Genomic Selection Model Development

SNP Panel Development and Validation

Interns will work on identifying, validating, and optimizing Single Nucleotide Polymorphism (SNP) panels for crop species. This involves analyzing genomic datasets, filtering markers based on allele frequency and linkage disequilibrium, and conducting cross-validation studies to ensure panel reliability for breeding applications.

Genomic Prediction Model Building

Interns will develop and train machine learning models (GBLUP, Ridge Regression, Random Forest) to predict complex agronomic traits from genomic data. They will work with training datasets, optimize model parameters, and evaluate predictive accuracy across different crop populations and environments.

Linkage Disequilibrium and Population Structure Analysis

Interns will analyze linkage disequilibrium patterns and population genetic structure using whole genome sequencing or SNP array data. This work includes creating LD decay plots, identifying marker blocks, and assessing how population stratification affects genomic selection accuracy.

Genomic Selection Pipeline Optimization

Interns will design and optimize bioinformatic workflows for processing raw genomic data through quality control, imputation, and prediction stages. They will develop scripts in R or Python to streamline large-scale genomic data analysis and improve pipeline efficiency for breeding programs.

Trait Heritability Estimation and QTL Fine-Mapping

Interns will estimate genomic heritability for agronomic traits and perform quantitative trait loci (QTL) fine-mapping using association studies and genomic data. This includes variance component analysis, identifying significant genomic regions, and validating findings across diverse germplasm collections.