Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection

AbstractCereal grains, primarily composed of starch, protein, and fat, are major source of staple for human and animal nutrition. Sorghum, a cereal crop, serves as a dietary staple for over half a billion people in the semi-arid tropics of Africa and South Asia. Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. Therefore, the use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits: amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Predicting whole environments by training with remaining environments in BMORS yielded higher average prediction accuracy than from STSE model. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

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Incorporating Genome-Wide Association Mapping Results Into Genomic Prediction Models for Grain Yield and Yield Stability in CIMMYT Spring Bread Wheat

Frontiers in Plant Science ◽

10.3389/fpls.2020.00197 ◽

2020 ◽

Vol 11 ◽

Cited By ~ 2

Author(s):

Deepmala Sehgal ◽

Umesh Rosyara ◽

Suchismita Mondal ◽

Ravi Singh ◽

Jesse Poland ◽

...

Keyword(s):

Grain Yield ◽

Association Mapping ◽

Bread Wheat ◽

Genomic Prediction ◽

Prediction Models ◽

Yield Stability ◽

Genome Wide Association ◽

Spring Bread Wheat ◽

Genome Wide

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Impact of sorghum racial structure and diversity on genomic prediction of grain yield components

Crop Science ◽

10.1002/csc2.20060 ◽

2020 ◽

Vol 60 (1) ◽

pp. 132-148 ◽

Cited By ~ 4

Author(s):

Sirjan Sapkota ◽

Richard Boyles ◽

Elizabeth Cooper ◽

Zachary Brenton ◽

Matthew Myers ◽

...

Keyword(s):

Grain Yield ◽

Genomic Prediction ◽

Yield Components ◽

Grain Yield Components

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Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

G3 Genes|Genome|Genetics ◽

10.1534/g3.118.200856 ◽

2019 ◽

pp. g3.200856.2018 ◽

Cited By ~ 7

Author(s):

Margaret R. Krause ◽

Lorena González-Pérez ◽

José Crossa ◽

Paulino Pérez-Rodríguez ◽

Osval Montesinos-López ◽

...

Keyword(s):

Grain Yield ◽

Genomic Prediction ◽

Hyperspectral Reflectance

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Multitrait, Random Regression, or Simple Repeatability Model in High‐Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield

The Plant Genome ◽

10.3835/plantgenome2016.11.0111 ◽

2017 ◽

Vol 10 (2) ◽

Cited By ~ 55

Author(s):

Jin Sun ◽

Jessica E. Rutkoski ◽

Jesse A. Poland ◽

José Crossa ◽

Jean‐Luc Jannink ◽

...

Keyword(s):

Grain Yield ◽

High Throughput ◽

Genomic Prediction ◽

Random Regression ◽

Wheat Grain ◽

High Throughput Phenotyping

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The Importance of Dominance and Genotype-by-Environment Interactions on Grain Yield Variation in a Large-Scale Public Cooperative Maize Experiment

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkaa050 ◽

2021 ◽

Author(s):

Anna R Rogers ◽

Jeffrey C Dunne ◽

Cinta Romay ◽

Martin Bohn ◽

Edward S Buckler ◽

...

Keyword(s):

Grain Yield ◽

High Throughput ◽

Genomic Prediction ◽

Environmental Data ◽

Environment Interaction ◽

Hybrid Performance ◽

Interaction Patterns ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Main Effect

Abstract High-dimensional and high throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

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Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

Frontiers in Plant Science ◽

10.3389/fpls.2021.709545 ◽

2021 ◽

Vol 12 ◽

Author(s):

Harsimardeep S. Gill ◽

Jyotirmoy Halder ◽

Jinfeng Zhang ◽

Navreet K. Brar ◽

Teerath S. Rai ◽

...

Keyword(s):

Winter Wheat ◽

Grain Yield ◽

Genomic Prediction ◽

Complex Traits ◽

Agronomic Traits ◽

Wheat Breeding ◽

Successful Implementation ◽

Limited Information ◽

Multiple Traits ◽

Breeding Lines

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

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Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction

Frontiers in Plant Science ◽

10.3389/fpls.2021.690059 ◽

2021 ◽

Vol 12 ◽

Author(s):

Juan Ma ◽

Yanyong Cao

Keyword(s):

Grain Yield ◽

Genomic Prediction ◽

Fixed Effects ◽

Prediction Accuracy ◽

Reproducing Kernel ◽

Genome Wide Association Study ◽

Kernel Weight ◽

Primary Objective ◽

High Yield ◽

Nucleotide Polymorphisms

High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait–marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.

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Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Agronomy ◽

10.3390/agronomy10091221 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1221 ◽

Cited By ~ 1

Author(s):

Sirjan Sapkota ◽

J. Lucas Boatwright ◽

Kathleen Jordan ◽

Richard Boyles ◽

Stephen Kresovich

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Recombinant Inbred Lines ◽

Inbred Lines ◽

Grain Composition ◽

Genomic Heritability ◽

Gross Energy ◽

High Prediction ◽

Highly Correlated ◽

Sorghum Grain

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

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