Costanzo Maria C, Harris Laura W, Ji Yue, McMahon Aoife, Burtt Noël P, Flannick Jason
Programs in Metabolism and Medical and Population Genetics, the Broad Institute of MIT and Harvard, Cambridge, MA, USA.
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
Nat Genet. 2025 May 29. doi: 10.1038/s41588-025-02210-5.
Genome-wide association studies (GWAS) identify regions of the genome in which genetic variation is associated with the risk of complex diseases, such as diabetes, or the magnitude of traits, such as blood pressure. Determining which 'effector genes' mediate the effects of GWAS associations is essential to using GWAS to understand disease mechanisms and develop new therapies. In recent years, GWAS authors have increasingly included effector gene predictions as part of their study results. However, the research community has not yet converged on standards for generating or reporting these predictions. In this Perspective, we illustrate the diversity of the evidence types used to support effector gene predictions and argue for future initiatives to increase their accessibility and usefulness.
全基因组关联研究(GWAS)可识别基因组中那些遗传变异与复杂疾病风险(如糖尿病)或性状大小(如血压)相关的区域。确定哪些“效应基因”介导GWAS关联的效应对于利用GWAS理解疾病机制和开发新疗法至关重要。近年来,GWAS的作者越来越多地将效应基因预测纳入其研究结果之中。然而,学术界尚未就生成或报告这些预测的标准达成共识。在这篇观点文章中,我们阐述了用于支持效应基因预测的证据类型的多样性,并主张未来采取举措以提高其可及性和实用性。