scholarly journals Genomic Prediction and Selection for Fruit Traits in Winter Squash

2020 ◽  
Vol 10 (10) ◽  
pp. 3601-3610
Author(s):  
Christopher O. Hernandez ◽  
Lindsay E. Wyatt ◽  
Michael R. Mazourek
Keyword(s):  
Genomic Selection ◽  
Fruit Quality ◽  
Quality Index ◽  
Recurrent Selection ◽  
Selection Model ◽  
Breeding Cycle ◽  
Quality Traits ◽  
Genomic Selection Model ◽  
Selection For ◽  
Winter Squash

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.

scholarly journals Genomic Selection for Fruit Quality Traits in Apple (Malus×domestica Borkh.)

PLoS ONE ◽  
2012 ◽  
Vol 7 (5) ◽  
pp. e36674 ◽  
Author(s):  
Satish Kumar ◽  
David Chagné ◽  
Marco C. A. M. Bink ◽  
Richard K. Volz ◽  
Claire Whitworth ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Fruit Quality ◽  
Malus Domestica ◽  
Quality Traits ◽  
Malus Domestica Borkh ◽  
Fruit Quality Traits ◽  
Selection For

scholarly journals Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience

2021 ◽  
Vol 12 ◽  
Author(s):  
◽  
Aline Fugeray-Scarbel ◽  
Catherine Bastien ◽  
Mathilde Dupont-Nivet ◽  
Stéphane Lemarié
Keyword(s):  
Genetic Diversity ◽  
Genomic Selection ◽  
Plant Species ◽  
Animal Species ◽  
Animal Breeding ◽  
Breeding Cycle ◽  
Breeding Programs ◽  
The Third ◽  
Wide Range ◽  
Selection For

The present study is a transversal analysis of the interest in genomic selection for plant and animal species. It focuses on the arguments that may convince breeders to switch to genomic selection. The arguments are classified into three different “bricks.” The first brick considers the addition of genotyping to improve the accuracy of the prediction of breeding values. The second consists of saving costs and/or shortening the breeding cycle by replacing all or a portion of the phenotyping effort with genotyping. The third concerns population management to improve the choice of parents to either optimize crossbreeding or maintain genetic diversity. We analyse the relevance of these different bricks for a wide range of animal and plant species and sought to explain the differences between species according to their biological specificities and the organization of breeding programs.

scholarly journals Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits

2020 ◽  
Vol 13 (10) ◽  
pp. 2704-2722 ◽  
Author(s):  
Jean Beaulieu ◽  
Simon Nadeau ◽  
Chen Ding ◽  
Jose M. Celedon ◽  
Aïda Azaiez ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Wood Quality ◽  
Spruce Budworm ◽  
White Spruce ◽  
Quality Traits ◽  
Selection For

scholarly journals Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits

PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0169234 ◽  
Author(s):  
Elisa Biazzi ◽  
Nelson Nazzicari ◽  
Luciano Pecetti ◽  
E. Charles Brummer ◽  
Alberto Palmonari ◽  
...  
Keyword(s):  
Association Mapping ◽  
Medicago Sativa ◽  
Genomic Selection ◽  
Forage Quality ◽  
Genome Wide Association ◽  
Quality Traits ◽  
Genome Wide ◽  
Selection For

scholarly journals FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenwu Xu ◽  
Xiaodong Liu ◽  
Mingfu Liao ◽  
Shijun Xiao ◽  
Min Zheng ◽  
...  
Keyword(s):  
Big Data ◽  
Genomic Selection ◽  
Computational Efficiency ◽  
Conditional Expectation ◽  
Large Scale ◽  
Predictive Ability ◽  
Selection Model ◽  
Prior Distributions ◽  
Sample Data ◽  
Genomic Selection Model

Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low computational efficiency, or an inability to handle large-scale sample data. We report the development of a genomic prediction model named FMixFN with four zero-mean normal distributions as the prior distributions to optimize the predictive ability and computing efficiency. The variance of the prior distributions in our model is precisely determined based on an F2 population, and genomic estimated breeding values (GEBV) can be obtained accurately and quickly in combination with an iterative conditional expectation algorithm. We demonstrated that FMixFN improves computational efficiency and predictive ability compared to other methods, such as GBLUP, SSgblup, MIX, BayesR, BayesA, and BayesB. Most importantly, FMixFN may handle large-scale sample data, and thus should be able to meet the needs of large breeding companies or combined breeding schedules. Our study developed a Bayes genomic selection model called FMixFN, which combines stable predictive ability and high computational efficiency, and is a big data-oriented genomic selection model that has potential in the future. The FMixFN method can be freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913).

scholarly journals Ear Quality in Three Sweet Corn Populations Selected for Resistance to Common Rust

1999 ◽  
Vol 124 (6) ◽  
pp. 641-643
Author(s):  
Bruce G. Abedon ◽  
William F. Tracy
Keyword(s):  
Zea Mays ◽  
Rust Resistance ◽  
Recurrent Selection ◽  
Sweet Corn ◽  
Environment Interaction ◽  
Quality Traits ◽  
Kernel Size ◽  
Puccinia Sorghi ◽  
Phenotypic Recurrent Selection ◽  
Selection For

Research was conducted to evaluate correlated effects of full-sib phenotypic recurrent selection for resistance to common rust (Puccinia sorghi Schw.) on ear quality traits in three sugary1 (su1) sweet corn (Zea mays L.) populations: Minn11, Minn14, and NECDR. Cycles 0, 1, 2, and 3 of each population were evaluated in both rust infested and nonrust infested environments. Generally, selection for rust resistance resulted in significant, but minor, decreases in ear and kernel size. Changes in specific traits varied with population. The nonsignificant cycle × environment interaction indicates similar responses occurred in all environments evaluated. Based on these results, selection for ear quality traits need not accompany selection for resistance to common rust if maintenance of ear quality is desired.

scholarly journals Bayesian genomic models boost prediction accuracy for survival to Streptococcus agalactiae infection in Nile tilapia (Oreochromus nilioticus)

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Rajesh Joshi ◽  
Anders Skaarud ◽  
Alejandro Tola Alvarez ◽  
Thomas Moen ◽  
Jørgen Ødegård
Keyword(s):  
Genomic Selection ◽  
Streptococcus Agalactiae ◽  
Nile Tilapia ◽  
Prediction Accuracy ◽  
Predictive Accuracy ◽  
Challenge Test ◽  
Bacterial Disease ◽  
Nucleotide Polymorphisms ◽  
Genomic Selection Model ◽  
Selection For

Abstract Background Streptococcosis is a major bacterial disease in Nile tilapia that is caused by Streptococcus agalactiae infection, and development of resistant strains of Nile tilapia represents a sustainable approach towards combating this disease. In this study, we performed a controlled disease trial on 120 full-sib families to (i) quantify and characterize the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia, and (ii) identify the best genomic model and the optimal density of single nucleotide polymorphisms (SNPs) for this trait. Methods In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were used in the challenge test. Mortalities were recorded every 3 h for 35 days. After quality control, genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. Genetic parameters were obtained using various genomic selection models (genomic best linear unbiased prediction (GBLUP), BayesB, BayesC, BayesR and BayesS) and a traditional pedigree-based model (PBLUP). The pedigree-based analysis used a deep 17-generation pedigree. Prediction accuracy and bias were evaluated using five replicates of tenfold cross-validation. The genomic models were further analyzed using 10 subsets of SNPs at different densities to explore the effect of pruning and SNP density on predictive accuracy. Results Moderate estimates of heritabilities ranging from 0.15 ± 0.03 to 0.26 ± 0.05 were obtained with the different models. Compared to a pedigree-based model, GBLUP (using all the SNPs) increased prediction accuracy by 15.4%. Furthermore, use of the most appropriate Bayesian genomic selection model and SNP density increased the prediction accuracy up to 71%. The 40 to 50 SNPs with non-zero effects were consistent for all BayesB, BayesC and BayesS models with respect to marker id and/or marker locations. Conclusions These results demonstrate the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia. Compared to the PBLUP and GBLUP models, Bayesian genomic models were found to boost the prediction accuracy significantly.

scholarly journals An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Plants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 745
Author(s):  
Ivana Plavšin ◽  
Jerko Gunjača ◽  
Zlatko Šatović ◽  
Hrvoje Šarčević ◽  
Marko Ivić ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Molecular Genetic ◽  
Wheat Quality ◽  
Quality Traits ◽  
Key Factors ◽  
Breeding Values ◽  
Factors Affecting ◽  
Selection For ◽  
Marker Information

Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.

scholarly journals Recurrent Selection for Specific Combining Ability for Yield in Four Pickling Cucumber Populations

HortScience ◽  
1996 ◽  
Vol 31 (4) ◽  
pp. 626a-626
Author(s):  
Christopher S. Cramer ◽  
Todd C. Wehner
Keyword(s):  
Fruit Quality ◽  
Combining Ability ◽  
Recurrent Selection ◽  
Block Design ◽  
Specific Combining Ability ◽  
Yield Traits ◽  
Breeding Lines ◽  
Pickling Cucumber ◽  
Per Se ◽  
Selection For

The combining ability (hybrid performance) of breeding lines is often determined to measure selection progress for yield. Plant breeders utilize this information to develop breeding lines with higher combining ability. The objectives of this study were to measure the specific combining ability for yield traits over three selection cycles from four pickling cucumber populations with Gy 14, a popular pickling cucumber inbred; and to determine the change in specific combining ability for yield traits in four populations improved through recurrent selection. Four pickling cucumber populations, North Carolina wide base pickle (NCWBP), medium base pickle (NCMBP), elite pickle 1 (NCEP1), and hardwickii 1 (NCH1), were developed and improved through modified half-sib selection from 1983 to 1992 to improve yield per se and fruit quality in each population. Eleven families were randomly selected from each of 3 selection cycles (early, intermediate, advanced) from each populations and were hybridized to Gy 14. Plants were sprayed with Paraquat to defoliate them and to simulate once-over harvest. The experiment was a randomized complete-block design with 22 replications per population arranged in a split plot with the four populations as whole plots and the three cycles as subplots. The combining ability for fruit quality rating of NCWBP and NCMBP increased as the number of selection cycles increased. Conversely, selection for higher yield per se decreased the combining ability of the NCEP1 population for improved fruit quality. In most instances, the combining ability of each population exhibited a constant response over selection cycles for each measured yield trait.

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