David Steinsaltz

The paper introduces LOCATER, a genealogy-based method that improves trait association power by leveraging haplotype diversity and allelic heterogeneity, identifying novel associations and recovering known loci in a Finnish cohort.

The paper introduces a Python package for applying Gaussian Process and Approximate Bayesian Computation to Integral Projection Models, demonstrated with a Soay sheep case study.

The paper introduces LOCATER, a method to test genealogical trees for association with traits, showing improved power over traditional methods in detecting allelic heterogeneity.

This paper introduces ABC GP IPM, a flexible IPM framework using Gaussian Processes and Approximate Bayesian Computation to improve demographic modeling across species.

The paper introduces an improved ABC-PMC method for high-dimensional modular models by leveraging precomputed MCMC samples and adaptive summary statistics, significantly reducing computational cost.

The paper introduces vsdistill, a method for improving power in genomics by distilling variant sets, and demonstrates its effectiveness through simulations and UK Biobank analysis.

The paper discusses the interpretation of negative heritability estimates and suggests they may reflect real biological features rather than just statistical noise.

The paper derives statistical properties of random-effects models for genetic heritability, showing small bias and exploring model behavior under violated assumptions.