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Recent studies have demonstrated the important role played by gut microbiota in regulating bone development, but the evidence of such causal relationship is still sparse in human population. The aim of this study is to assess the causal relationship from gut microbiota to bone development and to identify specific causal bacteria taxa via a Mendelian randomization (MR) approach. A genome-wide association study (GWAS) summary statistic based two-sample MR analysis was performed. Summary statistics of microbiome GWAS (MGWAS) in 1126 twin pairs of the TwinsUK study was used as discovery sample, and the MGWAS in 984 Dutch participants from the LifeLines-DEEP cohort was used as replication sample. Estimated heel bone mineral density (eBMD) GWAS in 426,824 participants from the UK biobank (UKB) cohort was used as outcome. Bacteria were grouped into taxa features at both order and family levels. In the discovery sample, a total of 25 bacteria features including 9 orders and 16 families were analyzed. Fourteen features (5 orders + 9 families) were nominally significant, including 5 orders (Bacteroidales, Clostridiales, Lactobacillales, Pasteurellales and Verrucomicrobiales) and 9 families (Bacteroidaceae, Clostridiaceae, Lachnospiraceae, Mogibacteriaceae, Pasteurellaceae, Porphyromonadaceae, Streptococcaceae, Verrucomicrobiaceae and Veillonellaceae). One order Clostridiales and its child taxon, family Lachnospiraceae, were successfully replicated in the replication sample (Clostridiales Pdiscovery = 3.32 × 10-3Preplication = 7.29 × 10-3; Lachnospiraceae Pdiscovery = 0.03 Preplication = 7.29 × 10-3). Our findings provided evidence of causal relationship from microbiota to bone development, as well as identified specific bacteria taxa that regulated bone mass variation, thus providing new insights into the microbiota mediated bone development mechanism.
Pubmed ID: 32971307
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International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
View all literature mentionsSoftware toolset that makes use of high-throughput SNP data typically seen in a genome-wide association study (GWAS) for applications such as family relationship inference and population structure identification (entry from Genetic Analysis Software)
View all literature mentionsSoftware R package for performing Mendelian randomization using genome wide association study summary data.
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