Author(s): Blair MW, Corts AJ, Penmetsa RV, Farmer A, CarrasquillaGarcia N,
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Abstract Single nucleotide polymorphism (SNP) detection has become a marker system of choice, because of the high abundance of source polymorphisms and the ease with which allele calls are automated. Various technologies exist for the evaluation of SNP loci and previously we validated two medium throughput technologies. In this study, our goal was to utilize a 768 feature, Illumina GoldenGate assay for common bean (Phaseolus vulgaris L.) developed from conserved legume gene sequences and to use the new technology for (1) the evaluation of parental polymorphisms in a mini-core set of common bean accessions and (2) the analysis of genetic diversity in the crop. A total of 736 SNPs were scored on 236 diverse common bean genotypes with the GoldenGate array. Missing data and heterozygosity levels were low and 94 \% of the SNPs were scorable. With the evaluation of the parental polymorphism genotypes, we estimated the utility of the SNP markers in mapping for inter-genepool and intra-genepool populations, the latter being of lower polymorphism than the former. When we performed the diversity analysis with the diverse genotypes, we found Illumina GoldenGate SNPs to provide equivalent evaluations as previous gene-based SNP markers, but less fine-distinctions than with previous microsatellite marker analysis. We did find, however, that the gene-based SNPs in the GoldenGate array had some utility in race structure analysis despite the low polymorphism. Furthermore the SNPs detected high heterozygosity in wild accessions which was probably a reflection of ascertainment bias. The Illumina SNPs were shown to be effective in distinguishing between the genepools, and therefore were most useful in saturation of inter-genepool genetic maps. The implications of these results for breeding in common bean are discussed as well as the advantages and disadvantages of the GoldenGate system for SNP detection.
This article was published in Theor Appl Genet
and referenced in Journal of Data Mining in Genomics & Proteomics