Statistical Methods for Enhanced Mapping of Microbiome Relationships

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PROJECT SUMMARY/ABSTRACT The human microbiome is integrally related to a vast range of human disorders and represents an imperative gateway toward mitigating the burden of these diseases, particularly as the microbiome is eminently modifiable. This has culminated in microbially oriented risk reduction and clinical interventions, which have proven efficacious in diverse situations ranging from infections to cancer immunotherapy. However, despite some high- profile successes, many other studies have failed. A central theme underlying these failures, and even many of the success stories, is our fundamentally limited understanding of how microbes interact with each other, with host genomics, and with outcomes. Recently, efforts to assess and map these relationships are taking place within large-scale profiling studies, but unfortunately, the tools used for elucidating these connections may be underpowered, difficult to interpret, or even subject to severe false positives due to strong underlying assumptions. Therefore, motivated by problems within three of the largest and richest microbiome profiling studies, this proposal seeks to fill critical gaps in the methodological literature by addressing four major areas. Specifically, we aim to develop a comprehensive suite of tools for (1) enhanced microbial co-occurrence network construction; (2) enhanced discovery of SNPs and rare variants associated with individual microbial taxa; and (3) assessing the role of microbes through Mendelian Randomization. These approaches are all based on rigorous prior data emphasizing the importance of the problems as well as the limitations of existing strategies. Our work is motivated by and will directly enable analyses in three of the largest and richest microbiome profiling studies, including the MEC and SOL cohorts which study the gut microbiome, and the PIN cohort, which explores the vaginal microbiome in pregnancy. Accordingly, the methods we develop have the potential to improve our fundamental knowledge of the microbiome and propel the field towards enhanced risk reduction and clinical interventions.