Literature DB >> 27255739

Challenges for case-control studies with microbiome data.

J Paul Brooks1.   

Abstract

PURPOSE: In case-control studies of the human microbiome, the goal is to evaluate whether cases differ from controls in the microbiome composition of a particular body habitat and which taxa are responsible for the differences. These studies leverage sequencing technology and spectroscopy that provide new measurements of the microbiome.
METHODS: Three challenges in conducting reproducible microbiome research using a case-control design are compensating for differences in observed and actual microbial community composition, detecting "rare" taxa in microbial communities, and choosing properly powered analysis methods. The significance of each challenge, evaluation of commonly held views, analysis of unanswered questions, and suggestions of strategies for solutions are discussed.
RESULTS: Understanding the effects of these choices on case-control analyses has been underappreciated, with an implicit assumption that further advances in technology will address all the current shortcomings.
CONCLUSIONS: It is recommended that research on the human microbiome include positive and negative control experiments to provide insight into bias, contamination, and technical variation. Research protocols such as these may afford a better opportunity to make quantitative and qualitative adjustments to data, thereby reducing the risk of falsely positive results, increasing power to discover true disease determinants, and enhancing interpretation across studies.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bias; Control experiments; Microbiome; Normalization; Rare taxa

Mesh:

Year:  2016        PMID: 27255739     DOI: 10.1016/j.annepidem.2016.03.009

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


  11 in total

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2.  Seasonal and diel patterns of abundance and activity of viruses in the Red Sea.

Authors:  Gur Hevroni; José Flores-Uribe; Oded Béjà; Alon Philosof
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-10       Impact factor: 11.205

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Review 4.  Perspectives for Consideration in the Development of Microbial Cell Reference Materials.

Authors:  Emma Allen-Vercoe; Joseph Russell Carmical; Samuel P Forry; Mitchell H Gail; Rashmi Sinha
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5.  LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

Authors:  Yingtian Hu; Glen A Satten; Yi-Juan Hu
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6.  Consistent and correctable bias in metagenomic sequencing experiments.

Authors:  Michael R McLaren; Amy D Willis; Benjamin J Callahan
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7.  Lung microbiome alterations in NSCLC patients.

Authors:  Leliang Zheng; Ruizheng Sun; Yinghong Zhu; Zheng Li; Xiaoling She; Xingxing Jian; Fenglei Yu; Xueyu Deng; Buqing Sai; Lujuan Wang; Wen Zhou; Minghua Wu; Guiyuan Li; Jingqun Tang; Wei Jia; Juanjuan Xiang
Journal:  Sci Rep       Date:  2021-06-03       Impact factor: 4.379

Review 8.  Experimental design and quantitative analysis of microbial community multiomics.

Authors:  Himel Mallick; Siyuan Ma; Eric A Franzosa; Tommi Vatanen; Xochitl C Morgan; Curtis Huttenhower
Journal:  Genome Biol       Date:  2017-11-30       Impact factor: 13.583

9.  Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Authors:  Kumar P Mainali; Sharon Bewick; Peter Thielen; Thomas Mehoke; Florian P Breitwieser; Shishir Paudel; Arjun Adhikari; Joshua Wolfe; Eric V Slud; David Karig; William F Fagan
Journal:  PLoS One       Date:  2017-11-16       Impact factor: 3.240

10.  Impact of DNA extraction method and targeted 16S-rRNA hypervariable region on oral microbiota profiling.

Authors:  Fei Teng; Sree Sankar Darveekaran Nair; Pengfei Zhu; Shanshan Li; Shi Huang; Xiaolan Li; Jian Xu; Fang Yang
Journal:  Sci Rep       Date:  2018-11-05       Impact factor: 4.379

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