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

Abstract

Background Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses. Results Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding. Conclusions We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

Evaluating the potential of (epi)genotype-by-low pass nanopore sequencing in dairy cattle: a study on direct genomic value and methylation analysis

“…Low pass sequencing has captured interest in later years due to the large amount of information it provides in genetic evaluations and because cost are decreasing fast (e.g., [ 22 , 23 ]). This is the first study evaluating the similarities between DGVs obtained from traditional SNP chips and different ONT chemistries in an epi-genotype-by-LPS framework, and simultaneously extracting methylation marks, which we called EpiGLowS.…”

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits

“…After the initial QC, the data were compared to the reference genome using Bwa [35] to determine the location of each read on the reference genome. Then, the genotypes were imputed using STITCH [36] and Beagle v5.1 [37] according to Wang et al [38]. A total of 20,125,019 high-quality SNPs (imputation accuracy above 98%) were used after stringent quality control.…”

Genotype imputation in F2 crosses of inbred lines

“…Although STITCH has been used successfully in a number of diverse populations ( Nicod et al 2016 , Zan et al 2019 , Scott et al 2021 , Zha et al 2023 , Blain et al 2024 , Li et al 2024 ), often the parameterization and the assessment of the required level of coverage are presented as a single best choice and with little exploration of alternative parameters and experimental designs. Previous work explored some of the parameters that influence STITCH accuracy in an Angora rabbit population ( Wang et al 2022 ), but this work did not explore the complex interplay between sequencing depth, population size, and cross structure.…”