Challenges and future directions for studying effects of host genetics on the gut microbiome

Kurilshikov, Alexander; Graaf, Adriaan van der; Fu, Jingyuan; Zhernakova, Alexandra

doi:10.1038/s41588-021-00983-z

Cited by 65 publications

(49 citation statements)

References 70 publications

(110 reference statements)

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“…The number of host phenotypes known to be impacted by the microbiome is ever-growing, from metabolism to behavior, including its influence on a range of disease risk factors (Cho and Blaser, 2012;Lynch and Hsiao, 2019;Zheng et al, 2020). However, we are only beginning to understand the contribution of host genetics in shaping microbiome composition (Davenport, 2016;Goodrich et al, 2014Goodrich et al, , 2016Lopera-Maya et al, 2022;Sanna et al, 2022). This interest stems not only from our desire to understand how evolution and coevolution has shaped host-microbiome interactions over both shorter and longer (i.e.…”

Section: Introductionmentioning

confidence: 99%

Relative abundance data can misrepresent heritability of the microbiome

Bruijning

Ayroles

Henry

et al. 2022

Preprint

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Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this question can be addressed by estimating the heritability (h2) of the microbiome – the proportion of variance in the abundance of each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample. We derived an analytical approximation for the heritability that one obtains when using such relative abundances and we uncovered three problems: 1) The interdependency between taxa leads to imprecise heritability estimates. 2) Large sample size leads to high false discovery rates, overestimating the number of heritable taxa. 3) Microbial co-abundances lead to biased heritability estimates. We conclude that caution must be taken when interpreting heritability estimates and comparing values across studies.

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Section: Introductionmentioning

confidence: 99%

Relative abundance data can misrepresent heritability of the microbiome

Bruijning

Ayroles

Henry

et al. 2022

Preprint

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“…Among a panel of 110 inbred mouse strains from the Hybrid Mouse Diversity Panel (HMDP), where confounding factors, such as dietary variation and age were controlled, heritability estimates for certain taxa were considerably higher than in humans [ 34 ]. Consistent with these observations, dozens of loci associated with gut bacterial abundance have been identified through genome-wide association studies (GWAS) in both mice and humans [ [34] , [35] , [36] ]. However, most loci have not been replicated across studies, at least in humans, even with sample sizes exceeding 1000 subjects [ 33 , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ].…”

Section: Factors Affecting Gut Bacterial Compositionmentioning

confidence: 86%

Role of gut microbe-derived metabolites in cardiometabolic diseases: Systems based approach

Aquino-Martínez¹,

Hutchison²,

Allayee³

et al. 2022

Molecular Metabolism

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“…However, unlike the high consistency of the findings in GWAS, considerable instability of the results from the microbiome research has been noted when heterogeneous signals of the genetic and dietary factors associated with the gut microbiome are reported across various studies (67,68). Such heterogeneities have been largely due to the highly individual or population-specific nature of the gut microbiome profiles, the temporal variability, the gut microbiomeimmune system interactions that establish the fragile equilibrium in the gut ecosystem, and the interactions with environmental factors, among others (69,70). In addition, although the species composition of the human microbiome has been deeply explored, detailed mechanistic studies linking the specific microbial species to host phenotypes are still nascent (71).…”

Section: Diet-gut Microbiome-host Interactionsmentioning

confidence: 99%

Nutrition for precision health: The time is now

2022

Obesity

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Precision nutrition has emerged as a boiling area of nutrition research, with a particular focus on revealing the individual variability in response to diets that is determined mainly by the complex interactions of dietary factors with the multi‐tiered “omics” makeups. Reproducible findings from the observational studies and diet intervention trials have lent preliminary but consistent evidence to support the fundamental role of gene–diet interactions in determining the individual variability in health outcomes including obesity and weight loss. Recent investigations suggest that the abundance and diversity of the gut microbiome may also modify the dietary effects; however, considerable instability in the results from the microbiome research has been noted. In addition, growing studies suggest that a complicated multiomics algorithm would be developed by incorporating the genome, epigenome, metabolome, proteome, and microbiome in predicting the individual variability in response to diets. Moreover, precision nutrition would also scrutinize the role of biological (circadian) rhythm in determining the individual variability of dietary effects. The evidence gathered from precision nutrition research will be the basis for constructing precision health dietary recommendations, which hold great promise to help individuals and their health care providers create precise and effective diet plans for precision health in the future.

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Challenges and future directions for studying effects of host genetics on the gut microbiome

Cited by 65 publications

References 70 publications

Relative abundance data can misrepresent heritability of the microbiome

Relative abundance data can misrepresent heritability of the microbiome

Role of gut microbe-derived metabolites in cardiometabolic diseases: Systems based approach

Nutrition for precision health: The time is now

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