Genomic Prediction in Maize Breeding Populations with Genotyping-by-Sequencing
José Crossa,Yoseph Beyene,Semagn Kassa,Paulino Pérez,John M. Hickey,Charles Chen,Gustavo de los Campos,Juan Burgueño,Vanessa S. Windhausen,Edward S. Buckler,Jean-Luc Jannink,Marco A. Lopez Cruz,Raman Babu +12 more
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TLDR
Evaluated methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments found consistent gains in prediction accuracy.Abstract:
Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.read more
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Journal ArticleDOI
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
José Crossa,Paulino Pérez-Rodríguez,Jaime Cuevas,Osval A. Montesinos-López,Diego Jarquin,Gustavo de los Campos,Juan Burgueño,Juan Manuel González-Camacho,Sergio Pérez-Elizalde,Yoseph Beyene,Susanne Dreisigacker,Ravi P. Singh,Xuecai Zhang,Manje Gowda,Manish Roorkiwal,Jessica Rutkoski,Rajeev K. Varshney +16 more
TL;DR: Based on GP results, it is speculated how GS in germplasm enhancement programs could accelerate the flow of genes from gene bank accessions to elite lines and recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
Journal ArticleDOI
Harvesting the promising fruits of genomics: Applying genome sequencing technologies to crop breeding
TL;DR: The current and future uses of next-generation sequencing technologies, both for developing crops with improved traits and for increasing the efficiency of modern plant breeding, as a step towards meeting the challenge of feeding a growing world population.
Journal ArticleDOI
Genetic Gains in Grain Yield Through Genomic Selection in Eight Bi-parental Maize Populations under Drought Stress
Yoseph Beyene,Kassa Semagn,Stephen Mugo,Amsal Tarekegne,Raman Babu,Barbara Meisel,Pierre Sehabiague,Dan Makumbi,Cosmos Magorokosho,Sylvester O. Oikeh,John Gakunga,Mateo Vargas,Michael Olsen,Boddupalli M. Prasanna,Marianne Bänziger,José Crossa +15 more
TL;DR: It is demonstrated that genomic selection is more effective than pedigree-based conventional phenotypic selection for increasing genetic gains in grain yield under drought stress in tropical maize.
Journal ArticleDOI
Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding.
Javaid Akhter Bhat,Sajad Ali,Romesh Kumar Salgotra,Zahoor Ahmad Mir,Sutapa Dutta,Vasudha Jadon,Anshika Tyagi,Muntazir Mushtaq,Neelu Jain,Pradeep Kumar Singh,G. P. Singh,Kumble Vinod Prabhu +11 more
TL;DR: The NGS-based genotyping have increased genomic-estimated breeding value prediction accuracies over other established marker platform in cereals and other crop species, and made the dream of GS true in crop breeding, but to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping for achieving the valuable genetic gain from complex traits.
Journal ArticleDOI
Modeling Epistasis in Genomic Selection.
Yong Jiang,Jochen C. Reif +1 more
TL;DR: It is concluded that prediction accuracy can be improved by modeling epistasis for selfing species but may not for outcrossing species, and why the RKHS model based on a Gaussian kernel captures epistatic effects among markers.
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