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. 2018 Jan 29;10(1):6.
doi: 10.1186/s13073-018-0515-8.

Host genetic variation and its microbiome interactions within the Human Microbiome Project

Raivo Kolde  1 , Eric A Franzosa  2   3 , Gholamali Rahnavard  2   3 , Andrew Brantley Hall  3 , Hera Vlamakis  3 , Christine Stevens  3 , Mark J Daly  3   4 , Ramnik J Xavier  5   6   7 , Curtis Huttenhower  8   9
Affiliations

Host genetic variation and its microbiome interactions within the Human Microbiome Project

Raivo Kolde et al. Genome Med. .

Abstract

Background: Despite the increasing recognition that microbial communities within the human body are linked to health, we have an incomplete understanding of the environmental and molecular interactions that shape the composition of these communities. Although host genetic factors play a role in these interactions, these factors have remained relatively unexplored given the requirement for large population-based cohorts in which both genotyping and microbiome characterization have been performed.

Methods: We performed whole-genome sequencing of 298 donors from the Human Microbiome Project (HMP) healthy cohort study to accompany existing deep characterization of their microbiomes at various body sites. This analysis yielded an average sequencing depth of 32x, with which we identified 27 million (M) single nucleotide variants and 2.3 M insertions-deletions.

Results: Taxonomic composition and functional potential of the microbiome covaried significantly with genetic principal components in the gastrointestinal tract and oral communities, but not in the nares or vaginal microbiota. Example associations included validation of known associations between FUT2 secretor status, as well as a variant conferring hypolactasia near the LCT gene, with Bifidobacterium longum abundance in stool. The associations of microbial features with both high-level genetic attributes and single variants were specific to particular body sites, highlighting the opportunity to find unique genetic mechanisms controlling microbiome properties in the microbial communities from multiple body sites.

Conclusions: This study adds deep sequencing of host genomes to the body-wide microbiome sequences already extant from the HMP healthy cohort, creating a unique, versatile, and well-controlled reference for future studies seeking to identify host genetic modulators of the microbiome.

Keywords: Association studies; Human Microbiome Project; Human genome sequence; Microbiome and human genetics; Microbiome metagenome sequence.

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Conflict of interest statement

Ethics approval and consent to participate

Recruitment protocols were approved by the appropriate institutional review boards (IRBs) at each HMP clinical site (Baylor College of Medicine, IRB protocols H-22895 (IRB 00001021) and H-22035 (IRB 00002649)); Washington University School of Medicine (IRB protocol HMP-07-001 (IRB 201105198)); and St. Louis University (IRB 15778). Written informed consent was obtained from all study participants to participate in the study and to allow data sharing through dbGaP. All participants consented for the sequencing of their own genetic material [47]. Research on human subjects was performed in accordance with the Declaration of Helsinki. The study was also reviewed by the J. Craig Venter Institute under IRB protocol 2008–084 (IRB 00003721). The study was determined to be exempt from IRB review at the Broad Institute.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Overview of the Human Microbiome Project host genome and metagenome coverage. Sequencing depth for each host genome (left) and number of reads for all available samples with whole metagenome sequencing
Fig. 2
Fig. 2
Distribution of genetic variants and comparison with other cohorts. a Discovered variants categorized by frequency and overlap with other cohorts. AC allele count, MAF minor allele frequency. b Distribution of the number of coding mutations by frequency and estimated impact
Fig. 3
Fig. 3
Correlation between high-level genetic variation and microbiome composition. a The first two components of the genetic principal component analysis are shown, based on common single nucleotide variants, overlaid by self-reported donor ethnicity. AA African-American. b Shown is how much variance in microbiome data on average can be explained by the genetic principal components, when compared to permutation on the same data. Values shown are Z-scores based on permutations, which were also used to calculate empirical p values. c Distribution of genetic principal component R2 values for different species and pathways in stool. Y-axis shows the variance explained, and the X-axis shows permutation-based empirical p values for each of those numbers. Only the names of species with false discovery rate (FDR) < 0.05 and pathways’ FDR < 0.01 are shown. The histogram below displays the distribution of empirical p values, and the Y-axis shows the number of species in a bin. Green bars under the pathway histogram show how the pathways that are associated with fermentation are ranked by R2
Fig. 4
Fig. 4
Kinship and microbiome similarity and replication of known associations. a Bray-Curtis similarity between the 12 pairs of close relatives (third degree or closer) identified from genetic data compared to similarities between other pairs. The p values correspond to results of t tests between similarity scores for relatives, against all other pairs. b Association between FUT2 secretor variant and B. longum. c Association between genetic variant rs4988235 near the LCT gene and B. longum. In both b and c we display log10 transformed relative abundance
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