(Internal) Imputation of High-Quality SNP Markers in SoyMAGIC Soybean Population Using GBS Data and Whole Genome Sequencing

Advisor: Milad Eskandari, Plant Agriculture

Proposed co-advisor: Mohsen Yoosefzadeh Najafabadi

The development of high-density genetic markers is critical for enhancing genomic selection and marker-assisted breeding in crops like soybean (Glycine max). This project focuses on the imputation of high-quality single nucleotide polymorphism (SNP) markers for a population of over 700 recombinant inbred lines (RILs) derived from an 8-parent Multiparent Advanced Generation Inter-Cross (SoyMAGIC) population. Using genotyping-by-sequencing (GBS) data, we aim to impute missing genotypes and increase the density of SNP markers by leveraging the current soybean reference genome and the whole genome sequencing data of the eight founder parents. 

The project will involve the application of various bioinformatics tools and algorithms to enhance the accuracy of imputation. By comparing these imputed datasets with the whole genome sequence of the parental lines, we aim to validate imputation accuracy and identify optimal algorithms for high-resolution mapping. The resulting imputed marker data will be used to construct a high-density genetic map, which will enable genome-wide association studies (GWAS) and the discovery of key genomic regions associated with agronomic and seed quality traits in soybean.

This is a one-semester project. The student is required to occasionally be on-site.

Knowledge/Skills

Data Handling & Processing - programming