A network-driven approach for genome-wide association mapping.
It remains a challenge to detect associations between genotypes and phenotypes because of insufficient sample sizes and complex underlying mechanisms involved in associations. Fortunately, it is becoming more feasible to obtain gene expression data in addition to genotypes and phenotypes, giving us new opportunities to detect true genotype-phenotype associations while unveiling their association mechanisms.



In this article, we propose a novel method, NETAM, that accurately detects associations between SNPs and phenotypes, as well as gene traits involved in such associations. We take a network-driven approach: NETAM first constructs an association network, where nodes represent SNPs, gene traits or phenotypes, and edges represent the strength of association between two nodes. NETAM assigns a score to each path from an SNP to a phenotype, and then identifies significant paths based on the scores. In our simulation study, we show that NETAM finds significantly more phenotype-associated SNPs than traditional genotype-phenotype association analysis under false positive control, taking advantage of gene expression data. Furthermore, we applied NETAM on late-onset Alzheimer's disease data and identified 477 significant path associations, among which we analyzed paths related to beta-amyloid, estrogen, and nicotine pathways. We also provide hypothetical biological pathways to explain our findings.



Software is available at http://www.sailing.cs.cmu.edu/



: epxing@cs.cmu.edu.
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Seunghak
S
Lee
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Soonho
S
Kong
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Eric P
EP
Xing
D002874:Chromosome Mapping; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D005838:Genotype; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide