Evidence map›Paper›PMID 30693016›Full record

ArticleFrontiers in genetics2018

Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests.

Wan-Yu Lin, Ching-Chieh Huang, Yu-Li Liu, Shih-Jen Tsai, Po-Hsiu Kuo

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
2.9field-weighted citation impact, top 9% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.

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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

5 authors at 3 institutions in 1 country.

Wan-Yu LinInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Ching-Chieh HuangInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Yu-Li LiuCenter for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Taiwan.
Shih-Jen TsaiDepartment of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.
Po-Hsiu KuoInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
National Taiwan University · TWNational Health Research Institutes · TWTaipei Veterans General Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The "adaptive combination of Bayes factors method" (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the "Set-Based gene-EnviRonment InterAction test" (SBERIA), "gene-environment set association test" (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The

Indexed as

diastolic blood pressuregene-alcohol interactionhypertensionmultiple testing correctionsystolic blood pressureTaiwan Biobank

Identifiers

PMID30693016
PMCPMC6339974
OpenAlexW2911032740

What OpenQuestion holds

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LicenceCC BY
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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.