Network analysis of GWAS data

被引:66
作者
Leiserson, Mark D. M. [1 ,2 ]
Eldridge, Jonathan V. [1 ,2 ]
Ramachandran, Sohini [2 ,3 ]
Raphael, Benjamin J. [1 ,2 ]
机构
[1] Brown Univ, Dept Comp Sci, Providence, RI 02912 USA
[2] Brown Univ, Ctr Computat Mol Biol, Providence, RI 02912 USA
[3] Brown Univ, Dept Ecol & Evolutionary Biol, Providence, RI 02912 USA
基金
美国国家科学基金会;
关键词
GENOME-WIDE ASSOCIATION; CANDIDATE DISEASE GENES; PROTEIN-INTERACTION NETWORK; MISSING HERITABILITY; TOPOLOGICAL SIMILARITY; INTERACTOME; PATHWAYS; PRIORITIZATION; VARIANTS; PHENOME;
D O I
10.1016/j.gde.2013.09.003
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
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.
引用
收藏
页码:602 / 610
页数:9
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