scholarly journals Genetic gains with rapid‐cycle genomic selection for combined drought and waterlogging tolerance in tropical maize ( Zea may s L.)

2020 ◽  
Vol 13 (3) ◽  
Author(s):  
Reshmi R. Das ◽  
M. T. Vinayan ◽  
Manish B. Patel ◽  
Ramesh K. Phagna ◽  
S. B. Singh ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Tropical Maize ◽  
Waterlogging Tolerance ◽  
Genetic Gains ◽  
Zea May ◽  
Selection For

scholarly journals Genetic gains with genomic versus phenotypic selection for drought and waterlogging tolerance in tropical maize (Zea mays L.)

2021 ◽  
Author(s):  
Reshmi Rani Das ◽  
Madhumal Thayil Vinayan ◽  
Kaliyamoorthy Seetharam ◽  
Manish Patel ◽  
Ramesh Kumar ◽  
...  
Keyword(s):  
Zea Mays ◽  
Zea Mays L ◽  
Phenotypic Selection ◽  
Tropical Maize ◽  
Waterlogging Tolerance ◽  
Genetic Gains ◽  
Selection For

scholarly journals Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population

2017 ◽  
Vol 7 (7) ◽  
pp. 2315-2326 ◽  
Author(s):  
Xuecai Zhang ◽  
Paulino Pérez-Rodríguez ◽  
Juan Burgueño ◽  
Michael Olsen ◽  
Edward Buckler ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Grain Yield ◽  
Genomic Selection ◽  
Breeding Period ◽  
Rapid Cycling ◽  
Tropical Maize ◽  
Training Population ◽  
Genetic Gains ◽  
Maize Population ◽  
Short Period

Abstract Genomic selection (GS) increases genetic gain by reducing the length of the selection cycle, as has been exemplified in maize using rapid cycling recombination of biparental populations. However, no results of GS applied to maize multi-parental populations have been reported so far. This study is the first to show realized genetic gains of rapid cycling genomic selection (RCGS) for four recombination cycles in a multi-parental tropical maize population. Eighteen elite tropical maize lines were intercrossed twice, and self-pollinated once, to form the cycle 0 (C0) training population. A total of 1000 ear-to-row C0 families was genotyped with 955,690 genotyping-by-sequencing SNP markers; their testcrosses were phenotyped at four optimal locations in Mexico to form the training population. Individuals from families with the best plant types, maturity, and grain yield were selected and intermated to form RCGS cycle 1 (C1). Predictions of the genotyped individuals forming cycle C1 were made, and the best predicted grain yielders were selected as parents of C2; this was repeated for more cycles (C2, C3, and C4), thereby achieving two cycles per year. Multi-environment trials of individuals from populations C0, C1, C2, C3, and C4, together with four benchmark checks were evaluated at two locations in Mexico. Results indicated that realized grain yield from C1 to C4 reached 0.225 ton ha−1 per cycle, which is equivalent to 0.100 ton ha−1 yr−1 over a 4.5-yr breeding period from the initial cross to the last cycle. Compared with the original 18 parents used to form cycle 0 (C0), genetic diversity narrowed only slightly during the last GS cycles (C3 and C4). Results indicate that, in tropical maize multi-parental breeding populations, RCGS can be an effective breeding strategy for simultaneously conserving genetic diversity and achieving high genetic gains in a short period of time.

scholarly journals Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 210
Author(s):  
Sang V. Vu ◽  
Cedric Gondro ◽  
Ngoc T. H. Nguyen ◽  
Arthur R. Gilmour ◽  
Rick Tearle ◽  
...  
Keyword(s):  
Moisture Content ◽  
Genomic Selection ◽  
Shell Length ◽  
Prediction Accuracy ◽  
Genomic Information ◽  
Morphometric Traits ◽  
Shell Weight ◽  
Shell Width ◽  
Crassostrea Angulata ◽  
Selection For

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

Predicted strong genetic gains from the application of genomic selection to improve growth related traits in barramundi (Lates calcarifer)

Aquaculture ◽  
2021 ◽  
pp. 737761
Author(s):  
DeanR. Jerry ◽  
David B. Jones ◽  
Marie Lillehammer ◽  
Cecile Massault ◽  
Shannon Loughnan ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Lates Calcarifer ◽  
Genetic Gains

Genomic Selection for the Traits Expressed after Pollination in Allogamous Plants

Crop Science ◽  
2014 ◽  
Vol 54 (4) ◽  
pp. 1448-1457 ◽  
Author(s):  
Shiori Yabe ◽  
Ryo Ohsawa ◽  
Hiroyoshi Iwata
Keyword(s):  
Genomic Selection ◽  
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 Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

2021 ◽  
Author(s):  
Vishnu Ramasubramanian ◽  
William Beavis
Keyword(s):  
Genomic Selection ◽  
Genetic Improvement ◽  
Genetic Variance ◽  
Breeding Strategies ◽  
Short Term ◽  
Genetic Gains ◽  
Mating Design ◽  
Genetic Potential ◽  
Breeding Populations

AbstractPlant breeding is a decision making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize rate of genetic improvement and minimize loss of useful genetic variance. For commercial plant breeders competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short term genetic gains from Genomic Selection (GS) are much greater than Phenotypic Selection (PS), while PS provides better long term genetic gains because PS retains useful genetic diversity during the early cycles of selection. With limited resources must a soybean breeder choose between the two extreme responses provided by GS or PS? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs and whether the breeding population should be organized as family islands. For breeding populations organized into islands decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to ten cycles using GS, a hub network mating design in breeding populations organized as fully connected family islands and migration rules allowing exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except a genomic mating design, instead of a hub networked mating design, is used. This strategy also resulted in realizing the greatest proportion of genetic potential of the founder populations. Weighted genomic selection applied to both non-isolated and island populations also resulted in realization of the greatest proportion of genetic potential of the founders, but required more cycles than the best compromise strategy.

Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy

Crop Science ◽  
2017 ◽  
Vol 57 (3) ◽  
pp. 1325-1337 ◽  
Author(s):  
Alexandra Duhnen ◽  
Amandine Gras ◽  
Simon Teyssèdre ◽  
Michel Romestant ◽  
Bruno Claustres ◽  
...  
Keyword(s):  
Protein Content ◽  
Genomic Selection ◽  
Prediction Accuracy ◽  
Seed Protein ◽  
Breeding Program ◽  
Seed Protein Content ◽  
Selection For ◽  
Program Data
Sign in / Sign up

Export Citation Format

Share Document