# Optimizing genomic predictions in maize using a diversity panel and a multiparental population

## TL;DR

The study shows that genomic selection in maize is more accurate within similar populations and less reliable when applied across genetically diverse groups.

## Contribution

The study evaluates genomic prediction accuracy in two maize populations and highlights the importance of genetic relatedness in training sets for effective genomic selection.

## Key findings

- Higher predictive ability was observed in the diversity panel compared to the MAGIC population for most traits.
- Cross-population prediction had very low accuracy due to differences in allele frequencies and linkage disequilibrium.
- Combining both populations in training did not improve prediction accuracy and sometimes reduced it.

## Abstract

Genomic selection allows the prediction of genetic values using SNP markers distributed across the genome. Its effectiveness depends on factors such as trait heritability, genetic similarity between training and validation sets, and population structure. Although results in homogeneous populations have been promising, its application in diverse germplasm remains a challenge. This study evaluates the predictive capacity of genomic best linear unbiased prediction models applied to agronomic and biochemical‐structural traits related to stover quality in two maize populations: a diversity panel and a multiparental advanced generation inter‐cross (MAGIC) population. Higher heritability was observed in the panel, especially for flowering traits (h
2 ≥ 0.88), with high intra‐population predictive abilities (PA = 0.15–0.75) for most traits, compared to MAGIC (PA = 0.14–0.37). However, when applying the models from one population to another (cross‐population prediction), the predictive ability was drastically reduced for most traits (PA < 0.05), possibly due to differences in allele frequencies and phases of linkage disequilibrium. Combining both populations in a single training set did not improve prediction (PA = 0.13–0.74) and even reduced it in some cases. These results indicate that genetic heterogeneity and differences in linkage disequilibrium between populations compromise the stability of marker effects. Therefore, it is critical to optimize the training set composition by considering genetic relatedness and population structure to improve the efficiency of genomic selection in diverse germplasm.

The accuracy of genomic prediction is strongly influenced by trait heritability and the genetic composition of the training and validation populations.Differences in linkage disequilibrium and allele frequencies between populations reduce the transferability of predictive markers between populations.Effective genomic selection in diverse germplasm requires training sets that balance diversity and relatedness, population structure, and biological context.

The accuracy of genomic prediction is strongly influenced by trait heritability and the genetic composition of the training and validation populations.

Differences in linkage disequilibrium and allele frequencies between populations reduce the transferability of predictive markers between populations.

Effective genomic selection in diverse germplasm requires training sets that balance diversity and relatedness, population structure, and biological context.

Genomic selection allows breeders to predict crop performance using DNA markers, accelerating the development of improved varieties. This study tested its effectiveness in two maize populations: a diverse panel and a structured population from multiple parents. Predictions were more accurate when models were trained and tested within the same population, especially for highly heritable traits such as flowering time. Cross‐population predictions were less reliable due to genetic differences, and combining both populations did not improve accuracy. In practical breeding programs where selection is applied to recombinant families with high linkage disequilibrium, genomic selection is particularly useful for identifying starting material, even when not all germplasm is genotyped. Therefore, tailoring training and validation strategies to the specific biological and genetic context of each breeding program enhances the success of genomic selection in maize.