Host Genetics and Gut Microbiome: Challenges and Perspectives

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Review

Host Genetics and Gut Microbiome: Challenges and Perspectives Alexander Kurilshikov,1 Cisca Wijmenga,1,2 Jingyuan Fu,1,3 and Alexandra Zhernakova1,* The mammalian gut is colonized by trillions of microorganisms collectively called the microbiome. It is increasingly clear that this microbiome has a critical role of in many aspects of health including metabolism and immunity. While environmental factors such as diet and medications have been shown to influence the microbiome composition, the role of host genetics has only recently emerged in human studies and animal models. In this review, we summarize the current state of microbiome research with an emphasis on the effect of host genetics on the gut microbiome composition. We focus particularly on genetic determinants of the host immune system that help shape the gut microbiome and discuss avenues for future research.

Trends A proportion of gut bacteria are heritable. The impact of host genetics on the gut microbiome in humans is being revealed through genome-wide association studies. The effect size of host genetics on the microbiome appears to be modest. Several associations are found between the microbiome and genes associated with diet, innate immunity, vitamin D receptors, and metabolism.

State of the Art in Gut Microbiome Studies The human gut microbiome is a complex ecosystem of thousands of species of bacteria, viruses, fungi, and protozoa that affect human metabolic and immune function. Gut microbiota influence the development of the immune system early in life and participate directly in human metabolism by mediating energy harvest from food [1,2]. The gut microbiome is initially shaped by maternal transmission during birth, then further influenced by human nutrition, lifestyle, immune status, infections, medication use, and other factors [3,4]. A direct role for gut bacteria in the maturation and function of the immune system has been reported in humans and animal studies [5–7]. It has been shown that part of the microbiome is heritable, implying that host genetics are an important factor in determining gut microbiome composition [8–12]. In this review, we summarize the state of the art in the microbiome field and highlight links between host genetics, the gut microbiome, and immunity.

A consistent genetic signal comes from pattern recognition receptor molecules, particularly C-type lectins.

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Studies of the human microbiome have been performed for more than a century, but the fastest progress in the field has come with the start of Human Microbiome Project in 2008, when nextgeneration sequencing methods [16S rRNA gene sequencing (16Sseq) and metagenomics sequencing (MGS) methods] became widely applied (Box 1) [3,13,14]. Initially, most studies utilized hypothesis-driven design and were focused on specific groups of participants such as obese individuals [8], elderly individuals [15], children with malnutrition [16], or individuals with diseases such as Crohn’s disease [17] or type 2 diabetes [18]. This led to the discovery of disease-related changes in the metagenome. More recently, comprehensive studies have been conducted in unselected populations of modest size but with extensive phenotypic information [3,4,9,19]. Analysis of dietary questionnaires, environment, medication use, and intrinsic factors can now explain 10–20% of interindividual gut microbiome variation [3,4,19,20]. Diet has been shown to have a strong effect on microbiome composition [3,19,21,22], particularly substantial changes in dietary patterns such as switching from a vegetarian to an omnivorous diet [23].

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University of Groningen, University Medical Center Groningen, Department of Genetics, P.O. Box 30001, 9700 RB Groningen, The Netherlands 2 K.G. Jebsen Coeliac Disease Research Centre, Department of Immunology, University of Oslo, Oslo, Norway 3 University of Groningen, University Medical Center Groningen, Department of Pediatrics, Groningen, The Netherlands

*Correspondence: [email protected] (A. Zhernakova).

http://dx.doi.org/10.1016/j.it.2017.06.003 © 2017 Elsevier Ltd. All rights reserved.

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Box 1. Current Progress in Microbiome Analyses Identification of the composition of this complex community has progressed enormously over the last few years with the development and application of high-throughput DNA sequencing technology. Two major nonculturing methods of microbiome analysis are widely used. The first is based on sequencing the 16S rRNA gene of bacteria and archaea (16S rRNA gene sequencing, shortened here as 16Sseq) [44]. Identification of variations in specific regions of this gene allows decent classification of bacterial taxa. 16Sseq is relatively inexpensive to perform but has limited resolution: not all bacteria can be classified and species-level identification may not be possible, nor can the 16Sseq method detect viruses and eukaryotic communities. The second method, MGS, is based on sequencing of all DNA fragments (isolated from a fecal sample in the case of the gut microbiome) and aligning them to reference databases from all domains of life [45]. MGS allows identification of not only bacterial, but also viral, fungal, and protozoan DNA. It produces a much better resolution of bacteria at the species level and allows for annotation of bacterial gene clusters and pathways based on direct sequencing of bacterial genes. The downsides of MGS are higher sequencing costs, higher bioinformatic load due to the large number of sequence reads produced, and the inability to analyze genomes absent in the reference databases or genes with unrecognized function. Contamination by host DNA is another challenge in MGS when biopsy or mucosal material is being collected. The statistical methods widely used in microbiome analysis mostly come from ecological studies. Because they are dealing with relatively large numbers of poorly distributed measures (such as abundances of bacterial taxa), microbiome researchers must employ diversity measures, multidimensional statistics, and complex statistical approaches: Alpha diversity describes diversity of species or other taxa within a sample. The list of widely used alpha diversity measures includes species richness (the number of taxa present in the sample) and the entropy-based Shannon and Simpson indexes. Beta diversity describes the difference in taxonomic composition between samples and can be represented as a square distance matrix. Commonly used beta diversity measures are UniFrac distance and Bray–Curtis dissimilarity. UniFrac distance is a distance metric that integrates phylogenetic distances between organisms present in the samples. P P Bray–Curtis dissimilarity is calculated as Dik = |xij xjk|/ (xij + xjk), where xij and xik are the values of species i in samples j and k, respectively. The advantage of Bray–Curtis dissimilarity is that species contribute to the distance measure relative to their proportions, so the effect of a potential environmental factor is scaled to the number of responding species. By contrast, when using Euclidean distance as a measure of dissimilarity, species contribute to distance relative to their proportion squared, which overweights the effect of an external factor on a highly abundant species compared with the effect on a less abundant one. Heritability is the proportion of variation in a trait (e.g., abundance of gut bacteria) that is explained by host genetics. Quantitative trait is a measurable phenotype that depends on various factors, usually including environment and genetics. A QTL is a segment of human DNA (locus) that correlates with the trait. mbQTL mapping analyzes the effect of genetic loci on microbiome-related quantitative traits (bacterial abundance, diversity, and function).

From food sources the microbiome produces metabolites such as short-chain fatty acids and trimethylamine-N-oxide that affect a range of metabolic factors, control adiposity, and predispose individuals to common diseases [24,25]. Medication use is another important influencing factor, with drugs such as antibiotics, proton-pump inhibitors, metformin, and antidepressants shown to dramatically shift the microbiota profile, in most cases to a less diverse composition [3,26–30]. The gut microbiome is also strongly linked with intrinsic factors such as host metabolic and immune status. For example, it has been estimated that variations in gut microbiome can explain up to 6% of the variation in blood lipid levels in healthy individuals [31]. An unfavorable lipid profile (low high-density lipoprotein, high triglycerides) and high body mass index (BMI) have been associated with increased abundance of Eggerthella and Blautia genus, decreases in the families Rikenellaceae and Christensenellaceae, and decreases in Akkermansia, Tenericutes, and other taxa [9,31,32]. Gut bacteria, including Prevotella copri and Bacteroides vulgatus, are associated with insulin resistance in humans and induce insulin

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resistance in mice [33]. A causal role for gut microbiota in metabolic syndrome has been established via fecal microbiota transplantation studies in mice and humans [2,24,34]. There is also a strong interplay between the gut microbiome and the immune system, with bidirectional interaction between them playing a role in the physiology of the whole organism. On the one side, the gut microbiome plays a major role in development of the immune system, both directly and via food metabolism. For example, germ-free mice show profound alterations in development of the innate and adaptive immune systems [35–37], a reduced diversity of gut bacteria that contributes to development of chronic immune diseases [38], and an interaction of gut bacteria with food that mediates immune-cell activation, cytokine production, and Tlymphocyte proliferation via short-chain fatty acid metabolism [39]. On the other side, the immune system regulates the colonization and species abundance of the microbiome and the response to commensal bacteria [40–43]. As will be discussed in the following sections, recent findings on the effect of genetic variants of immune genes on the abundance of gut bacteria further extend our understanding of the bidirectional interaction between the immune system and gut microbiome.

Heritability of the Microbiome Human and mouse studies have shown the role of host genetics in shaping both the overall microbiome composition and the individual bacterial taxa [8–12,46]. The classical approach used to distinguish the effects of shared genetics from those of shared environment is comparing the dissimilarity between the groups of monozygotic (MZ) and dizygotic (DZ) twins. Each pair of MZ and DZ twins shares the same environment, while the genetic similarity is different: 100% in MZ twins versus, on average, 50% in DZ twins. Assuming that both twin types share the same environment, heritability of traits can be directly estimated. The first two studies on microbiome heritability, performed using relatively small twin cohorts (54 and 87 twin pairs), showed that the overall microbiome composition (represented as between-sample distances) was not significantly different between MZ and DZ twins, although the difference between MZ twins was slightly smaller in both studies [8,46]. However, the observed effect of shared environment was much stronger than the effect of genetics: both MZ and DZ twins show more similarity in their microbiome than unrelated individuals. A larger study on the TwinsUK cohort (416 twin pairs) confirmed significant heritability of the overall microbiome composition and additionally estimated heritability of individual bacteria (Figure 1). Goodrich et al. [10] identified both heritable modules and, within these modules, highly heritable bacteria. Their first module includes the family Christensenellaceae, methanogenic Archaea, and genus Tenericutes, while the second module is [37_TD$IF]mainly composed of Bifidobacteriaceae. The abundance of Christensenellaceae was associated with lower BMI in twins, and its introduction into a mouse model led to reduced weight gain in treated mice compared with controls. This intervention study concluded that the microbiome can be an important mediator between host genetics and phenotype. More recently, Goodrich et al[38_TD$IF]. [10] extended the heritability estimations by significantly increasing the sample size to 489 DZ and 637 MZ twins [10]. This study showed good concordance with the previous heritability estimates and identified more heritable bacteria: the phyla Firmicutes, Actinobacteria, Tenericutes, and Euryarchaeota were shown to be more heritable, while the highly abundant Bacteroidetes phylum shows very little heritability. For a subset of TwinsUK participants (250 twins), MGS was performed and showed significant heritability not only for bacterial taxa but also for microbial gene ontology groups, including branched-chain amino acid biosynthesis pathways and a module for sulfur reduction [11]. With genotype information available, heritability can also be measured on the identity-by-state kinship matrix as a measure of genetic relation. This method was first applied to families of

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GEM, Canada, 2016 Huerites, USA, 2015 TwinsUK, UK, 2014/2016 Two or more papers

A:Acnomycetaceae Aa:Acnomyces Bba:Barnesiella Bbi:Bifidobacterium Bcb:Bacteroides Bcp:Bacteroidaceae _Prevotella C:Christensenellaceae CAM:Campylobacteraceae CAMc:Campylobacter CAd:Carnobacteraceae_Dolosigranulum CL:Clostridiaceae CM:Comamonas Cc:Christensenella

D:Dehalobacteriaceae Dd:Dehalobacterium Ec:Erysipelotrichaceae_cc_115 GP_P:Pasteurellaceae La:Lachnospiraceae_Anaerofilum Lb:Lachnospiraceae_Blaua Lc:Lachnospiraceae_Coprococcus Ld:Lachnospiraceae_Dorea Ll:Lachnospiraceae_Lachnobacterium Ma:Moraxellaceae_Acinetobacter Mm:Methanobrevibacter N:Neisseraceae O:Oxalobacteraceae

Oo:Oxalobacter P:Peptococcaeae PS:Peptostreptococcaceae Pp:Porphyromonadaceae_Parabacteroides Rr:Ruminococcus S:Selenomonadales Tm:Tenericutes_Mollicutes Tr:Tenericutes_RF3 Tt:Turicibacter Vp:Veillonellaceae_Phascolarctobacterium Vv:Veillonella c:Clostridium s:Clostridiacaee_SMB53

Figure 1. Heritable Bacterial Taxa within the Gut Microbiome Composition. This figure shows all taxa for which significant heritability was identified in one or more studies. For the Davenport et al[329_TD$IF]. [47] study of Hutterites in the USA, bacteria for which the confidence interval does not cross zero are reported. For the TwinsUK data, the taxa with heritability that passed study-wise FDR control are shown (Goodrich et al[30_TD$IF]. [9,10]; Xie et al. [11]). For the GEM study, Canada (Turpin et al[31_TD$IF]. [12]), only taxa which passed 10% false discovery rate (FDR) are reported.

North American Hutterites (n = 127), an isolated population with a communal lifestyle of living and eating together that is expected to reduce environmental differences [47]. Heritability in this group was identified for several bacterial taxa, with the largest heritability for class Gammaproteobacteria, family Campylobacteraceae, and genus Coprococcus [47].

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Given a cohort of related individuals, sample kinship can be calculated directly and then used to measure trait heritability. This approach was applied to a cohort of related individuals (n = 270) by Turpin et al[38_TD$IF]. [12]. In their study, 20 bacterial taxa were found to be heritable after correcting for multiple testing, including five taxa reported earlier by Goodrich et al. [10]. Given that both studies were performed on populations from very different geographic regions (the UK and Canada), this replication rate is reasonable and implies that some bacteria are affected by host genetics in the overall human population (Figure 1). In sum, human and mouse studies have thus far shown that a subset of gut bacteria is heritable and influenced by host genetics.

Microbial Quantitative Trait Mapping in Mice and Humans The gut microbiome can be considered as a complex trait like height, BMI, or blood lipid levels. Because the overall microbiome composition, as well as many individual microbial measures such as bacterial abundances or bacterial functions, has been shown to be dependent on the host genome, one can start to identify genetic loci that influence specific bacterial taxa or pathways using quantitative trait mapping [microbial quantitative trait loci (mbQTLs)]. To date, mbQTLs have been identified in mouse by studying intercrosses in inbred mice and the Hybrid Mouse Diversity Panel and in humans by large-scale genome-wide association studies (GWASs). In mice, a study by Benson et al. [39_TD$IF][48] based on a large murine advanced intercross line discovered mbQTLs for 26 of 64 highly abundant taxonomic groups. Some mbQTLs showed a pleiotropic effect, resulting in 13 different loci that influenced one or more microbial traits. Interestingly, pleiotropy was observed both for closely related and for unrelated bacteria: an mbQTL at chromosome 7 affects two phylogenetically close bacteria, Lactobacillus johnsonii and Turicibacter (both from Firmicutes); an mbQTL at chromosome 6 affects the whole Proteobacteria phylum and one of its low-level taxa, Helicobacter pylori; and a mbQTL at chromosome 10 affects the completely unrelated Lactococcus genus and Coriobacteriaceae family. Many of these mbQTLs can be linked to immunity and disease development. The pleiotropic mbQTL at chromosome 7, for example, contains the Irak2 gene known to modulate response of the Toll-like receptor 2 (TLR2) pathway, the lysozyme genes Liz1 and Liz2, and interferon-g and interleukin-22 ([340_TD$IF]IL-22), all of which are involved in mucosal immunity. McKnite et al. [341_TD$IF][49] and Org et al. [342_TD$IF][50] both performed mbQTL mapping on inbred mice and observed seven and five QTLs, respectively, and they linked several of these loci to obesity, lipid levels, immune response, and insulin segregation. In another study of wild and laboratory populations of house mice subspecies and their hybrids, Wang et al. [342_TD$IF][51] identified 14 independent loci that influence 29 bacterial features (including phylum Proteobacteria, genera Helicobacter and Bacteroides, and overall species richness) [51]. These studies confirmed the genetic effect on microbiome composition in mice, identified the pleiotropic effect of associated loci, and pointed to the genetic effect of several immune-related loci on microbiome composition. In humans, the first genetic studies on the microbiome were performed in candidate gene settings, where one or several host genetic variants were tested for their association with microbiome composition or function. For example, genetic variants in FUT2 genes were associated with microbial energy metabolism and mucosal inflammation [52], MEFV polymorphisms were associated with major shifts in bacterial phyla [53], and Crohn’s risk variants located in the NOD2 gene were associated with changes in abundance of Enterobacteriaceae [54]. The first genome-wide mbQTL mapping in humans was performed in 93 individuals from the Human Microbiome Project initiative for whom there is both metagenomic and genotype data [55] (Table 1). Despite the small sample size, there was suggestive evidence of a linkage

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Table 1. Summary of Genome-Wide mbQTL Mapping Studies in Humans Study

[14_TD$IF]Sequencing method

Population sample size

[324_TD$IF]Traits

Results

Major findings

Blekhman et al. (2015) [55]

WGSa[13_TD$IF]

Mixed N = 93

Bacterial taxa

83 associations with suggestive threshold [10_TD$IF]P < 1.16  10 5

Suggestive linkage of LCT locus polymorphism with abundance of Bifidobacterium

Davenport et al. (2015) [47]

16Sseq

Hutteritesb[326_TD$IF]5 N = 91 (summer) N = 93 (winter)

Bacterial taxa

Eight associations for trait-wise at FDR [17_TD$IF]q < 0.2

mbQTL loci found for Anaerostipes, Akkermansia, Lactobacillus, Bifidobacterium[18_TD$IF], and others

Goodrich [19_TD$IF]et al. (2016) [10]

16Sseq

UK (twins) [20_TD$IF]N = 1126

Beta-diversity, bacterial taxa

[21_TD$IF]Three loci linked to beta-diversity with [2_TD$IF]P < 5  10 8 28 Loci linked to bacterial taxa with P < 5  10 8[23_TD$IF]

Three SNPs linked to betadiversity: rs563779 within UHRF2 gene, and rs9997915 and rs1593554 on 4q22.3 (intergenic). Strongest associations with bacterial taxa: Clostridiaceae to SNP rs10055309 in SLIT3 gene ([24_TD$IF]P = 1.20  10 8), and Bifidobacterium with SNP rs1446585 near LCT gene (P = 4.38  10 8[25_TD$IF])

Bonder et al. (2016) [56]

WGS

Dutch N = 1514

Bacterial taxa, bacterial pathways

[26_TD$IF]Nine loci linked to taxa at P < 5  10 8 33 Loci linked to bacterial pathways and Gene Ontology terms at P < 5  10 8[27_TD$IF] 32 mbQTL loci linked to bacterial taxa and pathways at suggestive [10_TD$IF]P < 5  10 6 for targeted gene set

Most significant mbQTLs to bacterial taxa were observed for Blautia, Methanobrevibacter, Dialister invisus, Bacteroides xylanisolvens Strongest associations for bacterial pathways include steroid degradation, bile acid metabolism, sulfuric ester hydrolase activity[29_TD$IF], and others Several mbQTLs point to genes involved in innate immunity (CLEC4F-CD207, CLEC4K-FAM90A1, NOD1, NOD2) and energy metabolism (LINGO2, VANGL1, SORCS2, SLIT3)

[30_TD$IF]Turpin et al. (2016) [12]

16Sseq

Canadian (n = 1301) USA (n = 79) Israeli (n = 181) Ntotal = 1561

Bacterial taxa

58 Loci linked to individual taxa with [31_TD$IF]P < 5  10 8[28_TD$IF]

Four associations were replicated on the independent cohort from different [32_TD$IF]regions Strongest associations include [3_TD$IF]genus Acidaminococcus to SNPs from [34_TD$IF]three loci: first contains SPEN, second carries KCNV1[35_TD$IF] and RPSAP48, and third contains H2AFY2[36_TD$IF]; family Barnesiellaceae to CTNND2 locus; genus Atopobium[37_TD$IF] to LINC0051 locus; family Rikenellaceae [38_TD$IF]to PHOSPHO2 locus.

Wang [39_TD$IF]et al. (2016) [19]

16Sseq

German N = 2183

Beta-diversity, bacterial taxa

42 Loci linked to betadiversity at [10_TD$IF]P < 5  10

Numerous loci linked to betadiversity, including loci [40_TD$IF]carrying genes related to metabolism (VDR, POMC, GRID1) [41_TD$IF]and immunity (CLEC16A, IL1R2, MAP4K4) Loci linked to individual taxa carry broad range of genes, including lincRNAsa[42_TD$IF]

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40 loci linked to bacterial taxa at P < 5  10 8[10_TD$IF]

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Table 1. (continued) Study

[14_TD$IF]Sequencing method

Population sample size

[324_TD$IF]Traits

Major findings

Results

(LINC01192), metabolismrelated genes (SLC2A9, ABCA13[327_TD$IF]), and others a

Abbreviations: lincRNA, long noncoding RNA; WGS, whole-genome sequencing; FDR - false discovery rate. The Hutterites are an isolated founder population of European origin.

b

between increased Bifidobacterium abundance and [34_TD$IF]SNP rs56064699, which is located in the LCT gene known to be associated with lactose intolerance [55]. Association of another functional LCT variant, rs4988235, and its proxy rs1446585 (r2[36_TD$IF] = 0.85) with Bifidobacterium abundance was later observed in larger population cohorts from the UK [10], Hutterites [10], and [34_TD$IF]The Netherlands [56]. The study by Goodrich et al[38_TD$IF]. [10] in 1126 UK twins also reported results of mbQTL mapping. They applied two different approaches: GWAS on the microbiome beta-diversity applied only to unrelated individuals from twin pairs, and GWAS on individual taxa on all samples with correction for kinship. Two mbQTLs were found for microbial beta-diversity and 28 for the individual taxa at a genome-wide significance of 5  10 8 (Table 1). Recently, three independent mbQTL studies in large-scale population cohorts were published. Bonder et al. [56], Turpin et al. [12], and Wang et al. [19] performed high-resolution QTL mapping in Dutch, Canadian, and German populations, respectively. All three groups used similar discovery and replication designs on cohorts of comparable sizes (Table 1), but used different methods (Figure 2) and approaches. The differing methodologies and betweenpopulation diversity in genetics, diet, and lifestyle led to differences in the results obtained. Bonder et al. [346_TD$IF][56] used MGS data in a discovery cohort of 984 individuals and a replication cohort of 530 individuals to analyze not only taxonomical composition but also abundance of bacterial pathways and functional gene categories. Of the 42 loci that passed the genome-wide significance threshold (P < 5  10 8[345_TD$IF]), nine had an effect on different microbial taxa and 33 affected bacterial pathways and Gene Ontology categories. No overlap was observed between genetic loci determining taxonomic and functional microbiome composition except for suggestive association to CD5–CD6 SNPs associated with multiple sclerosis. Possible explanations for this discrepancy are lack of power and the fact that most genetic-driven pathways are driven by several taxa. Several loci associated with bacterial abundance contained genes related to immunity and energy metabolism such as LINGO2 and VANGL1. In the list of microbial pathways, the largest effect of host genetics was observed for the plant-derived steroid degradation pathway (PWY-6948). Two independent loci were linked with this pathway: one containing the SORCS2 gene and one containing the SLIT3 gene. Both genes are involved in metabolic processes. The SLIT3-containing locus was also shown to be significantly linked with bacterial abundance in Goodrich et al. [10]. Several associations were observed for Gene Ontology terms and loci containing C-type lectin genes, including CLEC4F-CD207 and CLEC4K-FAM90A1. With more relaxed thresholds for specific loci subsets, associations were observed for other loci related to innate immunity (NOD1, NOD2, CD5/CD6, LY86, CD209, and others). This study also identified clear interactions between LCT genotype, levels of Bifidobacterium, and the consumption of dairy products. These latter studies show the importance of genetic and environmental interactions in determining microbiome composition.

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Microbiome QTL studies in human populaons (N > 1000)

Sequencing method

16S sequencing

Trait for GWAS

WGS sequencing

Beta-diversity

Taxonomies

Pathways

Genomewide efficient mixed models (GEMMA)

Hurdle negave binomial model

Turpin et al. (2016)

Goodrich et al. (2016)

Wang et al. (2016)

Bonder et al. (2016)

58

28

40

42

Stascal model

Envfit (ordinaon-based, permutaon test for significance)

Microbiome GWAS (distance-based, parametric)

Combined two-part logit/ lognormal model

Paper

Wang et al. (2016)

Goodrich et al. (2016)

Number of mbQTLs reported (P<5×10-8)

42

3

Spearman correlaon excluding zero incidence

Figure 2. Analysis Scheme for Microbial Quantitative Trait Loci (mbQTL) Mapping Performed on Large-Scale Population Cohorts. This figure shows the different methods that have been used for mbQTL mapping in the genome-wide association studies (GWASs) published so far and a summary of results. GWAS hits with P < 5  10 8 are reported. Only studies with sample size greater than 1000 individuals are included in the figure. Abbreviation: WGS, whole-genome sequencing. See [10,12,19,56].

Turpin et al. [346_TD$IF][12] performed a GWAS on a discovery cohort of 1098 individuals and a replication cohort of 463 individuals. Among the 58 SNPs passing the genome-wide significance threshold in the discovery cohort, 33 associations were for heritable taxa and 25 were for taxa not found to be heritable. Even though their replication cohort was from different countries (United States and Israel) and much younger in age, four of these associations could be replicated. The strongest associations were observed for the loci containing CTNND2 (cadherin-associated protein d2), LINC0051, and the locus with known expression quantitative trait loci effect on pyridoxal phosphate phosphatase (PHOSPHO2) expression in the stomach. Wang et al. [347_TD$IF][19] utilized a different approach. They analyzed the bacterial beta-diversity, a metric that describes microbiome composition dissimilarity between individuals. This analysis was also done after correction of microbial variations for the effect of environmental factors, such as smoking and major nutritional components, derived from questionnaires. They made use of a discovery cohort of 1812 individuals with replication in 371 individuals. A total of 42 loci reached genome-wide significance, each explaining 0.67–0.97% of the total microbiome variance. Twenty-one of the loci were successfully replicated. Some of Wang et al.’s [19] observations were consistent with previous findings. For example, they found that the vitamin D receptor (VDR) gene had a high impact on the microbiome. This association was also shown in Vdr–/– mice, showing that Vdr gene loss affects 42% of the microbiome beta-diversity in a controlled setting [19,57]. A significant effect on beta-diversity was also observed for loci carrying the proopiomelanocortin gene, the serotonin and glutamate receptors, and other genes implicated in susceptibility to diseases. GWAS for individual bacterial taxa and OTU counts revealed 40 loci reaching genome-wide significance associated with 22 bacterial traits. These loci were enriched for genes involved in pathways localized in the gastrointestinal tract

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and epithelial cells. Interestingly, the pathways of vitamin and vitamin D response were both in the top five enriched pathways, in agreement with their beta-diversity GWAS findings. Surprisingly, little overlap was observed between loci reaching genome-wide significance in overall microbiome composition versus individual bacterial taxa. These results suggest that some genomic features may have a stronger effect on individual bacteria, while others have relatively weaker effects, but on many taxa. Going beyond the analysis of the effect of a single SNP on bacteria, large-scale population design also allows us to test if carrying a high load of disease-risk-associated genetic variants (affecting either one gene or the total risk score for a disease) has an impact on gut microbial composition and diversity. This approach was used to explore inflammatory bowel disease (IBD) risk loci and showed the association between the risk locus that carries SLC39A8 and the abundance of Anaerostipes, Coprococcus, Lachnospira, and SMB53 [58]. Another study showed the association of the NOD2-containing risk locus with the abundance of the Enterobacteriaceae family [54]. The combined risk score from 11 IBD SNPs has also been shown to be associated with a decreased abundance of the butyrate-producing genus Roseburia [59]. In sum, large-scale population studies and mouse studies with controlled environments have led to the detection of significant associations between genetic loci and microbiome. The associated genes with known functions point to the role of genetic loci linked to innate immunity and energy metabolism. With a few exceptions, most of the loci were reported in one study and have not (yet) been replicated in the other populations. Given the high number of tests in mbQTL studies, the current wide-scale analyses are still underpowered, and larger meta-analysis are needed to confirm the reported associations.

Novel Insights (Mechanisms) from Immune Genetics The bidirectional interaction of microbiome and immune system is evident from multiple mouse studies showing alterations in the development of the immune system of mice raised in germ-free conditions [60–62], in knockout of multiple immune genes in mice leading to altered microbiome composition and function [63–65], and in the association of gut bacterial species with immune and inflammatory diseases in humans [54,66–68]. The major findings of mbQTLs in mice and humans clearly highlight the importance of immune molecules such as C-type lectins and NOD genes in regulating the gut microbiome composition (Figure 3). Evidence is emerging quickly on the role of ‘innate immunity’ in shaping the microbiome. Many studies to date have highlighted the importance of pattern recognition receptors (PRRs): the group of innate molecules that sense microorganisms through their conserved molecular structure (Figure 3) [40,42,56,69–73]. PRRs include several families of receptors, including TLRs, nucleotide-binding oligomerization-like receptors, RIG-I-like receptors, and C-type lectin receptors (CLECs). Knockout studies of several of these molecules in mice showed evidence of gut dysbiosis. Mice lacking Tlr5 have altered gut microbiota and increased predisposition to metabolic syndrome [42]. Mice lacking the Card9 gene have microbiota changes that predispose them to developing colitis [74]. Multiple experiments in Nod2-deficient mice show that they have an increased load of commensal resident bacteria, a reduced ability to prevent intestinal colonization by pathogenic bacteria [69,70], and an increased susceptibility to bacterial infections [71]. Inflammasome-deficient mice also develop an impaired host–microbiome interaction that causes increased intestinal inflammation and metabolic syndrome abnormalities [40,72,73]. Genetic studies in mice have also identified polymorphisms in microbial sensing-signaling genes associated with gut microbiota composition in both mouse and human studies. Org et al.

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Bacteria, viruses, fungi and, protozoa

C-type lecns

Toll-like receptors TLR1/TLR6a

TLR2a

CD180b,*

CLEC16Ab* LY86a

CLEC4A/4E/6A/ 7A/FAM90A1a* MRC2a

CLEC4G/ CD209a CLEC4Fa*

NOD-like receptors Adapter molecules

NOD1a

NOD2a,c

CARD11a

MYD88a,b* TIRAPa TRAF2a

NF-kB

Cytokines

Figure 3. Genetic Variants in Innate Molecules That Have Been Linked to the Microbiome Composition in Humans. This figure shows the pattern recognition receptor pathway molecules that have been associated with (See figure legend on the bottom of the next page.)

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[50] found seven loci that showed association with bacterial species. One of them, located on chromosome 15 and associated with Roseburia species, contained the Irak4 gene, a kinase that activates the nuclear factor-kB pathway in TLR- and T [348_TD$IF]cell-receptor-signaling pathways. The expression of Irak4 was also correlated with abundance of Roseburia spp., suggesting a causal relationship. Benson et al. [39_TD$IF][48] found an association of several bacteria with the locus that contains the Irak3 gene, another regulator of TLR-signaling pathway. In human studies, consistent findings were observed for the association of PRR genes with microbiome composition and microbiome-associated disease. Genetic variants in NOD2 are strongly associated with Crohn’s disease, an inflammatory condition of the gut associated with dysbiosis [54,75]. Carriership of the NOD2 genetic risk for Crohn’s disease is associated with an increased relative abundance of Enterobacteriaceae, a bacterial family that includes many pathogens such as Escherichia coli, Klebsiella, and Shigella [54]. Consistent with this observation, polymorphisms in NOD2 were found to be associated with abundance of the microbial pathway of enterobactin biosynthesis [56]. This pathway is produced by E. coli and inhibits the bactericidal host enzyme myeloperoxidase, thereby providing a survival advantage for E. coli in the human gut [76]. SNPs in the NOD1 gene (a homolog of NOD2) are also associated with bacterial pathways and gene groups specific for E. coli [56]. The strongest association in genome-wide mbQTL studies was observed for the association of bacterial function clusters with three CLECs, CLEC4F–CD207 at 2p13.3, CLEC4A– FAM90A1 at 12p13, and CLEC16A at 16p13 [19,56]. A lower significance (P < 5  10 6[342_TD$IF]) was observed for the CLEC loci on chr19p13.2 containing CD209-CLEC4G, MRC2 (CLEC13E) on 17q23.2, and additional CLEC genes on 12p13 [CLEC6A (Dectin-2), CLEC4E (MINCLE), CLEC7A (Dectin-1)]. C-type lectins are a diverse group of PRRs originally defined by their ability to recognize carbohydrate structures [77]. Although these receptors have similar structural features, they can provide an inhibitory or activating signal and recognize diverse endogenous and exogenous ligands. Dectin-1, for example, can recognize fungal b-glucans, whereas Dectin-2 can recognize fungal carbohydrate structures, viral pathogens, and necrotic cells [77]. Dectin-1 and other CLEC molecules help to maintain tolerance to the microbiota, protect from intestinal inflammation, and control infections [78]. Dectin-1 has been shown to mediate the tolerogenic signal from mucus and commensal bacteria to dendritic cells [79]. The role of C-type lectins in the host–microbiome balance is also supported by recent studies in shrimp [80], mosquitos [81], and mice [82]. The resolution of genetic analysis does not allow narrowing of the association signal to one causal gene, but the fact that several different clusters of C-type lectin molecules are strongly associated with various bacterial function groups suggests that multiple C-type lectins are involved in regulating the homeostasis of gut bacteria. The effect of ‘adaptive immune response’ on the microbiome is less clear, although mutualistic relations have been also established between gut bacteria and adaptive immune cells. Rag1–/– mice lacking adaptive immunity have different microbiota profiles than wild-type mice [83], suggesting an overall role for adaptive immunity in host–microbiome composition. Multiple mouse studies have shown the effect of particular gut bacteria on specific T cell pathways: segmented filamentous bacteria have been shown to induce activation of intestinal Th17 cells and can trigger autoimmune arthritis [84], Bifidobacterium species can promote antitumor immunity [85], and a mixture of Clostridium species can induce intestinal Treg cells that mediate systemic inflammation [86]. In humans, several studies support the association of gut bacteria microbiome composition and function at P < 5  10 6[32_TD$IF]. Four classes of innate immune molecules have been reported to be associated: Toll-like receptors, C-type lectins, nucleotide-binding oligomerization-like receptors (NOD-like receptors), and adapter molecules. * indicates the association at genome-wide significance level (P < 5  10 8). aBonder et al. [56], b [3_TD$IF]Wang et al. [19], cKnights et al. [54]. Abbreviation: NF-kB, nuclear factor-kB.

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with autoimmune diseases such as multiple sclerosis [87] and rheumatoid arthritis [88]. It is, however, not possible to distinguish cause and consequence: Do gut bacteria trigger the autoimmune diseases? Or does the autoimmune condition lead to changes in the microbiome? Antigen-specific adaptive immune response can influence the microbiota–host relationship by modifying the gut microbiome. For example, production of bacteria-specific IgA has been reported in IBD [89], and this indicates a role for adaptive immunity in host–bacteria interaction. Moderate association (P < 5  10 6[348_TD$IF]) has been also observed for disease-associated autoimmune SNPs located in IL23R, IL10, CD5/CD6, CD86, and CCL2/CCL7/CCL8, suggesting that the microbiome can modify the manifestation of genetic predisposition to disease [56]. Overall, the associations observed for adaptive immune genes are less strong compared with those seen for innate molecules (in particular, PRR). However, we expect that more variants will also be identified in adaptive genes as discovery power increases in mbQTL studies with increased sample size.

Challenges and Perspectives Despite remarkable progress in generation of metagenomics data in large cohorts, analysis of the microbiome in relation to host genetics remains challenging. Major challenges include ([349_TD$IF]i) the effects of exogenous and environmental factors on the gut microbiome, which likely mask the effect of genetic variants; (ii) the complex structure of microbiome data, which makes analysis challenging and (iii) the high number of tests performed that require collection of extensive cohorts to overcome the issue of multiple testing. Large Effect of Environment The composition of the gut microbiome is highly variable across individuals [8,14] and it has recently been shown that many factors can affect the structure of this microbial community [3,4]. In population studies, the effect of host genetics on the gut microbiome is likely masked by interindividual differences in age, sex, individual diet, medication use, and other exogenous and external factors, differences which can all reduce the power of microbiome GWAS analysis [3,19]. Recent GWASs estimate that [350_TD$IF]established environmental factors explain 10–20% of microbiome variance, whereas the effect of genetics is identified as approximately 10% [19]. It is not yet clear what explains the remaining microbiome variance. The effect of exogenous and environmental factors is likely to be underestimated because not all potentially confounding factors have been investigated. It is also possible that the effect of the diet is underestimated when using food frequency questionnaires, which average the food intake over the reported period. In addition, the effect of gene–environment interaction has not yet been explored on a wide scale. The potential role of gene–environment interaction in explaining microbiome variance is highlighted by the reported interaction of LCT variants with diary intake in regulating the abundance of Bifidobacteria. Another challenge of genetic studies of microbiome traits is dealing with confounders, which are more critical here than in other genetic studies because they also reflect the cumulative effect of various factors over an individual’s lifetime beginning from early colonization in childhood. As it is not possible to trace all the covariates at the general population level, it is therefore important to create prospective microbiome cohorts to analyze the role of genetics over a lifetime and during perturbations like development of diseases. In particular, prospective cohorts of newborns followed until late adulthood will be important for understanding both the effect of genetics on shaping the gut ecosystem and the role of the microbiome in health and disease traits later in life. In general, accounting for detailed information about human health, diet, medication, stool consistency, and other possible covariates can potentially increase mbQTL analysis power and should be recommended for any further studies. However, these covariates should be added with caution because heritable diet preferences are speculated to be one of the mechanisms for how genetics affect the gut bacteria [9].

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Microbiome Structural Complexity As a complex trait, microbiome composition is not an ‘easy’ multidimensional space to deal with. The individual microbial features that comprise the microbiome are overdispersed, zero inflated, and usually have many outliers. The microbiome in general also has a high level of between-trait collinearity and complex correlation structure. This complexity can be biologically relevant because of the presence of bacterial co-occurrence clusters, or can be artificial due to the proportional nature of microbiome data. Numerous statistical methods were proposed to deal with this complexity, but there is still no ‘gold standard’ in this field [90,91]. It should be emphasized that current GWAS methodology is not directly applicable to studying mbQTLs. As an example, all three population studies published in 2016 used different methods to search for mbQTLs: Spearman correlation for nonzero samples [56], Hurdle negative binomial family models [19], and two-part logit/lognormal with nested model selection [12] (Figure 2), contributing to the differences in observed results. Developing a new methodology optimized for the broad methods of microbiome analyses is therefore an important focus for current and future research. Challenge of Multiple Comparisons Microbiome traits include hundreds of taxa and their pathways, making substantial sample sizes necessary to overcome the multiple testing issue. The established P value for GWAS on a single trait is 5  10 8, which is calculated from estimating the number of independent common SNPs as one million. However, including multiple microbial traits in the analysis requires increasing the significance thresholds for the number of independent tests. Although the sample size of microbiome studies has significantly increased in recent years, only a very small number of observed associations reach significance after proper correction for multiple testing. The solutions to overcome multiple testing issues are either selection of a subset of bacteria or SNPs or increasing sample size. For example, analyses can be focused on a selection of bacteria with known high heritability (as done by the TwinsUK study [10]) or on a selection of SNPs located in genes likely important for shaping the microbiome (as done in the Dutch GWAS [56]). However, similar to other complex traits, selection of candidate genes fails to capitalize on the major advantage of an unbiased GWAS approach: identification of novel genes and pathways. In the future, increasing sample size and performing the meta-analysis in multiple cohorts will increase the significance of results and allow us to catch smaller genetic effects and perform intercohort meta-analyses. Multiple groups are now working on an mbQTL meta-analysis of >15 000 subjects using genome-wide genotyping and 16Sseq data sets ([351_TD$IF]MiBioGen consortium). Similar to recent successes in identification of genetic findings in complex diseases and traits such as schizophrenia [92] and BMI [28], current estimations of effect size of genetic variants on the microbiome ensure that increasing the sample size to tens of thousands individuals will yield sufficient power to identify genes that drive the microbiome ecosystem.

Outstanding Questions How can the discovery power of [352_TD$IF]mbQTL studies be improved? We need to consider increasing the sample size, including an essential replication step, performing meta-analysis of multiple studies, and harmonizing DNA processing methods and data analysis pipelines. How can the effects of genetics and environmental factors be separated? One solution is using well-phenotyped cohorts that allow the inclusion of environment, disease, medication, and other nongenetic factors as covariants. How can the findings of human [352_TD$IF]mbQTL studies be validated? How useful are animal models for identification of casual genes? What mechanisms underline the genetics–microbiome association? How can the genetics–microbiome interaction contribute to susceptibility risk for complex diseases? How can the [352_TD$IF]mbQTL findings be used in practice? What is the potential of genes and food interaction as the basis for personalized nutrition? If the microbiome is a mediator between genetic predisposition and development of diseases, can it be used as a target for therapy and disease prevention?

Concluding Remarks A large step forward has been taken in understanding how the microbiome interacts with human metabolism and immunity. The substantial role of genetics was identified in large-scale twin and population cohorts, and these studies point to a bidirectional interaction between microbiome and immune system. In the future, increasing sample size and performing environment-controlled experiments and prospective and intervention studies are necessary steps toward further uncovering the role of the microbiome in human health. Identification of the interaction of individual genetics with food, lifestyle, and the microbiome is needed for the development of personalized nutrition and personalized microbiota targeting for the treatment and prevention of human diseases (see Outstanding Questions).

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Acknowledgments C.W. is funded by a European Research Council (ERC) advanced grant (FP/2007-2013/ERC grant 2012-322698), a Netherlands Organization for Scientific Research (NWO) Spinoza prize (NWO SPI 92-266), and the Stiftelsen Kristian Gerhard Jebsen Foundation (Norway). A.Z. holds a Rosalind Franklin Fellowship (University of Groningen) and ERC starting grant (715772). A.Z. and J.F. are also funded by CardioVasculair Onderzoek Nederland (CVON 2012-03). J.F. is funded by an NWO Vidi grant (NWO-VIDI 864.13.013). We thank Kate Mc Intyre for editorial assistance.

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