Effectively controlling for sample relatedness in large-scale GWAS: application to 79 EHR-derived longitudinal traits

Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPAGRM, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPAGRM can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPAGRM(CCT) to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPAGRM maintains well-controlled type I error rates and SPAGRM(CCT) can serve as a broadly effective method. Applying SPAGRM to 79 longitudinal traits extracted from UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.样本亲缘关系是全基因组关联分析（GWAS）中的主要混杂因素，若未进行恰当控制，可能导致一类错误率虚高。常用的校正策略是将与遗传亲缘关系矩阵（GRM）相关的随机效应纳入回归模型。然而，针对复杂性状（如纵向性状）的大规模全基因组关联分析，该方法面临诸多挑战。为此，本研究提出一种可扩展且精准的分析框架SPAGRM，该框架通过对基因型联合分布的精准近似来校正样本亲缘关系。SPAGRM可采用无GRM模型，因此适用于多种性状类型与统计方法，包括针对纵向性状的线性混合模型与广义估计方程。结合鞍点近似的混合策略，可大幅提升低频与罕见遗传变异的分析准确性，尤其适用于表型分布不均衡的场景。本研究还提出SPAGRM(CCT)，通过柯西组合检验（Cauchy combination test）整合不同模型下的分析结果。大量模拟实验与真实数据分析结果表明，SPAGRM可严格控制一类错误率，而SPAGRM(CCT)则是一种普适性较强的有效分析方法。将SPAGRM应用于从英国生物银行（UK Biobank）初级医疗数据中提取的79项纵向性状，本研究共鉴定出7463个遗传位点，首次针对这些性状开展纵向全基因组关联分析。