In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.
Bibliographical noteFunding Information:
The authors are grateful to the reviewers for helpful and constructive comments. This research was supported by NIH grant R01HL116720. W.P. was also supported by NIH grants R01GM113250, R01HL105397, and R01GM081535, and P.W. by R01CA169122 and R21HL126032. This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the WTCCC data is available from http://www.wtccc.org.uk . Funding for the WTCCC project was provided by the Wellcome Trust under award 076113.
© 2015 The American Society of Human Genetics.
- SSU tests
- aSPU test
- genome-wide association studies (GWASs)