Oscar E. Gaggiotti

The paper introduces MONET, a neutral evolution model for traits, and presents two tests to distinguish selection from neutral differentiation in quantitative traits.

This paper demonstrates the feasibility of generating whole genomes from humpback whale blow samples for population genomics.

The paper studies the diffusion of cooperative and solo bubble net feeding in humpback whales, finding strong evidence for social learning in the behavior's spread.

The paper identifies convergent molecular signals associated with viviparity in squamate reptiles, showing gene-level and site-level positive selection but no universal convergence across all clades.

The paper introduces a method to detect spatially divergent selection by incorporating population structure into genetic variance comparisons, showing improved performance over traditional methods.

The paper examines genomic adaptation in Hawaiian crickets following the introduction of a parasitoid fly, finding population size declines and local adaptation through male-silencing alleles.

This paper introduces GENEPAIR, a DNN interpretation framework for detecting gene-gene interactions in genomic data, demonstrating its ability to uncover both known and novel gene interactions related to BMI.

This paper introduces DeepGenomeScan, a deep learning framework that improves detection of genomic regions under natural selection, outperforming traditional methods in identifying loci associated with complex selection patterns.

KLFDAPC improves geographic origin prediction by enhancing population separability and accuracy compared to PCA and DAPC.

The paper assesses information-based summary statistics for spatial genetic structure inference using simulated data and machine learning methods, finding that q=1 diversity metrics and Shannon differentiation measures are most effective.