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

Springer Science and Business Media LLC - Tập 22 - Trang 1-7 - 2021
Xiujin Li1, Hailiang Song2, Zhe Zhang3, Yunmao Huang1, Qin Zhang4, Xiangdong Ding2
1Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, People’s Republic of China
2Key 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, Beijing, China
3Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, People’s Republic of China
4Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Taian, China

Tóm tắt

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

Tài liệu tham khảo

Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29. Hayes B, Goddard M. Genome-wide association and genomic selection in animal breeding. Genome. 2010;53(11):876–83. Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, et al. Genomic selection in plant breeding: methods, models, and Perspectives. Trends Plant Sci. 2017;22(11):961–75. Kolmodin R, Strandberg E, Madsen P, Jensen J, Jorjani H. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agric Scand - sect a. Anim Sci. 2002;52(1):11–24. Song H, Zhang Q, Misztal I, Ding X. Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single-step genomic reaction norm model. J Anim Breed Genet. 2020;137(6):523–34. Silva FF, Guimarães SEF, Lopes PS, Mulder HA, Bastiaansen JWM, Knol EF, et al. Sire evaluation for total number born in pigs using a genomic reaction norms approach. J Anim Sci. 2014;92(9):3825–34. Moore R, Casale FP, Jan Bonder M, Horta D, Heijmans BT, Peter PA, et al. A linear mixed-model approach to study multivariate gene–environment interactions. Nat Genet. 2019;51(1):180–6. Kerin M, Marchini J. Inferring gene-by-environment interactions with a Bayesian whole-genome regression model. Am J Hum Genet. 2020;107(4):698–713. Sargolzaei M, Schenkel FS. QMSim: A large-scale genome simulator for livestock. Bioinformatics. 2009;25(5):680–1. Tang Y, Liu X. G2P: a genome-wide-association-study simulation tool for genotype simulation, phenotype simulation and power evaluation. Bioinformatics. 2019;35(19):3852–4. Falconer DS, Mackay TFC. Introduction to Quantitative Genetics (Fourth Edition). 1996;12(7):280. Gianola D. Theory and analysis of threshold characters. J Anim Sci. 1982;54(5):1079–96. Gianola D, Foulley J. Sire evaluation for ordered categorical data with a threshold model. Genet Sel Evol. 1983;15(2):201–24. Wang CL, Ding XD, Wang JY, Liu JF, Fu WX, Zhang Z, et al. Bayesian methods for estimating GEBVs of threshold traits. Heredity (Edinb). 2013;110(3):213–9. Zhang Z, Li X, Ding X, Li J, Zhang Q. GPOPSIM: a simulation tool for whole-genome genetic data. BMC Genet. 2015;16(1):10. Song H, Zhang Q, Ding X. The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs. J Anim Sci Biotechnol. 2020;11:88. Li X, Lund MS, Zhang Q, Costa CN, Ducrocq V, Su G. Short communication: improving accuracy of predicting breeding values in Brazilian Holstein population by adding data from Nordic and French Holstein populations. J Dairy Sci. 2016;99(6):4574–9. Su G, Madsen P, Lund MS, Sorensen D, Korsgaard IR, Jensen J. Bayesian analysis of the linear reaction norm model with unknown covariates. J Anim Sci. 2006;84(7):1651–7. Chris Gaynor R, Gorjanc G, Hickey JM. AlphaSimR: An R package for breeding program simulations. G3 Genes Genomes Genet. 2021;11(2):jkaa017. Madsen P, Jensen J. A user’s guide to DMU. Center for Quantitative Genetics and Genomics Dept. of Molecular Biology and Genetics, University of Aarhus Research Centre Foulum Box 50, 8830 Tjele Denmark. 2013.
Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 22

1-7

Thông tin tác giả