Plant & Animal Genomes XVI Conference

January 12-16, 2008
Town & Country Convention Center
San Diego, CA

Gene Pair Haplotype Variation In The Octoploid Strawberry

Thomas M. Davis¹ , Kevin M, Folta² , Qian Zhang¹ , Denise C.M. Tombolato² , Viplav Mishra³ , Tamer Kahveci³

¹  Department of Plant Biology, University of New Hampshire, Rudman Hall, Durham, NH, 03824, USA

²  Plant Molecular and Cellular Biology Program and Horticultural Sciences Department, University of Florida, PO Box 110690, 1301 Fifield Hall, Gainesville, FL, 32611, USA

³  Department of Computer and Information Science and Engineering, CSE Building, University of Florida, Gainesville, FL, 32611, USA

The cultivated strawberry, Fragaria x ananassa, is a genomically complex octoploid, in which a single plant could harbor as many as eight different alleles at a “single locus”, and a progeny population could be segregating for as many as 16 different alleles. To address the challenge of differentiating among these many alleles, we have taken advantage of the small gene-to-gene distances in Fragaria to develop two new marker concepts: gene pair markers, and gene pair haplotypes. Based upon the known DNA sequences of two adjacent genes, forward and reverse PCR primers are targeted to conserved sites in exons of genes 1 and 2, respectively. On this basis, intergenic regions that are expected to be rich in sequence polymorphisms are being exploited for purposes of marker development, genotyping, and haplotyping. To simplify the dissection of inheritance patterns, a segregating population of pentaploid strawberries has been generated from a diploid x octoploid cross. For extending application of the gene pair approach to species without adequate genomic sequence databases, a computational tool named "Gene Pair Detective" was designed to compare EST collections from under-sequenced genomes against the complete genome sequences of reference organisms. The Gene Pair Detective program implements blastn to populate an M x N matrix, and a bipartite matching algorithm defines the best match. The program exploits the possibility of locally conserved gene order between subject and reference genomes to predict ESTs from the query (subject) set that may reside adjacent to one another in the subject genome.