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Major soybean maturity gene haplotypes revealed by SNPViz analysis of 72 sequenced sobyean genomes

Tiffany Langewisch, Hongxin Zhang, Ryan Vincent, Trupti Joshi, Dong Xu, Kristin Bilyeu
PLoS ONE 2014 v.9 no.4 pp. e94150
Glycine max, Glycine soja, alleles, breeding, computer software, cultivars, data collection, flowering, genetic variation, genomics, haplotypes, landraces, nucleotide sequences, phenology, plant architecture, plant growth, single nucleotide polymorphism, soybeans, wild relatives
In this Genomics Era, vast amounts of next generation sequencing data have become publicly-available for multiple genomes across hundreds of species. Analysis of these large-scale datasets can become cumbersome, especially when comparing nucleotide polymorphisms across many samples within a dataset and among different datasets or organisms. To facilitate the exploration of allelic haplotypes, we have developed and deployed computer software to categorize and visualize these haplotypes. The SNPViz software enables analysis of whole genome sequence SNP datasets for haplotypes of user-defined gene regions for different sequenced genomes. The examination of allelic variation and diversity of important soybean [Glycine max (L.) Merr.] flowering time and maturity genes may provide additional insight into flowering time regulation and enhance researchers’ ability to target soybean breeding for particular environments. For this study, we utilized two available soybean genomic datasets for a total of seventy-two soybean genotypes encompassing cultivars, landraces, and the wild species Glycine soja. The major soybean maturity genes E1, E2, and E3 along with the Dt1 gene for plant growth architecture were analyzed in an effort to determine the number of major haplotypes for each gene, the consistency of the allele haplotypes with characterized variant alleles, and for evidence for genetic bottlenecks or adaptive selection. The results indicated classification of a small number of predominant allele haplotypes for each gene and important insights into possible genetic bottlenecks and diversity of alleles for each gene within the context of known causative mutations. The software can be used to analyze other genes, with additional soybean datasets, or it can be used with similar genome sequence SNP datasets from other species.