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Activity Number: 340 - Novel Methods for Microbiome and Metabolomic Disease
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #313101
Title: Quantifying and Association Testing of Microbiome Alpha Diversity
Author(s): Shilan Li* and Jianxin Shi
Companies: NCI and National Cancer Institute
Keywords: Microbiome; Richness; Rarefaction; Weighted Regression

Richness, the number of unique operational taxonomy units (OTUs) of a microbiome community, depends on the sequencing depth. Rarefaction is a procedure that estimates richness by subsampling the same number of sequencing reads from all microbiome communities. While rarefaction has been a standard practice for epidemiology studies, multiple questions remain to be investigated, including computational efficiency, optimal association testing and multiple testing correction. To address these questions, we developed an implicit rarefaction procedure that does not require subsampling. We then developed an optimal weighted regression procedure that accounts for the difference of sequencing depths. We empirically examined the magnitude of between subject variance (BSV) and measurement error variance (MEV) of richness estimates and found that BSV was dominant over MEV when sequencing depth > 5000 in oral and gut microbiome data sets. Thus, the power for association testing was not influenced by sequencing depth using current technology. The impact of multiple testing correction will be exemplified in studying associations between gut microbiome and allergy traits.

Authors who are presenting talks have a * after their name.

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