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

Microbiome

Research

Xiang Zhan¹  Ni Zhao²  Hongjiao Liu³  Michael C. Wu³  Angela Zhang³  Qibin Qi⁴  Jee-Young Moon⁴  Robert D. Burk⁵  Anna M. Plantinga⁶  Rob Knight⁷  Wodan Ling⁸  Jessica S. Williams-Nguyen⁹  Xing Hua^1,10  Robert C. Kaplan^1,11 

[1]Department of Biostatistics and Beijing International Center for Mathematical Research, Peking University, 100191, Beijing, China

[2]Department of Biostatistics, Johns Hopkins University, 21205, Baltimore, MD, USA

[3]Department of Biostatistics, University of Washington, 98195, Seattle, WA, USA

[4]Public Health Sciences Division, Fred Hutchinson Cancer Center, 98109, Seattle, WA, USA

[5]Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 10461, Bronx, NY, USA

[6]Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 10461, Bronx, NY, USA

[7]Departments of Pediatrics

[8] Microbiology & Immunology

[9] and, Obstetrics, Gynecology & Women’s Health, Albert Einstein College of Medicine, 10461, Bronx, NY, USA

[10]Department of Mathematics and Statistics, Williams College, 01267, Williamstown, MA, USA

[11]Departments of Pediatrics, Computer Science & Engineering, and Bioengineering

[12] Center for Microbiome Innovation, University of California, 92093, San Diego, La Jolla, CA, USA

[13]Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, 10065, New York, NY, USA

[14]Institute for Research and Education to Advance Community Health, Washington State University, 98101, Seattle, WA, USA

[15]Public Health Sciences Division, Fred Hutchinson Cancer Center, 98109, Seattle, WA, USA

[16]Public Health Sciences Division, Fred Hutchinson Cancer Center, 98109, Seattle, WA, USA

[17]Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 10461, Bronx, NY, USA

关键词: Microbiome;    Beta-diversity;    GWAS;    Kernel machines;    Covariate adjustment;   

DOI  :  10.1186/s40168-023-01530-0

 received in 2022-12-09, accepted in 2023-03-21,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundUnderstanding 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.ResultsWe 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.ConclusionsOur 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.3iRoYfA_uxPN1-JktGz5GLVideo Abstract

【 授权许可】

CC BY   
© The Author(s) 2023

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