Selection Index with Parents, Populations, Progenies, and Generations Effects in Autogamous Plant Breeding

Resende, Marcos Deon Vilela de; Ramalho, Magno Antônio Patto; Carneiro, Pedro Crescêncio Souza; Carneiro, José Eustáquio Souza; Batista, Lorena; Góis, Itamara Bomfim

doi:10.2135/cropsci2015.05.0303

Cited by 24 publications

(58 citation statements)

References 19 publications

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“…SIPP was implemented via REML/BLUP (restricted maximum likelihood/best linear unbiased prediction) in SELEGEN software (Resende, 2016b). The experimental data were evaluated using information derived from the effects of progeny (F 2:4 ), populations and locations, under the following approaches: model without population effect and model with population effect, according to the SIPP-BLUP method (Resende et al, 2016a).…”

Section: Genetic Materials and Experimental Settingmentioning

confidence: 99%

“…Each year, a large number of progeny from several crosses are generated, which results in many different populations, and most genetic variation is among them. Thus, it would be appropriate that progeny receive greater weight according to their population effect (Resende et al, 2016a). However, in current breeding programmes, progeny selection is performed disregarding this effect.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, when the population effect is not included, selection gain is likely lower and the breeding-value order of progeny is altered (Duarte & Vencovsky, 2001;Pereira et al, 2017;Rocha & Vello, 1999). Therefore, it is important to apply a selection index of progeny that takes into account the effects of progeny and populations simultaneously (Resende et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

“…To gauge these effects, the SIPPPG index (selection index including parents, populations, progeny and generations) (Resende et al, 2016a) weights the increase in genetic variance among populations due to inbreeding by the fixation index F ST and the variance among progeny within populations by F IS . F IS represents the inbreeding coefficient of an individual with regard to nonrandom crosses within its respective population.…”

Section: Introductionmentioning

confidence: 99%

“…Efficiency in selecting the best progeny using the SIPP index has been proved in real field data only for the common bean crop by Resende et al (2016a). In this regard, contributions from data analysis using BLUP associated with SIPP in the selective breeding of soybean can be a differential to provide additional genetic gains with no extra costs to the programme.…”

Section: Introductionmentioning

confidence: 99%

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Selection of inbred soybean progeny (Glycine max): an approach with population effect

et al. 2018

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To meet the growing demand of the soybean consumer market, cultivars increasingly early, productive and resistant to biotic and abiotic stress are sought. Several populations are obtained in soybean breeding programmes, but progeny are selected without being weighted for their respective population effect. As a consequence, progeny originating from high-merit populations may be discarded too early. Given this scenario, this study proposes to employ the selection index with progeny and population effect via best linear unbiased prediction (SIPP-BLUP) for the genetic selection of early and productive soybean progeny. A total of 180 progeny derived from three populations were evaluated for yield-related traits. Genetic gains from selection, Spearman correlation and coincidence index were used to check the efficiency of the models with and without the population effect. The SIPP-BLUP index achieved greater selection accuracy and was efficient in the identification and future selection of early soybean progeny. Therefore, this study demonstrates that soybean breeding programmes should consider the population effect via SIPP-BLUP in progeny selection to obtain future lines that really contribute to genetic gain. K E Y W O R D Saccuracy, breeding values, earlier-maturing progeny, genetic gain, genotype × environment interaction, mixed models, soybean seed yield

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Section: Genetic Materials and Experimental Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Selection of inbred soybean progeny (Glycine max): an approach with population effect

et al. 2018

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Multipopulation recurrent selection: An approach with generation and population effects in selection of self‐pollinated progenies

et al. 2020

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Correlated information from different genetic sources is absent in most of annual self-pollinated crops using the recurrent selection strategy, which is a breeding strategy that improves crop traits consistently over years. In common bean (Phaseolus vulgaris L.) breeding programs, progenies coming from multiple biparental populations are evaluated across generations of inbred plants with the assumption that the data are not correlated. In this paper, in addition to the effects of progeny, we evaluate the effects of populations and generations and provide information for the selection process in a self-pollinated recurrent selection breeding program. Nineteen progenies were extracted from 20 breeding populations and evaluated at different sowing times across F 3:4 and F 3:5 generations. The evaluated traits were plant architecture, angular leaf spot resistance, grain appearance, and grain yield. Progenies were selected using three methods: means of progenies regardless of generation and population effects; the multigeneration index (MI), which considered the generation effect; and the selection index with parents, populations, progenies, and generations (SIPPPG). We showed that adding variation among progenies correctly weighted for different generations as well as variation among populations yield for an increase in genetic gain. Therefore, selection accuracies of the SIPPPG were the highest for all traits compared with those of MI and when generation and population effects were not considered.

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Accounting for heterogeneous genotypic variance of progenies within populations in autogamous plant breeding: An evaluation in common bean

et al. 2021

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In breeding of autogamous plants, breeders often deal with the evaluation of progenies derived from multiple populations. We hypothesize that selection strategies that account for the heterogeneous genotypic variability of progenies within these populations associated with the population means may improve progeny selection. Thus, the objective of this work was to use two mixed models with progenies nested to population effects to obtain and evaluate indexes for the genotypic values of progenies. We used simulations and a real dataset from a multipopulation recurrent selection program of common bean (Phaseolus vulgaris L.). Progenies from 20 populations were evaluated for grain yield in two different generations. The three studied indexes consider the merit of populations, but differently account for the genotypic variability of progenies within populations: index gM uses a mean estimate, index gH is based on heterogeneous estimates, and the index gW also uses a mean estimate but is weighted by the selection accuracy within populations. The studied populations were highly diverse in both generations, justifying the implementation of the two models to obtain the population means and specific estimates of genotypic variability of progenies for each population. The comparison among indexes suggested that index gW more appropriately explored the information from the genotypic profiles of the common bean studied populations in the genotypic values of progenies. Thus, the index gW has the potential to help breeders of self‐pollinated plant species to improve progeny selection from multiple populations.

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Selection Index with Parents, Populations, Progenies, and Generations Effects in Autogamous Plant Breeding

Cited by 24 publications

References 19 publications

Selection of inbred soybean progeny (Glycine max): an approach with population effect

Selection of inbred soybean progeny (Glycine max): an approach with population effect

Multipopulation recurrent selection: An approach with generation and population effects in selection of self‐pollinated progenies

Accounting for heterogeneous genotypic variance of progenies within populations in autogamous plant breeding: An evaluation in common bean

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