Teaching Gene Discovery and Population Genomics Through Authentic Analysis of Large-Scale Sequencing Datasets

Abstract

The increasing availability of affordable large-scale sequencing data has created new opportunities for undergraduate instruction that closely mirror contemporary genomics research. This paper describes classroom-tested teaching modules that introduce students to gene discovery and population genomics through authentic analysis of large public sequencing datasets. Using data from the Genome Aggregation Database (gnomAD) and the 1000 Genomes Project, students investigate the genetic basis of colour-blindness, population-specific adaptation, and complex evolutionary responses associated with the introduction of maize. The modules emphasize statistical signals of selection and association, guiding students to identify candidate loci and interpret genotype–phenotype relationships within diverse human populations. Analyses are conducted using DryLab workflows implemented on a cloud-computing platform, enabling accessibility across varying skill levels and institutional resources. Integration of Writing-to-Learn and Data-Intensive Science pedagogies supports conceptual understanding and critical interpretation of population-genomic data. By leveraging public datasets and authentic analytical frameworks, this approach enhances student engagement, strengthens data literacy, and provides a scalable model for teaching gene discovery and population genomics in undergraduate life-science education.