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. 2015 Sep 15;16(1):191.
doi: 10.1186/s13059-015-0759-1.

Host genetic variation impacts microbiome composition across human body sites

Ran Blekhman  1   2 , Julia K Goodrich  3   4 , Katherine Huang  5 , Qi Sun  6 , Robert Bukowski  6 , Jordana T Bell  7 , Timothy D Spector  7 , Alon Keinan  8 , Ruth E Ley  3   4 , Dirk Gevers  5   9 , Andrew G Clark  3
Affiliations

Host genetic variation impacts microbiome composition across human body sites

Ran Blekhman et al. Genome Biol. .

Abstract

Background: The composition of bacteria in and on the human body varies widely across human individuals, and has been associated with multiple health conditions. While microbial communities are influenced by environmental factors, some degree of genetic influence of the host on the microbiome is also expected. This study is part of an expanding effort to comprehensively profile the interactions between human genetic variation and the composition of this microbial ecosystem on a genome- and microbiome-wide scale.

Results: Here, we jointly analyze the composition of the human microbiome and host genetic variation. By mining the shotgun metagenomic data from the Human Microbiome Project for host DNA reads, we gathered information on host genetic variation for 93 individuals for whom bacterial abundance data are also available. Using this dataset, we identify significant associations between host genetic variation and microbiome composition in 10 of the 15 body sites tested. These associations are driven by host genetic variation in immunity-related pathways, and are especially enriched in host genes that have been previously associated with microbiome-related complex diseases, such as inflammatory bowel disease and obesity-related disorders. Lastly, we show that host genomic regions associated with the microbiome have high levels of genetic differentiation among human populations, possibly indicating host genomic adaptation to environment-specific microbiomes.

Conclusions: Our results highlight the role of host genetic variation in shaping the composition of the human microbiome, and provide a starting point toward understanding the complex interaction between human genetics and the microbiome in the context of human evolution and disease.

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Figures

Fig. 1
Fig. 1
Host genetic variation is correlated with microbiome composition. a Correlation of the first PC of host genetic data (x-axis) and alpha diversity of the anterior nares microbiome (y-axis). b Correlation of the first PC of host genetic data (x-axis) and first PC of the stool microbiome data (y-axis). c Identity-by-state between individual pairs calculated from host genome data (x-axis) is correlated with stool microbiome beta diversity (y-axis), which tabulates the magnitude of pairwise differentiation between the microbiomes of same pair of individuals. In all panels, solid and dashed gray lines represent a linear regression and loess regression fit to the data, respectively
Fig. 2
Fig. 2
Complex disease and functional SNPs are enriched among microbiome-correlated host genetic variation. a Enrichment of genes correlated with microbiome composition (y-axis) compared to all other genes that are significantly associated with a complex disease using a given P value threshold (x-axis). Each colored line represents a different complex disease with an enrichment of at least three-fold. b Enrichment of SNPs correlated with microbiome composition (y-axis) compared to all other SNPs that have been identified as eQTLs in the GTEx data using a given P value threshold (x-axis). Each colored line represents a different tissue type analyzed by GTEx. c Enrichment of SNPs (blue) and genes (red) correlated with microbiome composition in this study (y-axis) among SNPs and genes correlated with microbiome composition in the TwinsUK dataset using a given P value threshold (x-axis)
Fig. 3
Fig. 3
Correlation between coding genetic variation and bacterial abundance. a Manhattan plot illustrating the P values (y-axis, −log scale) for correlation of each tested coding SNP (shown as circles) by its genomic location (x-axis) with the abundance of Bifidobacterium in the gut. SNP colors alternate by chromosome, with red dots representing SNPs with P values that surpass genome-wide significance after FDR correction. b A close-up of the region of correlation within LCT. Genomic positions on chromosome 2 are on the x-axis, and the P values are on the y-axis (−log scale). Each dot represents a SNP tested using our model, and the color represents the linkage disequilibrium (r 2) between each dot and the top SNP, colored purple and indicated by its dbSNP rsID (inset legend indicates the spectrum of colors and matching r 2 values). Blue lines represent recombination rate calculated from the European samples in the 1000 Genomes Project. Gene regions are shown underneath, with LCT highlighted. c An interaction network generated using IPA showing pathways that are enriched among genes that harbor SNPs correlated with abundance of bacterial taxa (in orange). Lines represent known interactions between genes, and shapes represent types of proteins (see legend at the bottom left)
Fig. 4
Fig. 4
SNPs correlated with microbiome composition have high FST values between human populations. Each panel represents a comparison of a pair of human populations indicated in the title. Shown is the F ST median + 95 % CI (x-axis, calculated using bootstrapping) in SNPs where genetic variation is correlated with microbial taxa at P <10−4, separated by the body site (y-axis). Vertical dashed line represents the genome-wide median FST. Color highlight was used in cases where F ST in microbiome-correlated sites was significantly higher than the genome-wide value (FDR Q <0.05; using a permutation test of the median)
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References

    1. The Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14. doi: 10.1038/nature11234. - DOI - PMC - PubMed
    1. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. Bacterial community variation in human body habitats across space and time. Science. 2009;326:1694–7. doi: 10.1126/science.1177486. - DOI - PMC - PubMed
    1. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;8:e1002606. doi: 10.1371/journal.pcbi.1002606. - DOI - PMC - PubMed
    1. Ding T, Schloss PD. Dynamics and associations of microbial community types across the human body. Nature. 2014;509:357–60. doi: 10.1038/nature13178. - DOI - PMC - PubMed
    1. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–63. doi: 10.1038/nature12820. - DOI - PMC - PubMed

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