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


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Article Info

DATE

2015-12

AUTHORS

Genevieve L Wojcik, WH Linda Kao, Priya Duggal

ABSTRACT

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. More... »

PAGES

34

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http://scigraph.springernature.com/pub.10.1186/s12863-015-0191-2

DOI

http://dx.doi.org/10.1186/s12863-015-0191-2

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https://app.dimensions.ai/details/publication/pub.1035903240

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25887572


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45 schema:description 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.
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