gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels

Nicholas Larson, Shannon Mcdonnell, Lisa Cannon Albright, Craig Teerlink, Janet Stanford, Elaine A. Ostrander, William B. Isaacs, Jianfeng Xu, Kathleen A. Cooney, Ethan Lange, Johanna Schleutker, John D. Carpten, Isaac Powell, Joan E. Bailey-Wilson, Olivier Cussenot, Geraldine Cancel-Tassin, Graham G. Giles, Robert J. Macinnis, Christiane Maier, Alice S. WhittemoreChih Lin Hsieh, Fredrik Wiklund, William J. Catolona, William Foulkes, Diptasri Mandal, Rosalind Eeles, Zsofia Kote-Jarai, Michael John Ackerman, Timothy Mark Olson, Christopher Jon Klein, Stephen N Thibodeau, Daniel J Schaid

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results.

Original languageEnglish (US)
JournalGenetic Epidemiology
DOIs
StateAccepted/In press - 2017

    Fingerprint

Keywords

  • Gene set
  • Next-generation sequencing
  • Pathway
  • Rare variation

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

Cite this

Larson, N., Mcdonnell, S., Cannon Albright, L., Teerlink, C., Stanford, J., Ostrander, E. A., Isaacs, W. B., Xu, J., Cooney, K. A., Lange, E., Schleutker, J., Carpten, J. D., Powell, I., Bailey-Wilson, J. E., Cussenot, O., Cancel-Tassin, G., Giles, G. G., Macinnis, R. J., Maier, C., ... Schaid, D. J. (Accepted/In press). gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels. Genetic Epidemiology. https://doi.org/10.1002/gepi.22036