Evidence map›Paper›PMID 28878002›Full record

ArticleGenetics2017

COMBAT: A Combined Association Test for Genes Using Summary Statistics.

Minghui Wang, Jianfei Huang, Yiyuan Liu, Li Ma, James B Potash, Shizhong Han

Abstract read
In one paragraph

Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. The Myeloid BiomarkerInternational journal of molecular sciences · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. A twin analysis to estimate genetic and environmental factors contributing to variation in weighted gene co-expression network module eigengenes.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025
    Article
  11. Article
  12. Article
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  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
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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

6 authors.

Minghui WangDepartment of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Jianfei HuangDepartment of Psychiatry, University of Iowa, Iowa City, Iowa 52242.
Yiyuan LiuJoan & Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10021.
Li MaDepartment of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742.
James B PotashDepartment of Psychiatry, University of Iowa, Iowa City, Iowa 52242.
Shizhong HanDepartment of Psychiatry, University of Iowa, Iowa City, Iowa 52242 shan67@jhmi.edu.

Funding

Subject CollectionU10AA008401 · NIAAA · SUNY DOWNSTATE MEDICAL CENTER · PI JESSICA E SALVATORE · 1989 to 2026
$162.7M
JH/CIDR Genotyping for Genome-Wide Association StudiesU01HG004438 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI VALLE, DAVID · 2007 to 2011
$24.2M
The Collaborative Genetic Study of Nicotine DependenceP01CA089392 · NCI · WASHINGTON UNIVERSITY · PI BIERUT, LAURA J. · 2001 to 2012
$17.3M
Genome Wide Association Coordinating CenterU01HG004446 · NHGRI · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2007 to 2011
$6.6M
FAMILY STUDY OF COCAINE DEPENDENCER01DA013423 · NIDA · WASHINGTON UNIVERSITY · PI BIERUT, LAURA JEAN · 2000 to 2004
$2.2M
A SYSTEMS APPROACH TO THE GENETIC STUDY OF ALCOHOL DEPENDENCER01AA024486 · NIAAA · UNIVERSITY OF IOWA · PI HAN, SHIZHONG, TAN, KAI · 2017 to 2020
$1.6M
Fine mapping a gene sub-network underlying alcohol dependenceR01AA022994 · NIAAA · UNIVERSITY OF IOWA · PI HAN, SHIZHONG · 2014 to 2017
$1.3M
Study of Addiction: Genetics and EnvironmentU01HG004422 · NHGRI · WASHINGTON UNIVERSITY · PI BIERUT, LAURA J. · 2007 to 2010
$1.2M
NCI NIH HHS P01 CA089392NHGRI NIH HHS HHSN268200782096CNHGRI NIH HHS U01 HG004422NHGRI NIH HHS U01 HG004438NHGRI NIH HHS U01 HG004446NIAAA NIH HHS R01 AA022994NIAAA NIH HHS R01 AA024486NIAAA NIH HHS U10 AA008401NIDA NIH HHS R01 DA013423
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Traditional analysis of GWAS typically examines one marker at a time, usually single nucleotide polymorphisms (SNPs), to identify individual variants associated with a disease. However, due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. As a complementary approach to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known

Indexed as

SoftwareBipolar DisorderGenetic LociGenome-Wide Association StudyHumansPolymorphism, Single Nucleotideassociationcomplex diseasegene-based testGWASsummary statistics

Identifiers

PMID28878002
PMCPMC5676236

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.