Evidence map›Paper›PMID 28479869›Full record

ArticleCurrent genomics2016

Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics.

Ming Li, Changshuai Wei, Yalu Wen, Tong Wang, Qing Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Ming Li1Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, IN 47405, U.S.A; 2Department of Epidemiology and Biostatistics, University of North Texas Health Science Center, Fort Worth, TX 76107, U.S.A; 3Department of Statistics, University of Auckland, Auckland 1010, New Zealand; 4Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China; 5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.
Changshuai Wei1Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, IN 47405, U.S.A; 2Department of Epidemiology and Biostatistics, University of North Texas Health Science Center, Fort Worth, TX 76107, U.S.A; 3Department of Statistics, University of Auckland, Auckland 1010, New Zealand; 4Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China; 5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.
Yalu Wen1Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, IN 47405, U.S.A; 2Department of Epidemiology and Biostatistics, University of North Texas Health Science Center, Fort Worth, TX 76107, U.S.A; 3Department of Statistics, University of Auckland, Auckland 1010, New Zealand; 4Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China; 5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.
Tong Wang1Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, IN 47405, U.S.A; 2Department of Epidemiology and Biostatistics, University of North Texas Health Science Center, Fort Worth, TX 76107, U.S.A; 3Department of Statistics, University of Auckland, Auckland 1010, New Zealand; 4Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China; 5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.
Qing Lu1Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, IN 47405, U.S.A; 2Department of Epidemiology and Biostatistics, University of North Texas Health Science Center, Fort Worth, TX 76107, U.S.A; 3Department of Statistics, University of Auckland, Auckland 1010, New Zealand; 4Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China; 5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.

Funding

Arkansas Center for Clinical and Translational ResearchUL1TR000039 · NCATS · UNIV OF ARKANSAS FOR MED SCIS · PI BECK, CORNELIA MARIE, JAMES, LAURA P · 2012 to 2014
$10.0M
Study of Addiction: Genetics and EnvironmentU01HG004422 · NHGRI · WASHINGTON UNIVERSITY · PI BIERUT, LAURA J. · 2007 to 2010
$1.2M
Gene-Gene/Gene-Environment Interactions Associated with Nicotine DependenceK01DA033346 · NIDA · MICHIGAN STATE UNIVERSITY · PI LU, QING · 2013 to 2017
$833k
Arkansas Center for Clinical Translational ResearchKL2TR000063 · NCATS · UNIV OF ARKANSAS FOR MED SCIS · PI BECK, CORNELIA MARIE, JAMES, LAURA P · 2012 to 2014
$459k
High-dimensional Statistical Genetic Approach for Family-based Orofacial CleftsR03DE022379 · NIDCR · MICHIGAN STATE UNIVERSITY · PI LU, QING · 2012 to 2013
$441k
NCATS NIH HHS KL2 TR000063NCATS NIH HHS UL1 TR000039NHGRI NIH HHS U01 HG004422NIDA NIH HHS K01 DA033346NIDCR NIH HHS R03 DE022379
6 · The paper itself

Abstract

Many complex diseases, such as psychiatric and behavioral disorders, are commonly characterized through various measurements that reflect physical, behavioral and psychological aspects of diseases. While it remains a great challenge to find a unified measurement to characterize a disease, the available multiple phenotypes can be analyzed jointly in the genetic association study. Simultaneously testing these phenotypes has many advantages, including considering different aspects of the disease in the analysis, and utilizing correlated phenotypes to improve the power of detecting disease-associated variants. Furthermore, complex diseases are likely caused by the interplay of multiple genetic variants through complicated mechanisms. Considering gene-gene interactions in the joint association analysis of complex diseases could further increase our ability to discover genetic variants involving complex disease pathways. In this article, we propose a stepwise U-test for joint association analysis of multiple loci and multiple phenotypes. Through simulations, we demonstrated that testing multiple phenotypes simultaneously could attain higher power than testing one single phenotype at a time, especially when there are shared genes contributing to multiple phenotypes. We also illustrated the proposed method with an application to Nicotine Dependence (ND), using datasets from the Study of Addition, Genetics and Environment (SAGE). The joint analysis of three ND phenotypes identified two SNPs, rs10508649 and rs2491397, and reached a nominal

Indexed as

Nicotine dependencePleiotropyPopulation-based association studies

Identifiers

PMID28479869
PMCPMC5320542

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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.