Evidence map›Paper›PMID 38678264›Full record

ArticleBMC medical genomics2024

Poor statistical power in population-based association study of gene interaction.

Jiarui Ma, Jian Li, Yuqi Chen, Zhen Yang, Yungang He

Abstract read
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In one paragraph

Article in BMC medical genomics, 2024. 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
–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

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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.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jiarui Ma *Shanghai Key Laboratory of Medical Epigenetics, International Co-Laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Jian Li *Shanghai Key Laboratory of Medical Epigenetics, International Co-Laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Yuqi ChenShanghai Key Laboratory of Medical Epigenetics, International Co-Laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
Zhen YangCenter for Medical Research and Innovation of Pudong Hospital, Intelligent Medicine Institute, Fudan University, Shanghai, 200032, China.
Yungang HeShanghai Fifth People's Hospital, Intelligent Medicine Institute, Fudan University, Shanghai, 200032, PR China. heyungang@fudan.edu.cn.

Funding

National Natural Science Foundation of China 32170634, and 31871255Shanghai Municipal Science and Technology 2017SHZDZX01
6 · The paper itself

Abstract

backgroundStatistical epistasis, or "gene-gene interaction" in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven't found enough clues of statistical epistasis so far.

methodsWe developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene-gene interaction was calculated and then compared in different hypothesis scenarios.

resultsOur results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene-gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10

conclusionsThe lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

Indexed as

Epistasis, GeneticGenome-Wide Association StudyLinkage DisequilibriumHumansModels, GeneticModels, StatisticalPolymorphism, Single NucleotideGene interactionGenetic association studyStatistical epistasisStatistical power

Identifiers

PMID38678264
PMCPMC11055307

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