scholarly journals Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

2020 ◽  
Author(s):  
Elisa Cappetta ◽  
Giuseppe Andolfo ◽  
Antonio Di Matteo ◽  
Amalia Barone ◽  
Luigi Frusciante ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Breeding Value ◽  
Critical Parameters ◽  
Breeding Programs ◽  
Selection Strategies ◽  
Superior Genotypes ◽  
Tomato Breeding ◽  
Accuracy Of Prediction ◽  
Selection Of

Genomic selection (GS) is a predictive approach that was build up to increase the rate of genetic gain per unit of time in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effect. GS enables the prediction of breeding value of candidate genotypes for selection. In this work we address important issues related to GS and its implementation in tomato breeding context. Genomic constrains and critical parameters affecting the accuracy of prediction in such crop such as phenotyping, genotyping training population composition and size and statistical method should be carefully evaluated. Comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding program are also discussed. GS applied to tomato breeding has already shown to be feasible. We illustrated how GS can improve the rate of gain in elite lines selection, descendent and in backcross schemes. The GS schemes begin to be delineated and computer science can provide support for future selection strategies. A new breeding framework is beginning to emerge for optimizing tomato improvement procedures.

scholarly journals Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Plants ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1236
Author(s):  
Elisa Cappetta ◽  
Giuseppe Andolfo ◽  
Antonio Di Matteo ◽  
Amalia Barone ◽  
Luigi Frusciante ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Breeding Value ◽  
Critical Parameters ◽  
Breeding Programs ◽  
Selection Strategies ◽  
Genome Wide ◽  
Line Selection ◽  
Tomato Breeding ◽  
Accuracy Of Prediction

Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.

scholarly journals Non-Parametric Genetic Prediction of Complex Traits with Latent Dirichlet Process Regression Models

2017 ◽  
Author(s):  
Ping Zeng ◽  
Xiang Zhou
Keyword(s):  
Genomic Selection ◽  
Dirichlet Process ◽  
Complex Traits ◽  
Gene Expressions ◽  
Breeding Programs ◽  
Genetic Prediction ◽  
Robust Prediction ◽  
Selection Of ◽  
Effect Size Distribution ◽  
Non Parametric

AbstractUsing genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model (DPR). DPR is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare DPR with several commonly used prediction methods with simulations. We further apply DPR to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.

scholarly journals Identification of the Optimum Environments for the High Yield and Quality Traits of Lentil Genotypes Evaluated in Multi-Location Trials

2021 ◽  
Vol 13 (15) ◽  
pp. 8247
Author(s):  
Dimitrios N. Vlachostergios ◽  
Christos Noulas ◽  
Anastasia Kargiotidou ◽  
Dimitrios Baxevanos ◽  
Evangelia Tigka ◽  
...  
Keyword(s):  
Crude Protein ◽  
Agronomic Traits ◽  
High Yield ◽  
Breeding Programs ◽  
Growing Seasons ◽  
Yield And Quality ◽  
Food And Feed ◽  
Superior Genotypes ◽  
Selection Of ◽  
The Ideal

Lentil is a versatile and profitable pulse crop with high nutritional food and feed values. The objectives of the study were to determine suitable locations for high yield and quality in terms of production and/or breeding, and to identify promising genotypes. For this reason, five lentil genotypes were evaluated in a multi-location network consisting of ten diverse sites for two consecutive growing seasons, for seed yield (SY), other agronomic traits, crude protein (CP), cooking time (CT) and crude protein yield (CPY). A significant diversification and specialization of the locations was identified with regards to SY, CP, CT and CPY. Different locations showed optimal values for each trait. Locations E4 and E3, followed by E10, were “ideal” for SY; locations E1, E3 and E7 were ideal for high CP; and the “ideal” locations for CT were E3 and E5, followed by E2. Therefore, the scope of the cultivation determined the optimum locations for lentil cultivation. The GGE-biplot analysis revealed different discriminating abilities and representativeness among the locations for the identification of the most productive and stable genotypes. Location E3 (Orestiada, Region of Thrace) was recognized as being optimal for lentil breeding, as it was the “ideal” or close to “ideal” for the selection of superior genotypes for SY, CP, CT and CPY. Adaptable genotypes (cv. Dimitra, Samos) showed a high SY along with excellent values for CP, CT and CPY, and are suggested either for cultivation in many regions or to be exploited in breeding programs.

scholarly journals Selection strategies in agronomic characters in progenies F3:4 of transgenic soy RR

2021 ◽  
Vol 45 ◽  
Author(s):  
Anna Regina Tiago Carneiro ◽  
Osvaldo Toshiyuki Hamawaki ◽  
Ana Paula Oliveira Nogueira ◽  
Arthur Felipe Eustáquio e Silva ◽  
Raphael Lemes Hamawaki ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Indirect Selection ◽  
Direct Selection ◽  
Selection Strategies ◽  
Aggregate Information ◽  
Genetic Potential ◽  
Superior Genotypes ◽  
Selection Indexes ◽  
Total Gain ◽  
Selection Of

ABSTRACT The selection indexes aggregate information to multiple characters and, with this, they are able to carry out the selection of a set of variables simultaneously. The objective was to verify the genetic potential of agronomic traits and to select soybean F3:4 progenies based on different selection strategies. 123 progenies and the parents were sown in randomized blocks with two replications. The gains of direct selection by the indexes, the sum of “ranks” and the genotype-ideotype were lower for all characters when compared to the gains of direct and indirect selection. The rank sum index stood out for achieving the highest total gain with 37.11%. The index of the genotype-ideotype obtained a lower gain (-0.48%) for the character number of days for flowering compared to the sum index of “ranks” (-0.54%) and reached a negative gain for the attribute insertion height of the first pod with -1.82%. The genetic potential of the F3:4 population is high and allows different selection strategies to be applied to reach superior genotypes. The progenies UFU 72, UFU 116, UFU 86, UFU 45, UFU 117, UFU 56, UFU 5, UFU 106, UFU 6, UFU 4, UFU 73, UFU 101, UFU 96, UFU 90, UFU 123, UFU 116, UFU 88, UFU 65, UFU 70, UFU 3, UFU 69 and UFU 37 were selected by both selection indexes. The UFU 72, UFU 90, UFU 88 and UFU 69 progenies are agronomically superior both in direct and indirect selection, as in Mulamba and Mock (1978) sum of “ranks” selections and genotype-ideotype.

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3372
Author(s):  
Cesar A. Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Medicago Sativa ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Breeding Value ◽  
Phenotypic Traits ◽  
Genome Wide ◽  
Medicago Sativa L

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

scholarly journals Tolerance to preharvest sprouting and yield of wheat genotypes from different breeding programs

2015 ◽  
Vol 50 (8) ◽  
pp. 698-706
Author(s):  
Rafael Nörnberg ◽  
José Antonio Gonzalez da Silva ◽  
Henrique de Souza Luche ◽  
Elisane Weber Tessmann ◽  
Sydney Antonio Frehner Kavalco ◽  
...  
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Preharvest Sprouting ◽  
Rio Grande Do Sul ◽  
Falling Number ◽  
Breeding Programs ◽  
Wheat Genotypes ◽  
Randomized Complete Block Design ◽  
Superior Genotypes ◽  
Selection Of

Abstract:The objective of this work was to characterize the performance of elite wheat genotypes from different Brazilian breeding programs for traits associated with grain yield and preharvest sprouting. The study was conducted in 2010 and 2011 in the municipality of Capão do Leão, in the state of Rio Grande do Sul, Brazil, in a randomized complete block design with three replicates. Thirty-three wheat genotypes were evaluated for traits related to preharvest sprouting and grain yield. The estimate of genetic distance was used to predict potential combinations for selection of plants with high grain yield and tolerance to preharvest sprouting. The combined analysis of sprouted grains and falling number shows that the TBIO Alvorada, TBIO Mestre, Frontana, Fundacep Raízes, Fundacep Cristalino, and BRS Guamirim genotypes are tolerant to preharvest sprouting. Combinations of TBIO Alvorada and TBIO Mestre with Fundacep Cristalino show high potential for recovering superior genotypes for high grain yield and tolerance to preharvest sprouting.

scholarly journals Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 479 ◽  
Author(s):  
Larkin ◽  
Lozada ◽  
Mason
Keyword(s):  
Genomic Selection ◽  
Wheat Yield ◽  
Wheat Breeding ◽  
Breeding Value ◽  
Selection Marker ◽  
Successful Implementation ◽  
Head Blight ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
A Genome

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

2021 ◽  
Author(s):  
Cesar A Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Agronomic Traits ◽  
Association Studies ◽  
Breeding Value ◽  
Genome Wide Association Studies ◽  
Genome Wide

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa, previous results indicated that low to moderate prediction accuracy values (&lt;70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. This is the first report in alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

scholarly journals Marker assisted selection of heifers improved milk somatic cell count compared to selection on conventional pedigree breeding values

2008 ◽  
Vol 51 (1) ◽  
pp. 23-32 ◽  
Author(s):  
C. Kühn ◽  
F. Reinhardt ◽  
M. Schwerin
Keyword(s):  
Somatic Cell ◽  
Breeding Programs ◽  
Breeding Values ◽  
Udder Health ◽  
Selection Strategies ◽  
Commercial Breeding ◽  
Functional Mechanisms ◽  
Mastitis Susceptibility ◽  
Selection Of ◽  
Marker Information

Abstract. Although mastitis in cattle is an important factor for dairy economy and animal welfare and although udder health parameters have a substantial genetic variability, in many countries there is little or no improvement of udder health in the conventional commercial breeding programs. Strategies implementing information about Quantitative trait loci (QTL) via genetic marker information seem to offer new prospects to improve this situation. In a proof-of-principle approach, we show that selection of German Holstein heifers prior to first calving based on marker information regarding a confirmed QTL affecting somatic cell score (SCS) on bovine chromosome 18 (BTA18) (MAS strategy) indeed enabled prediction of halfsibs with a high (q) or a low (Q) number of somatic cells in milk already early in the first lactation. Compared to a strategy relying on conventional breeding values only (CON strategy), selection including marker information resulted in a stronger discrimination between and a higher uniformity within the MAS-Q and -q groups compared to the CON-Q and – q groups selected by conventional selection strategies. The selected heifers, which are clinically unaffected, however genetically different in their capacity of mammary gland protection against pathogens, will serve as tools for a comprehensive expression analysis to highlight molecular processes underlying a different genetic predisposition to mastitis susceptibility. Thus, functional mechanisms resulting in a decreased susceptibility of the Q individuals can be identified to further improve selection on udder health in cattle.

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