发表机构
Key Laboratory of Pig Digital and Intelligent Breeding, Ministry of Agriculture and Rural Affairs & College of Animal Science and Technology, Huazhong Agricultural University; Yazhouwan National Laboratory; School of Computer Science and Technology, Wuhan University of Technology; Hubei Hongshan Laboratory; Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education; Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education; College of Informatics, Huazhong Agricultural University; Institute of Food Nutrition and Health, Jingchu University of Technology（华中农业大学动物科学技术学院 农业农村部猪数字化与智能育种重点实验室; 崖州湾国家实验室; 武汉理工大学计算机科学与技术学院; 湖北洪山实验室; 教育部农业动物遗传育种与繁殖重点实验室; 教育部畜禽养殖与可持续生产前沿科学中心; 华中农业大学信息学院; 荆门理工学院食品营养与健康研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出统一无监督框架UCALM，直接从数据推断遗传同质亚组并结合分层荟萃分析，在异质群体中降低基因组膨胀并揭示被掩盖的关联位点。
AI 中文摘要
全基因组关联研究（GWAS）极大地推进了复杂性状和疾病相关遗传变异的发现。然而，在异质群体中，现有的GWAS策略通常要么在群体同质性的假设下汇集所有个体，要么在预定义的亚组之间进行荟萃分析，当潜在的遗传异质性削弱亚组特异性效应并掩盖真实关联，或亚组标签不精确时，这两种方法均受到限制。在此，我们提出UCALM，一个统一的无监督框架，它直接从数据中推断遗传同质亚组，并将亚组特异性GWAS与一种新颖的分层荟萃分析方法相结合，以捕获共享和亚组特异性的关联信号。通过广泛的模拟以及对大规模人类和牲畜队列的分析，包括英国生物银行（n≈487,000）和异质性猪队列（n≈85,000），我们证明UCALM将24个英国生物银行性状的平均基因组膨胀因子显著降低至1.17，而GLM为1.43，LDAK-KVIK为1.37，并进一步在猪队列中揭示了74个先前被传统方法掩盖的位点。我们的结果为结构化群体中的关联映射建立了一种稳健且广泛适用的策略，并提高了跨物种遗传信号的分辨率。
英文摘要
Genome-wide association studies (GWAS) have greatly advanced the discovery of genetic variants underlying complex traits and diseases. Yet in heterogeneous populations, existing GWAS strategies typically either pool all individuals under an assumption of population homogeneity or perform meta-analysis across predefined subgroups, both of which are limited when latent genetic heterogeneity attenuates subgroup-specific effects and masks true associations or subgroup labels are imprecise. Here we present UCALM, a unified unsupervised framework that infers genetically homogeneous subgroups directly from the data and integrates subgroup-specific GWAS with a novel layered meta-analysis method to capture both shared and subgroup-specific association signals. Through extensive simulations and analyses of large-scale human and livestock cohorts, including the UK Biobank ($n \approx 487{,}000$) and a heterogeneous pig cohort ($n \approx 85{,}000$), we demonstrate that UCALM substantially alleviated the mean genomic inflation across 24 UK Biobank traits to 1.17 compared with 1.43 for GLM and 1.37 for LDAK-KVIK, and further revealed 74 loci in the pig cohort that were previously obscured by conventional approaches. Our results establish a robust and broadly applicable strategy for association mapping in structured populations and improve the resolution of genetic signals across diverse species.
Comments73 pages, including Supplementary Information
