英国牛津大学Pier Francesco Palamara研究小组发现，一种可扩展的变分推断方法可用于增强混合模型关联的统计能力。相关论文于2025年1月9日在线发表在《自然—遗传学》杂志上。

研究人员开发了Quickdraws。利用变异效应的脉冲-板式先验、随机变分推断和图形处理单元加速，该方法在不牺牲计算效率的前提下，增加了定量性状和二元性状的关联能力。研究人员将Quickdraws应用于405088个英国生物库样本的79个定量性状和50个二元性状，发现比REGENIE多出4.97%和3.25%的关联，分别比FastGWA多出22.71%和7.07%。

Quickdraws的计算成本与REGENIE、FastGWA和SAIGE在英国生物库研究分析平台服务上的成本相当，同时比BOLT-LMM显著更快。这些结果凸显了在不牺牲统计能力或稳健性的前提下，利用机器学习技术进行可扩展全基因组关联研究（GWAS）的潜力。

据介绍，现代生物库的快速增长为大规模GWAS和复杂性状分析创造了新的机会。然而，在数百万样本上进行GWAS常常会在计算效率和统计能力之间产生权衡，减少大规模数据收集工作的收益。

附：英文原文

Title: A scalable variational inference approach for increased mixed-model association power

Author: Loya, Hrushikesh, Kalantzis, Georgios, Cooper, Fergus, Palamara, Pier Francesco

Issue&Volume: 2025-01-09

Abstract: The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71% and 7.07% more than FastGWA. Quickdraws had costs comparable to REGENIE, FastGWA and SAIGE on the UK Biobank Research Analysis Platform service, while being substantially faster than BOLT-LMM. These results highlight the promise of leveraging machine learning techniques for scalable GWASs without sacrificing power or robustness.

DOI: 10.1038/s41588-024-02044-7

Source: https://www.nature.com/articles/s41588-024-02044-7