Anurag Daware

The paper uses GWAS and machine learning to develop molecular markers and a genomic prediction framework for improving maize DUS testing using historical data.

This study identifies regulatory sequence variations that rewired gene networks, leading to differences in leaf development and photosynthesis between wild and cultivated rice.

GWAShub provides a web-based platform for conducting GWAS in crop plants, demonstrating its utility by identifying histone methyltransferase genes linked to flowering time in chickpea.

The paper develops a rice pan-genome genotyping array (RPGA) to capture genetic variation and improves GWAS by detecting novel loci and reducing reference bias.