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Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome

Nature Genetics volume 54, pages 52–61 (2022)Cite this article

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Abstract

The gut microbiome has been implicated in a variety of physiological states, but controversy over causality remains unresolved. Here, we performed bidirectional Mendelian randomization analyses on 3,432 Chinese individuals with whole-genome, whole-metagenome, anthropometric and blood metabolic trait data. We identified 58 causal relationships between the gut microbiome and blood metabolites, and replicated 43 of them. Increased relative abundances of fecal Oscillibacter and Alistipes were causally linked to decreased triglyceride concentration. Conversely, blood metabolites such as glutamic acid appeared to decrease fecal Oxalobacter, and members of Proteobacteria were influenced by metabolites such as 5-methyltetrahydrofolic acid, alanine, glutamate and selenium. Two-sample Mendelian randomization with data from Biobank Japan partly corroborated results with triglyceride and with uric acid, and also provided causal support for published fecal bacterial markers for cancer and cardiovascular diseases. This study illustrates the value of human genetic information to help prioritize gut microbial features for mechanistic and clinical studies.

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Fig. 1: Study design and workflow.
Fig. 2: Independent genetic variants and their explained variance of microbial features.
Fig. 3: Independent genetic variants and their explained variance of metabolic traits.
Fig. 4: Identifying 58 causal relationships for the microbial features and metabolic traits.
Fig. 5: Causal effects of genus Oscillibacter and Alistipes on decreasing blood triglyceride concentration.
Fig. 6: Causal effects of Proteobacteria and Escherichia coli on diseases.

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Data availability

The human reference hg38 datasets are publicly available from http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/. All summary statistics that support the findings of this study, including the associations between host genetics and microbiomes, host genetics and metabolites are publicly available from https://ftp.cngb.org/pub/CNSA/data2/CNP0000794/. The release of the data was approved by the Ministry of Science and Technology of China (Project ID: 2020BAT1137). Individual-level data including host genetics, metagenomics and metabolites have been uploaded to the GSA database (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA005334). Access to individual-level data has to be approved by corresponding authors (tao.zhang@genomics.cn, jiahuijue@genomics.cn), and is subject to the policies and approvals from the Human Genetic Resource Administration, Ministry of Science and Technology of the People’s Republic of China. The summary statistics data for 42 diseases and 59 blood quantitative traits in 212,453 Japanese individuals are available from Biobank Japan (http://jenger.riken.jp/en/result).

Code availability

The host genome reads were aligned to the latest reference human genome GRCh38/hg38 with BWA (v.0.7.15; http://bio-bwa.sourceforge.net/). The alignments were indexed in the BAM format using Samtools (v.0.1.18; http://samtools.sourceforge.net/) and PCR duplicates were marked for downstream filtering using Picardtools (v.1.62; http://broadinstitute.github.io/picard/). The genome variants calling were performed using GATK (v.3.8; https://gatk.broadinstitute.org/hc/en-us). The low-depth data were imputed using BEAGLE (v.5.0; https://faculty.washington.edu/browning/beagle/b5_0.html). The metagenome reads were aligned to hg38 using SOAP (v.2.22; http://soap.genomics.org.cn). Quality control, association analyses and GRS analyses were performed in PLINK (v.1.90; http://zzz.bwh.harvard.edu/plink/). We performed variance explained analysis using the REML model in GCTA (v.1.26.0; https://cnsgenomics.com/software/gcta) and one-sample MR analyses using the TSLS method in the AER package (v.1.2-9; https://www.rdocumentation.org/packages/AER/versions/1.2-9/topics/ivreg). Two-sample MR analyses were performed in GSMR (v.1.0.7; http://cnsgenomics.com/software/gsmr/) and TwoSampleMR (v.0.5.6; https://mrcieu.github.io/TwoSampleMR/). All statistics analyses and visualizations were performed in R (v.3.2.5; https://www.r-project.org).

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Acknowledgements

We are sincerely grateful for the support provided by China National GeneBank. We thank all the volunteers for their time and for self-collecting the fecal samples using our kit. We are very grateful to K. Kristiansen (Department of Biology, University of Copenhagen, Denmark; BGI-Qingdao, BGI-Shenzhen, China) for his support for the joint PhD program.

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Author notes

  1. These authors contributed equally: Xiaomin Liu, Xin Tong.

Authors and Affiliations

  1. BGI-Shenzhen, Shenzhen, China

    Xiaomin Liu, Xin Tong, Yuanqiang Zou, Xiaoqian Lin, Hui Zhao, Liu Tian, Zhuye Jie, Qi Wang, Zhe Zhang, Liang Xiao, Xuemei Qiu, Jin Zi, Rong Wang, Xun Xu, Huanming Yang, Jian Wang, Yang Zong, Weibin Liu, Yong Hou, Shida Zhu, Huijue Jia & Tao Zhang

  2. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Xiaomin Liu, Xiaoqian Lin, Hui Zhao & Qi Wang

  3. China National Genebank, BGI-Shenzhen, Shenzhen, China

    Haorong Lu

  4. Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, BGI-Shenzhen, Shenzhen, China

    Liang Xiao

  5. BGI-Qingdao, BGI-Shenzhen, Qingdao, China

    Liang Xiao

  6. James D. Watson Institute of Genome Sciences, Hangzhou, China

    Huanming Yang & Jian Wang

  7. Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, China

    Huijue Jia

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  1. Xiaomin Liu
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Contributions

H.J. and T.Z. conceived and organized this study. J.W. initiated the overall health project. X.X., H.Y., S.Z., Y.H., W.L. and Y. Zong contributed to organization of the cohort sample collection and questionnaire collection. H.L. led the DNA extraction and sequencing. X.Q., J.Z. and R.W. generated the metabolic data. X. Liu, T.Z. and X.T. processed the whole-genome data. Y. Zou, X. Lin, Z.Z., H.Z., L.T., Q.W., Z.J. and L.X. processed the metagenome data. X. Liu and X.T. performed the Mendelian randomization analyses. X. Liu and H.J. wrote the manuscript. All authors contributed to the data and text in this manuscript.

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Correspondence to Huijue Jia or Tao Zhang.

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Liu, X., Tong, X., Zou, Y. et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat Genet 54, 52–61 (2022). https://doi.org/10.1038/s41588-021-00968-y

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