Sebastian Ernesto Ramos-Onsins

The paper develops a theoretical framework to detect genetic signatures of rapid adaptation to extreme environmental changes using population genetics models.

The paper quantifies bias in genetic diversity and neutrality tests caused by neutral polymorphic structural variants and proposes corrected estimators.

The paper uses DEST 2.0 to analyze genetic variation in D. melanogaster across continents and time, identifying adaptation patterns and stable spatial genetic structures.

This study evaluates deep learning for predicting rice traits using structural and single-nucleotide genomic variants, finding that deep learning models outperform Bayesian models in 75% of cases.

The paper derives the conditional Site Frequency Spectrum for a genomic region linked to a focal mutation and presents formulas for neutrality tests in non-recombining regions.

The paper decomposes the site frequency spectrum into components of tree topology and waiting times, showing their impact on neutrality tests like Tajima's D and Fay and Wu's H.