Statistical methods for integrative analysis of multiple microbiome datasets

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Project Abstract: Recent research has highlighted the importance of human associated microbiota in many diseases and health conditions. However, in many areas, results are often inconsistent across studies due limited sample sizes, heterogeneous study populations (e.g., different race, gender, age), and technical variability (e.g., experimen- tal/analysis pipelines). For example, in HIV studies there is increasing evidence suggesting that gut dysbiosis contributes to HIV-associated inflammation. However, there is still a lack of consensus on its characteristics, such as whether HIV infection increases or decreases the microbial biodiversity in the gut and which taxa differ between HIV+ and HIV-. Integrative analysis, which aggregates information from multiple studies to increase the sample sizes and boost power, is necessary to move the field forward toward consistent and reproducible dis- coveries with the potential of suggesting prophylactic and therapeutic intervention. This, however, poses serious statistical challenges due to the differential biases and measurement error between studies. The objective of this proposal is to develop and validate statistical methods for integrative analysis of multiple microbiome datasets that are potentially generated using different laboratory and pre-processing procedures. We will use the study-specific characteristics, such as study populations, laboratory and pre-processing pipelines, and develop novel statistical models for characterizing changes in microbial alpha (within-sample) diversity, beta (between-sample) diversity, and abundances (Aim 1). We will analyze the data from the microbiome quality control project, a large community effort that sequenced the same set of samples through multiple pipelines, designed to identify technical variables that impact the microbiome sequencing data, and use this as a basis to determine how to best use the information in the proposed methods (Aim 2). We will apply the proposed methods to the HIV microbiome re-analysis project, in which we have compiled all available 16s rRNA gene sequencing data for gut microbiome in HIV for a comprehensive evaluation. We will also apply our proposed methods to the microbiome data collected from multiple cohorts from the Environmental influences of child health outcomes (ECHO) to investigate the role of microbiome in impacting the health of children and adolescents. We expect that the proposed methods will have broad impact on almost all areas of microbiome research and provide a foundation for analyzing 16s rRNA sequencing data.