ArticleGenetics, selection, evolution : GSE2022
Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing.
Article in Genetics, selection, evolution : GSE, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Low-Coverage Whole-Genome Resequencing in Livestock and Poultry: Statistical Foundations, Applications and Future Directions.Biology · 2026Review
- Integrative analysis of GWAS, Bayesian fine-mapping, Mendelian randomization and colocalization reveals genetic determinants underlying milk-related traits in dairy cattle.Genetics, selection, evolution : GSE · 2026Article
- Genomic Insights Into the Origin, Decline and Recovery of the Once Critically Endangered Iberian Lynx.Molecular ecology · 2025Review
- Integration Analysis of Transcriptome Sequencing and Whole-Genome Resequencing Reveal Wool Quality-Associated Key Genes in Zhexi Angora Rabbits.Veterinary sciences · 2024Article
- Article
- Genotype imputation in F2 crosses of inbred lines.Bioinformatics advances · 2024Article
- Evaluating the potential of (epi)genotype-by-low pass nanopore sequencing in dairy cattle: a study on direct genomic value and methylation analysis.Journal of animal science and biotechnology · 2023Article
- Marker density and statistical model designs to increase accuracy of genomic selection for wool traits in Angora rabbits.Frontiers in genetics · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundRabbit 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.
resultsDifferent 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.
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.