Evidence map›Paper›PMID 39697676›Full record

ArticleComputational and structural biotechnology journal2024

IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.

Ya-Wen Zhang, Xue-Lian Han, Mei Li, Ying Chen, Yuan-Ming Zhang

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 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

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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

Who cites it

1 citing paper in PubMed.

  1. 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

5 authors.

Ya-Wen ZhangCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Xue-Lian HanCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Mei LiCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Ying ChenCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Yuan-Ming ZhangCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although 3VmrMLM-MEJA and several indirect indicators have been employed to identify QTN-by-environment interactions (QEIs) in genome-wide association studies (GWAS), there is no convenient, flexible, and accurate method to comprehensively identify QEIs. To address this issue, 3VmrMLM-random was first extended to 3VmrMLM-fixed. Next, the two single-environment QTN detection methods were integrated with trait differences and regression parameters to indirectly detect QEIs. Finally, these indirect indicators were extended to include environmental factors (EFs, such as temperature) and four environmental variation indicators. As a result, both 3VmrMLM-random and 3VmrMLM-fixed, alongside all the indirect indicators, were incorporated into a new tool, IIIVmrMLM.QEI, designed for effective QEI identification. Simulation studies demonstrated that 3VmrMLM-fixed showed significantly higher powers than existing fixed-SNP-effect methods (MLM and EMMAX) because it takes into account all the possible effects and controls for all the possible polygenic backgrounds. 3VmrMLM-random and 3VmrMLM-fixed exhibited superior combination power to 3VmrMLM-MEJA. In the re-analysis of

Indexed as

Environmental factorFlowering timeGenome-wide association studiesIIIVmrMLM.QEIQTN-by-environment interactionVariation indicator

Identifiers

PMID39697676
PMCPMC11653143

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