) and animals that were
sold as replacements to other farmers (n=243) were not included in statistical analyses of
reproductive performance as lactating animals. After completion of the PGF2Îą -induced estrous
cycle, heifers were subjected to artificial insemination (AI) at the next detected “standing” estrus
or the next morning after standing estrus, and palpation of uterine contents 45 to 60 days after AI
was used to diagnose pregnancy. Heifers not diagnosed pregnant after AI were subjected to AI,
as just explained (except PGF2Îą was not used), up to 5 to 6 times. After calving, and starting at
45 DIM, cows were subjected to AI at standing estrus after either of two injections of PGF2Îą or at
fixed-time AI (using Ovsynch technology) at discretion of farm personnel and pregnancy
determined 35 d after AI (via palpation or ultrasonography). Cows exhibiting estrous behavior
before pregnancy diagnosis were inseminated at standing estrus while cows diagnosed not
pregnant were given a single PGF2Îą injection if a corpus luteum was present and inseminated at
the next standing estrus. Cows without CL were given two PGF2Îą injections spaced 11 d apart as
explained for heifers. This breeding regimen in cows continued up to 4 to 5 times.
Records on reproductive performance, level of milk production, health, and reasons for culling of
each individual animal at Green Meadow Farms were maintained in an on-the-farm computer
using the commercial DairyCOMP 305 software program. Relevant information for each cow
was recorded daily into DairyCOMP 305 by Green Meadow Farms’ managers and selected
employees.
Statistical analysis
Logistic regression was used with each model including a linear coefficient on serum
concentration of AMH to assess the effect of AMH on first service conception rate of animals
subjected to AI via regular or sexed semen, conception rate in embryo recipients, overall
64

conception rate and abortion rate. The same phenotypes were also examined by using AMH
quartiles as a factor with four levels followed by using nesting linear coefficients on AMH
within each quartile separately. This was pursued to allow for the possibility of threshold
effects; i.e., a linear relationship with AMH may only exist before or beyond a certain quartile.
Quartile means were separated using the Tukey test. The cutoff values for AMH quartiles are
given below:
Table 3.1: Cut off values of quartiles for AMH concentration

Cut off values

Quartile 1
(pg/ml)
<198.96

Quartile 2
(pg/ml)
>198.96 and <323.17

Quartile 3
(pg/ml)
>323.17 and <502.88

Quartile 4
(pg/ml)
>502.88

Furthermore, we examined differences in AMH concentrations for binary traits i.e. pregnant vs
non-pregnant on first service for each of regular semen, sexed semen and embryo transfer
recipients, pregnant vs non-pregnant for all animals that were bred at least once, and aborted vs
non-aborted animals using a one-way ANOVA with AMH concentration as a dependent
variable.
A Poisson regression model was used to study the effect of AMH concentration on the number of
services per conception. In a similar manner with the other traits, we also used AMH quartiles
and with separate linear coefficients on AMH within each quartile in separate analysis to model
the number of services per conception.
A simple linear regression model was also used to study the effect of AMH on age at first
calving and days open in the first lactation.

65

All analyses were done using the ‘glm’ function for Poisson and logistic regression and the ‘lm’
function for linear regression in R programming software version 3.3.1.
RESULTS
Probability of conception in embryo recipients
No overall relationship (P>0.3) between linear AMH concentration and probability of conception
was determined for embryo recipients using a classical logistic regression analysis. There was a
marginally significant relationship (P=0.03) between probability of conception in embryo
recipients and their AMH concentration with in the first quartile. However, no such relationship
was found for animals within the 2nd, 3rd and 4th quartiles. There was no significant difference in
conception rates between the quartiles separated using the Tukey test. Least squares mean AMH
concentration of animals that were successfully pregnant (336.97Âą11.76) on embryo transfer
were not significantly different from animals that failed to become pregnant (314.19Âą12.57).
Probability of conception to first service in artificial insemination recipients
No relationship was found between linear AMH concentration and probability of conception in
AI recipients using regular semen (P>0.9) and sexed semen (P>0.7). There was no significant
difference of probability of conception for regular and sexed semen recipients between the
quartiles separated using Tukey HSD.Least squares mean AMH concentration of animals that
were successfully pregnant to first service using regular AI semen was 293.67Âą9.38verses
298.74Âą10.08for non-pregnant animals respectively.
Least squares mean AMH concentration of animals that were successfully pregnant to first
service using sexed semen was 309.55Âą14.05verses 305.40Âą13.43for non-pregnant animals
respectively.
66

Table 3.2: Least square geometric mean AMH concentrations for different reproductive events
Event

Yes

No

P-value

Conception to Embryo transfer

336.97Âą11.76

314.19Âą12.57

0.21

Conception on first AI (regular semen)

293.67Âą9.38

298.74Âą10.08

0.71

Conception on first AI (sexed semen)

309.55Âą14.05

305.40Âą13.43

0.83

Conceived at least once

307.74Âą4.83

293.07Âą24.25

0.56

Abortion

307.54Âą21.76

307.68Âą4.95

0.99

Table 3.3: Relationship of AMH with probability of conception nested within quartiles
Event

Logistic regression P-values

Conception to Embryo
transfer
Conception on first AI
(regular semen)
Conception on first AI
(sexed semen)

Quartile 1
0.03
n=221
0.06
n=378
0.85
n=169

Quartile 2
0.13
n=226
0.757
n=378
0.89
n=164

Quartile 3
0.10
n=221
0.203
N=391
0.84
n=155

Quartile 4
0.30
n=256
0.89
n=343
0.69
n=169

Overall probability of conception
No relationship (P>0.4) was found between probability of conception and linked linear function
of AMH concentration. 2964 animals were pregnant out of 3072 animals that were bred at least
once. Least squares mean AMH concentration in animals that conceived (307.74Âą4.83) verses
those who never conceived (293.07Âą24.25) were not different from each other.
Probability of abortion
No relationship (P>0.5) was found between probability of abortion and linked linear function of
AMH concentration. 146 animals aborted out of 2964 that were confirmed pregnant. Least
67

squares mean AMH concentration in aborted (307.54Âą21.76) verses non aborted (307.68Âą4.95)
were not different from each other.
Days open
Number of days open after first lactation had a near-zero correlation with AMH (r=-0.02) such
that there was no statistically significant relationship (P>0.39) between them.

Figure 3.1: Plot of number of days open after first lactation and serum AMH concentration
Age at first calving
Age at first calving had a near-zero correlation with AMH (r=-0.004) such that there was no
statistically significant relationship (p>0.8) between the age at first calving and AMH
concentration.

68

P>0.8
r= -0.004

Figure 3.2: Plot of age at first calving and serum AMH concentration
Number of services per conception
Number of services per conception had a low negative correlation (r=0.019) with AMH which
was not statistically significant (P> 0.2) when analyzed using Poisson regression model
assuming quasi-Poisson distribution. When the AMH covariate was divided into quartiles, there
was no difference in number of services per conception between the quartiles.

69

DISCUSSION
Correlation between AMH with fertility traits has been studied in a number of different ways.
Redhead et al., (2017) placed animals into LOW, MEDIUM and HIGH AMH groups,
respectively. Jimenez-Krassel et al., (2015) divided the animals into quartiles based on AMH
concentrations as 11 to 12 months old heifers and examined differences in fertility traits between
quartiles. They concluded that the herd longevity was higher for second and third quartile than
the first and fourth quartile suggesting a quadratic relationship. Therefore, we adopted a set of
different strategies: first looking at overall linked linear relationships of fertility traits with AMH,
followed by linear coefficients on AMH within each quartile and between quartile comparisons.
Our results indicate that AMH appears to be moderately associated with probability of
conception in embryo recipients within the first quartile. However, no such relationship appears
to exist for animals in the other AMH quartiles, suggesting that there might be a threshold level
of AMH concentration required for successful pregnancy. That is, once this threshold is
achieved, higher AMH concentrations are not associated with higher probability of conception.
Lahoz et al., (2012) reported a similar observation in Rasa Aragonesa sheep, and suggested the
cut off value of 97 pg/ml to distinguish between females with low and high fertility.
We found no evidence of a relationship between AMH and probability of conception to first
service for heifers subjected to timed AI using sexed or regular semen and embryo recipients.
Ribeiro (2014) presented similar results that no relationship existed between AMH and
pregnancy for cows bred on timed AI protocol. However, cows with low AMH concentration
had lower pregnancy rates after first service for cows bred after detection of estrus. This is
probably due to the fact that estrus synchronization procedure might have optimized the follicle

70

development and ovulation process such that variation in AMH concentrations was no longer
associated with pregnancy.
We determined no evidence that overall probability of conception of heifers subjected to
multiple fixed time AI, and probability of abortion in heifers was associated with AMH.
Furthermore, no relationship was found between AMH with days open, number of services per
conception, age at first calving and probability of abortion in heifers. Jimenez-Krassel (2015)
showed similar results with a much smaller dataset. Age at first AI, at first conception, and at
each calving did not differ among the different AMH quartiles. This can possibly be explained
by considering the fact that AMH is correlated with the number of follicles but not the quality of
oocytes. Heifers being mono ovulatory animals, need only a single oocyte per successful
pregnancy. Therefore, their probability of conception may be independent of the number of
follicles in the ovary.
In contrast to heifers, dairy cows of mixed ages (up to 8 parities) with a low AFC, which is
correlated positively with AMH , had a significantly lower conception rate to first AI, required a
greater number of AI to conceive, and had a greater calving interval compared with cows with an
intermediate or higher AFC (Mossa et al., 2012). Cows with low AFC and AMH have abnormal
changes in endometrial thickness during the estrous cycle (Jimenez-Krassel et al., 2009), which
suggest that the endometria of such cows are less capable of conceptus implantation and
maintenance of pregnancy. Moreover, cows with low AFC also have a lower concentration of
progesterone (Jimenez-Krassel et al., 2015) which is required to play an important role in
maintenance of pregnancy. Ribeiro (2014) showed that lactating cows with low AMH had
greater risk of pregnancy loss than cows with intermediate or high AMH. Perhaps, compared
with heifers, fertility in cows with low compared with higher AMH is more sensitive
71

to the physiological stress related to lactation and decreased progesterone concentration (Sartori
et al., 2002). Therefore, heifers may not be the ideal model to study the effect of AMH on
fertility.
CONCLUSION
In summary, AMH concentration shows some tendency to predict the probability of conception
in embryo recipient heifers within the first quartile of AMH. Hence, there may be a threshold
level of AMH required for optimum probability of conception in embryo recipients as the
relationship only exists in the first quartile animals. However, other reproductive parameters in
heifers such as age at first calving, days open, conception rates, and probability of abortion are
not related with serum AMH concentrations.

72

OVERALL CONCLUSIONS AND FUTURE DIRECTIONS
Anti Mullerian hormone (AMH) is considered to be an endocrine marker of fertility and super
ovulatory response traits in cattle. We set out to estimate the genomic heritability of AMH in
Holstein heifers based on their genotype information using 60 k standard USDA SNP panel. The
heritability of AMH (0.36 Âą 0.02) that we estimated is relatively large compared to conventional
but economically relevant reproductive traits. Furthermore, its well documented association with
super ovulatory response hints at the possible use of AMH for animals that respond better to
superovulation. We also performed a GWA study to identify the QTL involved in AMH
regulation. The study resulted in identification of a ~4.28 MB window on chromosome 11 and a
1.35 MB window on chromosome 20. These regions likely contain a causative QTL for AMH
regulation given that we also identified potential candidate genes in those regions that appeared
to have a key role in AMH expression based on previous research. Some of these genes have
been previously reported to be involved in maintenance of cell structure, cell signaling, cell
viability, embryonic development of cardiac, gonadal and brain tissues, embryonic survival,
oocyte competence, cancer suppression and immunity.
We also looked at the correlations of AMH with reproductive traits in heifers. AMH
concentration shows some tendency (P=0.03) to predict the probability of conception in embryo
recipient heifers. There might be a threshold level of AMH required for optimum probability of
conception in embryo recipients as the relationship only exists in animals having low AMH.
Other reproductive parameters in heifers and first lactation cows like age at first calving, days
open, probability of conception, probability of abortion, and number of services per conception
did not appear to be related with serum AMH concentrations. However, it seems that these
associations should be studied in later parities as these heifers continue to mature.
73

Further research needs to be done on the candidate genes identified in this study. Some of these
genes have been studied extensively in the past. For example NR5A1 knock out mice showed
complete adrenal and gonadal agenesis (Luo et al., 1994), and mutations in this gene caused
ovarian insufficiency. (Lourenço et al., 2009). However, validation of other genes should be
done by a series of experiments. Firstly, fine sequencing should be done in the significant
genomic region to detect the causative QTL. These genes can be studied by differential gene
expression studies and knockout experiments to confirm the role of genes in that region in
regulation of AMH.
Secondly, wide spread application of AMH as a diagnostic tool to select superior dairy animals
for reproductive traits requires further research involving different species of livestock in
different stages of productive life and in different management conditions
In conclusion, AMH can be used to select animals for larger gonads, more number of follicles,
super-ovulatory response and herd longevity, but not for direct measures of fertility which are
considered to be economically important for a dairy farm.

74

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