Arthur Ko

The paper introduces Watershed-DL, a framework integrating sequence-to-omics predictions and multi-omics data to prioritize rare variants, finding it increases prioritized variants by 61.4% and identifies candidate disease alleles.

This study evaluates the diagnostic yield of long-read genome sequencing in cases where short-read sequencing failed, finding an incremental yield of 4.7% due to LR-GS's ability to detect structural variants and other complex genomic features.

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%.

Genome sequencing identifies pseudoexons as a common cause of Mendelian disease, with some variants missed by prior exome testing.

The paper develops Paraphase, a HiFi-based method to resolve highly similar paralogous genes, revealing genetic diversity and de novo events in segmental duplications.

Bisque estimates cell composition in bulk RNA-seq using single-cell data, improving accuracy and efficiency over existing methods.

This study identifies 588 SNP-metabolite associations using the METSIM cohort, revealing pleiotropy, temporal changes, and age-related genetic control shifts.

This paper introduces Reverse GWAS (RGWAS) to identify and validate phenotypic subtypes using genetics, finding three novel metabolic subtypes and showing differential treatment effects.

This study identifies genetic and environmental factors that disrupt trait correlations, showing environmental perturbations reduce correlations and genetic factors influence correlation patterns.

This paper introduces a reference-free method for generating polygenic risk scores (PRS) that avoids over-fitting and computational inefficiencies, and demonstrates applications in pleiotropy detection and mixed-model association testing.

This study identifies novel HPE genes and oligogenic inheritance patterns in holoprosencephaly by integrating clinical data with exome sequencing and gene prioritization.

The paper integrates gene expression and GWAS data to identify novel genes associated with obesity-related traits.