PT  - JOURNAL ARTICLE
AU  - Joshua Lynch
AU  - Karen Tang
AU  - Sambhawa Priya
AU  - Joanna Sands
AU  - Margaret Sands
AU  - Evan Tang
AU  - Sayan Mukherjee
AU  - Dan Knights
AU  - Ran Blekhman
TI  - HOMINID: A framework for identifying associations between host genetic variation and microbiome composition
AID  - 10.1101/081323
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 081323
4099  - http://biorxiv.org/content/early/2017/07/21/081323.short
4100  - http://biorxiv.org/content/early/2017/07/21/081323.full
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.