title: Pathway-based analysis of GWAS datasets: effective but caution required.
abstract: Pathway-based analysis is rapidly emerging as an alternative but powerful approach for searching for disease causal genes from genomic datasets and has been applied to many complex diseases recently, but it is only now beginning to be applied in psychiatry. Here, we discuss critical issues in the pathway-based approach by specifically comparing the first pathway analysis of genome-wide association studies (GWAS) datasets in neuropsychiatric disorders by O'Dushlaine and colleagues (Molecular Psychiatry 2010, doi:10.1038/mp.2010.7) with our analysis. We also computed the power of gene set enrichment analysis, hypergeometric test, and SNP ratio test in order to assist future applications of these methods in pathway-based analysis of GWAS datasets. Overall, we suggest that the pathway-based approach is effective but caution is needed in interpreting the results of such analysis.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Peilin
authors.initials: P
authors.lastname: Jia
authors.forename: Lily
authors.initials: L
authors.lastname: Wang
authors.forename: Herbert Y
authors.initials: HY
authors.lastname: Meltzer
authors.forename: Zhongming
authors.initials: Z
authors.lastname: Zhao
mesh_terms: D030541:Databases, Genetic; D020869:Gene Expression Profiling; D055106:Genome-Wide Association Study; D006801:Humans; D046228:Microarray Analysis; D020641:Polymorphism, Single Nucleotide; D012107:Research Design