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

doi:10.1186/s12864-021-08191-z

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

BMC Genomics. 2021 Dec 5;22(1):877. doi: 10.1186/s12864-021-08191-z.

Authors

Xiujin Li^#¹, Hailiang Song^#², Zhe Zhang³, Yunmao Huang¹, Qin Zhang⁴, Xiangdong Ding⁵

Affiliations

¹ 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.
² 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.
³ Guangdong 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.
⁴ Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, 271001, Taian, China.
⁵ 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. xding@cau.edu.cn.

^# Contributed equally.

Abstract

Background: With 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.

Results: We 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.

Conclusions: This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

Keywords: Data simulation; GPOPSIM2.0; Genotype-by-environment interaction; Threshold trait.

MeSH terms

Gene-Environment Interaction*
Genome-Wide Association Study*
Genomics
Models, Genetic
Software

Abstract

MeSH terms

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