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Modeling of genetic gain for single traits from marker-assisted seedling selection in clonally propagated crops.

Ru S, Hardner C, Carter PA, Evans K, Main D, Peace C - Hortic Res (2016)

Bottom Line: Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest.Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability.Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available.

View Article: PubMed Central - PubMed

Affiliation: Department of Horticulture, Washington State University , PO Box 646414, Pullman, WA 99164-6414, USA.

ABSTRACT
Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest. Traditionally, genetic potential is determined by phenotypic evaluation. With the availability of DNA tests for some agronomically important traits, breeders have the opportunity to include DNA information in their seedling selection operations-known as marker-assisted seedling selection. A major challenge in deploying marker-assisted seedling selection in clonally propagated crops is a lack of knowledge in genetic gain achievable from alternative strategies. Existing models based on additive effects considering seed-propagated crops are not directly relevant for seedling selection of clonally propagated crops, as clonal propagation captures all genetic effects, not just additive. This study modeled genetic gain from traditional and various marker-based seedling selection strategies on a single trait basis through analytical derivation and stochastic simulation, based on a generalized seedling selection scheme of clonally propagated crops. Various trait-test scenarios with a range of broad-sense heritability and proportion of genotypic variance explained by DNA markers were simulated for two populations with different segregation patterns. Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability. Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available.

No MeSH data available.


Simulated genetic gain from alternative seedling selection strategies for the population with three segregating genotypes and partial dominance (d3=a3/2). Each plot represents a selection scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each plot, the X-axis indicates the proportion of seedlings selected in the end of seedling selection, ranging from 0.05 to 0.95. The Y-axis indicates genetic gain from seedling selection based on the unit of simulated genotypic values. Error bars for each data point indicate the 95% confidence interval (Equation 11), which are not obvious because of extremely tight confidence intervals.
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fig4: Simulated genetic gain from alternative seedling selection strategies for the population with three segregating genotypes and partial dominance (d3=a3/2). Each plot represents a selection scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each plot, the X-axis indicates the proportion of seedlings selected in the end of seedling selection, ranging from 0.05 to 0.95. The Y-axis indicates genetic gain from seedling selection based on the unit of simulated genotypic values. Error bars for each data point indicate the 95% confidence interval (Equation 11), which are not obvious because of extremely tight confidence intervals.

Mentions: Optimal genetic gains based on derivation and simulation from marker-only matched closely in all scenarios and in all segregating populations (Supplementary Figure S3). In all populations, both simulated and derived genetic gain remained constant where TSP increased from 0.05 to the proportion of seedlings with the best marker genotype, for example, 0.25 for the population with three segregating genotypes and zero or partial dominance (Figure 4 and Supplementary Figure S3b). Genetic gain decreased as TSP increased to 0.95. The decrease of genetic gain from marker-only followed a smoother curve in the population with nine segregating genotypes compared with populations with three segregating genotypes (Supplementary Figure S3d). In all populations, where H and TSP remained constant, genetic gain increased as P increased; where P and TSP remained constant, increases in genetic gain were also observed as H increased. Genetic gain reached the highest values where both P and H were at the extreme value of 1, where all phenotypic variance was attributed to the marker locus/loci.


Modeling of genetic gain for single traits from marker-assisted seedling selection in clonally propagated crops.

Ru S, Hardner C, Carter PA, Evans K, Main D, Peace C - Hortic Res (2016)

Simulated genetic gain from alternative seedling selection strategies for the population with three segregating genotypes and partial dominance (d3=a3/2). Each plot represents a selection scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each plot, the X-axis indicates the proportion of seedlings selected in the end of seedling selection, ranging from 0.05 to 0.95. The Y-axis indicates genetic gain from seedling selection based on the unit of simulated genotypic values. Error bars for each data point indicate the 95% confidence interval (Equation 11), which are not obvious because of extremely tight confidence intervals.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837533&req=5

fig4: Simulated genetic gain from alternative seedling selection strategies for the population with three segregating genotypes and partial dominance (d3=a3/2). Each plot represents a selection scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each plot, the X-axis indicates the proportion of seedlings selected in the end of seedling selection, ranging from 0.05 to 0.95. The Y-axis indicates genetic gain from seedling selection based on the unit of simulated genotypic values. Error bars for each data point indicate the 95% confidence interval (Equation 11), which are not obvious because of extremely tight confidence intervals.
Mentions: Optimal genetic gains based on derivation and simulation from marker-only matched closely in all scenarios and in all segregating populations (Supplementary Figure S3). In all populations, both simulated and derived genetic gain remained constant where TSP increased from 0.05 to the proportion of seedlings with the best marker genotype, for example, 0.25 for the population with three segregating genotypes and zero or partial dominance (Figure 4 and Supplementary Figure S3b). Genetic gain decreased as TSP increased to 0.95. The decrease of genetic gain from marker-only followed a smoother curve in the population with nine segregating genotypes compared with populations with three segregating genotypes (Supplementary Figure S3d). In all populations, where H and TSP remained constant, genetic gain increased as P increased; where P and TSP remained constant, increases in genetic gain were also observed as H increased. Genetic gain reached the highest values where both P and H were at the extreme value of 1, where all phenotypic variance was attributed to the marker locus/loci.

Bottom Line: Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest.Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability.Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available.

View Article: PubMed Central - PubMed

Affiliation: Department of Horticulture, Washington State University , PO Box 646414, Pullman, WA 99164-6414, USA.

ABSTRACT
Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest. Traditionally, genetic potential is determined by phenotypic evaluation. With the availability of DNA tests for some agronomically important traits, breeders have the opportunity to include DNA information in their seedling selection operations-known as marker-assisted seedling selection. A major challenge in deploying marker-assisted seedling selection in clonally propagated crops is a lack of knowledge in genetic gain achievable from alternative strategies. Existing models based on additive effects considering seed-propagated crops are not directly relevant for seedling selection of clonally propagated crops, as clonal propagation captures all genetic effects, not just additive. This study modeled genetic gain from traditional and various marker-based seedling selection strategies on a single trait basis through analytical derivation and stochastic simulation, based on a generalized seedling selection scheme of clonally propagated crops. Various trait-test scenarios with a range of broad-sense heritability and proportion of genotypic variance explained by DNA markers were simulated for two populations with different segregation patterns. Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability. Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available.

No MeSH data available.