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Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

Published on: October 5, 2012

Pea genomic selection for Italian environments.

Paolo Annicchiarico¹, Nelson Nazzicari², Luciano Pecetti²

¹Council for Agricultural Research and Economics (CREA), Research Center for Animal Production and Aquaculture, viale Piacenza 29, 26900, Lodi, Italy. paolo.annicchiarico@crea.gov.it.

|July 24, 2019

Summary

Genomic selection (GS) improves the prediction of breeding values for pea (Pisum sativum L.) grain yield, enhancing selection efficiency for wide adaptation in Italian environments compared to traditional phenotypic selection (PS). This advancement is crucial for developing resilient pea varieties.

Keywords:

Breeding value Cross-population prediction Genotype × environment interaction Genotyping-by-sequencing Pisum sativum Predictive ability Yield

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Area of Science:

Plant breeding
Quantitative genetics
Genomics

Background:

Genomic selection (GS) verification for pea (Pisum sativum L.) grain yield prediction is needed.
High genotype × environment interaction (GEI) necessitates effective inter-environment prediction for breeding.
GS utility increases with accurate prediction across diverse environments and untrained germplasm (inter-population prediction).

Purpose of the Study:

To verify the predictive ability of GS for pea grain yield and other traits.
To assess inter-environment and inter-population prediction accuracies.
To compare the efficiency of GS versus phenotypic selection (PS) for pea breeding.

Main Methods:

Genotyping-by-sequencing of 306 RIL genotypes from three populations.
Phenotyping for grain yield, flowering, lodging, seed weight, and winter survival across three Italian environments.
Bayesian Lasso model applied with 6058 SNP markers, assessing various GS models and data configurations.

Main Results:

Large GEI observed for grain yield, favoring breeding for wide adaptation.
Intra-population inter-environment predictive ability: 0.30 for yield, 0.65 for flowering, 0.64 for seed weight, 0.28 for lodging.
Inter-population predictions showed reduced accuracy (e.g., 0.19 for yield).
GS demonstrated greater efficiency than PS, with at least 80% advantage for yield (intra-population).

Conclusions:

Genome-enabled predictions enhance pea line selection efficiency for wide adaptation in Italian environments.
GS offers a more efficient alternative to PS for various traits under different selection scenarios.