Evidence map›Paper›PMID 25568281›Full record

ArticleBioinformatics (Oxford, England)2015

Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data.

Qunyuan Zhang, Haley Abel, Alan Wells, Petra Lenzini, Felicia Gomez, Michael A Province, Alan A Templeton, George M Weinstock, Nita H Salzman, Ingrid B Borecki

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Tools for Analysis of the Microbiome.Digestive diseases and sciences · 2020
    Review
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Qunyuan ZhangDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Haley AbelDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Alan WellsDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Petra LenziniDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Felicia GomezDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Michael A ProvinceDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Alan A TempletonDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
George M WeinstockDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Nita H SalzmanDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
Ingrid B BoreckiDivision of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA, Department of Biology, Washington University, St. Louis, MO, USA, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA and Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.

Funding

Vervet Research Colony as a Biomedical ResourceP40OD010965 · OD · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Matthew Jorgensen · 2012 to 2026
$14.6M
Intestinal bacterial metagenome in pediatric NAFLDR01DK088831 · NIDDK · MEDICAL COLLEGE OF WISCONSIN · PI SALZMAN, NITA H, WEINSTOCK, GEORGE M · 2012 to 2016
$7.0M
Genetic Architecture of Adiposity in Multiple Large CohortsR01DK089256 · NIDDK · WASHINGTON UNIVERSITY · PI BARANSKI, THOMAS JOHN, LIU, CHING-TI · 2010 to 2016
$4.6M
Genetic Epidemiology of Metabolic Diseases of ObesityR01DK075681 · NIDDK · WASHINGTON UNIVERSITY · PI BORECKI, INGRID BERNADETTE · 2008 to 2011
$2.1M
Post-Doctoral Research Training in Genetic EpidemiologyT32HL091823 · NHLBI · WASHINGTON UNIVERSITY · PI RAO, DABEERU C · 2008 to 2017
$2.1M
NHLBI NIH HHS T32 HL091823NIDDK NIH HHS 1R01DK8925601NIDDK NIH HHS 5R01DK075681NIDDK NIH HHS R01 DK075681NIDDK NIH HHS R01 DK088831NIH HHS P40 OD010965
6 · The paper itself

Abstract

motivationEstablishment of a statistical association between microbiome features and clinical outcomes is of growing interest because of the potential for yielding insights into biological mechanisms and pathogenesis. Extracting microbiome features that are relevant for a disease is challenging and existing variable selection methods are limited due to large number of risk factor variables from microbiome sequence data and their complex biological structure.

resultsWe propose a tree-based scanning method, Selection of Models for the Analysis of Risk factor Trees (referred to as SMART-scan), for identifying taxonomic groups that are associated with a disease or trait. SMART-scan is a model selection technique that uses a predefined taxonomy to organize the large pool of possible predictors into optimized groups, and hierarchically searches and determines variable groups for association test. We investigate the statistical properties of SMART-scan through simulations, in comparison to a regular single-variable analysis and three commonly-used variable selection methods, stepwise regression, least absolute shrinkage and selection operator (LASSO) and classification and regression tree (CART). When there are taxonomic group effects in the data, SMART-scan can significantly increase power by using bacterial taxonomic information to split large numbers of variables into groups. Through an application to microbiome data from a vervet monkey diet experiment, we demonstrate that SMART-scan can identify important phenotype-associated taxonomic features missed by single-variable analysis, stepwise regression, LASSO and CART.

Indexed as

Decision TreesMicrobiotaModels, StatisticalAnimalsChlorocebus aethiopsGastrointestinal TractHumansLogistic ModelsPhenotypeRisk AssessmentRisk FactorsRNA, RibosomalRNA, Ribosomal

Identifiers

PMID25568281
PMCPMC4426830

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.