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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #260285

Title: Design And Performance Of 44,100 SNP Genotyping Array For Rice

item TUNG, CHIH-WEI - Cornell University
item WRIGHT, MARK - Cornell University
item ZHAO, KEYAN - Cornell University
item REYNOLDS, ANDY - Cornell University
item MONTGOMERY, JULIE - Affymetrix, Inc
item TANIMOTO, GENE - Affymetrix, Inc
item BARKOVICH, ROBERT - Affymetrix, Inc
item PIRANI, ALI - Affymetrix, Inc
item Eizenga, Georgia
item McClung, Anna
item BUSTAMANTE, CARLOS - Cornell University
item MCCOUCH, SUSAN - Cornell University

Submitted to: Plant and Animal Genome Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 11/30/2009
Publication Date: 12/11/2009
Citation: Tung, CW, Wright M, Zhao K, Reynolds A, Montgomery J, Tanimoto G, Barkovich R, Pirani A, Eizenga G, McClung A, Bustamante C, McCouch S. 2010. Design and performance of 44,100 SNP genotyping array for rice In: Proc. of the Plant & Animal Genomes XVIII Conf. 9-13 Jan. 2010. San Diego, California. Available at:

Interpretive Summary:

Technical Abstract: To document genome-wide allelic variation within and between the different subpopulations of both O. sativa and O. rufipogon, we developed an Affymetrix custom genotyping array containing 44,100 SNPs well distributed across the 400Mb rice genome. The SNPs on this array were selected from the MBML-intersection OryzaSNP dataset and O. rufipogon BAC-end sequences from the OMAP project based on allele frequencies, genome distribution and flanking sequence quality parameters. The 44K chip contains 12 probes per target with ±4 bp offsets and provides ~1SNP/10 kb. The preparation of rice DNA samples and labeled probes for hybridization onto the 44K SNP array differs from that used for human, mouse and other large-genome eukaryotes; conditions optimized for rice will be summarized. We used the 44K chip to genotype a collection of 500 diverse rice accessions (Ali poster) and confirmed a 94.1% call rate with 99.7% concordance among control samples, demonstrating the high quality of the array. Because O. sativa is a naturally inbreeding species and the the Rice Diversity Panel had a very low frequency of heterozygotes, we developed a novel allele calling algorithm, ALCHEMY (Wright poster), that does not rely on clustering of three genotypic classes and can be used with high accuracy even on small datasets. We have successfully used the 44K SNP dataset for admixture mapping and association analysis (Zhao poster) and as the basis for marker-assisted breeding applications (Thomson poster). Online reference for abstract: