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Nat Methods. 2015 Feb;12(2):154-9. doi: 10.1038/nmeth.3215. Epub 2014 Dec 22.

Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.

Author information

1
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.
2
1] Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. [2] Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA.
3
1] Center for Cancer Systems Biology (CCSB), Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. [2] Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.
4
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. [6] Canadian Institute for Advanced Research, Toronto, Ontario, Canada.

Abstract

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.

PMID:
25532137
PMCID:
PMC4480866
DOI:
10.1038/nmeth.3215
[Indexed for MEDLINE]
Free PMC Article

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