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Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative

Title
Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative
Authors
Wang, JunKurilshikov, AlexanderRadjabzadeh, DjawadTurpin, WilliamsCroitoru, KennethBonder, Marc JanJackson, Matthew A.Medina-Gomez, CarolinaFrost, FabianHomuth, GeorgRuehlemann, MalteHughes, DavidKim, Han-naSpector, Tim D.Bell, Jordana T.Steves, Claire J.Timpson, NicolasFranke, AndreWijmenga, CiscaMeyer, KatieKacprowski, TimFranke, LudePaterson, Andrew D.Raes, JeroenKraaij, RobertZhernakova, Alexandra|MiBioGen Consortium Initiative
Ewha Authors
김형래김한나
SCOPUS Author ID
김형래scopusscopusscopus; 김한나scopusscopus
Issue Date
2018
Journal Title
MICROBIOME
ISSN
2049-2618JCR Link
Citation
MICROBIOME vol. 6
Keywords
Gut microbiomeGenome-wide association studies (GWAS)Meta-analysis
Publisher
BMC
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Background: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture. Results: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories. Conclusion: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.
DOI
10.1186/s40168-018-0479-3
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연구기관 > 의과학연구소 > Journal papers
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