Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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
_version_ 1785028500917321728
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
work_keys_str_mv AT liuhongjiao kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT lingwodan kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT huaxing kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT moonjeeyoung kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT williamsnguyenjessicas kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT zhanxiang kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT plantingaannam kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT zhaoni kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT zhangangela kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT knightrob kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT qiqibin kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT burkrobertd kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT kaplanrobertc kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity
AT wumichaelc kernelbasedgeneticassociationanalysisformicrobiomephenotypesidentifieshostgeneticdriversofbetadiversity