American Journal of Human Genetics ( IF 9.8 ) Pub Date : 2022-04-13 , DOI: 10.1016/j.ajhg.2022.03.013 Shuang Song 1 , Wei Jiang 2 , Yiliang Zhang 2 , Lin Hou 3 , Hongyu Zhao 2
Heritability is a fundamental concept in genetic studies, measuring the genetic contribution to complex traits and bringing insights about disease mechanisms. The advance of high-throughput technologies has provided many resources for heritability estimation. Linkage disequilibrium (LD) score regression (LDSC) estimates both heritability and confounding biases, such as cryptic relatedness and population stratification, among single-nucleotide polymorphisms (SNPs) by using only summary statistics released from genome-wide association studies. However, only partial information in the LD matrix is utilized in LDSC, leading to loss in precision. In this study, we propose LD eigenvalue regression (LDER), an extension of LDSC, by making full use of the LD information. Compared to state-of-the-art heritability estimating methods, LDER provides more accurate estimates of SNP heritability and better distinguishes the inflation caused by polygenicity and confounding effects. We demonstrate the advantages of LDER both theoretically and with extensive simulations. We applied LDER to 814 complex traits from UK Biobank, and LDER identified 363 significantly heritable phenotypes, among which 97 were not identified by LDSC.
中文翻译:
利用 LD 特征值回归改进对 SNP 遗传力和混杂通货膨胀的估计
遗传力是遗传研究中的一个基本概念,用于衡量遗传对复杂性状的贡献,并带来对疾病机制的见解。高通量技术的进步为遗传力估计提供了许多资源。连锁不平衡 (LD) 评分回归 (LDSC) 通过仅使用全基因组关联研究发布的汇总统计数据来估计单核苷酸多态性 (SNP) 中的遗传力和混杂偏差,例如隐蔽相关性和群体分层。然而,在 LDSC 中仅利用了 LD 矩阵中的部分信息,导致精度损失。在这项研究中,我们通过充分利用 LD 信息提出 LD 特征值回归 (LDER),它是 LDSC 的扩展。与最先进的遗传力估计方法相比,LDER 提供更准确的 SNP 遗传力估计,并更好地区分由多基因性和混杂效应引起的膨胀。我们从理论上和广泛的模拟中证明了 LDER 的优势。我们将 LDER 应用于 UK Biobank 的 814 个复杂性状,LDER 识别出 363 个显着遗传表型,其中 97 个未被 LDSC 识别。