Morris A. Swertz

The paper evaluates how natural genetic diversity affects the thermodynamic impact of pathogenic variants in small human proteins, showing significant background-dependent stability changes in most tested variants.

DAVE uses explainable AI to interpret missense variants by integrating biophysically grounded features, improving variant classification and interpretability for genome diagnostics.

Del2Phen is a software tool that generates phenotype descriptions for chromosome deletions by matching patients to genotypically similar individuals and using gene-phenotype relationships.

The paper develops interpretable neural networks for multi-omics data to predict smoking status, age, and LDL levels, showing improved performance and generalizability across cohorts.

This study develops multi-ancestry polygenic risk scores for venous thromboembolism using diverse GWAS data and shows improved predictive performance across European- and African-ancestry populations.

This paper introduces SBayesRC, a method that improves polygenic prediction of complex traits by integrating GWAS summary statistics with functional genomic annotations, showing significant accuracy gains across ancestries.

The paper describes a unified data infrastructure for rare disease research, enabling collaborative analysis of multi-omics data across institutions.

The paper identifies shorter QRS duration in asymptomatic PLN c.40_42delAGA carriers and links it to genetic sensitivity in symptomatic carriers.

The paper analyzes the phenotypic spectrum of terminal and subterminal 6p deletions using a social media-derived cohort and literature review, identifying common and subgroup-specific features.

This study compares parent-reported phenotype data on chromosome 6 disorders to medical file data and literature, finding high consistency and greater data availability through parent reports.

The paper identifies co-expression QTLs (co-eQTLs) using single-cell RNA-seq data, revealing how genetic variants affect gene co-expression patterns and regulatory networks in specific cell types.

The paper identifies host genetic loci associated with gut microbiome composition and function in a large cohort, finding significant associations near LCT and ABO genes.

The paper integrates DNA methylation and gene expression networks to improve prediction of functional gene-gene associations using eQTMs and a novel integration pipeline.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

The paper uses allele-specific expression analysis in a large cohort to identify high-impact, autosomal-dominant variants that are compensated by imbalanced expression.

CAPICE is a machine learning method that prioritizes pathogenic variants in exomes, outperforming existing tools for rare and ultra-rare variants.

The paper introduces Decon2, a method to deconvolute bulk blood eQTL effects into immune cell subpopulations, showing high concordance with experimental and single-cell studies.

This paper introduces GADO, a tool that uses gene expression data to predict gene-phenotype associations and improve diagnostic yield in exome sequencing by prioritizing variants in patients with unknown genetic diagnoses.

The paper identifies 49 novel blood lipid loci associated with gene-sleep interactions in 126,926 individuals across 5 ancestry groups, with gene-short-sleep interactions explaining 4.25% of triglyceride variance.

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

The paper identifies 49 gene drivers of downstream transcriptional changes using population genomics data and RNA-sequencing from 3,072 individuals.

The paper uses single-cell RNA sequencing to identify cell-type specific cis-eQTLs in peripheral blood mononuclear cells and validates them against whole blood RNA-seq data.

The paper identifies trans-meQTLs linking disease variants to methylation changes at transcription factor binding sites, revealing eight-fold more downstream effects than trans-eQTLs.

The paper identifies genetic variants affecting gene expression using genotypes derived from public RNA-seq data, finding cis-eQTLs for 8,034 genes and strong ASE effects for 34 rare pathogenic variants.