Kelly Swarts

The paper introduces UME, a variant calling method that reduces coverage bias in maize genomes, generating an unbiased haplotype map with over 64.5 million SNPs from 818 diverse individuals.

The paper characterizes a maize near-isogenic line population to identify introgressions and map disease resistance QTL, revealing allelic variation in traits.

This study analyzes the evolution of root phenotypes in maize during domestication by linking ancient DNA, paleobotany, and modeling to environmental changes in Tehuacan Valley.

This study analyzes genetic variation in Marchantia polymorpha subspecies ruderalis to identify climate adaptation signals, finding significant differentiation between European and Japanese populations linked to temperature and precipitation.

The paper identifies genetic heterogeneity in plants and humans using machine learning, finding allelic heterogeneity in maize eQTL and potential synthetic associations in human GWAS data.

The paper introduces Multi-GBS, a high-throughput GBS protocol for conifer genomes, and demonstrates its ability to call over a million variants across multiple populations with minimal technical bias.

This study identifies genes underlying flowering time adaptation in maize landraces by integrating genomic mapping and association analysis across multiple environments.

This study identifies independent temperate adaptations in maize flowering time across different populations using GWAS and machine learning.

The paper examines convergent adaptation in maize to highland climates, finding limited nucleotide-level convergence but evidence of selection on standing variation and introgression from wild teosinte.