C. Kachulis

The paper presents a large reference panel of 515,579 genomes to improve rare-variant imputation across underrepresented populations, enhancing accuracy for variants with frequencies as low as 0.002%.

Celeste provides a scalable cloud infrastructure for population-scale sequencing projects, including a variant-calling pipeline optimized for clinical variant detection.

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

The paper introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

The paper compares ancestry calibration methods for a colorectal cancer polygenic risk score using All of Us data, finding that using admixture and diverse training sets improves normality and tail accuracy.

The paper selects and validates ten polygenic risk scores for clinical use in diverse populations, focusing on chronic diseases and addressing disparities in predictive performance.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This paper develops a genome-wide polygenic score for chronic kidney disease that incorporates APOL1 risk genotypes and shows consistent performance across four major continental ancestries.

The paper describes the process of obtaining an FDA IDE for returning genomic results via WGS in the All of Us Research Program and presents validation results.