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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.


Comparison between derived and simulated genetic gains from phenotype-only seedling selection for the population with three segregating genotypes with 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. Numbers on the right corner of each plot are correlation coefficients between mean genetic gains estimated based on derivation and simulation.
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fig3: Comparison between derived and simulated genetic gains from phenotype-only seedling selection for the population with three segregating genotypes with 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. Numbers on the right corner of each plot are correlation coefficients between mean genetic gains estimated based on derivation and simulation.

Mentions: In the population with three segregating genotypes and partial dominance, simulated genetic gain from phenotype-only decreased as TSP increased from 0.05 to 0.95 (Figure 3). The decrease in genetic gain followed a smooth curve in scenarios in which the phenotypic distribution was approximately normal, whereas where the normal distribution was violated, the decrease in genetic gain exhibited various patterns (Figure 3). For a constant value of TSP and H, simulated genetic gain tended to decrease with increasing P, and this was more pronounced at low values of TSP. Under the same TSP and P, simulated genetic gain increased as H increased from low to high (Figure 3). Derived and simulated genetic gains from phenotype-only were highly correlated in scenarios where the phenotypic distribution of the seedling population was approximately normal, whereas they were poorly correlated where the phenotypic distributions greatly deviated from normal distributions (Figure 3). For scenarios with similar phenotypic distributions (for example, Scenarios 12 and 15 in Figure 3), scenarios with high H values showed higher correlation coefficients between simulated and derived genetic gains compared with scenarios with low H values (Figure 3). Similar observations were also made in the same population where there was zero or complete dominance and in the population with nine segregating genotypes (Supplementary Figure S2).


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)

Comparison between derived and simulated genetic gains from phenotype-only seedling selection for the population with three segregating genotypes with 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. Numbers on the right corner of each plot are correlation coefficients between mean genetic gains estimated based on derivation and simulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Comparison between derived and simulated genetic gains from phenotype-only seedling selection for the population with three segregating genotypes with 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. Numbers on the right corner of each plot are correlation coefficients between mean genetic gains estimated based on derivation and simulation.
Mentions: In the population with three segregating genotypes and partial dominance, simulated genetic gain from phenotype-only decreased as TSP increased from 0.05 to 0.95 (Figure 3). The decrease in genetic gain followed a smooth curve in scenarios in which the phenotypic distribution was approximately normal, whereas where the normal distribution was violated, the decrease in genetic gain exhibited various patterns (Figure 3). For a constant value of TSP and H, simulated genetic gain tended to decrease with increasing P, and this was more pronounced at low values of TSP. Under the same TSP and P, simulated genetic gain increased as H increased from low to high (Figure 3). Derived and simulated genetic gains from phenotype-only were highly correlated in scenarios where the phenotypic distribution of the seedling population was approximately normal, whereas they were poorly correlated where the phenotypic distributions greatly deviated from normal distributions (Figure 3). For scenarios with similar phenotypic distributions (for example, Scenarios 12 and 15 in Figure 3), scenarios with high H values showed higher correlation coefficients between simulated and derived genetic gains compared with scenarios with low H values (Figure 3). Similar observations were also made in the same population where there was zero or complete dominance and in the population with nine segregating genotypes (Supplementary Figure S2).

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.