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MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition

The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic varian...

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Autores principales: Hua, Xing, Song, Lei, Yu, Guoqin, Vogtmann, Emily, Goedert, James J., Abnet, Christian C., Landi, Maria Teresa, Shi, Jianxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317577/
https://www.ncbi.nlm.nih.gov/pubmed/35886007
http://dx.doi.org/10.3390/genes13071224
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author Hua, Xing
Song, Lei
Yu, Guoqin
Vogtmann, Emily
Goedert, James J.
Abnet, Christian C.
Landi, Maria Teresa
Shi, Jianxin
author_facet Hua, Xing
Song, Lei
Yu, Guoqin
Vogtmann, Emily
Goedert, James J.
Abnet, Christian C.
Landi, Maria Teresa
Shi, Jianxin
author_sort Hua, Xing
collection PubMed
description The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by β diversity; that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome β diversity with or without interacting with an environmental factor. We show that the score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes p-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate p-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile–quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (p = 0.0032) and weighted (p = 0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.
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spelling pubmed-93175772022-07-27 MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition Hua, Xing Song, Lei Yu, Guoqin Vogtmann, Emily Goedert, James J. Abnet, Christian C. Landi, Maria Teresa Shi, Jianxin Genes (Basel) Article The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by β diversity; that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome β diversity with or without interacting with an environmental factor. We show that the score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes p-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate p-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile–quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (p = 0.0032) and weighted (p = 0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome. MDPI 2022-07-09 /pmc/articles/PMC9317577/ /pubmed/35886007 http://dx.doi.org/10.3390/genes13071224 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hua, Xing
Song, Lei
Yu, Guoqin
Vogtmann, Emily
Goedert, James J.
Abnet, Christian C.
Landi, Maria Teresa
Shi, Jianxin
MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition
title MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition
title_full MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition
title_fullStr MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition
title_full_unstemmed MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition
title_short MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition
title_sort microbiomegwas: a tool for identifying host genetic variants associated with microbiome composition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317577/
https://www.ncbi.nlm.nih.gov/pubmed/35886007
http://dx.doi.org/10.3390/genes13071224
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