Genome‐wide association study for egg production and quality in layer chickens. (17th March 2014)

• Record Type: Journal Article
• Authors: Wolc, A.; Arango, J.; Jankowski, T.; Dunn, I.; Settar, P.; Fulton, J.E.; O'Sullivan, N.P.; Preisinger, R.; Fernando, R.L.; Garrick, D.J.; Dekkers, J.C.M.
• Is Part Of: Journal of animal breeding and genetics. Volume 131:Number 3(2014:Jun.)
• Page Start: 173
• Page End: 182
• Publication Date: 2014-03-17
• Subjects: Livestock -- Breeding -- Periodicals; Livestock -- Genetics -- Periodicals; 636.0820
• DOI: 10.1111/jbg.12086
• Languages: English
• ISSNs: 0931-2668

Abstract

<abstract abstract-type="main" id="jbg12086-abs-0001"> <title>Summary</title> <p>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 <italic>et al</italic>. (<xref ref-type="link" rid="jbg12086-bib-0053">2012</xref>). The Bayesian whole genome prediction model – BayesB (Meuwissen <italic>et al</italic>. <xref ref-type="link" rid="jbg12086-bib-0033">2001</xref>) 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.</p> </abstract>