biostudies-literatureWang CFoundation for the National Institutes of HealthNCI NIH HHS121https://www.ebi.ac.uk/biostudies/studies/S-EPMC9354433biostudies-literatureUnknown10(1)<h4>Background</h4>With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction.<h4>Methods</h4>Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: (1) identifying a sub-community consisting of the signature microbial taxa associated with disease and (2) integrating the identified microbial taxa inMicrobiomeMicrobial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk.PMC9354433P20 CA252728P20CA252728R37 CA244775Segal LNLi HHu JAhn JWang CZhou BHayes RBfalseMicrobial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk.<h4>Background</h4>With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction.<h4>Methods</h4>Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: (1) identifying a sub-community consisting of the signature microbial taxa associated with disease and (2) integrating the identified microbial taxa in2022-01-01T00:00:00Z2022 Aug2026-06-04T04:39:30.404Z2025-04-03T21:30:20.056ZS-EPMC93544333593202910.1186/s40168-022-01310-2