P0203 Genome-wide Assessment of SNPs in Peanut Using Illumina (Solexa) Sequencing

Ratan Chopra , Texas Tech University, Lubbock, TX
Gloria Burow , ARS USDA, Lubbock, TX
Andrew Farmer , National Center for Genome Resources, Santa Fe, NM
Joann Mudge , National Center for Genome Resources, Santa Fe, NM
Ingrid Lindquist , National Center for Genome Resources, Santa Fe, NM
Gregory D. May , National Center for Genome Resources, Santa Fe, NM
Micheal Selvaraj Gomez , CIAT, Cali, Colombia
Zhanguo Xin , ARS USDA, Lubbock, TX
Charles Simpson , Texas AgriLife Research, Stephenville, TX
Naveen Puppala , New Mexico State University
Kelly D Chamberlin , USDA-ARS, Stillwater, OK
Thea Wilkins , Texas Tech University, Lubbock, TX
Mark D. Burow , Texas AgriLife Research, Lubbock, TX
Single nucleotide polymorphisms (SNP) are a ubiquitous type of genetic variation in eukaryotic genomes. SNPs are ideally suited for the construction of high-resolution genetic maps, investigation of population evolutionary history and discovery of marker–trait associations in association mapping experiments. In order to carry out genome-wide association studies using SNPs, we extracted total RNA from leaf, root and immature pod (yellow stage of development) tissue of greenhouse grown plants of four peanut cultivars namely OLin, NmValC, TamOLO7, and Jupiter. Total RNA was used for generating transcriptome sequence using next generation Solexa technologyon an Illumina sequencer. Sequences were aligned to a reference of 46,813 contigs, which was generated by combining of ESTs and Transcriptome Shotgun Assembly of Arachis hypogaea available at NCBI, removing the redundant contigs. SNPs and indels were analyzed with Alpheus software (NCGR) using SNP calling filters (allele frequency) >10% and >2 uniquely aligning reads with average quality scores ≥ 20%. A total of 65% of reads were aligned to reference, of which 55% were uniquely aligned and ca. 0.3 million SNP or indels were found in 36,102 contigs. From 4370 to 6922 SNPs distinguishing pairwise combination of the cultivars were found. In the future we will draw the consensus between the cultivar data and narrow down the amount of varaints using more stringent parameters and use these SNPs for developing a SNP chip assay for assessing the genetic resources. We expect to use this assay in breeding populations for QTL studies for biotic and abiotic stress response.