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Activity Number: 366 - SPEED: Recent Advances in Statistical Genomics and Genetics
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 11:15 AM
Sponsor: Biometrics Section
Abstract #332593
Title: A Two-Stage Microbial Association Mapping Framework with Advanced FDR Control
Author(s): Jiyuan Hu* and Huilin Li and Hyunwook Koh and Linchen He and Martin Blaser
Companies: New York University School of Medicine and New York University and NYU langone medical center and NYU langone medical center and New York University School of Medicine
Keywords: Two-stage microbial association mapping; Taxonomic tree; Microbial group association test; False discovery rate; Hierarchical BH; Selected subset testing

In microbiome studies, it is important to detect taxa which are associated with pathological outcomes at the lowest definable taxonomic rank, such as genus or species. Traditionally, taxa at the target rank are tested for association individually, and then the Benjamini-Hochberg (BH) procedure is applied to control for false discovery rate (FDR). However, this approach neglects the dependence structure among taxa and may lead to conservative results. We propose a two-stage microbial association mapping framework (massMap) which uses prior grouping information from the taxonomic tree to strengthen statistical power at the target rank. MassMap first screens the association of taxonomic groups at a pre-selected higher taxonomic rank using a powerful microbial group test OMiAT. Then it proceeds to test the association for each candidate taxon at the target rank within the significant taxonomic groups identified in the first stage. Hierarchical BH and selected subset testing procedures are evaluated to control the FDR for the two-stage structured tests. Extensive simulations and real data analyses have shown that massMap achieves higher statistical power and detects more taxa.

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

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