John W. Belmont

The paper introduces the Probability of Necessity (PN) to assess genetic diagnosis, showing it differs from penetrance and can resolve variant classification ambiguity in diseases like ischemic heart disease and Parkinson's.

The paper introduces the Causal Pivot (CP) method to detect causal genetic variants in complex diseases using structural causal models and applies it to UK Biobank data for three diseases.

This paper develops a genome-wide cardiovascular disease genetic risk assessment test that includes monogenic conditions, risk alleles, pharmacogenomic variants, and polygenic risk scores, showing high performance and scalability.

The paper describes the role of the Centers for Mendelian Genomics in advancing gene discovery through data sharing, collaboration, and resource development over the past decade.

This study uses DNA methylation analysis to identify diagnostic biomarkers and pathogenic mechanisms in diabetic embryopathy.

The paper investigates 17p13.3 deletions between YWHAE/CRK and PAFAH1B1, identifying white matter lesions and hypermobility, and narrows the critical region to 345 kb with RTN4RL1 as a candidate gene.

This study evaluates copy number variant detection in clinical whole genome sequencing, finding it as sensitive as microarrays with additional variants, and identifies clinically significant CNVs in 15% of cases.

The paper identifies additional DDX3X variants in individuals with intellectual disability and expands the known phenotypic spectrum of DDX3X disorders.