Computational methods identify genetic drivers of microbiome diversity

To identify the genetic drivers of microbial beta diversity, researchers need to develop methods with sufficient statistical power to map these complex gene-microbiome relationships. Therefore, we turned to the Covariate Adjusted Kernel RV (KRV) framework. Kernel-RV is “a statistic for measuring and testing dependencies between two groups of variables, such as a group of genetic variants and a microbiome,” he said. In previous microbiome GWAS studies, researchers “study associations between one genetic variation and one microbial taxon at a time,” but there are millions of genetic variants within a microbial community. Such an approach would pose enormous problems due to the presence of genetic variation and “hundreds of microbial taxa.” It’s burdensome with multiple tests and has low statistical power,” said Liu. To increase statistical power, the Wu team will “instead assess associations between groups of genetic variants within genes and microbiome-wide composition as measured by beta diversity at the community level.” selected. This new approach will allow researchers to “reduce the burden of multiple tests, allowing them to account for unique properties of genetic and microbiome data, such as interactions between genetic variants and phylogenetic relationships between microbial taxa.” These advantages of the KRV framework improve statistical power and help identify more loci associated with the human microbiome. “

To test whether this method has its capabilities, Liu et al. We applied it to the HCHS/SOL) cohort, Liu said. Narrowing down to specific populations has been biologically important in identifying genes that influence microbiome composition. This is because “different populations tend to be systematically different in their genetic and microbial profiles, and may also have different genetics and microbiome relationships.” were excited to discover four genes “associated with overall gut microbiota composition (or gut microbiota beta-diversity)” within the HCHS/SOL cohort, none of which were “the same.” identified using two other competing methods that focused on the genetic signature of species (that is, genes associated with the composition of the entire microbiome).Thus, these results suggest that We support the increasing power of our approach and contribute to exciting new discoveries for further understanding the relationship between genetics and the microbiome.” reproduced”, but no other relevance was previously known. “Three of the four genes identified in our microbiome GWAS analysis” have been previously shown to be involved in “immune function or immune-related diseases,” suggesting that the gut microbiome composition It has been suggested that immune-related genes may play an important role in the formation. One of these identified genes, “IL23R, is a pro-inflammatory cytokine that is intimately involved in autoimmunity” and is associated with “both the gut microbiome and inflammatory bowel disease (IBD).” gender is documented. Combined with previous studies, our findings further support that the gut microbiome may mediate host genetic influences on the development of inflammatory diseases like IBD,” said Liu. Shouted out.

The purpose of the Hispanic Community Health Study/Latino Cohort Study was to identify risk factors for health outcomes in the Hispanic/Latino population of the United States, and through the analysis of this data, “our study “It’s the first study to investigate the genetic impact on human health,” Liu said. “The results of our study help us understand important genetic risk factors for gut microbiome-related health outcomes in Hispanic/Latino populations.” Extending the traditional approach of “studying the associations between one genetic variation and one taxon” to examine “associations between intragenic variation and overall microbiome composition,” routine It will be an asset for microbiome analysis. . However, one of the greatest impacts of this methodological work is that it “develops tools to assist other researchers in their projects. We believe that will allow us to more strongly identify the genetic signatures associated with microbiome composition.Thus, our impact is not necessarily the development of our own methods, but the ultimate impact of others’ research. is.”