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Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity
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 micr...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116795/ https://www.ncbi.nlm.nih.gov/pubmed/37081571 http://dx.doi.org/10.1186/s40168-023-01530-0 |
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author | Liu, Hongjiao Ling, Wodan Hua, Xing Moon, Jee-Young Williams-Nguyen, Jessica S. Zhan, Xiang Plantinga, Anna M. Zhao, Ni Zhang, Angela Knight, Rob Qi, Qibin Burk, Robert D. Kaplan, Robert C. Wu, Michael C. |
author_facet | Liu, Hongjiao Ling, Wodan Hua, Xing Moon, Jee-Young Williams-Nguyen, Jessica S. Zhan, Xiang Plantinga, Anna M. Zhao, Ni Zhang, Angela Knight, Rob Qi, Qibin Burk, Robert D. Kaplan, Robert C. Wu, Michael C. |
author_sort | Liu, Hongjiao |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-023-01530-0. |
format | Online Article Text |
id | pubmed-10116795 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101167952023-04-21 Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity Liu, Hongjiao Ling, Wodan Hua, Xing Moon, Jee-Young Williams-Nguyen, Jessica S. Zhan, Xiang Plantinga, Anna M. Zhao, Ni Zhang, Angela Knight, Rob Qi, Qibin Burk, Robert D. Kaplan, Robert C. Wu, Michael C. Microbiome Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-023-01530-0. BioMed Central 2023-04-20 /pmc/articles/PMC10116795/ /pubmed/37081571 http://dx.doi.org/10.1186/s40168-023-01530-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Hongjiao Ling, Wodan Hua, Xing Moon, Jee-Young Williams-Nguyen, Jessica S. Zhan, Xiang Plantinga, Anna M. Zhao, Ni Zhang, Angela Knight, Rob Qi, Qibin Burk, Robert D. Kaplan, Robert C. Wu, Michael C. Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity |
title | Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity |
title_full | Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity |
title_fullStr | Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity |
title_full_unstemmed | Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity |
title_short | Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity |
title_sort | kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116795/ https://www.ncbi.nlm.nih.gov/pubmed/37081571 http://dx.doi.org/10.1186/s40168-023-01530-0 |
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