Evidence map›Paper›PMID 33795796›Full record

ArticleScientific reports2021

Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits.

Chao-Yu Guo, Reng-Hong Wang, Hsin-Chou Yang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed, 0 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Chao-Yu GuoDivision of Biostatistics, Department of Medicine, Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan. cyguo@ym.edu.tw.
Reng-Hong WangDivision of Biostatistics, Department of Medicine, Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan.
Hsin-Chou YangInstitute of Statistical Science, Academia Sinica, Taipei, Taiwan.
National Yang Ming Chiao Tung University · TWInstitute of Statistical Science, Academia Sinica · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

Indexed as

EnvironmentGene-Environment InteractionModels, GeneticMultifactorial InheritanceQuantitative Trait, HeritableQuantitative Trait LociAlgorithmsGenetic Association StudiesGenetic Predisposition to DiseaseGenetic TestingGenetic VariationGenome-Wide Association StudyHumansPhenotypeSoftware

Identifiers

PMID33795796
PMCPMC8016937
OpenAlexW3149207136

What OpenQuestion holds

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LicenceCC BY
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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.