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Abstract Background As a major economic trait in poultry, egg production efficiency attracts widespread interest in breeding and production. However, limited information is available about the underlying genetic architecture of egg production traits in ducks. In this paper, we analyzed six egg production-related traits in 352 F2 ducks derived from reciprocal crosses between mallard and Pekin ducks. Results Feed conversation ratio (FCR) was positively correlated with feed intake but negatively correlated with egg-related traits, including egg weight and egg production, both phenotypically and genetically. Estimates of pedigree-based heritability were higher than 0.2 for all traits investigated, except hip-width. Based on whole-genome sequencing data, we conducted genome-wide association studies to identify genomic regions associated with these traits. In total, 11 genomic regions were associated with FCR. No genomic regions were identified as significantly associated with hip-width, total feed intake, average daily feed intake, and total egg production. Analysis of selective sweeps between mallard and Pekin ducks confirmed three of these genomic regions on chromosomes 13, 3 and 6. Within these three regions, variants in candidate genes that were in linkage disequilibrium with the GWAS leader single nucleotide polymorphisms (SNPs) (Chr13:2,196,728, P = 7.05 × 10–14; Chr3:76,991,524, P = 1.06 × 10–12; Chr6:20,356,803, P = 1.14 × 10–10) were detected. Thus, we identified 31 potential candidate genes associated with FCR, among which the strongest candidates are those that are highly expressed in tissues involved in reproduction and nervous system functions of ducks: CNTN4, CRBR, GPR63, KLHL32, FHL5, TRNT1, MANEA, NDUFAF4, and SCD. Conclusions For the first time, we report the identification of genomic regions that are associated with FCR in ducks and our results illustrate the genomic changes that occurred during their domestication and are involved in egg production efficiency.

Abstract Background The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and H-AM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population. Results VC estimates from H-AM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in H-AM estimates arises when selective genotyping is used to construct the H-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of genetic variance from A-AM were also significantly overestimated when the effect of genomic selection was strong. Our results suggest that VC estimates from H-AM under random genotyping have the expected values. Predicted breeding values from H-AM were inflated when VC estimates were biased, and inflation differed between genotyped and ungenotyped animals, which can lead to suboptimal selection decisions. Conclusions We conclude that VC estimates from H-AM are biased with selective genotyping, but are close to expected values with random genotyping.VC estimates from A-AM in populations under genomic selection are also biased but to a much lesser degree. Therefore, we recommend the use of H-AM with random genotyping to estimate VC for populations under genomic selection. Our results indicate that it is still possible to use selective genotyping in selection, but then VC estimation should avoid the use of genotypes from one side only of the distribution of phenotypes. Hence, a dual genotyping strategy may be needed to address both selection and VC estimation.