title: Pathway-based approaches for sequencing-based genome-wide association studies.
abstract: For analyzing complex trait association with sequencing data, most current studies test aggregated effects of variants in a gene or genomic region. Although gene-based tests have insufficient power even for moderately sized samples, pathway-based analyses combine information across multiple genes in biological pathways and may offer additional insight. However, most existing pathway association methods are originally designed for genome-wide association studies, and are not comprehensively evaluated for sequencing data. Moreover, region-based rare variant association methods, although potentially applicable to pathway-based analysis by extending their region definition to gene sets, have never been rigorously tested. In the context of exome-based studies, we use simulated and real datasets to evaluate pathway-based association tests. Our simulation strategy adopts a genome-wide genetic model that distributes total genetic effects hierarchically into pathways, genes, and individual variants, allowing the evaluation of pathway-based methods with realistic quantifiable assumptions on the underlying genetic architectures. The results show that, although no single pathway-based association method offers superior performance in all simulated scenarios, a modification of Gene Set Enrichment Analysis approach using statistics from single-marker tests without gene-level collapsing (weighted Kolmogrov-Smirnov [WKS]-Variant method) is consistently powerful. Interestingly, directly applying rare variant association tests (e.g., sequence kernel association test) to pathway analysis offers a similar power, but its results are sensitive to assumptions of genetic architecture. We applied pathway association analysis to an exome-sequencing data of the chronic obstructive pulmonary disease, and found that the WKS-Variant method confirms associated genes previously published.
authors: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA.
authors: Guodong
authors: G
authors: Wu
authors: Degui
authors: D
authors: Zhi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D059472:Exome; D005787:Gene Frequency; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D029424:Pulmonary Disease, Chronic Obstructive / Q000235:genetics; D017422:Sequence Analysis, DNA; D059467:Transcriptome
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