RT Journal Article
SR Electronic
T1 HOMINID: A framework for identifying associations between host genetic variation and microbiome composition
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 081323
DO 10.1101/081323
A1 Joshua Lynch
A1 Karen Tang
A1 Sambhawa Priya
A1 Joanna Sands
A1 Margaret Sands
A1 Evan Tang
A1 Sayan Mukherjee
A1 Dan Knights
A1 Ran Blekhman
YR 2017
UL http://biorxiv.org/content/early/2017/07/21/081323.abstract
AB 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 composition data, identifies host 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 to 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.Availability and implementation Software, code, tutorial, installation and setup details, and synthetic data are available in the project homepage: https://github.com/blekhmanlab/hominid.Real dataset used here is from Blekhman et al. (Blekhman et al. 2015); 16S rRNA gene sequence data and OTU tables are available on the HMP DACC website (www.hmpdacc.org), and host genetic data are deposited in dbGaP under project number phs000228.