SIMER: an accurate and intelligent tool for simulating customizable population data across species in complex scenarios
文献类型： 外文期刊
作者： Yin, Dong 1 ; Zhang, Xuanning 4 ; Yin, Lilin 1 ; Zhang, Haohao 4 ; Tang, Zhenshuang 1 ; Xu, Jingya 1 ; Yuan, Xiaohui 4 ; Zhou, Xiang 5 ; Li, Xinyun 1 ; Zhao, Shuhong 1 ; Liu, Xiaolei 1 ;
作者机构： 1.Huazhong Agr Univ, Key Lab Agr Anim Genet Breeding & Reprod, Minist Educ, Wuhan 430070, Peoples R China; Huazhong Agr Univ, Coll Anim Sci & Technol, Wuhan 430070, Peoples R China
2.Hubei Hongshan Lab, Dept Anim Genet & Breeding, Wuhan 430070, Peoples R China
3.Frontiers Sci Ctr Anim Breeding & Sustainable Prod, Wuhan 430070, Hubei, Peoples R China
4.Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430070, Peoples R China
5.Univ Michigan, Dept Biostat, Ann Arbor, MI USA
6.Univ Michigan, Ctr Stat Genet, Ann Arbor, MI USA
7.Yazhouwan Lab, Sanya 572025, Peoples R China
8.Shenzhen Inst Nutr & Hlth, Shenzhen, Peoples R China
9.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Guangdong Lab Lingnan Modern Agr, Shenzhen Branch,Genome Anal Lab,Minist Agr, Shenzhen 518000, Guangdong, Peoples R China
关键词： Simulation; Breeding program; Genetic correlation; Linear mixed model (LMM); Repeated record trait; SIMER
期刊名称：JOURNAL OF BIG DATA （ 影响因子：6.4； 五年影响因子：13.4 ）
ISSN：
年卷期： 2025 年 12 卷 1 期
页码：
收录情况： SCI
摘要： Genetic and breeding studies require diverse simulations, which include structured populations with phenotypes in varying genetic complexity. Here, we introduce SIMER, which simulates data accurately and assists in the design of breeding programs. SIMER implements numerous individual selection methods and reproduction modes to generate genotype data for humans, animals, plants, and microorganisms. SIMER simulates phenotypes following linear mixed model theory, including multiple covariates, fixed and random effects, as well as their interactions. It also enables breeders to design a program, which includes the optimal selection indexes construction, and to choose individuals for genotype and phenotype testing to maximize genetic progress.
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