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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 and dwarf and normal broiler breeders fed ad-libitum or restricted to the point of lay. British Poultry Science 28: 493-506 (1987). 2) Robinson FE, Wilson JL, Yu MW, Fasenko GM, Hardin RT. The relationship between body-weight and reproductive efficiency in meat-type poultry. Poultry Science 72; 912-922 (1993) 3) Sharp, P J, Dunn, IC, Cerolini, S Neuroendocrine control of reduced persistence of egg-laying in domestic hens: evidence for the development of photorefractoriness. Journal of Reproduction and Fertility 94; 221-235 (1992). 4) Sewalem, A, Morrice, D M, Law, A S, Windsor, D, Haley, C S, Ikeobi, C O N, Burt, D W & Hocking, P M. Mapping of Quantitative Trait Loci (QTL) for body weight at 3, 6 and 9 weeks of age in a broiler layer cross. Poultry Science 81: 1775-1781 (2002). 5) Haley CS, Knott SA, Elsen JM Mapping quantitative trait loci in crosses between outbred lines using least-squares. Genetics 136: 1195-1207 (1994) 6) Dunn IC, Miao YW, Morris A, Romanov MN, Wilson PW and Waddington D. 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A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69: 315-324 (1992) Please press enter 22