Genetic Gain For Some Agronomical Characters By Dihaploid Breeding In Barley

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

Download Full-text

Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03847-6 ◽

2021 ◽

Author(s):

Pallavi Sinha ◽

Vikas K. Singh ◽

Abhishek Bohra ◽

Arvind Kumar ◽

Jochen C. Reif ◽

...

Keyword(s):

Statistical Methods ◽

Genetic Gain ◽

Crop Improvement ◽

Breeding Population ◽

Breeding Cycle ◽

Climate Resilience ◽

Heritability Estimation ◽

Improvement Programs ◽

Statistical Designs ◽

Novel Alleles

Abstract Key message Integrating genomics technologies and breeding methods to tweak core parameters of the breeder’s equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Abstract Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers’ fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder’s equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder′s equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

Download Full-text

Expected Genetic Contributions and Their Impact on Gene Flow and Genetic Gain

Genetics ◽

10.1093/genetics/153.2.1009 ◽

1999 ◽

Vol 153 (2) ◽

pp. 1009-1020 ◽

Cited By ~ 4

Author(s):

J A Woolliams ◽

P Bijma ◽

B Villanueva

Keyword(s):

Gene Flow ◽

Genetic Gain ◽

Overlapping Generations ◽

Predictive Accuracy ◽

Index Theory ◽

Previous Theory ◽

Selection Indices ◽

Breeding Groups ◽

Genetic Contributions

Abstract Long-term genetic contributions (ri) measure lasting gene flow from an individual i. By accounting for linkage disequilibrium generated by selection both within and between breeding groups (categories), assuming the infinitesimal model, a general formula was derived for the expected contribution of ancestor i in category q (μi(q)), given its selective advantages (si(q)). Results were applied to overlapping generations and to a variety of modes of inheritance and selection indices. Genetic gain was related to the covariance between ri and the Mendelian sampling deviation (ai), thereby linking gain to pedigree development. When si(q) includes ai, gain was related to E[μi(q)ai], decomposing it into components attributable to within and between families, within each category, for each element of si(q). The formula for μi(q) was consistent with previous index theory for predicting gain in discrete generations. For overlapping generations, accurate predictions of gene flow were obtained among and within categories in contrast to previous theory that gave qualitative errors among categories and no predictions within. The generation interval was defined as the period for which μi(q), summed over all ancestors born in that period, equaled 1. Predictive accuracy was supported by simulation results for gain and contributions with sib-indices, BLUP selection, and selection with imprinted variation.

Download Full-text

Multistage Selection for Genetic Gain by Orthogonal Transformation

Genetics ◽

10.1093/genetics/130.1.235a ◽

1992 ◽

Vol 130 (1) ◽

pp. 235B-235B

Author(s):

S Xu ◽

W M Muir

Keyword(s):

Genetic Gain ◽

Orthogonal Transformation ◽

Selection For ◽

Multistage Selection

Download Full-text

Genetic gain in the breeding program of common beans at IAC from 1989 to 2007

Cropp Breeding and Applied Biotechnology ◽

10.1590/s1984-70332010000400007 ◽

2010 ◽

Vol 10 (4) ◽

pp. 329-336 ◽

Cited By ~ 16

Author(s):

Alisson Fernando Chiorato ◽

Sérgio Augusto Morais Carbonell ◽

Roland Vencovsky ◽

Nelson da Silva Fonseca Júnior ◽

José Baldin Pinheiro

Keyword(s):

Grain Yield ◽

Common Bean ◽

Genetic Gain ◽

Grain Quality ◽

The State ◽

Breeding Program ◽

Common Beans ◽

The Common ◽

The Mean ◽

Second Period

The goal of the present work was to evaluate the genetic gain obtained in grain yield for the common bean genotypes from 1989 until 2007, at the Instituto Agronômico de Campinas, in the state of São Paulo. Genetic gain has been separated into two research periods; the first, from 1989 to 1996, and the second, from 1997 to 2007. In the first period, a genetic gain of 1.07 % per year was obtained, whereas for the second period, the gain was zero. However, the mean yield of the evaluated lines was approximately 1000 kg ha-1 superior to the figures obtained in the first period. The main cause for the absence of genetic gain in the second period is that the focus of the breeding program was changed to grain quality. The individualized analysis of the genotypes with carioca grains in the second period indicated the lack of genetic gain during the investigated period.

Download Full-text

A simulation study of open nucleus and closed nucleus breeding systems in a sheep population

Animal Science ◽

10.1017/s1357729800008213 ◽

1995 ◽

Vol 60 (1) ◽

pp. 117-124 ◽

Cited By ~ 7

Author(s):

J. A. Roden

Keyword(s):

Genetic Gain ◽

Live Weight ◽

Breeding Systems ◽

Breeding Value ◽

Linear Unbiased Prediction ◽

Sheep Population ◽

Best Linear Unbiased ◽

Nucleus Breeding ◽

Open Nucleus ◽

Selection Of

AbstractStochastic simulation was used to compare the results of alternative breeding systems in a sheep population divided into 10 flocks of 120 ewes. The breeding systems compared were selection within closed flocks (CF), a closed nucleus system (CNS), an open nucleus system (ONS) and open nucleus systems with the selection of nucleus replacements being restricted to either nucleus born males (ONSRm) or nucleus born females (ONSRf). Selection was for a best linear unbiased prediction of breeding value for lamb live weight which had a heritability of 0·17. The open nucleus breeding systems (ONS, ONSRm, ONSRf) resulted in higher rates of genetic gain, more predictable selection responses and lower rates of inbreeding than either the closed nucleus system (CNS) or selection within closed flocks (CF). Initial genetic differences between flocks resulted in higher rates of genetic gain in the nucleus breeding systems due to the use of between flock genetic variance. In the ONS system up to 25% of nucleus sires and approximately 50% of nucleus dams were born in base flocks. Nevertheless if selection of either nucleus sires or dams was restricted to nucleus born animals there was very little change in genetic gain or rate of inbreeding.

Download Full-text