To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10−8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10−20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10−10 < P < 5 × 10−8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.
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Full GWAS summary statistics for mbQTLs are available at www.mibiogen.org, built using the MOLGENIS framework80.
16S data availability:
BSPSPC and FOCUS data is available from the Sequence Read Archive (SRA) under accession PRJNA673102.
All CARDIA data, including 16S rRNA sequencing, cannot be made publicly available due to the confidentiality restrictions. The data can be requested from CARDIA Study Data Coordinating Center at the University of Alabama at Birmingham, following CARDIA Confidentiality Certification rules. The process for obtaining data through CARDIA is outlined at https://www.cardia.dopm.uab.edu/publications-2/publications-documents.
COPSAC data are available on SRA (PRJNA683912).
DanFunD data are not deposited on the public databases due to legal and ethical restrictions. Access to the data and biological material can be granted by the DanFunD steering committee (https://www.frederiksberghospital.dk/ckff/sektioner/SBE/danfund/Sider/How-to-collaborate.aspx).
FGFP data are available on the European Genome-Phenome Archive (EGA) under accession EGAS00001004420.
GEM data are available on the SRA (PRJEB14839).
Generation R and Rotterdam Study data cannot be made publicly available due to ethical and legal restrictions; these data are available upon request to the data manager of the Rotterdam Study (email@example.com) or of the Generation R Study (firstname.lastname@example.org), subject to local rules and regulations.
HCHS/SOL data are available from the European Nucleotide Archive (ENA) under accession ERP117287.
KSCS data are available at the public repository, Clinical and Omics data archives in the Korea National Institute of Health under accession R000635.
LLD and MIBS data are available from EGA (EGAS00001001704 and EGAS0000100924).
METSIM data are available on the SRA (SRP097785).
NGRC data are available on the ENA (ERP016332).
The NTR has a data access committee that reviews data requests and will make data available to interested researchers. The data come from extended twin families and pedigree structures with twins, which create privacy concerns and thus cannot be shared on publicly available databases. Researchers may contact email@example.com for data requests.
PNP is available on the ENA (PRJEB11532).
POPCOL is available on the EGA (EGAS00001004869).
SHIP and SHIP-TREND data can be obtained from the SHIP data management unit via an online data access application form (https://www.fvcm.med.uni-greifswald.de/dd_service/data_use_intro.php).
TwinsUK data are available on the ENA under accession ERP015317.
All code used in the study is available on the Consortium GitHub (https://github.com/alexa-kur/miQTL_cookbook) or on the websites of corresponding software packages.
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Information on cohort funding and acknowledgements is available in the Supplementary Note. We thank J. Senior and K. McIntyre for critically reading the manuscript.
All authors declare no competing interests.
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Supplementary Notes 1–4 and Supplementary Figs. 1–5
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Kurilshikov, A., Medina-Gomez, C., Bacigalupe, R. et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 53, 156–165 (2021). https://doi.org/10.1038/s41588-020-00763-1
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