Increased thousand-grain weight (TGW) is an important breeding target for indirectly improving grain yield (GY). Fourteen reported candidate genes known to enhance TGW were evaluated in two wheat independent and existing datasets of CIMMYT, the Elite Yield Trial (EYT) from 2015-16 (EYT2015-16) and the Wheat Association Mapping Initiative (WAMI) panel, to study their allele effects on TGW and to verify their suitability for marker-assisted selection. Of these, significant associations were detected for only one gene (TaGs3-D1) in the EYT2015-16 and two genes (TaTGW6, and TaSus1) in WAMI. The reported favorable alleles of TaGs3-D1 and TaTGW6 genes decreased TGW in the datasets. A haplotype-based genome wide association study was implemented to identify the genetic determinants of TGW on a large set of CIMMYT germplasm [4,302 lines comprising five EYTs], which identified 15 haplotype blocks to be significantly associated with TGW. Four of them, identified on chromosomes 4A, 6A and 7A, were associated with TGW in at least three EYTs. The locus on chromosome 6A (Hap-6A-13) had the largest effect on TGW and additionally GY with increases of up to 2.60g and 258 kg/ha, respectively. Discovery of novel TGW loci described in our study expands the opportunities for developing diagnostic markers and for multi-gene pyramiding to derive new allele combinations for enhanced TGW and GY in CIMMYT wheat.

Genomic prediction relates a set of markers to variability in observed phenotypes of cultivars and allows for the prediction of phenotypes or breeding values of genotypes on unobserved individuals. Most genomic prediction approaches predict breeding values based solely on additive effects. However, the economic value of wheat lines is not only influenced by their additive component but also encompasses a non-additive part (e.g., additive × additive epistasis interaction). In this study, genomic prediction models were implemented in three target populations of environments (TPE) in South Asia. Four models that incorporate genotype × environment interaction (G × E) and genotype × genotype (GG) were tested: Factor Analytic (FA), FA with genomic relationship matrix (FA + G), FA with epistatic relationship matrix (FA + GG), and FA with both genomic and epistatic relationship matrices (FA + G + GG). Results show that the FA + G and FA + G + GG models displayed the best and a similar performance across all tests, leading us to infer that the FA + G model effectively captures certain epistatic effects. The wheat lines tested in sites in different TPE were predicted with different precisions depending on the cross-validation employed. In general, the best prediction accuracy was obtained when some lines were observed in some sites of particular TPEs and the worse genomic prediction was observed when wheat lines were never observed in any site of one TPE.

Genomic prediction combines molecular and phenotypic data in a training population to predict the breeding values of individuals that have only been genotyped. The use of genomic information in breeding programs helps to increase the frequency of favorable alleles in the populations of interest. This study evaluated the performance of BLUP (Best Linear Unbiased Prediction) in predicting resistance to tan spot, spot blotch and Septoria nodorum blotch in synthetic hexaploid wheat. BLUP was implemented in single-trait and multi-trait models with three variations: (1) the pedigree relationship matrix (A-BLUP), (2) the genomic relationship matrix (G-BLUP), and (3) a combination of the two matrices (A+G BLUP). In all three diseases, the A-BLUP model had a lower performance, and the G-BLUP and A+G BLUP were statistically similar (p ≥ 0.05). The prediction accuracy with the single trait was statistically similar (p ≥ 0.05) to the multi-trait accuracy, possibly due to the low correlation of severity between the diseases.

Genomic selection is an extension of marker‐assisted selection by leveraging thousands of molecular markers distributed across the genome to capture the maximum possible proportion of the genetic variance underlying complex traits. In this study, genomic prediction models were developed by integrating phenological, physiological, and high‐throughput phenotyping traits to predict grain yield in bread wheat ( Triticum aestivum L.) under three environmental conditions: irrigation, drought stress, and terminal heat stress. Model performance was evaluated using both five‐fold cross‐validation and leave‐one‐environment‐out (LOEO) schemes. Under five‐fold cross‐validation, the model incorporating vegetation indices derived from spectral datasets from the grain‐filling phase achieved the highest accuracy. In LOEO validation, the model that included days to heading performed best under irrigation, whereas under drought stress, the model utilizing vegetation indices from the vegetative stage showed the highest accuracy. Under terminal heat stress, three models performed best: one incorporating genotype by environment interaction, one using vegetation indices during the vegetative stage, and one integrating spectral reflectance data from both the vegetative and grain‐filling phases. Although incorporating multiple covariates can improve prediction accuracy or reduce the normalized root mean square error, using an extended model with all available covariates is not recommended due to the marginal predictive accuracy gains, increases in phenotyping, costs and complexity of data collection analysis. Overall, our findings show the importance of tailored phenomic inputs to specific environmental contexts to optimize genomic prediction of wheat yield.

Este estudio se realizó para validar el locus de carácter cuantitativo (QTL- quantitative trait locus) HAP-6A-13, el cual fue reportado con asociación a peso de grano en trigos harineros de primavera del Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT). Para realizar la validación se diseñaron dos marcadores de competencia alelo específica (KASP- kompetitive allele specific PCR) para los polimorfismos de un solo nucleótido (SNP) T/C y A/G integrados en el QTL. La asociación de los marcadores KASP con el peso de mil granos y rendimiento se verificó mediante estudio de asociación del genoma completo (GWAS) en cuatro colecciones de trigo. Los dos marcadores KASP mostraron asociación significativa con el peso de mil granos y rendimiento (P ≤ 0.05); adicionalmente, se generaron 10 familias endogámicas heterogéneas (HIF-heterogeneous inbred families) y se evaluó el peso de mil granos, rendimiento y otros caracteres morfométricos del grano. En contraste con los resultados de GWAS, en las evaluaciones de las HIF el efecto del QTL HAP-6A-13 fue significativo (P ≤ 0.05) sólo en una HIF para peso de mil granos y en tres HIF para longitud de grano, lo que demuestra la dificultad de validar un QTL de efecto menor en trigo.