The predictive power of the microbiome exceeds that of genome-wide association studies in the discrimination of complex human disease

Tierney, Braden T.; Y, He; Church, George M.; Segal, Eran; Kostic, Aleksandar D.; Cj, Patel

doi:10.1101/2019.12.31.891978

Cited by 15 publications

(10 citation statements)

References 31 publications

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“…It is noticeable that predictions were better when only the microbiome influenced the phenotype than when the genome was the only source of variation, a phenomenon also observed with real data [11,12,19]. In this simulation, this occurs likely because the number of causative effects and of input variables (SNPs vs. OTUS) is smaller in the Microbiome or Indirect scenarios than in the Genome scenario.…”

Section: Resultssupporting

confidence: 53%

“…On the other hand, since the groundbreaking study of Meuwissen, Hayes and Goddard [15], the prediction of complex traits using genome information has been embraced in both plant [16] and animal breeding [17] as well as in human genetics [18]. Therefore, a natural step further is combining host’s genome and microbiome information to improve complex-trait prediction, a topic that is currently receiving much attention [12,19].…”

Section: Introductionmentioning

confidence: 99%

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Opportunities and limits of combining microbiome and genome data for complex trait prediction

Pérez‐Enciso

Zingaretti

Ramayo-Caldas

et al. 2020

Preprint

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The analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

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Section: Resultssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Pérez‐Enciso

Zingaretti

Ramayo-Caldas

et al. 2020

Preprint

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“…We suggest in this study that the response to fasting can also be individual and showed that it can depend on the baseline antioxidative status. Factors driving these differences would have to be evaluated in further studies including measurement of gut microbiome composition, as it was repeatedly showed to be an important factor driving personal susceptibility to disease ( Tierney et al, 2020 ). In addition, it was also shown that the gut microbiome dramatically changed during fasting, and were correlated to changes in energy metabolism ( Mesnage et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Interplay between oxidative damage, the redox status, and metabolic biomarkers during long-term fasting

Grundler

Mesnage

Goutzourelas

et al. 2020

Food and Chemical Toxicology

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Obesity and its related metabolic disorders, as well as infectious diseases like covid-19, are important health risks nowadays. It was recently documented that long-term fasting improves metabolic health and enhanced the total antioxidant capacity. The present study investigated the influence of a 10-day fasting on markers of the redox status in 109 subjects. Reducing power, 2,2’-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical cation(ABTS) radical scavenging capacity, and hydroxyl radical scavenging capacity increased significantly, and indicated an increase of circulating antioxidant levels. No differences were detected in superoxide scavenging capacity, protein carbonyls, and superoxide dismutase when measured at baseline and after 10 days of fasting. These findings were concomitant to a decrease in blood glucose, insulin, glycated hemoglobin (HbA1c), total cholesterol, low-density lipoprotein (LDL) and triglycerides as well as an increase in total cholesterol/high-density lipoprotein (HDL) ratio. In addition, the well-being index as well as the subjective energy levels increased, documenting a good tolerability. There was an interplay between redox and metabolic parameters since lipid peroxidation baseline levels (thiobarbituric acid reactive substances [TBARS]) affected the ability of long-term fasting to normalize lipid levels. A machine learning model showed that a combination of antioxidant parameters measured at baseline predicted the efficiency of the fasting regimen to decrease LDL levels. In conclusion, it was demonstrated that long-term fasting enhanced the endogenous production of antioxidant molecules, that act protectively against free radicals, and in parallel improved the metabolic health status. Our results suggest that the outcome of long-term fasting strategies could be depending on the baseline values of the antioxidative and metabolic status of subjects.

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“…Among humans, where the field of host microbiome research has been concentrated for the last decade, emerging results indicate the potential power of this research as a health indicator in more elusive species. Microbiome indicators of health outperform genome-wide association studies by nearly 20% [92], and certain microbial taxonomic groups have been linked with an increased probability of mortality [93]. In some cases, specific taxa have been linked with common diseases, such as autism [94], allergies [95], and obesity [96,97].…”

Section: Epidermal Microbial Variability and Individual Health Statusmentioning

confidence: 99%

Skin microbiome of beluga whales: spatial, temporal, and health-related dynamics

et al. 2020

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Background Host-specific microbiomes play an important role in individual health and ecology; in marine mammals, epidermal microbiomes may be a protective barrier between the host and its aqueous environment. Understanding these epidermal-associated microbial communities, and their ecological- or health-driven variability, is the first step toward developing health indices for rapid assessment of individual or population health. In Cook Inlet, Alaska, an endangered population of beluga whales (Delphinapterus leucas) numbers fewer than 300 animals and continues to decline, despite more than a decade of conservation effort. Characterizing the epidermal microbiome of this species could provide insight into the ecology and health of this endangered population and allow the development of minimally invasive health indicators based on tissue samples. Results We sequenced the hypervariable IV region of bacterial and archaeal SSU rRNA genes from epidermal tissue samples collected from endangered Cook Inlet beluga whales (n = 33) and the nearest neighboring population in Bristol Bay (n = 39) between 2012 and 2018. We examined the sequences using amplicon sequence variant (ASV)-based analyses, and no ASVs were associated with all individuals, indicating a greater degree of epidermal microbiome variability among beluga whales than in previously studied cetacean species and suggesting the absence of a species-specific core microbiome. Epidermal microbiome composition differed significantly between populations and across sampling years. Comparing the microbiomes of Bristol Bay individuals of known health status revealed 11 ASVs associated with potential pathogens that differed in abundance between healthy individuals and those with skin lesions or dermatitis. Molting and non-molting individuals also differed significantly in microbial diversity and the abundance of potential pathogen-associated ASVs, indicating the importance of molting in maintaining skin health. Conclusions We provide novel insights into the dynamics of Alaskan beluga whale epidermal microbial communities. A core epidermal microbiome was not identified across all animals. We characterize microbial dynamics related to population, sampling year and health state including level of skin molting. The results of this study provide a basis for future work to understand the role of the skin microbiome in beluga whale health and to develop health indices for management of the endangered Cook Inlet beluga whales, and cetaceans more broadly.

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The predictive power of the microbiome exceeds that of genome-wide association studies in the discrimination of complex human disease

Cited by 15 publications

References 31 publications

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Interplay between oxidative damage, the redox status, and metabolic biomarkers during long-term fasting

Skin microbiome of beluga whales: spatial, temporal, and health-related dynamics

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