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Title: Single nucleotide polymorphisms generated by genotyping by sequencing to characterize genome-wide diversity, linkage disequilibrium, and selective sweeps in cultivated watermelon

Author
item NIMMAKAYALA, P - West Virginia State University
item Levi, Amnon
item ABBURI, L - West Virginia State University
item ABBURI, V - West Virginia State University
item TOMASON, Y - West Virginia State University
item SAMINATHAN, T - West Virginia State University
item VAJJA, V - West Virginia State University
item REDDY, R - West Virginia State University
item WEHNER, T - West Virginia State University
item MITCHELL, S - West Virginia State University
item REDDY, U - West Virginia State University

Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2014
Publication Date: 9/8/2014
Publication URL: http://doi:10.1186/1471-2164-15-767
Citation: Nimmakayala, P., Levi, A., Abburi, L., Abburi, V.L., Tomason, Y.R., Saminathan, T., Vajja, V.G., Reddy, R., Wehner, T.C., Mitchell, S.E., Reddy, U.K. 2014. Single nucleotide polymorphisms generated by genotyping by sequencing to characterize genome-wide diversity, linkage disequilibrium, and selective sweeps in cultivated watermelon. Biomed Central (BMC) Genomics. 15:767.

Interpretive Summary: There is a need to evaluate watermelon varieties collected in different parts of the world to determine if they might contain valuable genes useful for enhancing watermelon fruit quality. In this study, an ARS scientist collaborated with a team of scientists at West Virginia State University and North Carolina State University using advanced genomic sequencing technologies to evaluate gene sequences of watermelon varieties collected in different parts of the world. Using the advanced methods, the scientists identified several regions of the watermelon genome that affect fruit quality traits, including watermelon fruit sweetness and flesh color and texture. The genetic information produced in this study should prove useful to seed companies, plant breeders, university scientists, and students interested in elucidating the genome of watermelon and in improving watermelon fruit quality and nutritional content using up-to-date DNA technology.

Technical Abstract: Large datasets containing single nucleotide polymorphisms (SNPs) are used to analyze genome-wide diversity in a robust collection of cultivars from representative accessions, across the world. The extent of linkage disequilibrium (LD) within a population determines the number of markers required for successful association mapping and marker-assisted selection. Here, we used genome-wide genetic diversity study, LD, selection sweep, and pairwise fixation index (FST) distribution to track domestication of worldwide cultivated watermelons. One hundred and eighty-three accessions of C. lanatus var. lanatus representing domesticated watermelon were used for genotyping by sequencing to generate a set of 11,484 single nucleotide polymorphism (SNP) markers. A set of 5,254 SNPs with minor allele frequency = 0.05 was spread across the genome. The SNP sets revealed the population structure of sweet watermelon. Sliding window analysis of pairwise FST values for measuring differentiation for the various chromosomes was used to resolve selection sweep. A strong selection for regions on chromosomes 3 and 9 were identified as associated with domestication. Linkage LD decay was estimated at the level of the whole genome using individual SNPs, as well as, with haplotype analysis. Linkage disequilibrium was also detected within individual genes on various chromosomes. Principle component and ancestry analysis were used to control population structure in genome-wide association (GWAS) study. Genes associated with soluble solids were mapped by using a mixed linear model. Information pertaining to LD, population structure and SNP resources developed in this study will be of use to mine agronomically important candidate genes from the genomic regions underlying selection in sweet watermelon and to map quantitative trait loci using GWAS mapping.