Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity

Abstract

Background Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics may influence health outcomes. Typical microbiome genome-wide association studies (GWAS) marginally assess the association between individual genetic variants and individual microbial taxa. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The KRV framework evaluates the association between genetics and microbes by comparing similarity in genetic profiles, based on groups of variants at the gene level, to similarity in microbiome profiles, based on the overall microbiome composition, across all pairs of individuals. By reducing the multiple-testing burden and capturing intrinsic structure within the genetic and microbiome data, the KRV framework has the potential of improving statistical power in microbiome GWAS. Results We apply the covariate-adjusted KRV to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) in a two-stage (first gene-level, then variant-level) genome-wide association analysis for gut microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in a previous microbiome genetic association study and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. In addition, simulation studies show that the covariate-adjusted KRV has a greater power than other microbiome GWAS methods that rely on univariate microbiome phenotypes across a range of scenarios. Conclusions Our findings highlight the value of the covariate-adjusted KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition.

Multivariate differential association analysis

“…Moreover, it can ensure exact control of the type I error rate for all test statistics under the null hypothesis. These attractive properties have led to the permutation approach being used across a wide range of settings (Liu et al, 2023; Song & Chen, 2020; Zhan et al, 2017). However, the permutation test has the requirement for exchangeability under the null hypothesis, and the permutation approach could be problematic if the assumption of exchangeability is violated (Chung & Romano, 2013, 2016; DiCiccio & Romano, 2017).…”

“…To make the new test work for a wide range of alternatives, we use the popular kernels, Gaussian kernel and linear kernel, as each kernel is suitable for different types of dependence relationships (Gretton et al, 2007; Liu et al, 2021, 2023). To accommodate both effects based on the Gaussian kernel and linear kernel, we apply the Cauchy combination test to obtain the omnibus $$$ p $$$‐value (Liu & Xie, 2020).…”

JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression

“…It allows for covariates and uses a genetic relatedness matrix (GRM) to correct for population structure. JASPER is applicable to to a wide range of association test statistics, including methods for associating a set of rare variants with a single continuous trait (SKAT [19], famSKAT [38], generalized SKAT [29] and MONSTER fixed-ρ [39]) or with a single binary trait [32], as well as kernel-based multitrait methods for testing association with a set of rare or common variants, when these are used with a linear or weighted linear kernel for the genotypes, such as DKAT [28], MSKAT [10], GAMuT [20], the kernel-based test statistics that are combined into the omnibus test in Multi-SKAT [12], wSKAT [40], and a kernel-based method for genetic analysis of the microbiome [41]. Simple special cases also include single-trait, single variant association tests for binary traits [33,34] and for quantitative traits (score and Wald tests for association in a linear mixed model).…”