Training Set Optimization for Sparse Phenotyping in Genomic ...

In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS.

Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time.

(PDF) Training Set Optimization for Sparse Phenotyping in Genomic ...

TRS optimization typically involves selecting a smaller TRS as a subset of a larger candidate set. ... ... TRS optimization typically involves ...

Training Set Optimization for Sparse Phenotyping in Genomic ...

Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview · Latest News · News · Privacy Overview.

Training Set Optimization for Sparse Phenotyping in Genomic ...

Keywords. training set optimization; genomic selection; genome-wide markers; statistical design; sparse phenotyping; genomic prediction; mixed models ...

Sparse kernel models provide optimization of training set design for ...

The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals.

Sparse kernel models provide optimization of training set design for ...

The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but ...

Sample size determination for training set optimization in genomic ...

A practical approach is developed to determine a cost-effective optimal training set for selective phenotyping in a genomic prediction study.

Design of training populations for selective phenotyping in genomic ...

Our results show that optimization methods that include information from the test set (targeted) showed the highest accuracies, indicating that ...

A comparison of methods for training population optimization in ...

A training set size of 50–55% (targeted) or 65–85% (untargeted) is needed to obtain 95% of the accuracy. Abstract. With the advent of genomic ...

Sparse kernel models provide optimization of training set design for ...

(2017) analyzed a CIMMYT wheat data set to evaluate the prediction performance of phenotypes across environments using single-step genomic and ...

Optimizing Sparse Testing for Genomic Prediction of Plant Breeding ...

The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a ...

Sparse Phenotyping and Haplotype-Based Models for ... - Rice

To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering ...

(PDF) Optimization of Sparse Phenotyping Strategies in Multi ...

Our research used genomic data and relationship measurements between the training (Full testing genotypes) and testing set (Sparse testing ...

Genomic selection in plant breeding: Key factors shaping two ...

(A) In genomic prediction, phenotypic and genotypic data as well as other covariates can be applied to develop and optimize various machine- ...

Training Set Optimization for Sparse Phenotyping in Genomic ...

Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview. Publicated to:Frontiers In Plant Science. 12 715910- - 2021-09 ...

Sparse testing designs for optimizing predictive ability in sugarcane ...

This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic ...

[PDF] Sample size determination for training set optimization in ...

A practical approach was developed to determine a cost-effective optimal training set for selective phenotyping in a genomic prediction study by applying ...

Optimization of Sparse Phenotyping Strategies in Multi ... - Sciety

Our research utilized genomic data and relationship measurements between the training (full testing genotypes) and testing set (sparse testing genotypes) to ...

Portability of genomic predictions trained on sparse factorial designs ...

Isidro y Sánchez J, Akdemir D (2021) Training Set Optimization for Sparse Phenotyping in Genomic Selection: 679. A Conceptual Overview ...