Evidence map›Paper›PMID 31132756›Full record

ArticleHuman heredity2018

Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes.

Youfei Yu, Lu Xia, Seunggeun Lee, Xiang Zhou, Heather M Stringham, Michael Boehnke, Bhramar Mukherjee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human heredity, 2018. 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. Gene-environment interactions in human health.Nature reviews. Genetics · 2024
    Review
  3. Review
  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

7 authors.

Youfei YuDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Lu XiaDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Seunggeun LeeDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Xiang ZhouDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Heather M StringhamDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Michael BoehnkeDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA, bhramar@umich.edu.

Funding

Identifying Genes for Type 2 Diabetes: FUSIONU01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2009 to 2021
$11.6M
Identifying Genes for Type 2 Diabetes:FUSIONR01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2003 to 2025
$6.7M
Design and Analysis of Human Gene Mapping StudiesR01HG009976 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 2018 to 2022
$2.5M
Statistical and computational methods for rare variant association analysisR01HG008773 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MUKHERJEE, BHRAMAR · 2016 to 2020
$1.9M
Identifying Genes for Type 2 Diabetes: FUSIONR56DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 2008 to 2008
$500k
NHGRI NIH HHS R01 HG008773NHGRI NIH HHS R01 HG009976NIDDK NIH HHS R01 DK062370NIDDK NIH HHS R56 DK062370NIDDK NIH HHS U01 DK062370
6 · The paper itself

Abstract

objectivesClassical methods for combining summary data from genome-wide association studies only use marginal genetic effects, and power can be compromised in the presence of heterogeneity. We aim to enhance the discovery of novel associated loci in the presence of heterogeneity of genetic effects in subgroups defined by an environmental factor.

methodsWe present a pvalue-assisted subset testing for associations (pASTA) framework that generalizes the previously proposed association analysis based on subsets (ASSET) method by incorporating gene-environment (G-E) interactions into the testing procedure. We conduct simulation studies and provide two data examples.

resultsSimulation studies show that our proposal is more powerful than methods based on marginal associations in the presence of G-E interactions and maintains comparable power even in their absence. Both data examples demonstrate that our method can increase power to detect overall genetic associations and identify novel studies/phenotypes that contribute to the association.

conclusionsOur proposed method can be a useful screening tool to identify candidate single nucleotide polymorphisms that are potentially associated with the trait(s) of interest for further validation. It also allows researchers to determine the most probable subset of traits that exhibit genetic associations in addition to the enhancement of power.

Indexed as

Gene-Environment InteractionGenome-Wide Association StudyCase-Control StudiesCholesterolCohort StudiesComputer SimulationC-Reactive ProteinFinlandGene FrequencyGenetic Predisposition to DiseaseHumansLipoproteins, LDLMeta-Analysis as TopicModels, GeneticPhenotypePolymorphism, Single NucleotideCholesterolC-Reactive ProteinLipoproteins, LDLGene-environment independenceGene-environment interactionsMeta-analysisOverlapping subjectsPleiotropic effectsSubset-based association testType 2 diabetes

Identifiers

PMID31132756
PMCPMC7034441

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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.