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

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.

On set‐based association tests: Insights from a regression using summary statistics

“…T Minp is also slightly conservative for small J but has correct test size when J > 50, and T Fisher has inflated type I error when the correlation among variants is strong (see the Supplementary Material). Our observations here are largely consistent with previous reports (e.g., Larson et al, 2017).…”

Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

“…A fourth limitation is the lack of a well-defined statistical-testing framework for the telescoping approach we used. Larson and others [ 64 ] pointed out that type-I error control has not been well studied for gene-set analyses, and they described corrections to SKAT to adjust for multiple testing when there may be substantial overlap of genes across multiple pathways. However, because of our small number of candidate pathways, there was only modest overlap of genes.…”

Efficient testing and effect size estimation for set‐based genetic association inference via semiparametric multilevel mixture modeling

“…It also should be noted that existing empirical Bayes methods for multiple testing with FDR control (e.g., Noma & Matsui, 2013;Stephens, 2017) are applicable only for screening single-variant effects, but our framework provides set-based screening for multiple variants. Moreover, we note that there are some attempts for screening multiple regions of rare variants (Cirulli et al, 2020;Larson et al, 2017), they do not necessarily provide efficient screening results and cannot compute additional information regarding the effect sizes. After presenting the proposed methods, we evaluate their performance by simulation studies and assess their practical usefulness via application to the PennCATH study (Reilly et al, 2011), a large GWAS for coronary artery disease.…”