TY - JOUR
T1 - Network analysis of GWAS data
AU - Leiserson, Mark D.M.
AU - Eldridge, Jonathan V.
AU - Ramachandran, Sohini
AU - Raphael, Benjamin J.
N1 - Funding Information:
BJR is supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, an Alfred P. Sloan Research Fellowship, a grant from the National Human Genome Research Institute (R01HG005690), an NSF CAREER Award (CCF-1053753) and NSF grant IIS-1016648. SR is supported by an Alfred P. Sloan Research Fellowship and by the Pew Charitable Trusts as a Pew Scholar in the Biomedical Sciences. MDML is supported by NSF Graduate Research Fellowship DGE 0228243. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PY - 2013/12
Y1 - 2013/12
N2 - Genome-wide association studies (GWAS) identify genetic variants that distinguish a control population from a population with a specific trait. Two challenges in GWAS are: (1) identification of the causal variant within a longer haplotype that is associated with the trait; (2) identification of causal variants for polygenic traits that are caused by variants in multiple genes within a pathway. We review recent methods that use information in protein-protein and protein-DNA interaction networks to address these two challenges.
AB - Genome-wide association studies (GWAS) identify genetic variants that distinguish a control population from a population with a specific trait. Two challenges in GWAS are: (1) identification of the causal variant within a longer haplotype that is associated with the trait; (2) identification of causal variants for polygenic traits that are caused by variants in multiple genes within a pathway. We review recent methods that use information in protein-protein and protein-DNA interaction networks to address these two challenges.
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U2 - 10.1016/j.gde.2013.09.003
DO - 10.1016/j.gde.2013.09.003
M3 - Review article
C2 - 24287332
AN - SCOPUS:84889595111
SN - 0959-437X
VL - 23
SP - 602
EP - 610
JO - Current Opinion in Genetics and Development
JF - Current Opinion in Genetics and Development
IS - 6
ER -