ArticleBMC genomics2026
Bayesian fine-mapping pinpoints candidate genes and pleiotropic loci of production traits from a chicken backcrossing scheme.
Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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8 authors.
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Abstract
backgroundUnderstanding the genetic architecture of economically important traits in poultry is critical for improving breeding strategies. In this study, we investigated a backcrossing scheme between a White Layer line and Araucana chickens. Genome-wide association studies (GWAS) were performed using array-genotyped and imputed data. We also explored the use of correlated traits as covariates, which helps to distinguish between pleiotropic and trait-specific associations. We applied two Bayesian fine-mapping methods to refine GWAS-identified QTLs and pinpoint candidate variants and genes associated with egg number (EN, from 20 to 71 weeks), egg weight (EW, from 30 to 70 weeks), and body weight (BW, at 32 weeks): the forward selection approach and functional annotation enrichment implemented in BFMAP, and the shotgun stochastic search algorithm implemented in FINEMAP.
resultsGWAS identified multiple significant loci associated with BW and EW, with high heritability estimated for these traits. EN showed a more polygenic architecture with lower heritability across most periods. Including correlated traits as covariates in GWAS revealed pleiotropic loci, particularly on chromosomes 1 and 4, that influenced both BW and EW, as well as loci specific to individual traits. Both fine-mapping methods successfully pinpointed candidate genes such as NCAPG, LCORL, and IGF2BP1, which are well known for their roles in growth and body size across species. Several novel candidate genes were also highlighted for EN. Notably, some fine-mapped results reflected patterns consistent with the covariate-adjusted GWAS results.
conclusionsThis study demonstrates the power of combining GWAS with imputation and fine-mapping methods in chickens to uncover the genetic basis of economically important traits. Furthermore, incorporating correlated traits as covariates in GWAS provided valuable insights, enabling the distinction between pleiotropic and trait-specific loci. Together, these approaches refine GWAS signals and deepened our understanding of the genetic architecture underlying complex traits.
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