Taşan Murat, Musso Gabriel, Hao Tong, Vidal Marc, MacRae Calum A, Roth Frederick P
1] Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. [3] Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. [4] Center for Cancer Systems Biology (CCSB), Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. [5] Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.
1] Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. [2] Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Nat Methods. 2015 Feb;12(2):154-9. doi: 10.1038/nmeth.3215. Epub 2014 Dec 22.
Genome-wide association (GWA) studies have linked thousands of loci to human diseases, but the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Reliance on published work to prioritize candidates is biased toward well-characterized genes. We describe a 'prix fixe' strategy and software that uses genome-scale shared-function networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from ∼100 GWA studies covering ten cancer types, our approach outperformed the common alternative strategy in ranking known cancer genes. As more GWA loci are discovered, the strategy will have increased power to elucidate the causes of human disease.
全基因组关联（GWA）研究已将数千个基因座与人类疾病联系起来，但这些基因座上的因果基因和变异通常仍不为人知。尽管研究人员通常关注与相关多态性最接近的基因，但因果基因往往距离更远。依赖已发表的研究来确定候选基因的优先级会偏向于特征明确的基因。我们描述了一种“固定套餐”策略和软件，该策略和软件利用全基因组规模的共享功能网络来识别跨越多个GWA基因座的相互功能相关的基因集。利用涵盖十种癌症类型的约100项GWA研究的关联数据，我们的方法在对已知癌症基因进行排名方面优于常见的替代策略。随着发现更多的GWA基因座，该策略将有更强的能力来阐明人类疾病的病因。
