Cristopher V. Van Hout

The paper identifies SLC39A5 as a gene whose loss-of-function variants are associated with reduced type II diabetes risk and improved liver function in metabolic disorders.

The paper identifies a BRCA1 founder variant in Orcadians and estimates its high prevalence, suggesting population screening for this variant.

The paper describes exome sequencing of 200,643 UK Biobank participants, providing a rich resource of coding variants for genetic and drug discovery research.

This study identifies parent-of-origin specific genetic associations with clinical traits from EHR in a large population study, finding that PoO effects are often captured by standard models but remain underpowered to detect.

The paper was withdrawn due to lack of full author consent.

The paper identifies recessive NOD2 inheritance as a major driver of early onset Crohn’s Disease in both pediatric and adult cohorts.

This paper develops a statistical framework to detect gene-environment interactions by focusing on variance-associated genetic variants, finding higher discovery rates for BMI and disease-related traits.

The paper performs exome-wide association studies in large cohorts to identify genetic variants linked to human age, finding new candidates and confirming known ones.

This study uses whole exome sequencing in 6,716 American Indians to identify metabolic trait-associated variants, finding unique pLOF and nonsynonymous variants linked to BMI, diabetes, and lipid levels.

U-PASS provides a tool for power analysis and validity assessment of genetic association studies for binary traits, visualizing the trade-off between risk allele frequency and odds ratio.

The paper identifies a missense variant in B4GALT1 associated with lower LDL-C and fibrinogen, supported by genetic, functional, and glycan profiling evidence.

Exomes of 49,960 UK Biobank participants reveal 231,631 predicted loss-of-function variants (>10-fold more than imputed data); burden tests implicate PIEZO1, MEPE, and COL6A1 in disease traits.

The paper analyzes cryptic relatedness in a large exome cohort, identifies numerous close relationships, reconstructs pedigrees, and demonstrates their utility in improving mutation phasing and identifying de novo mutations.