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Supplemental Materials for Bhatta et al. 2020

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posted on 2020-01-23, 16:11 authored by Madhav Bhatta, Lucia Gutierrez, Lorena Cammarotta, Fernanda Cardozo, Silvia Germán, Blanca Gómez-Guerrero, María Fernanda Pardo, Valeria Lanaro, Mercedes Sayas, Ariel J. Castro

File S1 contains supplementary Tables S1-S6 and Figures S1-S6. Table S1 contains basic summary statistics with BLUEs, standard error, minimum, maximum, coefficient of variation, broad sense heritability, and proportion of variance component for genotype by environment and genotypic effect for agronomic and malting quality traits evaluated across multiple locations and years. Table S2 contains an analysis of variance with mean squares for agronomic and malting quality traits. Table S3 contains genomic predictive ability for un-phenotyped environments using MT-CV1 and MT-CV2 models. Table S4-S6 contains predictive ability for agronomic and malting quality traits using MT-CV1 and MT-CV2 models. Figure S1 shows a principal component analysis of all individuals. Figure S2 contains a distribution of the 6,482 single nucleotide polymorphisms (SNPs) across the seven chromosomes of barley in 145 double haploid lines. Figures S3-S6 contain Pearson’s correlation among environments (locations-years) for grain yield, number of grains per square meter, grain plumpness, and thousand grain weight, respectively.

File S2 contains the BLUEs for the agronomic traits (grain yield, plumpness, thousand grain weight, and number of grains per square meter) from individual experiments (EELE15, EELE16, EEMAC15, EEMAC16, EEMAC17, MOSA15, MOSA16, MUSA15, and MUSA16), experiments across locations (EELE, EEMAC, MOSA, and MUSA), years (2015, 2016, and 2017), and all experiments combined (ALL).

File S3 contains the BLUEs for the malting quality traits (beta-glucan content, malt extract, protein content, and soluble nitrogen) from the experiments conducted in 2015 in three locations (EELE, EEMAC, and MOSA) and for the combined experiments (ALL15).


Article title

Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)

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