Mean, genetic variance, and usefulness of selfing progenies from intra- and inter-pool crosses in faba beans (Vicia faba L.) and their prediction from parental parameters

Gumber, R. K.; Schill, B.; Link, Wolfgang; Kittlitz, E. von; Melchinger, Albrecht E.

doi:10.1007/s001220051106

Cited by 16 publications

(26 citation statements)

References 13 publications

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“…Overall, it appears therefore that error in the estimates of marker effects, whether due to low heritability, sparse markers, or possibly small population size, has a more negative effect on the accuracy of estimates of s G than of m. This fact, along with the generally low ratio of var(s G ) to var(m), limits the parameter space wherein it may be valuable to account for s G in the estimation of superior progeny value. Field experiments from different crop species also indicated that the usefulness of a cross is mainly influenced by the midparent value (Gumber et al 1999;Utz et al 2001;Miedaner et al 2006).…”

Section: Discussionmentioning

confidence: 99%

Using Quantitative Trait Loci Results to Discriminate Among Crosses on the Basis of Their Progeny Mean and Variance

2007

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To develop inbred lines, parents are crossed to generate segregating populations from which superior inbred progeny are selected. The value of a particular cross thus depends on the expected performance of its best progeny, which we call the superior progeny value. Superior progeny value is a linear combination of the mean of the cross's progeny and their standard deviation. In this study we specify theory to predict a cross's progeny standard deviation from QTL results and explore analytically and by simulation the variance of that standard deviation under different genetic models. We then study the impact of different QTL analysis methods on the prediction accuracy of a cross's superior progeny value. We show that including all markers, rather than only markers with significant effects, improves the prediction. Methods that account for the uncertainty of the QTL analysis by integrating over the posterior distributions of effect estimates also produce better predictions than methods that retain only point estimates from the QTL analysis. The utility of including estimates of a cross's among-progeny standard deviation in the prediction increases with increasing heritability and marker density but decreasing genome size and QTL number. This utility is also higher if crosses are envisioned only among the best parents rather than among all parents. Nevertheless, we show that among crosses the variance of progeny means is generally much greater than the variance of progeny standard deviations, restricting the utility of estimates of progeny standard deviations to a relatively small parameter space. I N inbred line development, parents are crossed to generate segregating populations from which superior inbred progeny are selected. The value of a particular cross depends on the performance of its best progeny rather than on its mean progeny performance. In a typical breeding program, far too many crosses are possible between elite candidate parents for exhaustive evaluation. For example, among 50 elite parents there are 1225 possible crosses. Even if it were feasible to evaluate a sufficient set of progeny from all those crosses, it is unlikely that that would be efficient. Rather, one would want to predict, among possible crosses, which ones are most likely to lead to superior inbred lines.Schnell and Utz (1975) introduced the usefulness concept for line development. Their definition of the usefulness of the cross m was U m ¼ m m 1 DG m ¼ m m 1 is GðmÞ h m , where m m is the population mean of homozygous lines that can be derived from cross m, s 2 GðmÞ is the genetic variance among these lines, h m is the square root of the heritability, and i is the standardized selection intensity. Two other criteria for similar usefulness are the varietal ability (Wright 1974;Gallais 1979) and the probability of obtaining transgressive segregants ( Jinks and Pooni 1976). Here, rather than focus on the genetic gain that might be obtained within a cross, we sought a simpler characterization that expresses which crosses would...

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Section: Discussionmentioning

confidence: 99%

Using Quantitative Trait Loci Results to Discriminate Among Crosses on the Basis of Their Progeny Mean and Variance

2007

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“…The GV estimates showed broad confidence intervals (Pulcinelli, 1997) and, according to Gumber et al (1999), the error associated with such estimates may cause the lack of correlation with GD. Natural selection in the selfing generations may also have caused bias in the estimates and it is possible that the present errors and bias may have changed the magnitude of the GV estimates so that the correlation with the GD could not be detected.…”

Section: Correlation Between Genetic Distances and Genetic Variancesmentioning

confidence: 99%

Predicting performance of soybean populations using genetic distances estimated with RAPD markers

et al. 2003

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In order to verify whether genetic distance (GD) is associated with population mean (PM), genetic variance (GV) and the proportion of superior progenies generated by each cross in advanced generations of selfing (PS), the genetic distances between eight soybean lines (five adapted and three non-adapted) were estimated using 213 polymorphic RAPD markers. The genetic distances were partitioned according to Griffing's Model I Method 4 for diallel analysis, i.e., GD ij = GD + GGD i + GGD j + SGD ij . Phenotypic data were recorded for seed yield and plant height for 25 out of 28 populations of a diallel set derived from the eight soybean lines and evaluated from F 2:8 to F 2:11 generations. No significant correlation for seed yield was detected between GD and GV, while negative correlations were detected between GD and PM and between GD and PS (r = -0.74** and -0.75**, respectively). Similar results were observed for the correlation between GGD i + GGD j and PM and between GGD i + GGD j and PS (r = -0.78** and -0.80**, respectively). No significant correlation was detected for plant height. The magnitudes of the correlations for seed yield were high enough to allow predictions of the potential of the populations based on RAPD markers.

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“…Although GD estimates based on molecular marker estimates have been effective at grouping related germplasm (Melchinger et al, 1998), the relationship between GD in parents and genotypic variance components (GVCs) in their progenies has been reported as weak or non-significant across many studies (Helms et al, 1997;ManjarrezSandoval et al, 1997;Burkhamer et al, 1998;Melchinger et al, 1998;Bohn et al, 1999;Gumber et al, 1999;Brachi et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

et al. 2011

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Appropriate selection of parents for the development of mapping populations is pivotal to maximizing the power of quantitative trait loci detection. Trait genotypic variation within a family is indicative of the family's informativeness for genetic studies. Accurate prediction of the most useful parental combinations within a species would help guide quantitative genetics studies. We tested the reliability of genotypic and phenotypic distance estimators between pairs of maize inbred lines to predict genotypic variation for quantitative traits within families derived from biparental crosses. We developed 25 families composed of B200 random recombinant inbred lines each from crosses between a common reference parent inbred, B73, and 25 diverse maize inbreds. Parents and families were evaluated for 19 quantitative traits across up to 11 environments. Genetic distances (GDs) among parents were estimated with 44 simple sequence repeat and 2303 single-nucleotide polymorphism markers. GDs among parents had no predictive value for progeny variation, which is most likely due to the choice of neutral markers. In contrast, we observed for about half of the traits measured a positive correlation between phenotypic parental distances and within-family genetic variance estimates. Consequently, the choice of promising segregating populations can be based on selecting phenotypically diverse parents. These results are congruent with models of genetic architecture that posit numerous genes affecting quantitative traits, each segregating for allelic series, with dispersal of allelic effects across diverse genetic material. This architecture, common to many quantitative traits in maize, limits the predictive value of parental genotypic or phenotypic values on progeny variance.

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Mean, genetic variance, and usefulness of selfing progenies from intra- and inter-pool crosses in faba beans (Vicia faba L.) and their prediction from parental parameters

Cited by 16 publications

References 13 publications

Using Quantitative Trait Loci Results to Discriminate Among Crosses on the Basis of Their Progeny Mean and Variance

Using Quantitative Trait Loci Results to Discriminate Among Crosses on the Basis of Their Progeny Mean and Variance

Predicting performance of soybean populations using genetic distances estimated with RAPD markers

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

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