Josyf (Joe) C. Mychaleckyj

About

Josyf (Joe) C. Mychaleckyj is a Professor in the Department of Genome Sciences at the University of Virginia School of Medicine. His research focuses on the application of statistical, bioinformatic, and genomic methods to analyze large data sets emerging from human genetic susceptibility mapping projects. His work encompasses genetic epidemiology, the development of new statistical methods, and multi-dimensional data integration, with a particular emphasis on complex disease genetics such as diabetes and diabetic nephropathy. During the last 15 years, his research has involved mapping and cloning susceptibility genes for various forms of diabetes, diabetic nephropathy, and end-stage kidney disease. He has also contributed to diverse areas including malnutrition, prostate cancer, stem cells, host-pathogen interactions, and mouse pheromone memory. His projects include collaborations with the Joslin Diabetes Center on genetic factors influencing diabetic nephropathy, analysis for the Multi-ethnic Study of Atherosclerosis, and studies funded by the Bill and Melinda Gates Foundation on malnutrition and infectious disease risk factors. Additionally, he has worked on genetic risk factors underlying ischemic and recurrent stroke, utilizing high-throughput analysis and genomic data to advance understanding of complex phenotypes.

Research topics

• Genetics
• Biology
• Computational biology
• Computer Science
• Demography
• Endocrinology
• Internal medicine
• Medicine
• Evolutionary biology
• Bioinformatics

Selected publications

• Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

  Nature · 2021 · 2261 citations

  . In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These…

  DOI

• Inherited causes of clonal haematopoiesis in 97,691 whole genomes

  Nature · 2020 · 726 citations

  DOI

• Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data

  Nature Genetics · 2022 · 354 citations

  DOI

• De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population

  Proceedings of the National Academy of Sciences · 2020 · 110 citations

  ), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.

  DOI

• Whole genome sequence analysis of blood lipid levels in >66,000 individuals

  Nature Communications · 2022 · 73 citations

  Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants…

  DOI

Frequent coauthors

• Stephen S. Rich

  242 shared

• Jerome I. Rotter

  UCLA Medical Center

  226 shared

• Bruce M. Psaty

  180 shared

• Yongmei Liu

  Duke University

  177 shared

• Russell P. Tracy

  University of Vermont

  145 shared

• Xiuqing Guo

  127 shared

• Kent D. Taylor

  UCLA Medical Center

  125 shared

• Michèle M. Sale

  Modibbo Adama University of Technology

  123 shared

Similar researchers at University of Virginia

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