Bayesian fine-mapping pinpoints candidate genes and pleiotropic loci of production traits from a chicken backcrossing scheme

Background Understanding 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, that helps to distinguish shared genetic effects from those specific to individual traits. We applied a Bayesian-based fine-mapping approach to refine GWAS-identified QTLs and to 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). Results GWAS identified multiple significant loci associated with BW and EW, with higher heritability estimated. EN across periods showed a more polygenic architecture with lower heritability. 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. Bayesian fine-mapping successfully pinpointed candidate genes such as NCAPG , LCORL , and IGF2BP1 , 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 signals suggested indirect genetic effects on traits rather than direct causal relationships. Conclusions This study demonstrates the power of combining GWAS with imputation and Bayesian fine-mapping 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.