Evidence map›Paper›PMID 34865618›Full record

ArticleBMC genomics2021

The theory on and software simulating large-scale genomic data for genotype-by-environment interactions.

Xiujin Li, Hailiang Song, Zhe Zhang, Yunmao Huang, Qin Zhang, Xiangdong Ding

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In one paragraph

Article in BMC genomics, 2021. 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. A framework for simulating genotype-by-environment interaction using multiplicative models.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024
    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

6 authors.

Xiujin Li *Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangdong, 510225, Guangzhou, People's Republic of China.
Hailiang Song *Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China.
Zhe ZhangGuangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, People's Republic of China.
Yunmao HuangGuangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangdong, 510225, Guangzhou, People's Republic of China.
Qin ZhangShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, 271001, Taian, China.
Xiangdong DingKey Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China. xding@cau.edu.cn.

Funding

Key- Area Research and Development Program of Guangdong Province 2020B020222003National Key Research and Development Project 2019YFE0106800Pearl River S&T Nova Program of Guangzhou 201906010040
6 · The paper itself

Abstract

backgroundWith the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches.

resultsWe proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account.

conclusionsThis tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

Indexed as

Gene-Environment InteractionGenome-Wide Association StudyGenomicsModels, GeneticSoftwareData simulationGenotype-by-environment interactionGPOPSIM2.0Threshold trait

Identifiers

PMID34865618
PMCPMC8647494

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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.