Genetic Relatedness and Heterotic Grouping in MRIZP Elite Maize Inbred Lines Using SNP Markers from 25k SNP Array and...

Area of Science:

• * Agricultural Science
• * Genetics
• * Molecular Biology

Background:

• * Understanding genetic relationships and population structure is crucial for maize breeding programs aiming to exploit heterosis.
• * Evaluating the concordance between different marker technologies is essential for optimizing breeding strategies.

Purpose of the Study:

• * To compare the effectiveness of 25k SNP array and RNA-sequencing (RNA-seq) derived markers in assessing genetic relatedness and population structure in elite maize inbred lines.
• * To determine the impact of different Single Nucleotide Polymorphism (SNP) dataset generation methods from RNA-seq data on genetic inference.
• * To validate the accuracy of each platform in assigning lines to known heterotic groups.

Main Methods:

• * Analysis of 28 elite Maize Research Institute "Zemun Polje" (MRIZP) maize inbred lines and two public lines using a 25k SNP array.
• * Generation and comparative analysis of three SNP datasets (ALL, HOM, FINAL) from RNA-seq data, focusing on heterozygous site handling.
• * Comparison of genetic distances, population structure, and heterotic group assignments derived from both marker platforms.

Main Results:

• * Limited overlap (2-6 SNPs) was found between 25k SNP array and RNA-seq derived markers.
• * The 25k SNP array demonstrated high concordance (96.67%) with established heterotic groups, providing more informative genetic insights than pedigree data.
• * RNA-seq derived SNPs showed moderate concordance (56.67%) with heterotic groups, capturing some but not all of the underlying breeding structure.

Conclusions:

• * The 25k SNP array is a reliable and robust tool for evaluating genetic relatedness and population structure in maize breeding.
• * RNA-seq derived SNPs can provide complementary genetic information but are less suitable for routine heterotic group assignment compared to SNP arrays.
• * The choice of SNP dataset generation method significantly impacts RNA-seq data's utility in genetic inference.