4.5 Article

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

期刊

JOURNAL OF ANIMAL BREEDING AND GENETICS
卷 131, 期 3, 页码 173-182

出版社

WILEY
DOI: 10.1111/jbg.12086

关键词

Egg production; egg quality; GWAS; laying hens

资金

  1. BBSRC [BBS/E/D/20221656, BBS/E/D/20221655, BBS/E/D/20320000] Funding Source: UKRI
  2. Biotechnology and Biological Sciences Research Council [BBS/E/D/20320000, BBS/E/D/20221655, BBS/E/D/20221656] Funding Source: researchfish
  3. Biotechnology and Biological Sciences Research Council [BBS/E/D/20320000] Funding Source: Medline

向作者/读者索取更多资源

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 13000 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 etal. (). The Bayesian whole genome prediction model - BayesB (Meuwissen etal. ) was used to locate 1Mb regions that were most strongly associated with each trait. The posterior probability of a 1Mb 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) 1Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78Mb 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 1Mb windows were estimated to have a non-zero effect with posterior probability > 0.9 for one or more traits.

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