A high-throughput apple SNP genotyping platform using the GoldenGate™ assay.

Area of Science:

• * Plant genetics and genomics
• * Bioinformatics and computational biology
• * Agricultural science

Background:

• * Expressed Sequence Tag (EST) data is crucial for understanding gene expression in plants.
• * Identifying genetic variations like Single Nucleotide Polymorphisms (SNPs) is key for crop improvement.
• * Apple (Malus domestica) genetics research benefits from robust marker discovery.

Purpose of the Study:

• * To identify and characterize Single Nucleotide Polymorphisms (SNPs) from apple EST data.
• * To develop a reliable set of SNPs for high-throughput apple genotyping.
• * To assess the accuracy and potential of identified SNPs for genetic studies.

Main Methods:

• * Downloaded EST data from NCBI for 14 apple genotypes.
• * Mapped ESTs against a reference assembly to identify SNPs using specific similarity and coverage criteria.
• * Filtered SNPs based on flanking sequence length for genotyping assay design.
• * Validated a subset of SNPs using high-throughput genotyping assays.

Main Results:

• * Identified 37,807 SNPs across 6888 unique EST contigs, averaging one SNP per 187 bp.
• * Selected 12,299 SNPs with sufficient flanking sequences (≥ 60 bp) for assay design.
• * Validated 1411 SNPs, confirming approximately 60% as true SNPs.
• * Estimated that 26% of identified SNPs may originate from paralogous regions.

Conclusions:

• * A substantial number of SNPs were identified from apple EST data, providing valuable genetic resources.
• * The developed SNP set, with validated markers, is suitable for high-throughput genotyping in apple.
• * SNP validation revealed potential challenges, such as paralogous sequences, requiring careful consideration in marker application.