Evidence map›Paper›PMID 26916514›Full record

ArticleGenetic epidemiology2016

Binomial Mixture Model Based Association Testing to Account for Genetic Heterogeneity for GWAS.

Zhiyuan Xu, Wei Pan

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetic epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 28% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

2 authors at 1 institution in 1 country.

Zhiyuan XuDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Wei PanDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.
University of Minnesota · US

Funding

Association analysis of rare variants with sequencing dataR01HL116720 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, WEI, PENG · 2013 to 2020
$3.3M
Powerful Inference and Prediction for Genetic AssociationR01HL105397 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2011 to 2018
$2.9M
Statistical Methods for Genomic DataR01GM113250 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2014 to 2017
$702k
Medical Research Council G1001799Medical Research Council MR/N01104X/1NHLBI NIH HHS R01 HL105397NHLBI NIH HHS R01HL105397NHLBI NIH HHS R01 HL116720NHLBI NIH HHS R01HL116720NIGMS NIH HHS R01 GM113250NIGMS NIH HHS R01GM113250Wellcome TrustWellcome Trust 076113
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have confirmed the ubiquitous existence of genetic heterogeneity for common disease: multiple common genetic variants have been identified to be associated, while many more are yet expected to be uncovered. However, the single SNP (single-nucleotide polymorphism) based trend test (or its variants) that has been dominantly used in GWAS is based on contrasting the allele frequency difference between the case and control groups, completely ignoring possible genetic heterogeneity. In spite of the widely accepted notion of genetic heterogeneity, we are not aware of any previous attempt to apply genetic heterogeneity motivated methods in GWAS. Here, to explicitly account for unknown genetic heterogeneity, we applied a mixture model based single-SNP test to the Wellcome Trust Case Control Consortium (WTCCC) GWAS data with traits of Crohn's disease, bipolar disease, coronary artery disease, and type 2 diabetes, identifying much larger numbers of significant SNPs and risk loci for each trait than those of the popular trend test, demonstrating potential power gain of the mixture model based test.

Indexed as

Genetic HeterogeneityModels, StatisticalBipolar DisorderCase-Control StudiesCoronary Artery DiseaseCrohn DiseaseDiabetes Mellitus, Type 2Gene FrequencyGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansModels, GeneticPhenotypePolymorphism, Single NucleotideRiskHardy-Weinberg equilibrium testlikelihood ratio testlogistic regressionpowertrend test

Identifiers

PMID26916514
PMCPMC4814320
OpenAlexW2286223680

What OpenQuestion holds

Textmetadata
LicenceTDM
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.