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The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented \u2018genome optimization via virtual simulation (GOVS)\u2019 using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant \u2018optimal genotypes\u2019 or \u2018advantageous alleles\u2019 in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize.<\/jats:p>\n               <jats:p>Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant \u2018optimal genotypes\u2019 in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https:\/\/govs-pack.github.io\/) to the public may ultimately facilitate genomically designed breeding in maize.<\/jats:p>","DOI":"10.1093\/bib\/bbab447","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T13:33:48Z","timestamp":1633008828000},"source":"Crossref","is-referenced-by-count":5,"title":["Genome optimization via virtual simulation to accelerate maize hybrid breeding"],"prefix":"10.1093","volume":"23","author":[{"given":"Qian","family":"Cheng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, China"}]},{"given":"Shuqing","family":"Jiang","sequence":"additional","affiliation":[{"name":"National Maize Improvement Center of China Agricultural University, Beijing, China"}]},{"given":"Feng","family":"Xu","sequence":"additional","affiliation":[{"name":"National Maize Improvement Center of China Agricultural University, Beijing, China"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"National Maize Improvement Center of China Agricultural University, Beijing, China"}]},{"given":"Yingjie","family":"Xiao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences and Technology at Huazhong Agricultural University, Wuhan, China"}]},{"given":"Ruyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Maize Research Center at Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"}]},{"given":"Jiuran","family":"Zhao","sequence":"additional","affiliation":[{"name":"Maize Research Center at Beijing Academy of Agriculture and Forestry Sciences, Beijing, China"}]},{"given":"Jianbing","family":"Yan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences and Technology at Huazhong Agricultural University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9612-7898","authenticated-orcid":false,"given":"Chuang","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-0859","authenticated-orcid":false,"given":"Xiangfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Sanya Institute of China Agricultural University, Hainan, China"}]}],"member":"286","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"2022011921411429500_ref1","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/s41587-019-0152-9","article-title":"Breeding crops to feed 10 billion","volume":"37","author":"Hickey","year":"2019","journal-title":"Nat 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