Identifying candidate genetic variants for egg number by analyzing over 1,000 fully sequenced layers.

Background: Egg production over a long laying cycle until 700 days of age is preferred for modern layer chicken breeding. It is influenced by the onset of laying, stability during the peak period, and persistence at late laying stages. Conventional single-single nucleotide polymorphism (SNP) association analyses have identified additive loci, but few studies have explored dominance effects or integrated multiomics data to investigate the genetic basis of egg production traits from the onset to 700 days of age. A full diallel cross of 1,004 chickens was·subjected to whole-genome sequencing. Transcriptome data from the ovary were available for a subset of 120 chickens. A genome-wide association study (GWAS) was conducted using an additive-dominance model for cumulative egg number and egg number at different stages. Expression quantitative trait loci (eQTL) mapping was applied to investigate associations between SNPs and gene expression. A transcriptome-wide association study (TWAS) was conducted to explore the associations between gene expression and egg production traits to identify candidate genes.

Results: The additive-dominance model identified 5,892 significant SNPs, comprising 805 additive SNPs and 360 dominance SNPs shared between 2 or more traits. By integrating loci identified through GWAS with eQTL mapping, the expression level of 27 genes was associated with significant SNPs. Further integration with TWAS results revealed 4 novel candidate genes. For the loci with significant SNP effects, we found a positive but insignificant correlation between the ratios of dominance to additive effects and observed heterosis. Observed heterosis was positively correlated with heterosis predicted based on dominance effects and allele frequencies of all SNPs.

Conclusions: We identified candidate genetic variants for egg production traits by analyzing 1,004 fully sequenced layers. Detection benefited from incorporating dominance into the GWAS model. Traits with higher heterosis tended to be more affected by genes with a dominant mode of action. Moreover, multiomics data allowed for the contribution to deciphering genetic mechanisms underlying egg production by establishing connections between genetic variants, gene expression, and egg number.