Wei‐Qi Wei

This paper introduces mixWAS, a one-shot, lossless algorithm for integrating distributed EHR datasets to identify cross-cohort genetic associations across multiple outcomes.

The paper develops a pipeline for integrated breast cancer risk assessment using polygenic risk scores and other factors in the eMERGE study, showing high concordance between API and UI results.

The paper uses a polygenic risk score for colorectal cancer in a PheWAS to find genetic correlations with other phenotypes from electronic health records.

The paper examines how covariates affect polygenic score performance across European and African cohorts, finding significant differences in R2 and interaction effects, and proposes methods to improve model performance.

The paper shows the APOL1 p.N264K variant reduces the risk of FSGS and kidney disease in individuals with G2 APOL1 risk alleles.

This paper introduces PhenoPLIER, a method that maps gene-trait and drug associations into gene modules to reveal disease mechanisms and drug targets.

This study investigates the clinical consequences of a genetic predisposition to lower white blood cell counts using a polygenic score and finds associations with bone marrow biopsy outcomes and treatment responses.

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 study investigates the association between APOL1 risk variants and sepsis occurrence in Black patients with infections, finding that the increased risk is largely due to pre-existing severe renal disease.

This study evaluates how ancestry, GWAS sample size, and LD reference panels affect BMI PRS performance across different age groups and ancestries.

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 introduces polygenic risk vectors (PRV) to improve genetic risk stratification for cardio-metabolic diseases by combining additive and dominant genetic effects, outperforming traditional polygenic risk scores.

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

The paper uses multi-ancestry biobank data and integrative methods to identify gene-disease associations across different ancestry groups, finding shared and ancestry-specific genetic links.

Neptune delivers structured genomic data to EHR systems through customizable reports and variant reanalysis, supporting over 15,000 clinical reports.

This paper describes the implementation of HL7 FHIR standards for representing clinical genomics results in the eMERGE Networks, improving interoperability for genetic data management.

The paper identifies HLA-B*55:01 as a genetic risk factor for penicillin allergy through genome-wide analysis of electronic health records and imputed HLA data.

The paper identifies a missense variant in the MARC1 gene associated with protection against cirrhosis and lower liver enzyme and cholesterol levels.

The paper develops and evaluates mappings from ICD-10 and ICD-10-CM codes to PheCodes, showing high coverage and reproducibility for chronic diseases.

The paper uses genetic variants to investigate side effects and repurposing potential of antihypertensive drugs, finding a link between calcium channel blockers and increased diverticulosis risk.

The paper uses non-negative matrix factorization to analyze electronic health records and identify associations between a genetic variant in LPA and disease topics, finding positive and negative correlations with cardiovascular diseases and lung cancer.

This paper introduces SAIGE, a method that improves the accuracy of genetic association tests in unbalanced case-control studies by using saddlepoint approximation, and demonstrates its effectiveness on UK Biobank data.