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Metagenome-wide association studies: fine-mining the microbiome

Key Points

  • Metagenome-wide association studies (MWAS) of human disease are now possible, owing to advances in DNA sequencing and the development of reference gene catalogues and gene clustering methods.

  • MWAS have identified associations between the microbiome and several major diseases, despite the relatively small sample sizes that have been examined by these studies compared with genome-wide association studies (GWAS).

  • Common changes to the taxa and functions of the gut microbiota have emerged from MWAS of metabolic diseases.

  • To advance from the detection of associations to the demonstration that elements of the microbiota contribute to disease will require a range of validations, including mechanistic studies both in vivo and in vitro. However, even associations that are shown to be non-causal could form the basis of diagnostic markers.

  • In the future, MWAS may be further developed in numerous ways, including the use of multiomic data, the analysis of genetic variants in metagenomic data and the study of the non-bacterial components of the microbiome.

Abstract

Metagenome-wide association studies (MWAS) have enabled the high-resolution investigation of associations between the human microbiome and several complex diseases, including type 2 diabetes, obesity, liver cirrhosis, colorectal cancer and rheumatoid arthritis. The associations that can be identified by MWAS are not limited to the identification of taxa that are more or less abundant, as is the case with taxonomic approaches, but additionally include the identification of microbial functions that are enriched or depleted. In this Review, we summarize recent findings from MWAS and discuss how these findings might inform the prevention, diagnosis and treatment of human disease in the future. Furthermore, we highlight the need to better characterize the biology of many of the bacteria that are found in the human microbiota as an essential step in understanding how bacterial strains that have been identified by MWAS are associated with disease.

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Figure 1: Identifying associations using MWAS.
Figure 2: Changes to the gut microbiome that are associated with type 2 diabetes.
Figure 3: Model for a gut microbial basis for the development of colorectal cancer.
Figure 4: The oral and gut microbiomes of individuals with rheumatoid arthritis.

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Acknowledgements

This study was supported by the Natural Science Foundation of China (grants 30890032, 30725008 and 30811130531), the Shenzhen Municipal Government of China (grants JSGG20140702161403250, DRC-SZ[2015]162 and CXB201108250098A), the Danish Strategic Research Council (grant 2106-07-0021) and the Ole RØmer grant from the Danish Natural Science Research Council and Solexa project (272-07-0196). The authors thank their colleagues at BGI, Shenzhen, China, especially J. Li, Z. Lan, S. Liang, H. Xie, D. Zhang, X. Luo, M. Arumugam and K. Kristiansen, for their help in the preparation of this Review. The authors also thank Y. Xie at Michigan State University, East Lansing, USA, for helpful discussions regarding this manuscript.

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Correspondence to Jun Wang or Huijue Jia.

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J.W. is the CEO of iCarbonX.

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The ecological community of microorganisms at a given site.

16S rRNA gene amplicon sequencing

Amplification and sequencing of the variable regions in 16S ribosomal RNA genes for the taxonomic profiling of bacteria and archaea in a sample.

Dysbiosis

An imbalance of the microbiota at a body site that is caused by an overgrowth of pathogenic microorganisms or a lack of commensal micoorganisms.

Contigs

Contiguous DNA sequences that are assembled from shorter, overlapping sequencing reads.

Short-chain fatty acids

(SCFAs). Fatty acids that have fewer than six carbon atoms. In the context of the microbiome, SCFAs usually refer to acetate, propionate and butyrate, which are produced by various species of bacteria.

Metformin

A biguanide drug that is commonly prescribed as a treatment for type 2 diabetes.

Supervised machine learning

Machine learning in which the training data are labelled (for example, as cases or controls). Using the training data, the algorithm learns to classify new data according to these labels.

Area under the receiver operating characteristic curve

(AUC). The area under a receiver operating characteristic (ROC) curve of true-positive rates versus false-positive rates, which depicts the performance of a binary classifier. AUCs typically range between 0.5 and 1, corresponding to a random and a perfect classification, respectively.

Dyslipidaemia

An abnormal amount of lipids in the blood.

Adenomas

Benign tumours that are formed from glands or that have characteristics of glands.

Periodontitis

Inflammation of the tissue that surrounds the teeth, which leads to the progressive loss of the alveolar bone and the loosening or loss of teeth.

Rheumatoid factor

An autoantibody against the constant region (known as the fragment crystallisable (Fc) region) of immunoglobulin G.

Anti-cyclic citrullinated peptide autoantibodies

Autoantibodies against proteins that contain the modified amino acid citrulline. Cyclic citrullinated peptides are used to clinically detect these antibodies.

Guilt by association

A concept from genome-wide association studies (GWAS) that describes associations in such studies as 'guilt' of a gene for a trait of interest, which means that the gene is of interest for further investigation.

Specific pathogen-free mice

Laboratory mice that are free of particular pathogens that could interfere with experiments. The excluded pathogens include both viral and bacterial pathogens.

Organoids

Organ-like structures that are grown in the laboratory.

Mobile genetic elements

DNA sequences that can be transferred between genomes or between loci of the same genome.

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Wang, J., Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat Rev Microbiol 14, 508–522 (2016). https://doi.org/10.1038/nrmicro.2016.83

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