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Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results. In this article, we present XSim Version 2 that is an open-source tool and has been extensively redesigned with additional features to meet the needs in modern breeding programs. It seamlessly incorporates multiple statistical models for genetic evaluations, such as GBLUP, Bayesian alphabets, and neural networks, and it can effortlessly simulate successive generations of descendants based on complex mating schemes by the aid of its modular design. Case studies are presented to demonstrate the flexibility of XSim Version 2 in simulating crossbreeding in animal and plant populations. Modern biotechnology, including double haploids and embryo transfer, can all be simultaneously integrated into the mating plans that drive the simulation. From a computing perspective, XSim Version 2 is implemented in Julia, which is a computer language that retains the readability of scripting languages (e.g. R and Python) without sacrificing much computational speed compared to compiled languages (e.g. C). This makes XSim Version 2 a simulation tool that is relatively easy for both champions and community members to maintain, modify, or extend in order to improve their breeding programs. Functions and operators are overloaded for a better user interface so they may concatenate, subset, summarize, and organize simulated populations at each breeding step. With the strong and foreseeable demands in the community, XSim Version 2 will serve as a modern simulator bridging the gaps between theories and experiments with its flexibility, extensibility, and friendly interface.
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http://dx.doi.org/10.1093/g3journal/jkac032 | DOI Listing |
G3 (Bethesda)
April 2022
Department of Animal Science, University of California, Davis, CA 95616, USA.
Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results.
View Article and Find Full Text PDFG3 (Bethesda)
May 2015
Department of Animal Science, Iowa State University, Ames, Iowa 50011-3150
Real or imputed high-density SNP genotypes are routinely used for genomic prediction and genome-wide association studies. Many researchers are moving toward the use of actual or imputed next-generation sequence data in whole-genome analyses. Simulation studies are useful to mimic complex scenarios and test different analytical methods.
View Article and Find Full Text PDFExp Cell Res
July 2003
Université Pierre et Marie Curie, UMR7622-CNRS Biologie Moléculaire et Cellulaire du Développement, 9 quai St Bernard, 75252 Paris Cedex 05, France.
Transcription factors belonging to the basic helix-loop-helix Per-Arnt-Sim (bHLH/PAS) family control a wide variety of biological processes in mammalian and/or Drosophila. We have previously isolated bHLH/PAS Xenopus amphibian homologs of Single-minded (xSim) and aryl receptor nuclear translocator (xArnt) and characterized their expression pattern during embryogenesis. We show in this paper that xSim protein is a functional homolog of Drosophila or mammalian Sim(s).
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