整合结构变异的奶山羊体尺全基因组关联分析
收稿日期: 2025-11-06
修回日期: 2026-01-14
网络出版日期: 2026-02-05
基金资助
国家自然科学基金项目(3257200948);国家自然科学基金项目(U23A20228);新疆维吾尔自治区重大科技专项(2024A02004-2)
Genome-wide association study of body size traits in dairy goats integrating structural variants
Received date: 2025-11-06
Revised date: 2026-01-14
Online published: 2026-02-05
Supported by
National Natural Science Foundation of China(3257200948);National Natural Science Foundation of China(U23A20228);Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region(2024A02004-2)
体尺性状作为衡量体型与生长水平的重要表型特征，与生产性能密切相关。为揭示萨能奶山羊体尺性状的遗传基础并发掘潜在分子标记，本研究基于635只萨能奶山羊的低深度全基因组测序数据，依托本课题组构建的山羊参考面板进行基因型填充后，共获得14M的单核苷酸多态位点(single nucleotide polymorphism，SNP)和45K的结构变异(structural variant，SV)。本研究利用SNP估计遗传参数，分别对SNP与SV开展单性状(single-trait，ST)和多性状全基因组关联分析(multi-trait genome-wide association study，MT-GWAS)。结果显示，体高、体长和十字部高3个性状均为中等遗传力，性状间的遗传与表型均为正相关。ST-GWAS共鉴定出56个显著SNP位点和3个显著SV位点，注释到TNFSF11、HDAC4、MURC等30个候选基因；MT-GWAS检测出ST-GWAS未发现的2个显著SNP位点和2个显著SV位点，注释后新增4个候选基因(S100A11、LOC108635595、GALNTL6和FSIP2)。值得注意的是，在第10号染色体的NRXN3附近发现了体长与十字部高的重叠关联信号，共定位分析支持该区域存在共享因果变异。KEGG富集分析显示候选基因主要富集于脂肪酸生物合成及相关代谢通路。本研究表明，整合SV的MT-GWAS可提供SNP之外的关联信号，为开展分子标记辅助选择与体型精准选育提供了理论基础。
关键词： 萨能奶山羊; 体尺性状; 结构变异; 多性状全基因组关联分析; 共定位分析
冯衍帅, 李嘉鑫, 赵炯尧, 曹家乐, 王兴权, 姚晓婷, 傅家琪, 王喜宏 . 整合结构变异的奶山羊体尺全基因组关联分析[J]. 遗传, 2026 , 48(4) : 393 -406 . DOI: 10.16288/j.yczz.25-293
Body size traits serve as crucial phenotypic indicators of body conformation and growth, showing a close correlation with production performance. To elucidate the genetic basis of these traits and identify potential molecular markers in Saanen dairy goats, we analyzed low-coverage whole-genome sequencing (lcWGS) data from 635 individuals. Following genotype imputation based on an in-house goat reference panel, we obtained 14 million single-nucleotide polymorphisms (SNPs) and 45 thousand structural variants (SVs). Genetic parameters were estimated using SNP data. Subsequently, single-trait (ST) and multi-trait genome-wide association studies (MT-GWAS) were conducted using both SNP and SV datasets. Results indicated that body height, body length, and rump height possess moderate heritability, with positive genetic and phenotypic correlations observed among these traits. ST-GWAS identified 56 significant SNPs and 3 significant SVs, mapping to 30 candidate genes, including TNFSF11, HDAC4, and MURC. Furthermore, MT-GWAS detected 2 significant SNPs and 2 significant SVs missed by ST-GWAS, identifying 4 additional candidate genes (S100A11, LOC108635595, GALNTL6, and FSIP2). Notably, overlapping association signals for body length and rump height were observed near NRXN3 on chromosome 10, with colocalization analysis supporting the existence of a shared causal variant in this region. KEGG enrichment analysis indicated that candidate genes were primarily enriched in fatty acid biosynthesis and related metabolic pathways. In conclusion, this study shows that integrating structural variants into MT-GWAS can reveal association signals beyond those captured by SNPs, providing a theoretical basis for marker-assisted selection and precision breeding for body conformation.
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