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

来源：

CURRENT OPINION IN GENETICS & DEVELOPMENT | 2013年 / 23卷 / 06期

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

页码：602 / 610

页数：9

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