Janet E. Fulton

Development of a high density 600K SNP genotyping array for chicken

BackgroundHigh density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.ResultsWe report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources (experimental, commercial broiler and layer lines) by pooling 10–15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses.ConclusionsThis Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants.

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

BackgroundGenomic 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.MethodsThe 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.ResultsUsing 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 Across Generations of Linkage Disequilibrium in Commercial Layer Chicken Breeding Populations

Recent studies report a surprisingly high degree of marker-to-marker linkage disequilibrium (LD) in ruminant livestock populations. This has important implications for QTL mapping and marker-assisted selection. This study evaluated LD between microsatellite markers in a number of breeding populations of layer chickens using the standardized chi-square (χ2′) measure. The results show appreciable LD among markers separated by up to 5 cM, decreasing rapidly with increased separation between markers. The LD within 5 cM was strongly conserved across generations and differed among chromosomal regions. Using marker-to-marker LD as an indication for marker-QTL LD, a genome scan of markers spaced 2 cM apart at moderate power would have good chances of uncovering most QTL segregating in these populations. However, of markers showing significant trait associations, only 57% are expected to be within 5 cM of the responsible QTL, and the remainder will be up to 20 cM away. Thus, high-resolution LD mapping of QTL will require dense marker genotyping across the region of interest to allow for interval mapping of the QTL.

Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens.

The pioneering work by Professor Soller et al., among others, on the use of genetic markers to analyze quantitative traits has provided opportunities to discover their genetic architecture in livestock by identifying quantitative trait loci (QTL). The recent availability of high-density single nucleotide polymorphism (SNP) panels has advanced such studies by capitalizing on population-wide linkage disequilibrium at positions across the genome. In this study, genomic prediction model Bayes-B was used to identify genomic regions associated with the mean and standard deviation of egg weight at three ages in a commercial brown egg layer line. A total of 24,425 segregating SNPs were evaluated simultaneously using over 2900 genotyped individuals or families. The corresponding phenotypic records were represented as individual measurements or family means from full-sib progeny. A novel approach using the posterior distribution of window variances from the Monte Carlo Markov Chain samples was used to describe genetic architecture and to make statistical inferences about regions with the largest effects. A QTL region on chromosome 4 was found to explain a large proportion of the genetic variance for the mean (30%) and standard deviation (up to 16%) of the weight of eggs laid at specific ages. Additional regions with smaller effects on chromosomes 2, 5, 6, 8, 20, 23, 28 and Z showed suggestive associations with mean egg weight and a region on chromosome 13 with the standard deviation of egg weight at 26-28 weeks of age. The genetic architecture of the analyzed traits was characterized by a limited number of genes or genomic regions with large effects and many regions with small polygenic effects. The region on chromosome 4 can be used to improve both the mean and standard deviation of egg weight by marker-assisted selection.

Analysis of polymorphisms in the major expressed class I locus (B-FIV) of the chicken.

Abstract We analyzed the polymorphic nature of eleven alleles expressed by the major class I locus (B-FIV) in chickens. Similar to mammalian class I loci, the nucleotide substitutions with high variability occur in exons 2 and 3 encoding the α1 and α2 domains. However, the nonsynonymous to synonymous ratio of nucleotide substitutions in exon 3 encoding the α helix and β sheets is reversed compared with HLA. The region of exon 3 encoding the α2 helix demonstrates a much lower nonsynonymous to synonymous ratio, suggesting evolutionary selection of a more conserved α2 helix in B-FIV compared with HLA. Amino acid residues with high Wu-Kabat variability are typically located in positions predicted to impact antigen presentation. B-FIV amino acid residues predicted to interact with the CDR1α region of the T-cell receptor (Tcr) demonstrate less variability than in mouse and human class I alleles. The combination of a reduced nonsynonymous to synonymous ratio in exon 3 encoding the α2 helix and the limited variability in CDR1α contact residues is discussed with regard to concerted evolution between a minimal major histocompatibility complex and compaction of Tcr variable gene segments in the chicken.

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

BackgroundThe 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.MethodsThe 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.ResultsPedigree 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.

Extent and Consistency of Linkage Disequilibrium and Identification of DNA Markers for Production and Egg Quality Traits in Commercial Layer Chicken Populations

BackgroundThe 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.ResultsThe 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 (φ > 0.2) and an additional ten 3-SNP windows that had a sum of φ greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses.ConclusionHigh 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.

A New Chicken Genome Assembly Provides Insight into Avian Genome Structure

The importance of the Gallus gallus (chicken) as a model organism and agricultural animal merits a continuation of sequence assembly improvement efforts. We present a new version of the chicken genome assembly (Gallus_gallus-5.0; GCA_000002315.3), built from combined long single molecule sequencing technology, finished BACs, and improved physical maps. In overall assembled bases, we see a gain of 183 Mb, including 16.4 Mb in placed chromosomes with a corresponding gain in the percentage of intact repeat elements characterized. Of the 1.21 Gb genome, we include three previously missing autosomes, GGA30, 31, and 33, and improve sequence contig length 10-fold over the previous Gallus_gallus-4.0. Despite the significant base representation improvements made, 138 Mb of sequence is not yet located to chromosomes. When annotated for gene content, Gallus_gallus-5.0 shows an increase of 4679 annotated genes (2768 noncoding and 1911 protein-coding) over those in Gallus_gallus-4.0. We also revisited the question of what genes are missing in the avian lineage, as assessed by the highest quality avian genome assembly to date, and found that a large fraction of the original set of missing genes are still absent in sequenced bird species. Finally, our new data support a detailed map of MHC-B, encompassing two segments: one with a highly stable gene copy number and another in which the gene copy number is highly variable. The chicken model has been a critical resource for many other fields of study, and this new reference assembly will substantially further these efforts.

Mapping QTL affecting resistance to Marek's disease in an F6 advanced intercross population of commercial layer chickens

BackgroundMareks disease (MD) is a T-cell lymphoma of chickens caused by the Mareks disease virus (MDV), an oncogenic avian herpesvirus. MD is a major cause of economic loss to the poultry industry and the most serious and persistent infectious disease concern. A full-sib intercross population, consisting of five independent families was generated by crossing and repeated intercrossing of two partially inbred commercial White Leghorn layer lines known to differ in genetic resistance to MD. At the F6 generation, a total of 1615 chicks were produced (98 to 248 per family) and phenotyped for MD resistance measured as survival time in days after challenge with a very virulent plus (vv+) strain of MDV.ResultsQTL affecting MD resistance were identified by selective DNA pooling using a panel of 15 SNPs and 217 microsatellite markers. Since MHC blood type (BT) is known to affect MD resistance, a total of 18 independent pool pairs were constructed according to family × BT combination, with some combinations represented twice for technical reasons. Twenty-one QTL regions (QTLR) affecting post-challenge survival time were identified, distributed among 11 chromosomes (GGA1, 2, 3, 4, 5, 8, 9, 15, 18, 26 and Z), with about two-thirds of the MD resistance alleles derived from the more MD resistant parental line. Eight of the QTLR associated with MD resistance, were previously identified in a backcross (BC) mapping study with the same parental lines. Of these, 7 originated from the more resistant line, and one from the less resistant line.ConclusionThere was considerable evidence suggesting that MD resistance alleles tend to be recessive. The width of the QTLR for these QTL appeared to be reduced about two-fold in the F6 as compared to that found in the previous BC study. These results provide a firm basis for high-resolution linkage disequilibrium mapping and positional cloning of the resistance genes.

Genome-wide association study for egg production and quality in layer chickens

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome-wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13,000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. (2012). The Bayesian whole genome prediction model--BayesB (Meuwissen et al. 2001) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non-zero effect with posterior probability > 0.9 for one or more traits.