Literature DB >> 32976571

Power of Microbiome Beta-Diversity Analyses Based on Standard Reference Samples.

Mitchell H Gail, Yunhu Wan, Jianxin Shi.   

Abstract

A simple method to analyze microbiome beta-diversity computes mean beta-diversity distances from a test sample to standard reference samples. We used reference stool and nasal samples from the Human Microbiome Project and regressed an outcome on mean distances (2 degrees-of-freedom (df) test) or additionally on squares and cross-product of mean distances (5-df test). We compared the power of 2-df and 5-df tests with the microbiome regression-based kernel association test (MiRKAT). In simulations, MiRKAT had moderately greater power than the 2-df test for discriminating skin versus saliva and skin versus nasal samples, but differences were negligible for skin versus stool and stool versus nasal samples. The 2-df test had slightly greater power than MiRKAT for Dirichlet multinomial samples. In associating body mass index with beta-diversity in stool samples from the American Gut Project, the 5-df test yielded smaller P values than MiRKAT for most taxonomic levels and beta-diversity measures. Unlike procedures like MiRKAT that are based on the beta-diversity matrix, mean distances to reference samples can be analyzed with standard statistical tools and shared or meta-analyzed without sharing primary DNA data. Our data indicate that standard reference tests have power comparable to MiRKAT's (and to permutational multivariate analysis of variance), but more simulations and applications are needed to confirm this. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2020. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Entities:  

Keywords:  MiRKAT; PERMANOVA; beta-diversity; microbiome; power; standard reference samples; standard reference tests

Mesh:

Year:  2021        PMID: 32976571      PMCID: PMC7936037          DOI: 10.1093/aje/kwaa204

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  16 in total

1.  Overdispersion in allelic counts and θ-correction in forensic genetics.

Authors:  Torben Tvedebrink
Journal:  Theor Popul Biol       Date:  2010-07-13       Impact factor: 1.570

2.  Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

Authors:  Qiong Wang; George M Garrity; James M Tiedje; James R Cole
Journal:  Appl Environ Microbiol       Date:  2007-06-22       Impact factor: 4.792

3.  Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test.

Authors:  Ni Zhao; Jun Chen; Ian M Carroll; Tamar Ringel-Kulka; Michael P Epstein; Hua Zhou; Jin J Zhou; Yehuda Ringel; Hongzhe Li; Michael C Wu
Journal:  Am J Hum Genet       Date:  2015-05-07       Impact factor: 11.025

4.  T cell-mediated regulation of the microbiota protects against obesity.

Authors:  Charisse Petersen; Rickesha Bell; Kendra A Klag; Soh-Hyun Lee; Raymond Soto; Arevik Ghazaryan; Kaitlin Buhrke; H Atakan Ekiz; Kyla S Ost; Sihem Boudina; Ryan M O'Connell; James E Cox; Claudio J Villanueva; W Zac Stephens; June L Round
Journal:  Science       Date:  2019-07-26       Impact factor: 47.728

5.  Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies.

Authors:  Rashmi Sinha; Jun Chen; Amnon Amir; Emily Vogtmann; Jianxin Shi; Kristin S Inman; Roberto Flores; Joshua Sampson; Rob Knight; Nicholas Chia
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2015-11-24       Impact factor: 4.254

6.  Associating microbiome composition with environmental covariates using generalized UniFrac distances.

Authors:  Jun Chen; Kyle Bittinger; Emily S Charlson; Christian Hoffmann; James Lewis; Gary D Wu; Ronald G Collman; Frederic D Bushman; Hongzhe Li
Journal:  Bioinformatics       Date:  2012-06-17       Impact factor: 6.937

7.  An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea.

Authors:  Daniel McDonald; Morgan N Price; Julia Goodrich; Eric P Nawrocki; Todd Z DeSantis; Alexander Probst; Gary L Andersen; Rob Knight; Philip Hugenholtz
Journal:  ISME J       Date:  2011-12-01       Impact factor: 10.302

8.  American Gut: an Open Platform for Citizen Science Microbiome Research.

Authors:  Daniel McDonald; Embriette Hyde; Justine W Debelius; James T Morton; Antonio Gonzalez; Gail Ackermann; Alexander A Aksenov; Bahar Behsaz; Caitriona Brennan; Yingfeng Chen; Lindsay DeRight Goldasich; Pieter C Dorrestein; Robert R Dunn; Ashkaan K Fahimipour; James Gaffney; Jack A Gilbert; Grant Gogul; Jessica L Green; Philip Hugenholtz; Greg Humphrey; Curtis Huttenhower; Matthew A Jackson; Stefan Janssen; Dilip V Jeste; Lingjing Jiang; Scott T Kelley; Dan Knights; Tomasz Kosciolek; Joshua Ladau; Jeff Leach; Clarisse Marotz; Dmitry Meleshko; Alexey V Melnik; Jessica L Metcalf; Hosein Mohimani; Emmanuel Montassier; Jose Navas-Molina; Tanya T Nguyen; Shyamal Peddada; Pavel Pevzner; Katherine S Pollard; Gholamali Rahnavard; Adam Robbins-Pianka; Naseer Sangwan; Joshua Shorenstein; Larry Smarr; Se Jin Song; Timothy Spector; Austin D Swafford; Varykina G Thackray; Luke R Thompson; Anupriya Tripathi; Yoshiki Vázquez-Baeza; Alison Vrbanac; Paul Wischmeyer; Elaine Wolfe; Qiyun Zhu; Rob Knight
Journal:  mSystems       Date:  2018-05-15       Impact factor: 6.496

9.  A core gut microbiome in obese and lean twins.

Authors:  Peter J Turnbaugh; Micah Hamady; Tanya Yatsunenko; Brandi L Cantarel; Alexis Duncan; Ruth E Ley; Mitchell L Sogin; William J Jones; Bruce A Roe; Jason P Affourtit; Michael Egholm; Bernard Henrissat; Andrew C Heath; Rob Knight; Jeffrey I Gordon
Journal:  Nature       Date:  2008-11-30       Impact factor: 49.962

10.  A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping.

Authors:  Hyunwook Koh; Martin J Blaser; Huilin Li
Journal:  Microbiome       Date:  2017-04-24       Impact factor: 14.650

View more
  1 in total

1.  Beta-diversity distance matrices for microbiome sample size and power calculations - How to obtain good estimates.

Authors: 
Journal:  Comput Struct Biotechnol J       Date:  2022-04-27       Impact factor: 6.155

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.