Inbreeding depression in cassava for productive traits

Freitas, Juan Paulo Xavier de; Santos, V. da S.; Oliveira, E. J. de

doi:10.1007/s10681-016-1649-7

Cited by 30 publications

(19 citation statements)

References 15 publications

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“…LIMA et al (1984) associated low values of inbreeding depression with the low participation of dominance genetic effects (deviations) in the expression of some traits. For quantitative traits, the contribution of heterozygous loci is high, so there is a tendency toward increased inbreeding depression (ARNHOLD et al, 2010;FREITAS et al, 2016). In the present study, characteristics that showed inheritance with the highest dominance effect had high estimated inbreeding depression, indicating the importance of dominant gene action as a factor in inbreeding depression.…”

Section: Resultsmentioning

confidence: 44%

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Inbreeding depression and average genetic components in green corn genotypes

et al. 2017

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This study aimed to evaluate the inbreeding depression and average genetic components in seven hybrids and two open pollinated cultivars for green corn production, as well as to select the best genotypes in order to obtain base populations for inbred lines extraction. The experiment was carried out in a randomized complete block design with four replications, in a split plot design, with inbreeding levels arranged in the plots and the different genotypes distributed over the subplots, in the municipality of Sabáudia - PR, during the 2014/15 crop season. Higher values of dominance and inbreeding depression were observed for yield of both unhusked and commercial ears. Higher additive effects were detected on length and diameter of commercial ears. The genotypes AM811, Cativerde 02 and AG4051 showed greater probability of obtaining highly productive and higher quality green corn inbred lines.

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Section: Resultsmentioning

confidence: 44%

“…The reduction of the adaptive value of a genotype derived from the crossing of related genotypes is called "inbreeding depression", and it depends on the frequency of deleterious recessive alleles involved in the trait control (FREITAS et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Inbreeding depression and average genetic components in green corn genotypes

et al. 2017

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“…These high values are expected because of the complexity of these traits. Arnhold et al (2010) and Freitas et al (2016) argue that for quantitative inheritance traits, the contribution of heterozygous loci is high and influences the increase of inbreeding depression. Bernini et al (2013) found inbreeding depression rates for the trait GW in hybrids of 48.1%, 39.2%, and 36.3%, corroborating with the results found in the present study.…”

Section: Resultsmentioning

confidence: 99%

Inbreeding depression and performance of partially self-fertilized maize progenies in a top cross

Somera

Ragagnin

Costa

et al. 2018

Chil. j. agric. res.

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Information on the effect of endogamy and combining ability of inbred families is essential in hybrid maize (Zea mays L.) breeding programs. The objective of this study was to evaluate the effect of inbreeding in a maize population and the performance of top-cross hybrids from this population, with a broad genetic base tester. First, 110 S1 families were generated and established in the field along with families from the original population. The experiment was arranged in a randomized block design with three replicates, in 3.0 m plots with 15 plants. Traits of agronomic importance, inbreeding depression, and the general effect of dominance deviations were estimated the first stage. In the second stage, 69 inbred families (S2) with low inbreeding depression were evaluated by top-cross tests using the F2 generation of the commercial hybrid AG 6040 as tester. The same traits from the first stage were evaluated. Inbreeding depression in the first stage was higher for traits related to production, which indicates that the heterozygous loci contribute more to these traits, with the additive effects being less important. In the second stage, the best estimates of the general combining ability (GCA) for grain weight were found in progenies 51, 33, 17, 9, 3, and 67, showing good performance and good potential for use in breeding programs.

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“…Considering that inbreeding depression is quite pronounced for cassava yield traits such as fresh root yield (range of 1.78% to 55.20%, mean 19.38%) and dry root yield (range of 0% to 55.57%, mean 17.54%) [6,61], most cassava breeding programs use heterozygous progenitors to generate highly segregant F 1 populations [3], and therefore, each F 1 individual is a single recombinant event. In general, this hinders the selective process in the early stages of breeding, since heritability estimates in the seedling stage are quite biased.…”

Section: Discussionmentioning

confidence: 99%

Cassava yield traits predicted by genomic selection methods

et al. 2019

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Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions’ BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 –RR-BLUP to 0.4756—RKHS) and dry root yield (0.4689 –G-BLUP to 0.4818—RKHS) in comparison with dry matter content (0.5655 –BLASSO to 0.5670 –RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99–1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.

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Inbreeding depression in cassava for productive traits

Cited by 30 publications

References 15 publications

Inbreeding depression and average genetic components in green corn genotypes

Inbreeding depression and average genetic components in green corn genotypes

Inbreeding depression and performance of partially self-fertilized maize progenies in a top cross

Cassava yield traits predicted by genomic selection methods

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