Comparative analyses of the control of mammalian microbiomes by host genetic architecture reveal striking conserved features that have implications for the evolution of host–microbiome interactions

Benson, Andrew K.

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University of Nebraska - Lincoln
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Faculty Publications in Food Science and
Technology Food Science and Technology Department
2015
Host genetic architecture and the landscape of
microbiome composition: humans weigh in
Andrew K. Benson
University of Nebraska-Lincoln, abenson1@unl.edu
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RESEARCH HIGHLIGHT Open Access
Host genetic architecture and the landscape
of microbiome composition: humans weigh in
Andrew K Benson
See related Research article: http://www.genomebiology.com/2015/16/1/191
Abstract
Comparative analyses of the control of mammalian
microbiomes by host genetic architecture reveal
striking conserved features that have implications for
the evolution of host–microbiome interactions.
Introduction
Genetic and environmental factors converge in many
different ways and bring about remarkable diversity in
complex traits. Our growing understanding of the role
of the microbiome in complex diseases naturally leads
us to examine the concept of the microbiome as a
complex trait, and hence to measure genetic versus
environmental control of microbiome composition.
Quantitative genetic analyses in mouse models, which
allow many environmental factors to be measured or
controlled, have established that genetic variation at a
number of loci affects compositional features of the
gut microbiome, supporting our view of the micro-
biome as a complex, polygenic trait [1–3]. A study by
Ran Blekhman and colleagues [4], published recently
in Genome Biology, is the first to use genome-wide
analyses in a human study population to attribute
variation in microbiome composition to genetic vari-
ation at specific loci in the host genome.
Human genetic factors and complex traits
Accurate dissection of genetic and environmental contri-
butions to the control of complex traits in human study
populations is a holy grail of complex trait research. This
goal is elusive because of the confounding effects of
environmental factors that cannot be controlled or
estimated. Indeed, initial studies of microbiome
Correspondence: abenson1@unl.edu
Department of Food Science and Technology, University of Nebraska, Food
Innovation Center, North 21 Street, Lincoln, Nebraska 68588-6205, USA
composition in a large cohort of obese and lean twin
pairs that relied on relatively crude statistical analyses
showed only a trend toward greater differences between
the microbiomes of dizygotic twin pairs than between
those of monozygotic pairs (but no statistically signifi-
cant difference) [5]. Analysis of data from a twin study
in the U.K. using a more sophisticated approach to esti-
mate the heritability of each taxon later detected meas-
urable heritability and, hence, a significant contribution
of host genetic variation [6]. Thus, the stage was set for
an initial attempt at genome-wide associations.
First glimpse of the genetic architecture of
microbiome control in humans
Blekhman et al. [4] have developed a sophisticated ana-
lysis to pull out associations from genome-wide scans of
two different populations. Their analysis focused initially
on a subset of subjects from the Human Microbiome
Project (HMP), in which genotypes were inferred from a
large number of high-quality single nucleotide polymor-
phisms (SNPs) gleaned from human DNA that contami-
nated stool metagenomic data. They found that genetic
variation in the host has a measurable effect on diversity
in the microbiome. Furthermore, the loci that made
measurable contributions are linked to loci for disease
susceptibility and for innate and adaptive immune func-
tions. The authors subsequently tested a second popula-
tion, the UK Twins cohort, for genome-wide associations
between host genetics and microbiome diversity using
genotype data obtained directly from each of the subjects,
and obtained very similar results. Of the 83 significant
host loci identified from the HMP population, there were
several instances in which variation affects the abundances
of multiple taxa from microbiomes across different body
sites. Once again, innate and adaptive immunity genes
and disease susceptibility genes were involved.
© 2015 Benson. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
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Benson Genome Biology  (2015) 16:203 
DOI 10.1186/s13059-015-0775-1
Conserved features of genetic architecture and
microbiome landscapes
The methodologies used by Blekhman and colleagues
are an elegant display of the power of bioinformatics
and quantitative genomics. Equally exciting is the oppor-
tunity for these techniques to be used in comparative
studies to reveal a broad understanding of mammalian
genetic architectures that affect the compositional land-
scape of the microbiome. One of the first observations
from such comparative studies is that the genetic architec-
ture revealed by any single study is highly context-
dependent and only a small number of associations are
replicated across studies. The context-dependency stems
from the relatively strong contributions of environmental
and stochastic factors that affect microbiome composition.
Despite the context-dependency, four general features of
genetic architecture that influence microbiome compos-
ition are shared between humans and mice, and these
features merit discussion because they shed light on the
very nature of host–microbiome interactions and their
evolutionary relationships.
Host genetic variation affects microbial taxa across the
phylogenetic space of the microbiome
This feature was consistently observed in mouse stud-
ies of the gut microbiota, and data from the human
populations reported by Blekhman et al. [4] extend
this feature across multiple body sites. Phylogenetic
signal in the microbiota generally reflects the phyl-
ogeny of the host [7, 8], and though some of this
shared signal may be attributed to the host's ability to
adapt to the microbiome, we would expect the major-
ity to arise from the influence of genetic diversity
within the host species in constraining the primary
framework of the microbiome. Admittedly, such con-
straint manifests largely through other complex traits
such as dietary preference, physiology, and behavioral
traits. Nevertheless, the genetic signal seems likely to
play direct roles in the constraint because many of
the microbial taxa that show the highest heritability
form important hubs in co-occurrence networks [6].
This suggests that the taxa that are under control do
indeed play important roles in configuring the micro-
biome, perhaps as keystone taxa. This feature will
continue to intrigue us as a comprehensive list of
quantitative trait loci (QTLs) and traits begins to
emerge from future studies.
Effect size of a given microbiome QTL is relatively small
In general, the effect sizes of the associations account
for less than 10 % of the phenotypic variation, with
the largest effect size reported for any microbiome
QTL being 19 %. This general feature teaches us that
the cumulative effect of genetic variation on a
number of taxa, rather than large effects from a small
number of loci on a few taxa, is the main driver of
microbiome composition. This feature is likely to ex-
plain why factors such as diet can overcome genetic
influence [2, 9]. From an evolutionary perspective, the
importance of cumulative small effects might suggest
that the influence of host genetic diversity on the
microbiome could be more of a 'bystander effect' than
an 'intended target'. A 'bystander effect' might be
achieved if pathogens were the primary drivers of
allele frequencies at microbiome QTLs, a possibility
pointed out by Blekhman et al. [4]. It is worth noting
that there is some overlap between QTLs that control
susceptibility and resistance to pathogens and micro-
biome QTLs, but the sample size of pathogens for
which such data are available is too small to allow a
definitive conclusion.
Pleiotropy is common among microbiome QTLs
In each of the studies, multiple instances are observed in
which QTL peaks for different taxa of the microbiome
overlap, implying that variation at a single genomic region
influences several different microorganisms, some of
which are phylogenetically diverse. In several instances,
genes that are involved in innate immunity underlie these
pleiotropic QTL peaks, providing a mechanistic basis for
how such pleiotropy could be mediated. Blekhman et al.
[4] extend this observation further, showing evidence that
these genes are enriched and evolving at a higher rate than
expected. From an evolutionary standpoint, pleiotropy is
generally believed to constrain the rate at which a gene
evolves. This traditional view is, however, being chal-
lenged, with alternative splicing, structural modularity,
and mutable domains being offered as mechanisms that
could overcome such constraint [10].
Pleiotropy is commonly observed among complex
disease phenotypes and microbiome phenotypes
Perhaps the most intriguing conserved feature of the
architecture is the consistent finding of overlapping
QTLs that are associated with both the control of the
microbiome and predisposition to complex diseases or
physiological traits related to complex disease. Fig. 1
illustrates an example of one such region, on chromo-
some 10 of the mouse genome, which was originally
detected by Benson et al. [1]. This intriguing feature
underscores the role of dysbiosis in complex diseases
and, more importantly, it illuminates the concept that
genetic predisposition to such diseases occurs, in part,
indirectly through imbalance in the microbiome [1].
With the evidence from Blekhman et al. [4] showing
that genes that are associated with microbiome QTLs
are often under positive selection, such pleiotropy, if
Benson Genome Biology  (2015) 16:203 Page 2 of 4
real, further implies that balancing selection associ-
ated with the microbiome maintains variation in the
human population that may elevate the likelihood of
disease predisposition.
Concluding remarks
Clearly, there is much to learn about host–micro-
biome interactions and their relationships with human
and animal disease. For now, humans have 'weighed-
in’ among the models in which genome–environment
interactions and the microbiome are being studied,
and these studies will provide tremendous insights
into the mechanistic details of diseases as well as
their distributions across populations.
Abbreviation
QTL: Quantitative trait locus.
Competing interests
The author declares that he has no competing interests.
Acknowledgements
The author’s research on this subject has been funded by the National
Institutes of Health (RC1DK087346) and the United States Department of
Agriculture (2011-67005-30060).
References
1. Benson AK, Kelly SA, Legge R, Ma F, Low SJ, Kim J, et al. Individuality in gut
microbiota composition is a complex polygenic trait shaped by multiple
environmental and host genetic factors. Proc Natl Acad Sci U S A.
2010;107:18933–8.
MMU10
100
105
110
115
120
125
130
Streptococcaceae
Coriobacteriaceae
Insq4
Nhdlq4
Tgq21
T2dm1sa
Scc9
Obsty3
IL22
LYSP/M
IFNG
IRAK3
Fig. 1 Apparent pleiotropy on mouse chromosome 10. An ideogram of the mouse chromosome 10 is shown with a 100–125 Mb region expanded
to the right. The confidence intervals for different quantitative trait loci (QTLs) are indicated by colored solid lines. The QTLs affect both divergent
microbial taxa (members of the bacterial families Coriobacteriaceae and Streptococcaceae) and complex diseases, including obesity (Obsty 3 (Obesity
QTL3), Nhdlq4 (non-HDL QTL4) and Tgq21 (Triglyceride QTL21)), diabetes (Insq4 (Insulin sensitivity QTL4) and T2dm1sa (Type 2 diabetes mellitus 1)),
and colonic tumor susceptibility (scc9). The position of genes associated with inflammation and innate immunity (IL22, IRAK3) and the two primary
bacterial cell wall-degrading enzymes (LysP/M) are indicated on the far right by red bars
Benson Genome Biology  (2015) 16:203 Page 3 of 4
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Host genetic architecture and the landscape
of microbiome composition: humans weigh in

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Host genetic architecture and the landscape of microbiome composition: humans weigh in

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