A. D. Kern

Estimation of spatial demographic maps from polymorphism data using a neural network

This paper introduces a neural network method to estimate spatial demographic maps from SNP data, showing it can infer population density and dispersal rates across landscapes.

Cephalopod Sex Determination and its Ancient Evolutionary Origin

This study presents the first chromosome-scale genome of a female California two-spot octopus, revealing a ZO sex chromosome system and demonstrating the ancient origin of the sex chromosome in cephalopods.

A Quantitative Genetic Model of Background Selection in Humans

This paper develops a quantitative genetic model to estimate purifying selection parameters from linked selection in humans, finding strong selection across samples but uncertainty in predicting divergence levels.

Evaluating evidence for co-geography in the Anopheles Plasmodium host-parasite system

The paper evaluates whether spatial genetic correlation between Anopheles gambiae and Plasmodium falciparum indicates co-dispersal or spatial sampling bias.

Standing genetic variation and chromosome differences drove rapid ecotype formation in a major malaria vector

The paper uses whole-genome sequencing to show that standing genetic variation and chromosomal differences drove rapid ecotype formation in Anopheles funestus, a malaria vector, adapting to different habitats.

Shared evolutionary processes shape landscapes of genomic variation in the great apes

This study shows strong correlations between genetic diversity and divergence in great apes, suggesting evolutionary processes like selection and mutation rate variation play a key role.

Dispersal inference from population genetic variation using a convolutional neural network

This paper introduces disperseNN, a convolutional neural network that estimates dispersal distance from genetic data without requiring geographic coordinates, outperforming existing methods in simulations and applied to 12 species.

Discovery of ongoing selective sweeps within Anopheles mosquito populations using deep learning

This paper introduces a deep learning method to detect ongoing selective sweeps in mosquito populations, finding strong overrepresentation of sweeps at insecticide resistance loci.

Evaluating Human Autosomal Loci for Sexually Antagonistic Viability Selection in Two Large Biobanks

The paper evaluates evidence for sexually antagonistic viability selection on autosomal loci in humans using two large biobanks but finds no significant replication of associations, suggesting limited ongoing selection on single locus variation.

A community-maintained standard library of population genetic models

The paper introduces stdpopsim, a community-maintained library for population genetic models, to standardize simulations and facilitate benchmarking of inference tools.

Inferring the landscape of recombination using recurrent neural networks

This paper introduces ReLERNN, a deep learning method for estimating genome-wide recombination maps with high accuracy, and applies it to Drosophila melanogaster populations.

diploS/HIC: an updated approach to classifying selective sweeps

The paper introduces diploS/HIC, a deep learning method that classifies selective sweeps using unphased genotypes, showing effectiveness at moderate sample sizes.

Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia

This paper introduces FILET, a supervised machine learning method to detect introgressed loci between Drosophila species, finding significant introgression from D. simulans to D. sechellia.

Machine Learning for Population Genetics: A New Paradigm

This paper reviews the application of supervised machine learning in population genetics, highlighting its potential to outperform traditional methods in analyzing genomic data.

Soft sweeps are the dominant mode of adaptation in the human genome

This study finds that soft sweeps, not hard sweeps, are the dominant mode of recent human adaptation across six populations.

Exact calculation of the joint allele frequency spectrum for isolation with migration models

This paper presents a Markov chain method for exact calculation of the joint allele frequency spectrum under isolation with migration models and applies it to Drosophila melanogaster data.