Genome-wide association studies for egg quality traits in White Leghorn layers using low-pass sequencing and SNP chip data.

Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.

The impact of endogenous Avian Leukosis Viruses (ALVE) on production traits in elite layer lines.

Avian Leukosis Virus subgroup E (ALVE) integrations are endogenous retroviral elements found in the chicken genome. The presence of ALVE has been reported to have negative impacts on multiple traits, including egg production and body weight. The recent development of rapid, inexpensive and specific ALVE detection methods has facilitated their characterization in elite commercial egg production lines across multiple generations. The presence of 20 ALVE was examined in 8 elite lines, from 3 different breeds. Seventeen of these ALVE (85%) were informative and found to be segregating in at least one of the lines. To test for an association between specific ALVE inserts and traits, a large genotype by phenotype study was undertaken. Genotypes were obtained for 500 to 1500 males per line, and the phenotypes used were sire-daughter averages. Phenotype data were analyzed by line with a linear model that included the effects of generation, ALVE genotype and their interaction. If genotype effect was significant, the number of ALVE copies was fitted as a regression to estimate additive ALVE gene substitution effect. Significant associations between the presence of specific ALVE inserts and 18 commercially relevant performance and egg quality traits, including egg production, egg weight and albumen height, were observed. When an ALVE was segregating in more than one line, these associations did not always have the same impact (negative, positive or none) in each line. It is hypothesized that the presence of ALVE in the chicken genome may influence production traits by 3 mechanisms: viral protein production may modulate the immune system and impact overall production performance (virus effect); insertional mutagenesis caused by viral integration may cause direct gene alterations or affect gene regulation (gene effect); or the integration site may be within or adjacent to a quantitative trait region which impacts a performance trait (linkage disequilibrium, marker effect).

Heritability of perching behavior and its genetic relationship with incidence of floor eggs in Rhode Island Red chickens.

BACKGROUND: As cage-free production systems become increasingly popular, behavioral traits such as nesting behavior and temperament have become more important. The objective of this study was to estimate heritabilities for frequency of perching and proportion of floor eggs and their genetic correlation in two Rhode Island Red lines. RESULTS: The percent of hens observed perching tended to increase and the proportion of eggs laid on the floor tended to decrease as the test progressed. This suggests the ability of hens to learn to use nests and perches. Under the bivariate repeatability model, estimates of heritability in the two lines were 0.22 ± 0.04 and 0.07 ± 0.05 for the percent of hens perching, and 0.52 ± 0.05 and 0.45 ± 0.05 for the percent of floor eggs. Estimates of the genetic correlation between perching and floor eggs were - 0.26 ± 0.14 and - 0.19 ± 0.27 for the two lines, suggesting that, genetically, there was some tendency for hens that better use perches to also use nests; but the phenotypic correlation was close to zero. Random regression models indicated the presence of a genetic component for learning ability. CONCLUSIONS: In conclusion, perching and tendency to lay floor eggs were shown to be a learned behavior, which stresses the importance of proper management and training of pullets and young hens. A significant genetic component was found, confirming the possibility to improve nesting behavior for cage-free systems through genetic selection.

Identification of recombination hotspots and quantitative trait loci for recombination rate in layer chickens.

BACKGROUND: The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL (quantitative trait loci) for recombination rate in two purebred layer chicken lines. METHODS: A total of 1200 white-egg layers (WL) were genotyped with 580 K SNPs and 5108 brown-egg layers (BL) were genotyped with 42 K SNPs (single nucleotide polymorphisms). Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each 0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL. RESULTS: Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL, respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines. CONCLUSIONS: Differences in the recombination rates, hotspot locations, and QTL regions associated with genome-wide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait.

Genetics of male reproductive performance in White Leghorns.

The ability to produce viable progeny is a complex trait, involving both male and female components. In poultry, mating ratios are usually 1 male to 6 to 12 females. Consequently, the impact of male reproductive failure is much greater than that for a female. In this study, the genetic determination of male reproductive performance, by natural mating and artificial insemination (AI), was evaluated. Semen quality was studied in 1,575 pre-selected (using a selection index of multiple egg production and quality traits) White Leghorn males of a single pure line from multiple generations. A subset of individuals with satisfactory semen quality (based on sperm count and motility) were further tested for subsequent fertility and hatchability. Genetic parameters for fertility (FER), hatch of fertile (HOF), hatch of set (HOS), sperm motility (SM), sperm count (SC), and fertility using AI (FER-AI) were estimated using single- and multi-trait animal models, with generation as fixed effect. Selected birds were genotyped using the 600K Affymetrix SNP chip. Genomic data were analyzed with the BayesB method. FER, HOS, and HOF were highly correlated, both genetically (0.82 to 0.99) and phenotypically (0.28 to 0.99), but genetic correlations with semen quality traits were not strong (0.05 to 0.43) and phenotypic correlations varied between generations (-0.13 to 0.14). Birds used for fertility and hatchability tests were pre-selected based on SM and SC, which could contribute to the lack of strong correlations between these traits (due to truncation of the distribution). Based on pedigree information, low to moderate heritabilities were estimated for reproductive traits (0.08 to 0.21). Markers explained a low proportion of phenotypic variance (0.04 to 0.15), probably due to stringent selection of genotyped individuals and the limited training set size. No genes with large effects were identified. Genomic estimated breeding values were more accurate than pedigree-based estimates but only for HOF and FERT-AI. Despite low estimates of accuracy in validation, genetic trends were positive for all analyzed traits. In conclusion, continued long-term selection can result in genetic improvement of reproductive performance of roosters.

Effects of number of training generations on genomic prediction for various traits in a layer chicken population.

BACKGROUND: Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line. METHODS: Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated. RESULTS: On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions. CONCLUSIONS: The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.

Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions.

BACKGROUND: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci (QTL) with large effects, while other traits have one or several easily detectable QTL with large effects. METHODS: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect. A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP panel. RESULTS: Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1 % with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using BayesB but had no predictive power with GBLUP. CONCLUSIONS: In conclusion, In the presence of large effect QTL, BayesB did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.

Response and inbreeding from a genomic selection experiment in layer chickens.

BACKGROUND: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken. METHODS: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production. RESULTS: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line. CONCLUSIONS: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

Genetic variation within the Mx gene of commercially selected chicken lines reveals multiple haplotypes, recombination and a protein under selection pressure.

The Mx protein is one of the best-characterized interferon-stimulated antiviral mediators. Mx homologs have been identified in most vertebrates examined; however, their location within the cell, their level of activity, and the viruses they inhibit vary widely. Recent studies have demonstrated multiple Mx alleles in chickens and some reports have suggested a specific variant (S631N) within exon 14 confers antiviral activity. In the current study, the complete genome of nine elite egg-layer type lines were sequenced and multiple variants of the Mx gene identified. Within the coding region and upstream putative promoter region 36 SNP variants were identified, producing a total of 12 unique haplotypes. Each elite line contained from one to four haplotypes, with many of these haplotypes being found in only one line. Observation of changes in haplotype frequency over generations, as well as recombination, suggested some unknown selection pressure on the Mx gene. Trait association analysis with either individual SNP or haplotypes showed a significant effect of Mx haplotype on several egg production related traits, and on mortality following Marek's disease virus challenge in some lines. Examination of the location of the various SNP within the protein suggests synonymous SNP tend to be found within structural or enzymatic regions of the protein, while non-synonymous SNP are located in less well defined regions. The putative resistance variant N631 was found in five of the 12 haplotypes with an overall frequency of 47% across the nine lines. Two Mx recombinants were identified within the elite populations, indicating that novel variation can arise and be maintained within intensively selected lines. Collectively, these results suggest the conflicting reports in the literature describing the impact of the different SNP on chicken Mx function may be due to the varying context of haplotypes present in the populations studied.

Pedigree and genomic analyses of feed consumption and residual feed intake in laying hens.

Efficiency of production is increasingly important with the current escalation of feed costs and demands to minimize the environmental footprint. The objectives of this study were 1) to estimate heritabilities for daily feed consumption and residual feed intake and their genetic correlations with production and egg-quality traits; 2) to evaluate accuracies of estimated breeding values from pedigree- and marker-based prediction models; and 3) to localize genomic regions associated with feed efficiency in a brown egg layer line. Individual feed intake data collected over 2-wk trial periods were available for approximately 6,000 birds from 8 generations. Genetic parameters were estimated with a multitrait animal model; methods BayesB and BayesCπ were used to estimate marker effects and find genomic regions associated with feed efficiency. Using pedigree information, feed efficiency was found to be moderately heritable (h(2) = 0.46 for daily feed consumption and 0.47 for residual feed intake). Hens that consumed more feed and had greater residual feed intake (lower efficiency) had a genetic tendency to lay slightly more eggs with greater yolk weights and albumen heights. Regions on chromosomes 1, 2, 4, 7, 13, and Z were found to be associated with feed intake and efficiency. The accuracy from genomic prediction was higher and more persistent (better maintained across generations) than that from pedigree-based prediction. These results indicate that genomic selection can be used to improve feed efficiency in layers.

Genome-wide association study for Marek's disease mortality in layer chickens.

A genome-wide association study (GWAS) using Bayesian variable selection was performed to determine genomic regions associated with mortality due to Marek's disease virus (MDV) infection in layers. Mortality (%) under experimental disease challenge (500 plaque-forming units of a very virulent plus MDV strain) was recorded for progeny groups (average 15.5 birds; range 3 to 30) of 253 genotyped sires from four generations of a brown-egg layer line. An additional generation of 43 sires with progeny data was used to validate results. Sires were genotyped with a 42K Illumina single-nucleotide polymorphism (SNP) chip. Methods BayesB (pi = 0.995) and BayesCpi, with or without weighting residuals by the size of progeny groups were applied. The proportion of genetic variance contributed by SNPs within each 1-megabase (Mb) genomic region was quantified. Average mortality was 33% but differed significantly between generations. Genetic markers explained about 11% of phenotypic variation in mortality. Correlations between genomic estimated breeding values and percentage of progeny mortality for the validation generation (sons of individuals in training) were 0.12, 0.17, 0.02, and 0.16 for BayesB, weighted BayesB, BayesCpi, and weighted BayesCpi, respectively, when using the whole genome, and 0.03, 0.20, -0.06, and 0.14, when using only SNP from the 10, 1-Mb regions, explaining the largest proportion of genetic variance according to each method. Results suggest that regions on chromosomes 2, 3, 4, 9, 15, 18, and 21 are associated with Marek's disease resistance and can be used for selection and that accounting for the size of progeny groups has a large impact on correct localization of such genomic regions.

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens.

BACKGROUND: The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information. METHODS: The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability. RESULTS: Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model.

BACKGROUND: Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line. METHODS: The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV. RESULTS: Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.

Extent and consistency of linkage disequilibrium and identification of DNA markers for production and egg quality traits in commercial layer chicken populations.

BACKGROUND: The genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods (SNP-wise association and genome-wise Bayesian analysis) to identify markers associated with egg-quality and egg-production phenotypes. RESULTS: The LD between markers pairs was high at short distances (r2 > 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time (phi > 0.2) and an additional ten 3-SNP windows that had a sum of phi greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses. CONCLUSION: High LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations.