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CSG 15
MINISTRY OF AGRICULTURE, FISHERIES AND FOOD
Research and Development
Final Project Report
(Not to be used for LINK projects)
Two hard copies of this form should be returned to:
Research Policy and International Division, Final Reports Unit
MAFF, Area 6/01
1A Page Street, London SW1P 4PQ
An electronic version should be e-mailed to [email protected]
Project title
MARKER ASSISTED SELECTION FOR IMPROVED REPRODUCTIVE
PERFORMANCE IN BROILER BREEDERS
MAFF project code
LS3101
Contractor organisation
and location
Roslin Institute, Roslin, Midlothian, EH25 9PS
Total MAFF project costs
Project start date
£ 386,137
1/07/00
Project end date
30/06/03
Executive summary (maximum 2 sides A4)
Objectives
This project aimed to a) identify regions of the chicken chromosome responsible for reproductive traits and b) develop
genetic markers to assist in the selection of broiler meat type poultry to improve reproductive traits. Meat type poultry
have relatively poor reproductive performance. Any reproductive improvements would reduce the inputs required to
produce the 800 million broiler chicks placed in the UK per annum and contribute to the sustainability of this industry. It
may also be possible to reduce the dependence on restricted feeding to control reproductive performance with positive
benefits for animal welfare.
Two complementary approaches were used;
i) Quantitative trait loci (QTL) detection. To locate regions of the chicken genome which explain variation in
reproductive traits, total number of eggs produced and the age at first egg, in the F2 generation of a broiler x layer cross.
The latter is a reflection of the onset of puberty. Additionally egg weight and the number of double yolked eggs was
examined. Egg weight can influence fertility and chick quality whilst double yolked eggs are an indication of disrupted
ovarian follicular hierarchy.
ii) Candidate genes are hypothesised to be responsible for a significant proportion of trait variation. We tested alleles of
candidate genes for reproductive traits for their effect on the reproductive performance of pedigree broiler breeders. The
candidate genes were selected on results from previous positive candidates, from new physiological candidates and a
combination of results from the QTL and physiological studies.
Method: i) Reproductive quantitative trait loci. Broiler x layer cross F2 families where reproductive phenotypes (Egg
number, Age at first egg (AFE), egg weight and number of double yolked eggs) were recorded were genotyped utilising a
top and tail approach. The top and bottom 25% of the egg production phenotype from the 436 animals (29 families) were
genotyped (150 markers). The location of putative QTL were refined and validated by typing more markers at the loci on
the whole population. Analysis was performed using the Haley and Knott programme to locate quantitative trait loci in F2
designs implemented in the QTL express package. QTL for total egg production were analysed using 24-week body
CSG 15 (Rev. 12/99)
1
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
weight as a covariate and age at first egg was analysed using body weight at first egg as a covariate. Egg weight and
double yolked eggs were analysed on an age basis relative to first egg using AFE and egg production as a covariate.
Background genetic effects were also fitted.
Method: ii) Candidate genes.
i) Candidate genes were selected because of their function alone or because of their function combined with their position
in a QTL. ii) DNA sequence at the candidate gene loci was characterised and alleles were identified in the pedigree
populations using nucleotide markers. iii) Genotyping assays for the candidate gene loci were developed and applied. iv)
Collection of phenotypic and descriptive data (total egg production, number of double yolked eggs, age at first egg, hatch
date, body weight at 6 and 35 wks of age and parental information) from about 1000 hens and their sires and dams from
each of 3 lines. Information was obtained from more animals but a set for analysis comprising sire families with more
than 10 members was used for analysis. v) Association analysis of genotype and phenotype was carried out by fitting all
offspring of heterozygous sires for either an additive or dominance model (Model: sire+hatch + hatchxflock + markers).
Findings: i) Reproductive quantitative trait loci
 Puberty. QTL were detected with F values of genome wide significance for age at first egg on chromosome 2 at
288 cM and chromosome 13 at 50 cM. These were both additive and represented 3.9± 0.9 and 3.6± 0.7 days
advancement relative to the layer strain respectively. QTL with suggestive F values were observed for age at first
egg on chromosome 1 at 42 cM and chromosome 3 at 28 cM. The QTL effect on chromosome 3 was also additive
and represented 3.0± 0.8 days advancement. The QTL effect on chromosome 1 was dominant and represented
8.2± 2.6 days advancement relative to the layer strain.
 Egg production. QTL were detected with F values of genome wide significance for egg production on
chromosome 6 at 54 cM and chromosome 13 at 55 cM. The QTL effect on chromosome 13 was additive and
represented 13.7± 3.5 eggs more from the layer strain. The QTL effect on chromosome 6 was dominant and
represented 16.2± 5.4 eggs more from the layer strain. A QTL with suggestive F value was observed for egg
production on chromosome 1 at 126 cM that was additive and represented 9.8± 3.6 eggs extra from the layer
strain.
 Egg weight. A QTL with F values of genome wide significance for egg weight in the middle period of lay was
detected on chromosome 6. A number of suggestive QTL were observed for egg weight late in the laying period
on chromosome 1, 2 and 9.
 Double yolked eggs. Double yolked eggs are caused by multiple ovulations that are the result of abnormal
development of the ovarian hierarchy, which is a problem in broiler lines. A suggestive QTL was detected on
chromosome 2 at position 258 cM for double yolked eggs over the whole experimental period. This QTL had a
dominant effect of 0.56 double yolked eggs relative to the layer line. A number of suggestive QTL were observed
in the first period of production for double yolked eggs. This is the period when most double yolked eggs are laid.
Findings: ii) Candidate genes.
 The following allele markers were used on the DNA from the 3 commercial populations;
a) A deletion/insertion in the NPY promoter had previously been demonstrated to be associated with age at first egg.
Allele frequency of the polymorphism distinguished with the restriction enzyme DraI. Frequency of DraI + in line 1, 0.48;
2, 0.50; 3, 0.77.
b) An SNP marker in intron 1 of GNRH-R which had previously been demonstrated to be associated with double yolked
eggs in one line. This was due to a thymidine –cytosine transition. Allele frequency of the polymorphism, which can be
distinguished with the restriction enzyme CelII, in line 1 is, 0.75; 2, 0.87; 3, 0.49.
c) Luteinising hormone receptor (LHR) (complex haplotype 3 SNPs genotyped) Luteinising hormone/chorionic
gonadotropin receptor, (LHCGR); A 1.2 Kb region between exon 7 and exon 9 was examined. It contained up to 17
polymorphic loci which was amongst the largest number we have observed. These formed a complex set of haplotypes
that complicated interpretation however we chose to genotype three SNPs. These were diagnosed by RFLPs using AvaII
for 3 nucleotide polymorphisms in 5 region, AflIII a guanine - adenine transition, NheI a guanine - adenine transition.
The Frequency of AflIII + in line 1, 0.84; 2, 0.44; 3, 0.66. AvaII + in line 1, 0.91; 2, 0.53; 3, 0.72. NheI + in line 1, 0.55;
2, 0.73; 3, 0.71.
d) Prolactin (PRL) Prolactin promoter region and intronic regions were examined. SNPs were described in intron 2
however none was detected in the promoter region. Three SNPs were characterised 2 of which formed one haplotype.
CSG 15 (1/00)
2
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
One of these, a thymidine –cytosine transition, was diagnosed by a HpyCH4V RFLP at position 3996 in AF288765. This
was subsequently made into a semi automatic acycloprime assay. Frequency of G in line 1 was, 0.73; 2, 0.58; 3, 0.85.
e) Melanopsin (OPN4) The genomic structure of this gene was examined experimentally. Two SNPs were detected in
intron 5 and Acycloprime assays were designed and used successfully. These were at position 303 (a thymidine –cytosine
transition) and 711 (a guanine - adenine transition) in the intron sequence. Frequency of C at 303 in line 1, 0.15; 2, 0.43;
C, 0.57; Frequency of G at 711 in line 1, 0.61; 2, 0.14; 3, 0.13.
f) Oestrogen receptor (ER) We failed to reveal polymorphisms in the published gene sequence so using a combination
of de novo sequence and published sequence the 5’ promoter sequence was examined for polymorphism. A polymorphic
loci (a guanine - adenine transition) at position 2572 in U60211 was diagnosed using an HhaI RFLP and subsequently an
acyloprime assay was developed for genotyping. Frequency of G line 1, 0.56; 2, 0.66; 3, 0.72.

Significant (P<0.05) association was indicated between alleles of GNRH-R and double yolked eggs with the
difference between the two homozygotes being 0.88 eggs (additive model). No other associations appeared to be
significant.
Significant events: i) Reproductive quantitative trait loci.
 Five QTL significant at the genome wide level have been detected for reproductive traits. Two for age at first egg
which is a measurement of puberty, two for total egg production and one for egg weight.
 A further 2 suggestive QTL for age at first egg were detected. Four suggestive QTL for egg weight and 3 for double
yolked eggs were also found
Significant events: ii) Candidate genes.
 New SNP markers for Alleles in were discovered in Luteinising hormone/chorionic gonadotropin receptor, (LHCGR)
Prolactin (PRL), Melanopsin (OPN4) and oestrogen receptor (ER). Haplotypes were discovered for LHCGR
 A resource population was established which is available for testing association of reproductive candidate genes from
any source. This comprised sires and dams and offspring across three closed pedigree lines of broiler breeders.
 A method devised by Michael Schouten was implemented to attribute haplotypes.
 The analysis indicates that a significant association exists between the candidate gene GNRH-R and the trait of
double yolked eggs. This is in line with the observation in a previous study on a single line. This is extremely
encouraging indicating that previous results could be replicated.
Future Options


A number of QTL have been described. The position of these QTL can be mapped more precisely and candidate
genes from the loci can be used in association studies to determine if the loci have effects in commercial populations
that can be used for selection.
Confirmation of a possible association between GNRH-R and double yolked egg number was indicated. The role of
this gene in the pituitary and ovary in relation to the control of the ovarian hierarchy should be further investigated
and possible-flanking markers typed to strengthen the association.
CSG 15 (1/00)
3
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Scientific report (maximum 20 sides A4)
Background
Selection for rapid growth rate and increased feed conversion efficiency in broilers is associated with poor reproductive
performance. In order to reduce the number of broiler breeders required in poultry meat production and to improve their
competitiveness, broiler breeder companies need improved methods to select for reproductive efficiency without reducing
selection pressure for growth related traits. It may also be possible to reduce the dependence on restricted feeding to
control reproductive performance with positive benefits for animal welfare.
The objective of this project was to identify genetic markers for marker-assisted selection for improved reproductive
performance in broiler breeders. The research is based on the hypothesis that polymorphisms associated with “candidate”
genes, identified from either quantitative trait loci (QTL) analysis or physiology may be used for marker-assisted
selection.
The project determined both the location of QTL and tested if markers in candidate genes were associated with
reproductive performance. This latter work was done in commercial broiler lines in collaboration with the Cobb Breeding
Company.
The work supported MAFF’s policy objective of improving the reproductive efficiency of livestock in environments that
conform to UK animal welfare, food safety and environmental codes and regulations (LS31).
Introduction
The poor reproductive efficiency of broiler breeders is caused initially by problems in the formation of the ovarian
hierarchy and subsequently by poor persistency of egg laying and erratic production (1, 2, 3). The excessive and
disorganised ovarian growth at the onset of lay has been a corollary of selection for growth rate and genes closely linked
to that selection. Poor productivity later in the reproductive cycle is associated with genes responsible for persistency.
Because of the methods used for selection for growth the pressure on reproductive traits has been much less in broiler
than in commercial egg-laying strains where persistency is good. For this reason selection for rapid growth rate and feed
conversion efficiency in broilers is associated with poor reproductive efficiency in broiler breeders.
The aim of the research is to identify genetic markers for marker assisted selection (MAS) for improved reproductive
performance in broiler breeders. Selection pressure for growth and feed conversion efficiency are not compatible with
high selection pressure for improved reproductive performance. Methodology is required to allow selection for
reproductive performance without seriously compromising growth traits. Our proposal is to develop DNA markers to
select for improved reproductive performance in broiler breeders. DNA markers that are linked to desirable reproductive
traits can be used before sexual maturity in both sires and dams to select for dam reproductive performance. This would
allow breeders of meat type birds to increase effective selection pressure for reproductive traits by increasing the size of
the population on which selection is applied. Ideally these markers will be in the causative trait genes themselves. We
proposed to use inter-related approaches to improve the likelihood of success.
The work was appropriate for MAFF support because it tested novel methods to aid selection. These are designed to
improve reproductive efficiency of breeding livestock produced by UK companies in environments that conform to UK
animal welfare, food safety and environmental codes and regulations (LS31). The work will reduce the inputs required to
maintain broiler meat production since the breeder flocks will become more efficient. It may also be possible to reduce
the dependence on restricted feeding to control reproductive performance with positive benefits for animal welfare.
The work is strategic in nature, testing a novel hypothesis to produce markers for reproductive efficiency and is receiving
substantial support from the Cobb breeding company. However, the identification of markers, which explain significant
portions of the variance in reproductive traits, would be expected to move rapidly to implementation
CSG 15 (1/00)
4
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Experimental approaches
Two approaches were used, but both were ultimately designed to locate markers for the genes controlling reproduction.
1)
Detection of the chromosomal location of genes controlling reproductive traits using whole genome scans
(QTL analysis).
QTL analysis background
Reproductive traits are quantitative and the regions of the genome that control them are termed quantitative trait loci
(QTL). It is assumed that there are a relatively small number of genes with large effects on the quantitative traits. This has
been supported by the detection of QTL in a number of experimental crosses of farm animals, including for growth traits
from the same broiler layer cross used in this study (4).
In these studies polymorphic markers spaced along the genome were used to scan for the location of these genes by
regression with the trait. Genetic linkage allows the inheritance of blocks of DNA to be tracked from the founder broiler
and layer strains with these markers to locate with relative precision the genes that influence variation in the trait (5).
The GM7 experimental cross was established at Roslin by Dave Burt and Paul Hocking and was one of a series of
populations designed to identify molecular markers that are closely linked to the genes that determine characteristics of
economic importance in chickens (4). The F0 generation comprised inbred broiler and layer lines that were crossed to
produce an F1 and subsequently F2 populations of which GM7 is one. The founder lines differed in reproductive
performance that makes them especially suitable for the study of these traits, the layer strain being relatively prolific and
persistent whilst the broiler line was not. The GM7 population had information on growth rate, egg production, age and
weight at first egg and egg weight throughout production. It was also possible to derive an estimate of the number of
double yolked eggs laid by individual hens from the egg weight data (See methods).
A whole genome scan was carried out using micro-satellite markers that have been tested and validated at Roslin over a
number of projects and allows the definition of QTL to regions containing between 200 and 400 genes.
Genotyping
113 micro-satellite markers were used in the initial genome survey on the 237 birds comprising the top and bottom 25%
of the population for egg production. 136 markers were finally typed once areas containing potential QTL were identified.
These chromosomes, 1, 2, 3, 6 and 13 had complete genotyping carried out in all available animals (474 individuals). For
some traits the number of animals actually available for analysis was smaller due to missing data.
2)
The candidate gene approach.
Candidate gene background.
Genes that are thought to be responsible for variance in economic traits are called candidate genes. Candidate genes
alleles which are found to have significant association with a quantitative trait can also be markers of a QTL. In this study
candidate genes that we knew to be involved in reproduction were selected using a number of criteria. Genes were
selected because i) we had demonstrated association in previous studies which we wished to replicate (6), ii) because they
were in the region of QTL detected in a whole genome scan and iii) because the genes had a demonstrable role in
reproduction. The candidate gene approach to find suitable markers for selection has had a number of successes and was
well stated by Rothschild and Soller (7). Alleles of the candidate genes were tested to determine if there was significant
association between them and the reproductive performance of pedigree broilers. The application of these technologies to
commercial populations is key to delivering benefit from experimental techniques.
Choice of candidate genes
The rapid decrease in ovarian activity in broiler breeders after they have been in lay for a few weeks is a direct
consequence of decreased gonadotrophin secretion (3). A major factor controlling decreased gonadotrophin secretion is a
decrease in the release of hypothalamic gonadotrophin releasing hormone (GNRH). Gonadotrophin releasing hormone is
the ‘pivotal’ gene in the reproductive axis. The neurones in which it is expressed integrate the environmental information
which determines reproductive activity and the decapeptide which they release stimulates the release of gonadotrophins
5
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
from the pituitary (8). GNRH is therefore the start of the cascade which produces the appropriate growth and maturation
of the gonads. Previous studies failed to find polymorphisms in the chicken GNRH gene in Cobb broiler lines (6). The
receptor for GNRH, GNRH-R was selected for the same reasons as GNRH. It transduces the GNRH signal from the
hypothalamus into the release of gonadotrophins from the pituitary (9). Differences in the responsiveness of the pituitary
to GNRH exists throughout the reproductive cycle and it represents an important potential site where genetic variation
might result in changes in reproductive activity. In a previous study association was detected between GNRH-R and the
number of double yolked eggs (6). Further down this axis we examined the receptor for luteinising hormone (LHCGR).
Luteinising hormone is released from the pituitary and has its action in the gonads, in the case of hens the ovary, through
this receptor. Here it promotes differentiation and proliferation of the granulosa layer of the follicles once they have been
selected into the rapidly growing hierarchy (10). This gene is therefore part of the system which controls atresia and may
be important in determining the size of the follicular hierarchy (11). A result of signalling through the LHCGR receptor is
the synthesis and secretion of oestrogen which has negative feedback effects in the hypothalamus through its receptor the
oestrogen receptor (ER). The estrogen receptor was therefore chosen as a candidate gene for that reason and because it
has previously been implicated in the fertility of strains of pig (12) and observations on its role in influencing the onset of
puberty (13). Neuropeptide Y (NPY) is not a component in the primary hypothalamo pituitary axis but it was chosen
because it is known to influence reproduction and in particular the release of GNRH from the median eminence of the
hypothalamus during a progesterone induced surge of luteinising hormone (14). Furthermore, the expression of NPY is
critical in controlling food intake in birds (15) and this may be part of a mechanism which matches satiety to
reproductive activity and controls the timing of critical events such as puberty. It is suggested that polymorphisms
associated with the NPY gene might produce markers for differences in the age of the onset of lay in rapidly growing
broiler strains and, through its role in the control of ovulation, influence egg production rate. We also had evidence from a
previous study that alleles of the gene were associated with age at first egg in a commercial broiler breeder line (6).
Prolactin was chosen because of its role in broodiness which is a period of reproductive inhibition when hens incubate
their eggs and subsequently rear their offspring. Prolactin levels increase many fold during incubation and is responsible
for maintaining this behaviour (16). It is hypothesised that prolactin levels although not producing manifest signs of
incubation they might result in reproductive inhibition on the GNRH neurones (17) or the pituitary (18).
The melanopsin (OPN4) gene was targeted because it transduces information about day length in the brain of the chicken
(19). This is important as it may determine ovulation rate by the synchronisation of ovulation and the circadian time
system (20). The gene is a member of the opsin family, but unlike the more studied photopigments this class of G protein
coupled receptors is not involved in the transduction of vision but rather is the candidate gene for the deep brain
photoreceptor involved in transducing daylength information in birds.
Scientific objectives
The stated scientific objectives of the programme were therefore;
01) To identify genetic markers of QTL for increased egg production.
A whole genome scan using marker sets will be carried out to identify potential QTL for egg production in the
broiler layer cross. Animals from the top and tail of the phenotypic distribution will be analysed in this study.
Construct maps of known genes and genes inferred from comparative information to be present in at least 2 egg
production QTL. Select up to 4 candidates genes.
02) To confirm and replicate QTL in Cobb pedigree parent broiler breeder populations.
Screen 20-40 animals from the parent populations to detect polymorphism in up to 3 Kb from at least 4
candidate genes from 01
03) To select candidate genes from the QTL identified in 01 and confirmed in 02.
Likely candidates genes for the control of egg production will be selected from the loci indicated in 01 and
confirmed in 02 using information from the chicken genome map and comparative genome information from
human and mouse maps. If the candidate genes have not previously been mapped their position will be
confirmed. To genotype markers for candidate genes from at least 2 QTL in cobb pedigree parent broiler
breeder populations.
6
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
04) To collect a panel of DNA and trait information from parent lines of broiler breeders.
Samples of whole blood will be collected from commercial pedigree flocks. Samples will be processed to
produce genomic DNA samples of a quality for use in PCR based techniques. The size of the sample will be
3000 animals with their respective parents (total approx. 6000). These will be collected from at least 3 parent
lines.
05) To characterise polymorphism in candidate genes for egg QTL in parent lines of broiler breeder.
Genomic DNA samples from 20-40 individual broiler breeder parents will be screened for polymorphism at the
candidate gene loci for use as markers. Frequency of polymorphism will be estimated and those below 10 %
abandoned. Candidate genes will be obtained from physiological sources, 03 and from QTL studies already
completed.
06) To genotype the candidate gene loci from 05 in the samples collected in 04 and establish whether the
candidate genes are associated with egg production.
The polymorphic forms of the candidate genes will detected in the panel of DNA from commercial lines of
broiler breeders. The association between the polymorphic markers of the gene and the egg production trait will
be analysed. Offspring from heterozygote sires for each loci will be analysed to reduce background genetic
effects.
Extent to which the scientific objectives have been met
All the scientific objectives outlined for this study have been met. The whole genome scan was completed and
QTL related to egg production were detected. In addition to the stated objectives analysis were carried out for
QTL linked to egg mass and the number of double yolked eggs. The establishment of a large resource
population based on Cobb commercial pedigree broiler breeders was completed and a strategy to maximise the
power of association studies was devised and carried out. A number of candidate genes were scanned for
polymorphism. These were derived from QTL and/or physiology or their success in previous experiments.
Polymorphisms suitable for association analysis were available for GNRH-R, and NPY, LHCGR, Prolactin,
Melanopsin and Oestrogen Receptor. Haplotype analysis was instigated as we developed several SNPs markers
for some of the genes.
Results
QTL
 Puberty. QTL were detected with F values of genome wide significance for age at first egg on
chromosome 2 at 288 cM and chromosome 13 at 50 cM (Figure 1 and 2, Table 1). These were both
additive and represented 3.9± 0.9 and 3.6± 0.7 days advancement relative to the layer strain
respectively. QTL with a suggestive F values was observed for age at first egg on chromosome 3 at 28
cM. The QTL effect on chromosome 3 was also additive and represented 3.0± 0.8 days advancement
relative to the layer strain.
7
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Figure 1. QTL for age at first egg on chromosome 2
Figure 2. QTL for age at first egg on chromosome 13

Egg production. QTL were detected with F values of genome wide significance for egg production on
chromosome 6 at 54 cM and chromosome 13 at 55 cM (Figure 3, Table 1). The QTL effect on
chromosome 13 was additive and represented 13.7± 3.5 eggs more from the layer strain. The QTL effect
on chromosome 6 was dominant and represented 16.2± 5.4 eggs more from the layer strain.
Figure 3. QTL for egg production on chromosome 6
8
Project
title

MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Egg weight. A QTL with F values of genome wide significance for egg weight in the late period of lay
was detected on chromosome 6. A number of suggestive QTL were observed for egg weight late in the
laying period on chromosome 1, 2 and 9 (Table 1, Figure 4).
Figure 4. Suggestive QTL for egg weight on chromosome 9

Double yolked eggs. Double yolked eggs are caused by multiple ovulation which is itself the result of
abnormal development of the ovarian hierarchy which is a problem in broiler lines. A suggestive QTL
was detected on chromosome 2 at position 258 cM for double yolked eggs over the whole experimental
period (Table 1). This QTL had a dominant effect of 0.56 double yolked eggs relative to the layer line.
A suggestive QTL were observed in the first period of production for double yolked eggs on
chromosome 1 which was dominant (Table 1, Figure 5). Other suggestive QTL are found on
chromosome 3, 7 and 11 (Table 1). This first period of production is when most double yolked eggs are
laid.
Figure 5. Suggestive QTL for double yolked eggs on chromosome 1
9
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Age at first egg
Trait
Chr.
Position
95% CI
F statistic
Add ±SE
Dom ±SE
Age at first egg
Age at first egg
Age at first egg
Age at first egg
1
13
2
3
42cM
50cM
288cM
28cM
42-473cM
43-86cM
225-295cM
19-237cM
5.48
9.13
12.28
6.75
1.6 ± 1.5
-3.9 ± 0.9
-3.6 ± 0.7
-3.0± 0.8
-8.2 ± 2.6
-1.2 ± 1.8
1.9±1.1
-0.7± 1.3
Chr.
3
1
13
6
Position
276cM
126cM
55cM
54cM
95% CI
0-282cM
31-462cM
35-115 cM
37-63cM
F statistic
4.51
5.22
8.19
8.12
Add ±SE
5.2 ± 3.3
9.8 ± 3.6
13.7 ± 3.5
8.31 ± 3.1
Dom ±SE
-14.8±5.7
-8.9±5.4
3.2±5.6
16.2±5.4
Chr
2
6
1
9
Position
260cM
18cM
212cM
76cM
CI
53-454 cM
8 –37 cM
76-505 cM
0-126 cM
F-statistic
7.32
8.05
7.06
7.92
Add ±SE
-11.8 ± 5.1
-15.8 ± 5.9
5.7 ± 11.5
-30.2 ± 10.2
Dom ±SE
25.7 ± 8.1
26.6 ± 9.6
85.3 ± 23.2
52.1 ± 21.1
Position
256cM
CI
3-292 cM
F-statistic
7.02
Add ±SE
0.19 ± 0.12
Dom ±SE
0.65 ± 0.20
Position
112cM
20cM
258cM
0cM
CI
0-285 cM
6-70 cM
31-499 cM
0-63 cM
F-statistic
5.42
5.58
6.90
5.82
Add ±SE
-0.11 ± 0.04
-0.13 ± 0.05
-0.28 ± 0.12
0.20 ± 0.06
Dom ±SE
0.09 ± 0.06
-0.19 ± 0.08
-10.4 ± 0.36
-0.08 ± 0.08
Position
0cM
CI
0-124 cM
F-statistic
6.15
Add ±SE
0.02 ± 0.05
Dom ±SE
0.35 ± 0.10
Egg production
Trait
12 month egg number
12 month egg number
12 month egg number
12 month egg number
Late period egg weight
Trait
Egg Weight 315-371
Egg Weight 315-371
Egg Weight 315-371
Egg Weight 258-314
Total period double yolked eggs
Trait
Double Yolked Eggs1-371
Chr
2
Early period double yolked eggs
Trait
Double Yolked Egg1-29
Double Yolked Egg1-29
Double Yolked Egg1-29
Double Yolked Egg1-29
Chr
3
11
1
7
Late period double yolked eggs
Trait
Double Yolked Egg87-143
Chr
13
Table 1. Summary of whole genome scan results including position, 95% CI, F statistic and additive and
dominance effects with errors.
Candidate genes
1) Gonadotrophin hormone releasing hormone receptor (GNRH-R) In a previous study (6) association
was detected between GNRH-R and the number of double yolked eggs, which we wished to confirm in this
study. Intron 1 of the GNRH-R contained a cytosine-thymidine transition which was used for genotyping.
An assay based on the presence or absence of a recognition site for the restriction enzyme CelII in a PCR
product amplified from genomic DNA using primers GNRHRmap5 and GNRHRmap8 was validated for
genotyping. The allele frequencies for the cut allele in line 1, 2 and 3 were 0.76, 0.87 and 0.50 respectively.
This gene has been previously mapped to chromosome 10 and this was confirmed in the sequencing of the
chicken genome to be on chromosome 10 (18.5 Mb). Analysis of the alleles of this gene with reproductive
trait data in the Cobb populations replicated the previous observation of association between the alleles and
10
Project
title
MAFF
project code
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
LS3101
number of double yolked eggs produced. The additive model was significant at P=0.014 with the difference
between the two homozygotes being 0.88 eggs (95% CI, 0.60-0.98)
2) Neuropeptide Y (NPY). A 4 bp deletion/insertion about 700 bp upstream of the NPY transcription start
site used as a marker. This had been described in a previous study (6) and was associated with age at first
egg. An assay based on the presence or absence of a recognition site for the restriction enzyme DraI in a
PCR product amplified from genomic DNA using primers NPYmap9 and NPYmap10 had previously been
validated for genotyping and was used in the current study. The allele frequencies for the cut allele in line 1,
2 and 3 were 0.44, 0.52 and 0.80 respectively. This has been confirmed in the sequencing of the chicken
genome to be on chromosome 2 (39.0 Mb) but is outside of the regions connected with QTL. The
observation made in the previous study on association with the alleles and age at first egg was not replicated
in this study.
3) Oestrogen receptor (ER). Initial investigations around intron 1 failed to reveal any polymorphic loci
therefore the promoter region was examined. No continuous sequence was available at the time this was
attempted and we constructed the upstream region including exon 1 by cloning and sequencing about 250
bp of unpublished genomic sequence. Direct sequencing primers spanning this region were designed and
one polymorphic loci was detected in the cobb population which was a guanine - adenine transition at
position 2572 in the EMBL data base sequence, accession number U60211. An assay based on the presence
or absence of a recognition site for the restriction enzyme HhaI in a PCR product amplified from genomic
DNA using primers Oer1304f and OerHhaI was developed. The allele frequencies of the cut allele in line 1,
2 and 3 were 0.55, 0.66 and 0.72 respectively. This polymorphism was subsequently converted into an
acycloprime fluorescence polarisation assay (Perkin Elmer Life Sciences) (Figure 6) for the genotyping of
the whole population. This gene is found on chromosome 3 in the region of suggestive QTL for age at first
egg. This has been confirmed in the sequencing of the chicken genome to be on chromosome 3 (48.0 Mb).
However analysis of the alleles of this gene with reproductive trait data in the Cobb populations indicated
no significant associations.
Homozygous **
320
Heterozygous **
280
Y axis
TAMRA (mP)
240
200
160
120
80
Blank or
no product
40
Homozygous **
0
0
20
40
60
80
100
120
140
160
180
X axis
R110 (mP)
Figure 6. An example of the Acycloprime assay used to genotype the alleles in the Cobb population for Oestrogen receptor.
4) Melanopsin (OPN4) The genomic structure of this gene was determined experimentally and intron 5 and
its flanking exons were examined in detail. Two SNPs were detected for which Acycloprime assays were
11
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
designed and successfully tested these were locus 1 position 303 (a thymidine–cytosine transition) and locus
2 position 711 (a guanine - adenine transition) in the intron sequence. The allele frequencies of the C allele
for locus 1 in line 1, 2 and 3 were 0.15, 0.43 and 0.57 respectively. The allele frequencies of the G allele
for locus 1 in line 1, 2 and 3 were 0.61, 0.14 and 0.13 respectively. Melanopsin was predicted to be on
chromosome 2 in the region of QTL for age at first egg using comparative genomics. However recent
information from sequencing the chicken genome does not support this predicted position. The true position
is on chicken chromosome 4 (37.7 Mb). Analysis of the alleles of this gene with reproductive trait data in
the Cobb populations indicated no significant associations.
5) Luteinising hormone receptor (LHCGR) A 1.2 Kb region between exon 7 and exon 9 was examined. It
contained up to 17 polymorphic loci which was amongst the largest number we have observed. Five SNPs
were more closely evaluated, locus 1 was characterised by a AflIII RFLP in a PCR product from primer pair
LHR7f-LHRmap4, locus 2 was characterised by a AvaII RFLP in a PCR product from primer pair LHR7fLHRmap4, locus 3 was characterised by a HaeIII RFLP in a PCR product from primer pair LHRmap5map6, locus 4 was characterised by a AatII RFLP in a PCR product from primer pair LHRmap5-map6 and
locus 5 was characterised by a NheI RFLP in a PCR product from primer pair LHR7f-LHRmap4. The
allele frequencies of the cut allele for locus 1 in line 1, 2 and 3 were 0.84, 0.44 and 0.66 respectively; for the
cut allele for locus 2 in line 1, 2 and 3 were 0.91, 0.53 and 0.72 respectively and for the cut allele for locus 3
in line 1, 2 and 3 were 0.33, 0.43 and 0.28 respectively; for the cut allele for locus 4 in line 1, 2 and 3 were
0.93, 0.88 and 0.89 respectively and for the cut allele for locus 5 in line 1, 2 and 3 were 0.55, 0.73 and 0.71
respectively. These formed a complex set of haplotypes that complicated interpretation however we chose
to genotype three SNPs, locus 1, 2 and 5. This gene is found on chromosome 3 in the region of a suggestive
QTL for age at first egg. This has been confirmed in the sequencing of the chicken genome to be on
chromosome 3 (9.5 Mb). Analysis of the alleles of this gene with reproductive trait data in the Cobb
populations indicated no significant associations.
6) Prolactin (PRL) Prolactin promoter region, exon 1 and 2 and intronic regions were examined. SNPs were
described in intron 2 however none was detected in the promoter region examined. Three SNPs and a
deletion were characterised. The deletion and two of the SNPs were linked. One of these, locus 1, a
thymidine –cytosine transition, was diagnosed by a HpyCH4V RFLP at position 3996 in AF288765 in a
PCR product amplified from genomic DNA using primers PRL12R316 and PRL12F316. Another unlinked
SNP, locus 2, was characterised by a DdeI RFLP in a PCR product amplified from genomic DNA using
primers PRL12R171 and PRL12F171 at position 4113 in AF288765 was a guanine –adenine transition. The
allele frequencies of the cut allele for locus 1 in line 1, 2 and 3 were 0.70, 0.58 and 0.85 respectively and for
the cut allele for locus 2 in line 1, 2 and 3 were 0.61, 0.14 and 0.13 respectively. Prolactin is on
chromosome 2 in the region of a QTL for age at first egg. An acycloprime assay was set up for locus 1
using the diagnostic primer PRLex2SNPr and the flanking primers PRL12R316 and PRL12F316 to
genotype the population. No significant association was found between alleles of the marker and aspects of
reproductive performance. Prolactin was mapped to chromosome 2 in the region of QTL for age at first egg.
Recent information from sequencing the chicken genome supports this mapped position on chromosome 2
(58.4 Mb). Analysis of the alleles of this gene with reproductive trait data in the Cobb populations indicated
no significant associations.
Significant events
1) Mapping reproductive QTL.
Several QTL with genome wide significance were detected in this study, notably on chromosome 13 for egg
production and on chromosome 2 and 13 for age at first egg. These were additive effects that accounted for
over 13 eggs or almost 4 days earlier production in the case of age at first egg.
12
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Candidate genes have been identified in the region of these QTL and these will be pursued in a follow up
project with the Cobb breeding company in commercial lines of broiler breeders. This will allow us to assess if
alleles for genes in the QTL contribute significantly to variation in these traits in commercial lines. If this is true
the markers of the genes could be used for selection to improve reproduction and contribute to the sustainability
of this industry. The suggestive QTL for double yolked eggs may be potentially valuable since double yolked
eggs are symptomatic of a disorganised follicular hierarchy that is usually dealt with by food restriction.
Selection against the production of double yolked eggs is one strategy to reduce the dependence of the industry
on quantitative feed restriction as a tool.
2) Candidate genes.
This study confirmed the presence of association between genetic markers in the gonadotrophin releasing
hormone receptor and the number of double yolked eggs. This is particularly important for several reasons.
Firstly this association was first noted in a pilot study on a single population of broiler breeders and this result
replicates this observation. Secondly the occurrence of double yolked eggs, which cannot be set for hatching, is
one of the main reasons that broiler breeders are food restricted and any methods that might reduce this
requirement could be valuable.
Conclusions
The use of a segregating F2 population derived from broiler and layer parents has allowed the detection of a
number of chromosomal regions that explain relatively large amounts of variation in reproductive traits. Some
of these we can be confident in, however others have lower confidence such as those for double yolked eggs,
and were not the primary focus of the study. If more genotyping was carried out around the loci of these QTL
both the significance and the confidence intervals could be improved. Identification of the QTL loci for
reproductive traits allows us to target the regions for the selection of candidate genes connected with these
traits. The publication of the chicken genome has facilitated this task making it easy to determine the nature of
the genes within the QTL (21, 22). This will inform our future candidate gene studies in commercial broiler
populations. Until this study our ability to detect candidate genes for reproductive traits in broiler breeders has
been guided principally by our knowledge of the reproductive axis (6). As this study has progressed, and in the
future, it will be possible to select candidates from QTL results. Previously it has been confirmed that QTL
discovered in the broiler layer F2 population for growth can be transferred to detect QTL within a commercial
broiler population (23). This suggests that it will be possible to do the same for reproductive traits. The
commercial populations that we have established in this study contain a large number of offspring with DNA
and phenotype information that has allowed us to test these genes and will allow us to test candidates based on
positional information in the future. This is projected for a follow up study commencing in September 2004.
This current study has highlighted one significant association with a reproductive trait. However this result
replicates results from a previous study that gives us confidence that the effect is real, if somewhat small in
production terms. By repeating analysis in subsequent generations and different genetic lines, it ensures that
associations observed by chance or due to linkage with a distant loci would be eliminated from consideration
since any spurious or distant linkage would be broken. This demonstrates that we have the methodology to
bring studies in experimental crosses to a potential application in the primary breeding industry. We hope this
will be even more successful if the candidates are informed by whole genome QTL studies. This will allow us
to develop strategies that may preserve reproductive potential in meat type birds where reproduction is
relatively poor. These approaches are important if we are to sustain the broiler industry that is an important
contributor to the production of affordable meat and which requires relatively small inputs in to produce.
We expect that these types of studies will become easier because of the availability of information from the
chicken genome (21, 22) and the large number of chicken ESTs available through a BBSRC funded initiative
(24). In this and previous studies we have had to derive information on the genomic DNA surrounding genes
13
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
ourselves. However for at least 95% of the genome this information can now be derived from databases,
speeding up the process considerably. The EST resource allows us to predict where polymorphic sites might
exist in genes and whether they are likely to alter the protein sequence and structure. This again speeds up the
process of discovering single nucleotide polymorphisms and greatly increases the chances of discovering loci
which are likely to have a functional effect and which should be more likely to account for variance in
reproductive traits in a population. This is not to say that polymorphic loci outside the coding exons of genes
will not influence a trait. Databases for genomic DNA are also being compiled which will allow prediction of
SNPs in these regions without necessarily sequencing representatives from the study population (25).
The methods we have established along with the improved semi-automated methods of genotyping used in this
study should allow an expansion of the approach and ensure that genomic technologies can be used in
commercial breeding flocks to improve a range of desirable and previously difficult to select traits. In this
regards one of the major challenges is going to be gathering high quality information on the trait in the pedigree
populations.
Methods
1) Broiler x Layer F2 population for whole genome scan (GM7 population).
Data storage and generation
Hens were reared in individual cages to facilitate the recording of egg production and age at first egg. Egg
production was recorded daily. The date of the first egg laid was recorded as well as the weight of the hen on
the day of laying its first egg (Figure 8), body weight at 24 weeks of age was also recorded. Data on hatch and
position in the battery were also available. Egg production was analysed as total eggs laid up to 12 months of
age (Figure 7). I believe this is most meaningful commercially and seems to give the best estimate of
reproductive performance, especially as age at first egg (AFE) seems to be of limited effect in this population.
The complexity of laying pattern also meant that analysis using other approaches such as regression of the
decline in egg production seemed inappropriate because of the range and complexity of laying patterns.
All part records were removed i.e. Dead birds. All hens laying less than 40 eggs at 12 months of age were
removed (n=25) because of the chance they may have been sick.
In order to select the top and tail for initial genotyping a general linear models was used to fit the data. For
information individual factors and variables were examined for their ability to explain variance of the 12-month
egg count:
Variable or factor
Percentage of variance
Sire Family
Weight at 24 wks
Weight at 1st egg
Hatch
Age at first Egg
Tier
16.2%
11.8%
4.7%
2.3%
1.3%
0.2%
On the basis of this information it was important to include the weight at 24 weeks of age in the model.
Somewhat surprisingly, there was little evidence that age at first egg had much influence on the egg number,
however it was included it in the analysis. The model below accounts for 27% of the variance.
Constant + hatch + tier + hatch.tier + family + age at first egg + weight at 24 weeks of age
14
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
The ranks of the residuals from fitting the above model was used to select 237 birds which represented 25%
from each tail of the distribution. Eight birds (4 from the top 4 from the tail) were chosen from each family. 2
families were excluded due to having small size.
Although age at first egg was not the basis of the selection of the top and tail it was also used as a trait for the
detection of QTL. Although the top and tail population was not designed specifically for this trait any QTL that
were discovered would be valid, and not subject to an initial overestimation of the size of the effect seen in the
top and tail trait.
The distribution of the data for egg production and age at first egg that was used in the analysis can be seen in
figures 7 and 8.
Frequency
Number of eggs laid to 12
months of age
Figure 7. Distribution of the data for the trait of egg production in the GM7 population.
Frequency
Age at first egg
(days)
Figure 8. Distribution of the data for the trait of age at first egg in the GM7 population.
Data on egg weight was also available for the GM7 population however because of double yolked eggs that are
much heavier than normal eggs (Figure 9) some data manipulation had to be performed.
15
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Normal
eggs
Double
yolked
eggs
Egg weight (gms)
Figure 9. Distribution of the data for the egg weight in the GM7 population between 87 and 143 days after
first egg showing the two populations of eggs, normal and double yolked.
Egg weight also varies over time so the data was examined in separate time periods and to equalise the records
all the time periods were referenced to age at first egg for each individual. Egg weights that were greater than
2.5 times the standard deviation from the mean were removed from the analysis. The exception was the first
production period that contains a large number of double yolked eggs and a figure of 2 standard deviations was
used. Those that were eliminated at the top of the distribution represent double yolked eggs. The count of these
eggs was used as a separate trait of double yolked eggs.
Therefore the traits available for analysis were;
Trait
Unit of measurement
Egg production to 12 months of age
Number of eggs
Age at first egg
Days
Weight at first egg
Grams
Egg weight between day 1-29 after age at first egg
Grams
Egg weight between day 30-86 after age at first egg
Grams
Egg weight between day 87-143 after age at first egg
Grams
Egg weight between day 144-200 after age at first egg
Grams
Egg weight between day 201-257 after age at first egg
Grams
Egg weight between day 258-314 after age at first egg
Grams
Egg weight between day 315-371 after age at first egg
Grams
1
Total number of double yolked eggs
Number of eggs
1
Total number of double yolked eggs in the above periods relative to first egg were also available
Genotyping
113 micro-satellite markers were used in the initial genome survey on the 237 birds comprising the top and
bottom 25% of the population for egg production. 136 markers were finally typed once areas containing
potential QTL were identified. These chromosomes, 1, 2, 3, 6 and 13 had complete genotyping carried out in all
available animals (474 individuals). For some traits the number of animals actually available for analysis was
smaller due to missing data. Genotyping was carried out on a on a ABI 3700 automated sequencer and analysed
using Genotyper Version 3.6 NT. Genetic Maps for the selected QTL regions were initially based on the
Consensus 2000 map (26). The maps for these regions were further refined to be GM7 specific using Crimap
and the "Build function”.
16
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Table 2. Microsatellite markers used on the GM7 population
ROS0008
MCW0007
ROS0044
ADL0148
ADL0150
ADL0160
ADL0183
MCW0107
LEI0068
ADL0319
LEI0101
MCW0112
LEI0146
MCW0168
MCW0010
LEI0079
LEI0071
ROS0081
ADL0188
MCW0036
ADL0209
MCW0249
ADL0308
ROS0112
MCW0097
ROS0111
LEI0110
ADL0044
ADL0240
ADL0147
ADL0214
ROS0083
MCW0244
MCW0340
MCW0213
ADL0310
ADL0225
MCW0123
LEI0083
MCW0080
LEI0258
ADL0199
ROS0027
ROS0022
MCW0094
ROS0018
ADL0114
ADL0157
ADL0176
ADL0236
ADL0267
ROS0074
LEI0163
MCW0056
MCW0157
LEI0117
LEI0127
ADL0343
ADL0196
LEI0147
ROS0018
ADL0114
LEI0127
ROS0023
LEI0090
ADL0289
ROS0113
ADL0285
LEI0074
ROS0071
ADL0299
ROS0085
ROS0095
ROS0001
HUJ0006
ADL0177
ADL0306
MCW0169
MCW0083
MCW0187
MCW0040
LEI0166
MCW0037
ADL0370
LEI0118
MCW0252
MCW0127
LEI0115
ADL0371
LEI0265
ADL0237
ADL0266
ROS0015
ADL0317
HMG14a
ADL0166
ADL0292
ADL0298
ROS0084
LEI0082
MCW0090
ROS0013
MCW0013
ROS0028
ADL0138
ROS0062
ADL0323
ADL0142
ROS0003
ROS0019
ADL0180
LEI0064
ROS0021
ADL0154
ADL0179
ADL0258
ADL0278
ROS0075
MCW0305
MCW0095
MCW0100
MCW0160
ROS0026
ROS0030
ROS0078
MCW0134
MCW0135
ROS0073
MCW0249
ADL0022
ADL0201
ROS0072
MCW0055
LEI0075
LEI0111
LEI0121
MCW0241
MCW0294
MCW0123
LEI0258
ADL0199
MCW0094
ROS0113
ROS0071
ROS0073
Table 3. Distance between markers in Centimorgans on the chicken macro and microchromosomes as used in the
analysis for QTL
Chromosome 1 MCW0168 4.800 ROS0008 0.100 ADL0160 43.200 MCW0010 61.00 ADL0188 10.500 LEI0068 12.300 LEI0146
26.000 MCW0007 3.00 MCW0112 2.100 ADL0150 2.700 ADL0319 32.400 LEI0101 108.0 ROS0044 14.00
ROS0081. 12.800 ADL0148 30.600 MCW0036 55.600 ADL0183 5.800 LEI0079 115.200 MCW0107
Chromosome 2 LEI0163 21.700 ADL0343 94.300 ADL0176 18.000 ROS0018 91.000 ADL0196 20.500 ADL0157 16.000
ADL0267 11.000 LEI0127 10.400 LEI0147 4.200 ROS0023 1.300 ADL0236 15.600 ROS0074 11.300 ADL0114
31.700 MCW0056 126.300 MCW0157
Chromosome 3 MCW0169 0.100 ADL0177 22.500 MCW0083 15.900 HUJ0006 36.400 ROS0001 25.1 LEI0115 12.800 MCW0187
11.00 MCW0127 8.100 LEI0118 16.100 MCW0252 29.400 ADL0306 11.200 LEI0265. 19.200 ADL0237. 8.000
MCW0040 19.600 LEI0166 21.800 MCW0037
Chromosome 4 ADL0317 3.000 HMG14a 67.000 ROS0015 56.000 ADL0266
Chromosome 5 LEI0082 25.000 MCW0090 22.000 ROS0013. 4.000 ADL0292 6.000 ROS0084 73.000 ADL0166 36.000 ADL0298
Chromosome 6 ROS0062 33.000 ROS0003 18.000 ADL0142 37.000 ADL0323
Chromosome 7 LEI0064 101.000 ROS0019 8.000 ADL0180
Chromosome 8 ADL0179 46.000 ADL0154 48.000 ROS0075
Chromosome 9 ROS0078 60.000 MCW0135 67.000 ROS0030 5.000 MCW0134
Chromosome 10 ADL0209
Chromosome 11 MCW0097 0.100 LEI0110 19.900 ROS0111 31.000 ADL0308 19.000 ROS0112
Chromosome 12 ADL0240 33.000 ADL0044
Chromosome 13 MCW0340 32.000 ADL0147 38.000 ADL0225
Chromosome 14 MCW0123
17
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Chromosome 15 LEI0083 45.000 MCW0080
Chromosome 16 LEI0258 0.10 LEI0258
Chromosome 17 ADL0199
Chromosome 18 ROS0022 23.000 ROS0027
Chromosome 19 MCW0094
Chromosome 23 LEI0090 0.100 ADL0289
Chromosome 24 ROS0113
Chromosome 26 ADL0285 0.10 LEI0074
Chromosome 27 ROS0071
Chromosome 28 ROS0095 0.10 ROS0085 39.000 ADL0299
Chromosome E38 ROS0073
Chromosome W25 MCW0249
Chromosome Z ADL0022 15.000 MCW0055 23.000 ROS0072 49.000 ADL0201 14.000 MCW0241 7.000 MCW0294 17.000
LEI0111 13.000 LEI0121 27.000 LEI0075.
All trait, genotype and map position data was held in the ResSpecies data base (27). This allowed the data to be
extracted in a form which was compatible with the analysis programme QTLexpress. QTL express (28)
implements the F2 analysis method developed for inbred lines by (29) and extended for outbred lines by Haley
et al. (5). The method calculates the probability of inheriting DNA from the broiler or layer line throughout the
genome using the actual marker genotypes. Regression of the trait data with the genotype is carried out and
known covariates and cofactors are fitted in the model. An F value is attributed to the data which can be
compared with a bootstrapped estimate on a chromosome wide level. However because we are estimating over
the whole genome slightly more stringent criteria may be required. A previous study using this cross modelled
genome wide significance values (4) which were set for values above 6.00 as suggestive, above 8.00 significant
<0.05 and above 12 highly significant <0.01.
2) Candidate genes
Populations
Three populations of pedigree broiler hens were sampled to produce a large resource. It was our aim to collect
enough offspring to ensure that for any polymorphic locus we could have a large enough population of
offspring from sires heterozygous at that loci. To increase the effectiveness of our genotyping we additionally
selected offspring from sire families (half sibs) with at least 10 members. The numbers of offspring that were
finally available for analysis were Line 1, 1027 offspring, 45 sires, 231 dams; Line 2, 1160 offspring, 50 sires,
245 dams; Line 3, 577 offspring, 28 sires, 152 dams.
Sample collection methods
Blood samples were taken from the brachial vein using EDTA treated syringes and transferred to EDTA tubes
for storage at 4˚C. Aliquots of whole blood were transferred to 96 well plates and stored at both 4˚C and –20˚C.
Genomic DNA was prepared from whole blood using GFX genomic blood DNA purification kits (Amersham
biosciences) using the standard kit protocol with slight modification. Genomic DNA was also aliquoted and
stored in duplicate at 4˚C and –20˚C. Phenotypic data was received electronically in an Excel compatible
format in the form of extracts from Cobbs’ pedigree database. Sample collection and corresponding data for
these were entered into excel spreadsheets. This was collected over a considerable period of time which
unfortunately caused some problems with animal ID’s. When the birds are on the ground, they are given an
abbreviated wingband code that relates to a full ID code held in the Cobb Breeding companies database.
Because of the period of time elapsed from start to finish of the collection of genomic DNA, some of our
sample ID’s were duplicated. When genotype data and Cobb phenotype data was combined using the Microsoft
Access database system there were some conflicts because of this. This meant considerable efforts were made
to merge our genomic DNA data with the Cobb phenotypic data, this was eventually achieved and only a small
amount of data had to be discarded. This was unfortunately delayed due to the lack of staff available to consult
with due to the run down of the Cobb UK breeding operation.
18
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
Phenotypic measurement
Birds from three pedigree broiler lines were housed at 16 weeks of age in pens containing between 12 and 14
birds and were fed a restricted diet to ensure body weight did not exceed ~ 3.8 kg at 60 weeks of age as detailed
in the Cobb 500 breeder management guide (Cobb Breeding Company, East Hanningfield, UK). Prior to that
age birds were reared on ad-libitum feeding to 7 weeks of age and thereafter those birds selected for their
growth characteristics were fed on a modified restricted diet aimed to gain the target weight of 3.17 kg at 26
weeks of age. Diet composition and lighting are all according to the Cobb 500 breeder management guide. The
study population was 3 flocks, each of which were produced from 7 consecutive hatches. Data on egg
production including total egg production, age at first egg and number of double yolked eggs was collected
daily using trap nests to identify individual birds. The data for individual hens were collated over a seven month
period and recording commenced at 22 weeks of age in a similar manner to a previous study (6). Phenotypic
measurements were age at first egg, number of eggs laid in months 1-6 and number of double-yolked eggs laid
in months 1-6 of lay. Records were kept for a seventh month period on most birds, but those killed after 6
months had lower egg production in month 7. These birds were retained in the analysis by considering a 6,
rather than 7 month laying period. and the traits were either transformed to approximate normality, or modelled
with appropriate distributions. Numbers of double yolked eggs were approximately distributed as an overdispersed Poisson variable, and were analysed as a generalised linear model with parameters estimated on the
logarithmic scale. Other variables were shifted and rescaled using a log transformation to give approximate
normality and equality of variance. Total number of eggs was negatively skewed, and was analysed as the log
of total number of eggs subtracted from a hypothetical upper limit of 170. Age at first egg was positively
skewed, and had a value of 140 subtracted before taking logarithms.
:
Lafe=log(AFE-140)
Lneggs=log(170-no of eggs in months 1-6)
No of double-yolked eggs in months 1-6 varied as an over-dispersed Poisson distribution, and were fitted
using a generalised linear model.
SNP assay development
SNP’s were identified by direct sequencing in candidate genes. A sample sire population was used to establish
the allele frequency of the respective SNP’s.
Two types of SNP assay were used- Restriction fragment length polymorphism (RFLP) and AcycloPrime-FP
SNP Detection Kit (PerkinElmer Life Sciences, Zaventem, Belgium). RFLP data was manually inputted to the
database for further analysis. Acycloprime assays were read using the SNPscorer™ program to call alleles. The
program generated an excel results file that could be transferred directly into the database.
Data analysis
Not all sire families were used in the analysis, only the offspring of heterozygous sires. This substantially
reduced the amount of data, but gave marker effects estimated within families, and was therefore less likely to
be affected by other background genetic differences between sires. These may lead to false positive associations
between the candidate gene and the primary trait, which might happen, even when the candidate gene and the
true trait gene are on different chromosomes. For example when 2 genes are sufficiently close to genes
descended from a population founder which have both been under selection for a second, desirable trait. In
addition, there may be other QTLs for the primary trait. The within sire analysis will remove some of the
effects of these QTLs when their allele frequencies are not balanced for the 2 candidate gene alleles across
sires. The number of heterozygous sires at each location is given in table 4.
Gene
Number of
19
Number of
Project
title
MAFF
project code
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
heterozygous
LS3101
offspring
sires
GNRH-R
59
1141
NPY
51
994
ER
51
820
PRL
47
865
Table 4. The number of heterozygous sires and their offspring used in analysis
For LHCG-R there were 3 SNPs that were genotyped which gave rise to a complex haplotype. The estimated
haplotype frequency is given in table 5.
Line
1
2
3
Total
Haplotype
112
0
194
102
296
121
0
53
29
82
122
0
11
0
11
212
0
19
15
34
221
191
101
12
304
222
192
167
103
462
1027
1160
Count
577
2764
Table 5 Haplotypes for frequencies for LHCG-R. The 3 SNPs (Ava,Afl,Nhe) were combined into
haplotypes. The haplotypes are labelled as triplets of 1s and 2s for the 3 SNPs. Only 6 were found and some
were not found in one of the 3 lines studied – line 1 was always 22 for the first 2 SNPs. The association test was
carried out with the trait versus the sire haplotype inherited.
The effects of hatch (h), flock (f) and their interaction together with sires (s) and the
marker genotypes (g) were fitted, as fixed effects, to the expectation of the transformed responses (y), as
E ( y ijkl )  si  h j  f k  h. f jk  ml
Linear models were fitted by regression analysis and generalised linear models by iteratively re-weighted least
squares, followed by Student’s t-tests to assess possible marker effects. Additive effects of markers were
estimated as the differences between homozygote means, and dominance effects as the difference between
twice the heterozygote mean and the sum of the homozygote means.
References
20
Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
1) Hocking, PM, Gilbert, AB, Walker, M & Waddington, D. Ovarian follicular structure of white leghorns fed ad-libitum
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Project
title
MARKER ASSISTED SELECTION FOR IMPROVED
REPRODUCTIVE PERFORMANCE IN BROILER
BREEDERS
MAFF
project code
LS3101
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