Genomic prediction for broad and specific adaptation in sorghum accommodating differential variances of SNP effects

• Autores: Velazco, Julio Gabriel; Jordan, David R.; Hunt, Colleen H.; Mace, Emma S.; van Eeuwijk, Fred A.
• Año de publicación: 2020
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Crop Science 60 (3) : 1-33 (May -June 2020)
• Materia: Sorgos; Mejora Genética; Fitomejoramiento; Forrajes; Sorghum Grain; Genetic Gain; Plant Breeding; Forage
• Institución: Instituto Nacional de Tecnología Agropecuaria

This paper reports a first study exploring genomic prediction for adaptation of sorghum [Sorghum bicolor (L.) Moench] to drought‐stress (D‐ET) and non‐stress (W‐ET) environment types. The objective was to evaluate the impact of both modeling genotype‐by‐environment interaction (G × E) and accounting for heterogeneous variances of marker effects on genomic prediction of parental breeding values for grain yield within and across environment types (ET). For this aim, different genetic covariance structures and different weights for individual markers were investigated in BLUP‐based prediction models. The BLUP models used a kinship matrix combining pedigree and genomic information, termed K‐BLUP. The dataset comprised testcross yield performances under D‐ET and W‐ET as well as pedigree and genomic data. In general, modeling G × E increased predictive ability and reduced empirical bias of genomic predictions for broad adaptation across both ETs compared to models that ignored G × E by fitting a main genetic effect only. Genomic predictions for specific adaptation to D‐ET or to W‐ET were also improved by K‐BLUP models that explicitly accommodated G × E and used data from both ETs, relative to prediction models that used data from the targeted ET exclusively or models that used all the data but assumed no G × E. Allowing for heterogeneous marker variances through weighted K‐BLUP produced clear increments (between 43% and 72%) in predictive ability of genomic prediction for grain yield in all adaptation scenarios. We conclude that G × E as well as locus‐specific genetic variances should be accommodated in genomic prediction models to improve adaptability of sorghum to variable environmental conditions.
EEA Pergamino
Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda
Fil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia
Fil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia
Fil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia
Fil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda