Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana

The Plant Genome
Moses NyineJaroslav Doležel

Abstract

Improving the efficiency of selection in conventional crossbreeding is a major priority in banana ( spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to tradit...Continue Reading

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Citations

Jul 13, 2018·Critical Reviews in Food Science and Nutrition·Delphine AmahMaryke Labuschagne
Sep 19, 2019·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·Moses NyineRony Swennen
Sep 3, 2020·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·Dorcus C GemenetHugo Campos
Jan 14, 2020·Euphytica; Netherlands journal of plant breeding·Ivan Kabiita ArinaitweRofina Yasmin Othman
Mar 3, 2020·Frontiers in Plant Science·Laura M ZingarettiMiguel Pérez-Enciso
Jan 2, 2021·Journal of Plant Physiology·Hao Tong, Zoran Nikoloski
Jan 8, 2021·BMC Genomics·Osval Antonio Montesinos-LópezJosé Crossa

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