Discuss the relevance of training population in genomic selection and describe briefly the important considerations during creation of a suitable training population.

The training population is a critical component of genomic selection (GS) and plays a crucial role in developing accurate prediction models for estimating the breeding values of individuals based on their genotypic information. The relevance of the training population lies in its ability to capture the genetic diversity and phenotypic variation present in the breeding germplasm. Here are some key aspects of the training population in genomic selection and important considerations for its creation:

Representativeness:

 

·         The training population should represent the genetic diversity present in the breeding germplasm. It should include individuals from diverse genetic backgrounds, including different breeding lines, landraces, wild relatives, and elite cultivars.

·         Care should be taken to ensure that the training population adequately covers the range of genetic variation for the traits of interest, including both favorable and unfavorable alleles.

Phenotypic Data:

·         Phenotypic data collected on individuals in the training population are essential for establishing the relationship between marker genotypes and phenotypic traits.

·         Phenotypic data should be collected under relevant environmental conditions and using standardized protocols to ensure consistency and accuracy.

·         Traits measured should be heritable and economically important for the breeding program, and efforts should be made to collect data on multiple traits to enhance the prediction accuracy of the model.

Marker Density:

·         The marker density in the training population should be sufficient to capture the genetic variation present in the breeding germplasm.

·         High-density genotyping platforms, such as single nucleotide polymorphism (SNP) arrays or genotyping-by-sequencing (GBS), are often used to genotype individuals in the training population to ensure comprehensive coverage of the genome.

Population Size:

·         The size of the training population should be large enough to capture the genetic complexity of the traits being targeted.

·         A larger training population size generally leads to more accurate prediction models, especially for traits with low heritability or controlled by multiple genes.

Population Structure and Relatedness:

·         Population structure and relatedness among individuals in the training population can influence the accuracy of genomic predictions.

·         Strategies such as controlling for population structure using principal component analysis (PCA) or incorporating kinship matrices into prediction models can help account for genetic relatedness and population stratification.

Cross-Validation:

·         Cross-validation techniques, such as leave-one-out cross-validation or k-fold cross-validation, are commonly used to assess the predictive ability of the model and validate its performance.

·         The training population is typically divided into training and validation sets, with the prediction model trained on the training set and evaluated on the validation set to estimate prediction accuracy.

Long-Term Stability:

·         The training population should be maintained over time to ensure the long-term stability and relevance of the prediction models.

·         Regular updates to the training population may be necessary to incorporate new germplasm, phenotypic data, or advances in genotyping technologies.

In summary, the training population is a foundational element of genomic selection, providing the genetic and phenotypic data needed to develop accurate prediction models for estimating the breeding values of individuals. Careful consideration of representativeness, phenotypic data quality, marker density, population size, population structure, cross-validation, and long-term stability is essential during the creation of a suitable training population to ensure the success of genomic selection in plant breeding programs.