Next Generation Resequencing of 108 Soybean Germplasm for Trait Discovery

With the advances in next generation sequencing (NGS) technology and significant reduction in sequencing costs it is now possible to sequence large sets of crop germplasm and generate whole genome scale structural variations and genotypic data. In depth informatics analysis of the genotypic data can provide better understanding of the links with the observed phenotypic changes. This approach can be used to further understand and study different traits for the improvement of crops by design.

We have conducted resequencing of 108 soybean germplasm lines selected for major traits including oil, protein, soybean cyst nematode resistance (SCN), abiotic stress resistance (drought, heat and salt) and root system architecture. We have conducted initial bioinformatics analysis of the NGS data from these 108 lines. The analysis identified SNPs and insertion, deletions by comparisons against the soybean reference genome, Williams 82 using GATK, GSNAP and Samtools software. We have also conducted structural variation analysis in terms of copy number variations (CNV) using CNV-seq software. As a case study, we have selected 25 out of the 108 resequenced genomes for the in depth analysis of SCN resistance and further classified them into four different categories of resistance and susceptibility levels. The GWAS analysis helped identify major SNPs associated with the phenotypic changes between these lines. We have performed analysis using Haploview for linkage disequilibrium, haplotype identification and Cladogram tree generation. We have also applied generalized linear models (GLM) and mixed linear models (MLM) using TASSEL for identifying SNPs significant for phenotypic changes between the various resistance and susceptibility categories of this trait.

The informatics analysis results help set a strong foundation for handling and analysis of subsequent large scale resequencing efforts in future. The NGS resequencing data represents a rich source of information and can lead to significant discoveries when it comes to mining genotypic data for phenotypic inferences. All data including GWAS, SNP and genome structure information generated from this resequencing project can be accessed through Soybean Knowledge Base (SoyKB) at http://soykb.org.