Transposons in a maize population

Transposable elements (TEs), or "jumping genes", are hard to study due to their repetitive nature and abundance in crop genomes. However, when using short-read sequencing data to characterize them in a population, things get even more complicated. In this study, we implemented a machine learning method to classify the presence and absence of TEs in a diversity panel with 500+ maize lines. By calculating linkage disequilibrium (LD) between TEs and single nucleotide polymorphisms (SNPs), I observed that nearly 20% of the TEs were not being tagged by an SNP. This result potentially represents information that has not been well captured in previous SNP-based marker-trait association studies. The step-by-step of the LD analysis can be found on this Github page.