Selection: Experimental Results

Hallauer, Arnel R.; Carena, M. J.; Filho, J. B. Miranda

doi:10.1007/978-1-4419-0766-0_7

Cited by 9 publications

(13 citation statements)

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“…In experimental populations of nonmodel and model species such as fruit flies (Drosophila melanogaster) and laboratory mice (Mus musculus), selection experiments and assessments of genetic trends have been used to study the genetics of complex traits, selection limits, and to test quantitative genetic models and assumptions (Hill and Caballero, 1992). In crop species, estimation of realized DG t is typically done after conducting selection experiments with the objective of comparing different breeding strategies (Hallauer et al, 2010) and, to a lesser extent, to assess the In crop species, estimation of realized DG t is typically done after conducting selection experiments with the objective of comparing different breeding strategies (Hallauer et al, 2010) and, to a lesser extent, to assess the…”

Section: Researchmentioning

confidence: 99%

Section: Researchmentioning

confidence: 99%

“…When a genetic trend is linear, the rate of genetic gain per year (DG t ) that is realized can be estimated by regressing the average breeding value on year (Eberhart, 1964). In crop species, estimation of realized DG t is typically done after conducting selection experiments with the objective of comparing different breeding strategies (Hallauer et al, 2010) and, to a lesser extent, to assess the ABSTRACT Routine estimation of the rate of genetic gain (DG t ) realized by a breeding program has been proposed as a means to monitor its effectiveness. In experimental populations of nonmodel and model species such as fruit flies (Drosophila melanogaster) and laboratory mice (Mus musculus), selection experiments and assessments of genetic trends have been used to study the genetics of complex traits, selection limits, and to test quantitative genetic models and assumptions (Hill and Caballero, 1992).…”

mentioning

confidence: 99%

“…In livestock, reports of genetic trends are used to assess the effectiveness of breeding programs and to determine if adjustments are needed (Oldenbroek and van der Waaij, 2015). In crop species, estimation of realized DG t is typically done after conducting selection experiments with the objective of comparing different breeding strategies (Hallauer et al, 2010) and, to a lesser extent, to assess the ABSTRACT Routine estimation of the rate of genetic gain (DG t ) realized by a breeding program has been proposed as a means to monitor its effectiveness. Several methods of realized DG t estimation have been used in other studies, but none have been objectively evaluated in a plant breeding context.…”

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confidence: 99%

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Estimation of Realized Rates of Genetic Gain and Indicators for Breeding Program Assessment

Rutkoski

2019

Crop Science

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R ealized genetic gain is the change in average breeding value of a population over at least one cycle of selection for a particular trait or index of traits. Change in breeding or genetic values of populations over many cycles or years is referred to as genetic trend. When a genetic trend is linear, the rate of genetic gain per year (DG t ) that is realized can be estimated by regressing the average breeding value on year (Eberhart, 1964). Assuming the breeding process remains unchanged and the trait of interest is quantitatively inherited according to the infinitesimal model (Fisher, 1918), this estimate is can be used to predict future genetic gain.Analyses of genetic trends, often accompanied by estimation of realized DG t , have been conducted to analyze the outcomes of breeding programs or selection experiments in model species, livestock, and crops. In experimental populations of nonmodel and model species such as fruit flies (Drosophila melanogaster) and laboratory mice (Mus musculus), selection experiments and assessments of genetic trends have been used to study the genetics of complex traits, selection limits, and to test quantitative genetic models and assumptions (Hill and Caballero, 1992). In livestock, reports of genetic trends are used to assess the effectiveness of breeding programs and to determine if adjustments are needed (Oldenbroek and van der Waaij, 2015). In crop species, estimation of realized DG t is typically done after conducting selection experiments with the objective of comparing different breeding strategies (Hallauer et al., 2010) and, to a lesser extent, to assess the ABSTRACT Routine estimation of the rate of genetic gain (DG t ) realized by a breeding program has been proposed as a means to monitor its effectiveness. Several methods of realized DG t estimation have been used in other studies, but none have been objectively evaluated in a plant breeding context. Stochastic simulations of 80 rice (Oryza sativa L.) breeding programs over 28 yr were done to generate data used to evaluate five methods of realized DG t estimation in terms of error, precision, efficiency, and correlation between true and predicted annual mean breeding values. Two indicators of DG t , the expected DG t , and the average number of equivalent complete generations (EqCg), were described and evaluated. At best, estimates of realized DG t were over or underestimated by 15 and 27% when considering all 28 yr and the past 15 yr of breeding, respectively. The best methods were the estimated breeding value, control population, and era trial methods. Among these, correlations between true and estimated DG t were at best 0.59, indicating that these methods cannot very accurately rank breeding programs in terms of realized DG t . The expected DG t and the average EqCg were shown to be useful indicators for determining if a nonzero genetic gain is expected. Determining which of the three best realized DG t estimation methods evaluated, if any, would be appropriate for any given breeding program should be done with c...

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Section: Researchmentioning

confidence: 99%

Section: Researchmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Estimation of Realized Rates of Genetic Gain and Indicators for Breeding Program Assessment

Rutkoski

2019

Crop Science

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“…In livestock, reports of genetic trends are used to assess the effectiveness of breeding programs and to determine if adjustments are needed (Oldenbroek and van der Waaij, 2015). In crop species, estimation of realized Δ G t is typically done after conducting selection experiments with the objective of comparing different breeding strategies (Hallauer et al, 2010) and to a lesser extent to assess the effectiveness of breeding programs. This may be because the adoption of varieties by growers in the targeted region has been considered to be the ultimate measure of success of the program (Brennan and Byerlee, 1991).…”

Section: Introductionmentioning

confidence: 99%

Estimation of realized rates of genetic gain and indicators for breeding program assessment

Rutkoski

2018

Preprint

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1 1 2 3 4 5 1 AbstractRoutine estimation of the rate of genetic gain ( Δ G t ) realized by a breeding program has been proposed as a means to monitor its effectiveness. Several methods of realized Δ G t estimation have been utilized in other studies, but none have been objectively evaluated in a plant breeding context. Stochastic simulations of 80 rice (Oryza sativa) breeding programs over 28 years were done to generate data used to evaluate five methods of realized Δ G t estimation in terms of error, precision, efficiency and correlation between true and predicted annual mean breeding values. Two indicators of Δ G t , the expected Δ G t and the average number of equivalent complete generations (EqCg), were described and evaluated. At best, estimates of realized Δ G t were over or underestimated by 15% and 27% when considering all 28 years and the past 15 years of breeding respectively. The best methods were the control population, estimated breeding value, and ERA trial methods. Among these, correlations between true and estimated Δ G t were at best 0.59, indicating that these methods cannot very accurately rank breeding programs in terms of realized Δ G t . The expected Δ G t and the average EqCg were shown to be useful indicators for determining if a non-zero genetic gain is expected. Determining which of the three best realized Δ G t estimation methods evaluated, if any, would be appropriate for any given breeding program should be done with careful consideration of the objectives, resources, seed stocks, and structure of the data available. 2 6 7 8 contains the residual variances of y ijkl to account for heterogeneous error variance. EBV:After excluding terminal years of the breeding programs where phenotypic data from all testing stages was not present, breeding values were estimated based on the model

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Technological advances in maize breeding: past, present and future

Andorf¹,

et al. 2019

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Maize has for many decades been both one of the most important crops worldwide and one of the primary genetic model organisms. More recently, maize breeding has been impacted by rapid technological advances in sequencing and genotyping technology, transformation including genome editing, doubled haploid technology, parallelled by progress in data sciences and the development of novel breeding approaches utilizing genomic information. Herein, we report on past, current and future developments relevant for maize breeding with regard to (1) genome analysis, (2) germplasm diversity characterization and utilization, (3) manipulation of genetic diversity by transformation and genome editing, (4) inbred line development and hybrid seed production, (5) understanding and prediction of hybrid performance, (6) breeding methodology and (7) synthesis of opportunities and challenges for future maize breeding.

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Selection: Experimental Results

Cited by 9 publications

References 131 publications

Estimation of Realized Rates of Genetic Gain and Indicators for Breeding Program Assessment

Estimation of Realized Rates of Genetic Gain and Indicators for Breeding Program Assessment

Estimation of realized rates of genetic gain and indicators for breeding program assessment

Technological advances in maize breeding: past, present and future

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