Daniel J. Balick

The paper analyzes the distribution of simple repeat lengths across mammalian genomes and shows that long repeats are abundant due to mutational dynamics, not natural selection.

The paper uses primate genetic data to predict human variant pathogenicity with a deep learning classifier trained on 4.3 million common missense variants.

This paper develops a field theory framework for non-equilibrium population genetics under strong selection, providing closed-form approximations for allele frequency dynamics in various population size scenarios.

The study finds that genes with higher non-additive variance in expression show stronger purifying selection and lower synonymous variation in Arabidopsis lyrata.

This study develops a method to detect recessive purifying selection in genes using population genetics simulations and applies it to empirical gene sets, finding significant enrichments for recessive selection in specific gene categories.

This paper introduces MOI-Pred, a tool for predicting recessive inheritance of missense variants, showing high accuracy in distinguishing recessive disease-causing variants.

The paper calculates a deleterious load score using purifying selection weights and finds strong associations with 27 medical traits in the UK Biobank.

The paper assesses the applicability of the mutation-selection balance model to human protein-truncating variants using simulations and data from exome datasets.

The paper quantifies frequency-dependent genetic architectures and negative selection effects across 25 UK Biobank traits using a statistical model and imputed genotypes.

The paper estimates genome-wide selection coefficients for heterozygous protein truncating variants using exome data from 60,706 individuals and identifies genes under strong selection.

The paper introduces a statistical test to infer recessive selection by analyzing the response to population bottlenecks and re-expansion.