Patrick M. Abou‐Sleiman

The paper identifies epistatic SNP-SNP interactions in long-range LD across five diseases using UK Biobank and eMERGE data, finding associations involving conserved genes and essential genes.

This study uses EHR data to identify gene-phenotype associations and develop phenotype risk scores for variant interpretation in hereditary cancer genes.

The paper identifies pathogenic variants in arrhythmia genes in eMERGE-III participants and reclassifies variants of uncertain significance using functional studies.

The paper validates genetic sex using a 96-SNP PCR panel to track samples in clinical testing and identify inconsistencies.

This paper identifies a genetic variant associated with primary non-response to anti-TNFα therapy in IBD patients and links it to increased TNF secretion through autophagy mechanisms.

This study identifies 22 novel genetic loci associated with low polygenic risk score type 1 diabetes by analyzing rare variants in two independent cohorts.

This study identifies genetic correlations between COVID-19 and various diseases and traits using GWAS data, validating known risk factors and reporting novel associations.

This paper develops a machine learning model to interpret DNA variants in MODY genes using ACMG criteria, achieving over 95% accuracy for gene-specific classification.

This paper identifies novel genetic variants associated with PCOS using EHR-based GWAS and validates them in independent cohorts.

The paper investigates genetic correlations between psychiatric and immune-related phenotypes using GWAS summary data, finding significant shared genetic variants.

This study identifies pharmacogenetic variants associated with albuterol bronchodilator response in minority children with asthma using whole genome sequencing.

The paper uses PheWAS across large cohorts to identify genetic associations with drug targets and predict drug safety and efficacy.