Abstract
Genome Wide Association Studies (GWAS) are a standard approach for large-scale common variation characterization and for identification of single loci predisposing to disease. However, due to issues of moderate sample sizes and particularly multiple testing correction, many variants of smaller effect size are not detected within a single allele analysis framework. Thus, small main effects and potential epistatic effects are not consistently observed in GWAS using standard analytical approaches that consider only single SNP alleles. Here, we propose unique methodology that aggregates variants of interest (for example, genes in a biological pathway) using GWAS results. Multiple testing and type I error concerns are minimized using empirical genomic randomization to estimate significance. Randomization corrects for common pathway-based analysis biases, such as SNP coverage and density, linkage disequilibrium, gene size and pathway size. Pathway Analysis by Randomization Incorporating Structure (PARIS) applies this randomization and in doing so directly accounts for linkage disequilibrium effects. PARIS is independent of association analysis method and is thus applicable to GWAS datasets of all study designs. Using the KEGG database as an example, we apply PARIS to the publicly available Autism Genetic Resource Exchange GWAS dataset, revealing pathways with a significant enrichment of positive association results.
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Acknowledgments
This work was supported in part by funding to the Autism Genome Project from Autism Speaks (JSS and JLH) Medical Research Council (UK), Health Research Board (Ireland), Genome Canada and the Hilibrand Foundation; NIH grants R01 LM010040 to MDR, R01 NS049261 to JSS, and P01 NS026630 and R01 MH080647 to MPV and JLH. The authors would like to gratefully acknowledge the advice and consultation regarding methods development from many investigators in the AGP. We would also like to thank the Computational Genomics Core at Vanderbilt University for their assistance in bringing the ideas to a computational reality. We also wish to gratefully acknowledge the resources provided by the AGRE consortium and the participating Autism Genetic Resource Exchange (AGRE) families. The AGRE resource is supported by the NIMH and Autism Speaks.
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Yaspan, B.L., Bush, W.S., Torstenson, E.S. et al. Genetic analysis of biological pathway data through genomic randomization. Hum Genet 129, 563–571 (2011). https://doi.org/10.1007/s00439-011-0956-2
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DOI: https://doi.org/10.1007/s00439-011-0956-2