Mean and variance heterogeneity loci impact kernel compositional traits in maize.

Area of Science:

• Plant Genetics
• Agricultural Science
• Genomics

Background:

• Maize (Zea mays) kernel composition is vital for food, feed, and industrial uses.
• Understanding the genetic basis of kernel traits like starch, protein, oil, fiber, and ash is crucial for crop improvement.

Purpose of the Study:

• To identify genetic loci influencing both the mean and variability of maize kernel traits.
• To explore the utility of variance genome-wide association studies (vGWAS) as a complementary approach to standard GWAS.
• To assess the predictive accuracy of different genomic selection models for kernel composition traits.

Main Methods:

• Genome-wide association studies (GWAS) and variance genome-wide association studies (vGWAS) were performed on 954 maize inbred lines.
• Ten significant single nucleotide polymorphisms (SNPs) associated with five kernel traits were detected.
• Genomic selection models, including ridge-regression best linear unbiased prediction, reproducing kernel Hilbert space, and random forest, were employed.

Main Results:

• GWAS identified 10 significant SNPs for five kernel traits, with some colocalizing with known genes (e.g., waxy1, gras7).
• vGWAS revealed additional loci not identified by standard GWAS, demonstrating its complementary value.
• Genomic selection models achieved moderate prediction accuracies (0.41-0.55), with parametric and semi-parametric models showing lower prediction bias.
• Findings from unreplicated genebank seed were consistent with replicated trials for protein and starch.

Conclusions:

• Genebank-derived high-quality samples are valuable for initial genomic analysis of maize kernel traits.
• Combining GWAS and vGWAS provides a more comprehensive understanding of trait genetics.
• Genomic selection shows potential for predicting kernel composition traits in maize.
• These results support using existing seed resources and high-throughput phenotyping for identifying candidate loci and prioritizing traits for future validation.