A Simple Package to Script and Simulate Breeding Schemes: The Breeding Scheme Language

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Authors Shiori Yabe, Hiroyoshi Iwata, Jean-Luc Jannink
Journal/Conference Name Crop Science
Paper Category
Paper Abstract It is difficult for plant breeders to determine an optimal breeding strategy given that the problem involves many factors, such as targettrait genetic architecture and breeding-resource availability. There are many possible breeding schemes for each breeding program. Although simulation study may be useful to help choose a better (or the best) breeding scheme, it is difficult for breeders to take the first step in conducting breeding simulation because of the complexity of building a simulation platform or even using existing simulation tools. We present here a simple and flexible simulation platform, the breeding scheme language (BSL). This simulation platform works in the statistical computing environment R. Users define their target species, trait genetic architectures, and breeding schemes by writing simple, selfexplanatory scripts. We believe the BSL will be useful for breeders to evaluate breeding schemes and to choose an optimal breeding strategy among a number of possible ones, as well as for training plant breeders. S. Yabe, and H. Iwata, Dep. of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Science, The Univ. of Tokyo, Bunkyo, Tokyo 113-8657, Japan; S. Yabe and J.-L. Jannink, Cornell Univ., Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Ithaca, NY 14853; S. Yabe, Division of Basic Research, Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8518, Japan; S. Yabe, JST, PRESTO, Kawaguchi, Saitama 332-0012, Japan; J.-L. Jannink, USDA–ARS, RW Holley Center for Agriculture and Health, Ithaca, NY 14853. Received 21 June 2016. Accepted 16 Nov. 2016. *Corresponding author ( jeanluc.jannink@ars.usda.gov). Abbreviations: BSL, breeding scheme language; GS, genomic selection; PS, phenotypic selection; QTL, quantitative trait loci. Published in Crop Sci. 57:1347–1354 (2017). doi: 10.2135/cropsci2016.06.0538 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published June 16, 2017
Date of publication 2017
Code Programming Language R

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