Ribeiro Diogo M, Hofmeister Robin J, Rubinacci Simone, Delaneau Olivier
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Nat Genet. 2025 Aug 6. doi: 10.1038/s41588-025-02288-x.
Large biobanks with whole-genome sequencing (WGS) now enable the association of noncoding rare variants with complex human traits. Given that >98% of the genome is available for exploration, the selection of noncoding variants remains a critical yet unresolved challenge in these analyses. Here we leverage knowledge of blood gene regulation and deleteriousness scores to select noncoding variants pertinent for association with blood-related traits. Integrating WGS and 42 blood cell count and biomarker measurements for 166,740 UK Biobank samples, we perform variant collapsing tests, identifying hundreds of gene-trait associations involving noncoding variants. However, we demonstrate that most of these noncoding rare variant associations (1) reproduce associations known from previous studies and (2) are driven by linkage disequilibrium between nearby common and rare variants. This study underscores the prevailing challenges in rare variant analysis and the need for caution when interpreting noncoding rare variant association results.
拥有全基因组测序(WGS)技术的大型生物样本库,如今能够实现非编码罕见变异与复杂人类性状之间的关联研究。鉴于超过98%的基因组可供探索,在这些分析中,非编码变异的选择仍然是一个关键但尚未解决的挑战。在此,我们利用血液基因调控知识和有害性评分,来选择与血液相关性状关联的非编码变异。整合了166,740例英国生物样本库样本的WGS数据以及42项血细胞计数和生物标志物测量数据后,我们进行了变异合并测试,识别出数百个涉及非编码变异的基因与性状之间的关联。然而,我们证明这些非编码罕见变异关联中的大多数(1)重现了先前研究中已知的关联,并且(2)是由附近常见变异和罕见变异之间的连锁不平衡所驱动。这项研究强调了罕见变异分析中普遍存在的挑战,以及在解释非编码罕见变异关联结果时需要谨慎。