Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

Abstract

Wool traits of rabbits are important in fiber production and model organism research on hair growth, while the genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a well-known fiber breed. Balancing genotyping cost and variant detection, we proposed low-coverage whole genome sequencing (LCS) followed by genotype imputation for genotyping. Different genotype imputation strategies, sequencing coverages and sample sizes were compared, and we found by BaseVar + STITCH, genotyping reached high accuracy (>0.97) at a depth of 1.0X and a sample size > 300. Multivariate GWAS followed by conditional GWAS and confidence interval estimation of QTLs were used to reveal the genetic architecture of wool traits. Six QTLs were detected with phenotypic variation contribution ranging from 0.42% to 7.50%. Gene-level mapping implicated FGF10 associated with fiber growth and diameter, which supported previous function research on fibroblast growth factor family in other species and provided genetic information for wool rabbit breeding. We suggest LCS as a cost-effective alternative for assessing common variants. GWAS combined with LCS can excavate QTLs and fine-map genes associated with quantitative traits. This study provides a powerful analysis mentality for investigating complex traits, which lays the foundation for genomic breeding.