Relative performance of gene- and pathway-level methods as secondary analyses for genome-wide association studies

BMC Genetics

Priya Duggal²  WH Linda Kao²  Genevieve L Wojcik¹ 

[1] Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA;Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

关键词: Biological Pathways;    Gene Set;    Genome-wide Association Studies;   

Others  :  1164857
DOI  :  10.1186/s12863-015-0191-2

 received in 2014-10-28, accepted in 2015-03-19,  发布年份 2015

【 摘 要 】

Background

Despite the success of genome-wide association studies (GWAS), there still remains “missing heritability” for many traits. One contributing factor may be the result of examining one marker at a time as opposed to a group of markers that are biologically meaningful in aggregate. To address this problem, a variety of gene- and pathway-level methods have been developed to identify putative biologically relevant associations. A simulation was conducted to systematically assess the performance of these methods. Using genetic data from 4,500 individuals in the Wellcome Trust Case Control Consortium (WTCCC), case–control status was simulated based on an additive polygenic model. We evaluated gene-level methods based on their sensitivity, specificity, and proportion of false positives. Pathway-level methods were evaluated on the relationship between proportion of causal genes within the pathway and the strength of association.

Results

The gene-level methods had low sensitivity (20-63%), high specificity (89-100%), and low proportion of false positives (0.1-6%). The gene-level program VEGAS using only the top 10% of associated single nucleotide polymorphisms (SNPs) within the gene had the highest sensitivity (28.6%) with less than 1% false positives. The performance of the pathway-level methods depended on their reliance upon asymptotic distributions or if significance was estimated in a competitive manner. The pathway-level programs GenGen, GSA-SNP and MAGENTA had the best performance while accounting for potential confounders.

Conclusions

Novel genes and pathways can be identified using the gene and pathway-level methods. These methods may provide valuable insight into the “missing heritability” of traits and provide biological interpretations to GWAS findings.

【 授权许可】

   
2015 Wojcik et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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