Evidence map›Paper›PMID 27296983›Full record

ArticleBioinformatics (Oxford, England)2016

A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits.

Micol Marchetti-Bowick, Junming Yin, Judie A Howrylak, Eric P Xing

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026
    Review
  3. Article
  4. 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

4 authors.

Micol Marchetti-BowickMachine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Junming YinDepartment of Management Information Systems, University of Arizona, Tucson, AZ, USA.
Judie A HowrylakDivision of Pulmonary and Critical Care Medicine, Penn State University, Milton S. Hershey Medical Center, Hershey, PA, USA.
Eric P XingMachine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.

Funding

Penn State Clinical and Translational Science InstituteUL1TR002014 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI KRASCHNEWSKI, JENNIFER L. · 2016 to 2025
$33.7M
Penn State Clinical and Translational Science InstituteUL1TR000127 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI SINOWAY, LAWRENCE I · 2012 to 2015
$17.1M
Penn State Institutional Career Development CoreKL2TR002015 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI VAN SCOY, LAUREN JODI · 2016 to 2025
$7.8M
NIDA Center of Excellence OF Computational Drug Abuse Research (CDAR)P30DA035778 · NIDA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BAHAR, IVET, XIE, XIANG-QUN · 2014 to 2018
$5.4M
Time/Space-Varying Networks of Molecular Interactions: A New Paradigm for StudyinR01GM093156 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI XING, ERIC P · 2010 to 2014
$2.2M
Toward PanOmic and Personalized Association Study of Complex Diseases - A New Statistical and Computational Paradigm for Personalized MedicineR01GM114311 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI WENZEL, SALLY E, WU, WEI · 2015 to 2018
$2.2M
NCATS NIH HHS KL2 TR002015NCATS NIH HHS UL1 TR000127NCATS NIH HHS UL1 TR002014NIDA NIH HHS P30 DA035778NIGMS NIH HHS R01 GM093156NIGMS NIH HHS R01 GM114311
6 · The paper itself

Abstract

motivationDespite the widespread popularity of genome-wide association studies (GWAS) for genetic mapping of complex traits, most existing GWAS methodologies are still limited to the use of static phenotypes measured at a single time point. In this work, we propose a new method for association mapping that considers dynamic phenotypes measured at a sequence of time points. Our approach relies on the use of Time-Varying Group Sparse Additive Models (TV-GroupSpAM) for high-dimensional, functional regression.

resultsThis new model detects a sparse set of genomic loci that are associated with trait dynamics, and demonstrates increased statistical power over existing methods. We evaluate our method via experiments on synthetic data and perform a proof-of-concept analysis for detecting single nucleotide polymorphisms associated with two phenotypes used to assess asthma severity: forced vital capacity, a sensitive measure of airway obstruction and bronchodilator response, which measures lung response to bronchodilator drugs. AVAILABILITY AND IMPLEMENTATION: Source code for TV-GroupSpAM freely available for download at http://www.cs.cmu.edu/~mmarchet/projects/tv_group_spam, implemented in MATLAB. CONTACT: epxing@cs.cmu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Models, GeneticPolymorphism, Single NucleotideChromosome MappingGenomeGenome-Wide Association StudyHumansPhenotype

Identifiers

PMID27296983
PMCPMC5942717

What OpenQuestion holds

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Registered trials

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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.