HOMINID: a framework for identifying associations between host genetic variation and microbiome composition

Abstract

Recent studies have uncovered a strong effect of host genetic variation on the composition of host-associated microbiota. Here, we present HOMINID, a computational approach based on Lasso linear regression, that given host genetic variation and microbiome taxonomic composition data, identifies host single nucleotide polymorphisms (SNPs) that are correlated with microbial taxa abundances. Using simulated data, we show that HOMINID has accuracy in identifying associated SNPs and performs better compared with existing methods. We also show that HOMINID can accurately identify the microbial taxa that are correlated with associated SNPs. Lastly, by using HOMINID on real data of human genetic variation and microbiome composition, we identified 13 human SNPs in which genetic variation is correlated with microbiome taxonomic composition across body sites. In conclusion, HOMINID is a powerful method to detect host genetic variants linked to microbiome composition and can facilitate discovery of mechanisms controlling host-microbiome interactions.

Now published in GigaScience doi: 10.1093/gigascience/gix107

Joshua Lynch 1Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA2Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteKaren Tang 1Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA2Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteSambhawa Priya 1Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA2Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteJoanna Sands 1Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA2Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteMargaret Sands 1Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA2Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteEvan Tang 1Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA2Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteDan Knights 4Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA5Biotechnology Institute, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: blekhman@umn.edu dknights@umn.eduRan Blekhman 1Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA2Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: blekhman@umn.edu dknights@umn.edu

A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/gix107 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

These peer reviews were as follows:

Reviewer 1: http://dx.doi.org/10.5524/REVIEW.100893

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HOMINID: a framework for identifying associations between host genetic variation and microbiome composition

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