Tristan J. Hayeck

PRIME is a Bayesian model that improves estimation of sub-genic intolerance by sharing information across regions, identifying disease-critical protein regions and enhancing variant interpretation.

The paper develops MoDIFI, a Bayesian framework to prioritize cell lines and regulatory regions for testing noncoding variants associated with neurodevelopmental disorders.

This paper introduces CATMINT, a method to estimate gene-level intolerance across variant categories, showing it outperforms existing metrics and reveals category-specific intolerance patterns in disease genes.

This study shows that adjusting for ancestry improves genome-wide estimates of regional intolerance by incorporating demographic history, leading to better variant classification.

The paper develops a Bayesian method to detect balancing selection across different sequencing technologies and identifies novel genes under selection in complex genomic regions.

Whole-genome sequencing in thousands of families reveals variants relevant to simplex autism in a brain enhancer of the well-established neurodevelopmental disorder gene EBF3.

The paper improves pathogenic variant localization by using a hierarchical Bayesian model to estimate sub-regional intolerance, showing better performance in identifying ClinVar pathogenic variants and improving classification of de novo missense variants.