Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of methodologies that provide reproducible discoveries is essential for the understanding of complex diseases and precision drug development. Without statistical reproducibility guarantees, valuable efforts are spent on researching false positives. Therefore, scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods are urgently needed, especially, for complex polygenic diseases and traits. In this work, we propose the Screen-T-Rex selector, a fast FDR-controlling method based on the recently developed T-Rex selector. The method is tailored to screening large-scale biobanks and it does not require choosing additional parameters (sparsity parameter, target FDR level, etc). Numerical simulations and a real-world HIV-1 drug resistance example demonstrate that the performance of the Screen-T-Rex selector is superior, and its computation time is multiple orders of magnitude lower compared to current benchmark knockoff methods.

翻译：基因组生物库是包含数千种表型（例如疾病、性状）和数百万个单核苷酸多态性的信息宝库。开发能够提供可重复发现的方法对于理解复杂疾病和精准药物研发至关重要。若缺乏统计可重复性保证，宝贵的研究努力将耗费在假阳性结果上。因此，迫切需要可扩展的多变量高维错误发现率控制变量选择方法，尤其适用于复杂的多基因疾病和性状。本研究提出Screen-T-Rex选择器，这是一种基于近期开发的T-Rex选择器的快速错误发现率控制方法。该方法专为大规模生物库筛选而设计，无需选择额外参数（稀疏参数、目标错误发现率水平等）。数值模拟和真实世界HIV-1耐药性案例表明，Screen-T-Rex选择器的性能优越，其计算时间较当前基准的Knockoff方法降低了多个数量级。