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. 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. 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.

中文翻译：

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通过低覆盖测序经济有效地剖析兔子复杂羊毛性状的遗传结构

兔毛特性在纤维生产和毛发生长模型生物研究中很重要，但它们的遗传结构仍然不清楚。在这项研究中，我们重点研究了安哥拉兔的羊毛特性，安哥拉兔是一种以羊毛质量着称的品种。考虑到生成群体规模序列数据的成本和使用芯片数据检测变异的偏差，使用低覆盖率全基因组测序 (LCS) 数据开发有效的基因分型策略对于进行遗传分析是必要的。不同的基因型插补策略（BaseVar + STITCH、Bcftools + Beagle4 和 GATK + Beagle5）、测序覆盖率（0.1X、0.5X、1.0X、1.5X 和 2.0X）和样本量（100、200、300、400） 、500 和 600) 进行了比较。我们的结果表明，在测序深度为 1 时使用 BaseVar + STITCH。样本量大于 300 的 0X 导致最高的基因分型准确度，基因型一致性高于 98.8%，基因分型准确度高于 0.97。我们进行了多变量全基因组关联研究 (GWAS)，然后进行了条件 GWAS 和数量性状位点 (QTL) 置信区间的估计，以研究羊毛性状的遗传结构。检测到 6 个 QTL，解释了 0.4% 到 7.5% 的表型变异。基因水平作图确定成纤维细胞生长因子 10 (FGF10) 基因与纤维生长和直径相关，这与之前对其他物种 FGF 基因家族的功能数据分析的结果一致，并且与毛兔育种相关。我们建议，LCS 后接插补可以作为阵列和深度测序评估常见变异的一种经济高效的替代方法。GWAS 结合 LCS 可以识别与数量性状相关的新 QTL 和候选基因。本研究为研究复杂性状的遗传结构提供了一种经济高效且功能强大的方法，这将有助于基因组育种应用。