Can Cross-Country Genomic Predictions Be a Reasonable Strategy to Support Germplasm Exchange? – A Case Study With Hydrogen Cyanide in Cassava - Torres, Lívia Gomes; Oliveira, Eder Jorge de; Ogbonna, Alex C.; Bauchet, Guillaume J.; Mueller, Lukas A.; Azevedo, Camila Ferreira; Fonseca e Silva, Fabyano; Simiqueli, Guilherme Ferreira; Resende, Marcos Deon Vilela de (Frontiers in Plant Science, 2021)
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’s products usage and processing. This work aimed to: i) evaluate the feasibility of using cross-country (CC) GP between germplasm’s 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 on an 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, ~14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed by: i) Embrapa genotypic and phenotypic data, ii) IITA genotypic and phenotypic data, and iii) the joint datasets. Comparisons on GEBVs’ 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’ effects that were trained with data from other research institute/country’s germplasm to estimate their clones’ GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.80-0.85) compared to the prediction accuracy of either germplasm’ sources individually (0.51-0.67). CC GP proved to have potential use under the present study’s scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa’s germplasm, whereas for IITA’s it was 0.10. 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.