Evidence map›Paper›PMID 30659261›Full record

ArticleEuropean journal of human genetics : EJHG2019

Enriched power of disease-concordant twin-case-only design in detecting interactions in genome-wide association studies.

Weilong Li, Jan Baumbach, Afsaneh Mohammadnejad, Charlotte Brasch-Andersen, Fabio Vandin, Jan O Korbel, Qihua Tan

Abstract read
In one paragraph

Article in European journal of human genetics : EJHG, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. 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

7 authors.

Weilong LiEpidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.ORCID 0000-0001-9448-7953
Jan BaumbachDepartment of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Afsaneh MohammadnejadEpidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.
Charlotte Brasch-AndersenUnit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Fabio VandinDepartment of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Jan O KorbelEuropean Molecular Biology Laboratory, Genome Biology Unit, 69117, Heidelberg, Germany.
Qihua TanEpidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark. qtan@health.sdu.dk.ORCID 0000-0003-3194-0030

Funding

Lundbeckfonden (Lundbeck Foundation) R170-2014-1353Sundhed og Sygdom, Det Frie Forskningsråd (Medical Sciences, Danish Council for Independent Research) DFF-6110-00114
6 · The paper itself

Abstract

Genetic interaction is a crucial issue in the understanding of functional pathways underlying complex diseases. However, detecting such interaction effects is challenging in terms of both methodology and statistical power. We address this issue by introducing a disease-concordant twin-case-only design, which applies to both monozygotic and dizygotic twins. To investigate the power, we conducted a computer simulation study by setting a series of parameter schemes with different minor allele frequencies and relative risks. Results from the simulation study reveals that the disease-concordant twin-case-only design largely reduces sample size required for sufficient power compared to the ordinary case-only design for detecting gene-gene interaction using unrelated individuals. Sample sizes for dizygotic and monozygotic twins were roughly 1/2 and 1/4 of sample sizes in the ordinary case-only design. Since dizygotic twins are genetically similar as siblings, the enriched power for dizygotic twins also applies to affected siblings, which could help to largely extend the application of the powerful twin-case-only design. In summary, our simulation reveals high value of disease-concordant twins and siblings in efficiently detecting gene-by-gene interactions.

Indexed as

Genetic Predisposition to DiseaseComputer SimulationDiseases in TwinsFemaleGene FrequencyGenetic Diseases, InbornGenome-Wide Association StudyHumansMaleRiskSample SizeSiblingsTwins, DizygoticTwins, Monozygotic

Identifiers

PMID30659261
PMCPMC6460588

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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.