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Plant Sci."],"abstract":"<jats:p>Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship\/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava\u2019s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm\u2019s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, \u223c14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs\u2019 estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute\/country and might need to use the markers\u2019 effects that were trained with data from other research institutes\/country\u2019s germplasm to estimate their clones\u2019 GEBV. Fixation index (F<jats:sub>ST<\/jats:sub>) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8\u20130.85) compared to the prediction accuracy of either germplasm\u2019s sources individually (0.51\u20130.67). CC GP proved to have potential use under the present study\u2019s scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa\u2019s germplasm, whereas for IITA\u2019s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.<\/jats:p>","DOI":"10.3389\/fpls.2021.742638","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T04:52:23Z","timestamp":1638939143000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Can Cross-Country Genomic Predictions Be a Reasonable Strategy to Support Germplasm Exchange? \u2013 A Case Study With Hydrogen Cyanide in Cassava"],"prefix":"10.3389","volume":"12","author":[{"given":"L\u00edvia Gomes","family":"Torres","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eder Jorge de","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alex C.","family":"Ogbonna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume J.","family":"Bauchet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas A.","family":"Mueller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camila Ferreira","family":"Azevedo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabyano","family":"Fonseca e Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilherme Ferreira","family":"Simiqueli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcos Deon Vilela de","family":"Resende","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0224920","article-title":"Cassava yield traits predicted by genomic selection methods.","volume":"14","author":"Andrade","year":"2019","journal-title":"PLoS One"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1006287","article-title":"New opportunities and challenges to engineer disease resistance in cassava, a staple food of African small-holder farmers.","volume":"13","author":"Bart","year":"2017","journal-title":"PLoS Pathog."},{"key":"B3","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1002\/(sici)1097-0010(19990315)79:4<593::aid-jsfa222>3.0.co;2-2","article-title":"Picrate paper kits for determination of total cyanogens in cassava roots and all forms of cyanogens in cassava products.","volume":"79","author":"Bradbury","year":"1999","journal-title":"J. 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