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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.


Phenotypic distributions in 16 scenarios for the population with three segregating genotypes and partial dominance (d3=a3/2). Black lines indicate phenotypic distributions of each single genotype, and red lines indicate phenotypic distributions of all seedlings in the population. Each graph represents phenotypic distributions of a scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each graph, the X-axis indicates phenotypic value and the Y-axis is the proportion of seedlings with a phenotypic value.
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fig2: Phenotypic distributions in 16 scenarios for the population with three segregating genotypes and partial dominance (d3=a3/2). Black lines indicate phenotypic distributions of each single genotype, and red lines indicate phenotypic distributions of all seedlings in the population. Each graph represents phenotypic distributions of a scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each graph, the X-axis indicates phenotypic value and the Y-axis is the proportion of seedlings with a phenotypic value.

Mentions: In the population with three segregating genotypes and partial dominance, the proportion of the total phenotypic variance explained by the marker locus (or loci) increased as P and H increased, which was indicated by greater differences between the mean phenotypic values of different genotypes (Figure 2). The phenotypic distributions of all seedlings deviated further from normal distributions as P and H increased (Figure 2). Multiple peaks were observed where H and P both reached 0.8. Where both P and H reached 1, phenotypic values of seedlings were arranged in discrete distributions where the phenotypic value of a seedling was determined only by its marker genotype. Similar patterns were also observed in the same population with zero or complete dominance and the population with nine segregating genotypes (Supplementary Figure. S1).


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)

Phenotypic distributions in 16 scenarios for the population with three segregating genotypes and partial dominance (d3=a3/2). Black lines indicate phenotypic distributions of each single genotype, and red lines indicate phenotypic distributions of all seedlings in the population. Each graph represents phenotypic distributions of a scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each graph, the X-axis indicates phenotypic value and the Y-axis is the proportion of seedlings with a phenotypic value.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Phenotypic distributions in 16 scenarios for the population with three segregating genotypes and partial dominance (d3=a3/2). Black lines indicate phenotypic distributions of each single genotype, and red lines indicate phenotypic distributions of all seedlings in the population. Each graph represents phenotypic distributions of a scenario with a given broad-sense heritability (H) of the trait and predictiveness (P) of the DNA test. In each graph, the X-axis indicates phenotypic value and the Y-axis is the proportion of seedlings with a phenotypic value.
Mentions: In the population with three segregating genotypes and partial dominance, the proportion of the total phenotypic variance explained by the marker locus (or loci) increased as P and H increased, which was indicated by greater differences between the mean phenotypic values of different genotypes (Figure 2). The phenotypic distributions of all seedlings deviated further from normal distributions as P and H increased (Figure 2). Multiple peaks were observed where H and P both reached 0.8. Where both P and H reached 1, phenotypic values of seedlings were arranged in discrete distributions where the phenotypic value of a seedling was determined only by its marker genotype. Similar patterns were also observed in the same population with zero or complete dominance and the population with nine segregating genotypes (Supplementary Figure. S1).

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.