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

• Cross-ancestry comparison of aptamer and antibody protein measures

  UNC Libraries · 2026-03-17

  articleOpen access
  PublisherDOI

• Cross-ancestry comparison of aptamer and antibody protein measures

  Nature Communications · 2026-01-22 · 2 citations

  articleOpen access

  Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants and phenotypes. Here, we examine 2157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2157 proteins follow a bimodal distribution (median r = 0.30). We evaluate protein measure associations with genetic variants, and find approximately 25-30% of the signals on each platform are likely driven by protein-altering variants. We highlight 80 proteins that correlate differently across ancestry groups likely in part due to differing protein-altering variant frequencies by ancestry. Furthermore, adjustment for protein-altering variants with opposite directions of effect by platform improves inter-platform protein measure correlation and results in more concordant genetic and phenotypic associations. Hence, protein-altering variants need to be accounted for across ancestries to facilitate platform-concordant and accurate protein measurement. Affinity-proteomics platforms often yield poorly correlated measurements. Here, the authors show that protein-altering variants drive a portion of inter-platform inconsistency and that accounting for genetic variants can improve concordance of protein measures and phenotypic associations across ancestries.

  PublisherOA PDFDOI

• Differences in heart DNA methylation between sudden infant death syndrome and other sudden deaths

  Clinical Epigenetics · 2026-03-18

  articleOpen access

  BACKGROUND: Sudden Infant Death Syndrome (SIDS) remains a leading cause of infant mortality worldwide, yet its underlying mechanisms are largely unknown. SIDS etiology is complex and involves the interplay between a vulnerable infant, a critical developmental period, and external stressors. Cardiac function, including cardiorespiratory autonomic control, is considered a major contributor to SIDS. Emerging evidence suggests that epigenetic modifications of heart tissue, particularly DNA methylation (DNAm), may contribute to SIDS risk. We conducted an epigenome-wide association study (EWAS) to determine if DNA methylation patterns of post-mortem heart tissue differ between SIDS cases and non-SIDS controls, and to evaluate the role of DNAm in timing of SIDS events. METHODS: Using a case-control study design, we generated DNA methylation data using Illumina EPIC Methylation V2 arrays and performed single site EWAS, differentially methylated region (DMR), and epigenome network cluster analyses to assess differential DNA methylation between SIDS cases and non-SIDS controls. Post-mortem heart tissues from 324 infants (202 SIDS cases) were obtained from the Chicago Infant Mortality Study (CIMS) and the NIH NeuroBioBank (NBB). Inclusion criteria required SIDS cases to meet standardized SIDS definitions, as determined from a through case investigation (i.e., autopsy, death scene examination, and clinical history). RESULTS: Seven CpG sites were differentially methylated between SIDS cases and non-SIDS control (adjusted (adj.) p < 0.05). Two were located within DTNB (cg1703363; adj. p = 0.02) and NDUFS8 (cg12070987; adj. p = 0.02). There were two differentially methylated regions overlapping the KRTCAP3 and RUNX3 genes. Of the top ten most significant CpG-SIDS diagnosis interactions, two of them were located within mitochondrial genes, ACSL1 and AK2. CONCLUSIONS: This study identified differentially methylated CpG sites and regions from heart tissue in SIDS cases versus controls, notably in DTNB, NDUFS8, KRTCAP3, and RUNX3. Our findings highlight pathways enriched for mitochondrial function, immune cell regulation, and cardiac dysfunction, which indicate the heterogenous nature of SIDS and supports the need for larger multi-tissue collaborative studies to identify SIDS subtypes, underlying pathologies, and determine the causal nature of such findings.

  PublisherDOI

• Alterations in DNA Methylation, Proteomic, and Metabolomic Profiles in African Ancestry Populations with APOL1 Risk Alleles

  Journal of the American Society of Nephrology · 2025-04-07 · 1 citations

  articleOpen access

  Key Points We aimed to elucidate potential methylation, proteomic, and metabolomic mechanisms by which APOL1 variants may be linked to kidney disease. We report distinct methylation profiling between APOL1 risk allele carriers and noncarriers, many near APOL gene family. We report higher APOL1 protein and lower C18:1 cholesteryl ester in two risk allele carriers. Background The APOL1 high-risk haplotype has been associated with CKD and the deterioration of kidney function, particularly in populations with West African ancestry. However, the mechanisms by which APOL1 risk variants increase the risk for kidney disease and its progression have not been fully elucidated. Methods We compared methylation ( N =3191; 715 [22%] carriers), proteomic ( N =1240; 169 [14%] carriers), and metabolomic ( N =6309; 674 [11%] carriers) profiles in African and Hispanic/Latino carriers of two APOL1 high-risk alleles (G1/G1, G2/G2, G1/G2) and noncarriers (G0/G0), excluding heterozygotes (G0/G1, G0/G2), from the Population Architecture using Genomics and Epidemiology Consortium and UK Biobank. In each study, the associations between the APOL1 high-risk haplotype and up to 722,719 cytosine-phosphate-guanine (CpG) sites, 2923 proteins, or 836 metabolites were estimated using covariate-adjusted linear regression models, followed by fixed-effects sample size–weighted meta-analyses. Results Significant associations were observed between APOL1 high-risk haplotype and methylation at 52 CpG sites, with 48 located on chromosome 22 and 18 in the vicinity of APOL1–4 and MYH9 . All significant CpG sites near APOL2 were hypomethylated, whereas those near APOL3 and APOL4 were hypermethylated. APOL1 -associated CpG sites were also identified in genes involved in ion transport and mitochondrial stress pathways. Sensitivity analyses indicated consistent yet attenuated effects among heterozygotes, supporting an additive effect of APOL1 risk alleles. Further analyses of the 52 CpG sites identified two near APOL4 exhibiting G1-specific effects, eight associated with CKD but none with eGFR, and three showing heterogeneity by CKD status. In addition, carrying two APOL1 risk alleles was associated with higher plasma APOL1 protein ( β =1.12, P FDR = 2.26e-70) and lower C18:1 cholesteryl ester metabolite (Z=−4.50, P FDR = 4.83e-3). Conclusions Our results demonstrate differential methylation, proteomic, and metabolomic profiles associated with APOL1 high-risk haplotypes.

  PublisherOA PDFDOI

• Exome-Wide Analysis Identifies a Rare EXD3 Missense Variant Associated With Diabetic Kidney Disease

  Kidney International Reports · 2025-10-23 · 2 citations

  articleOpen access

  Introduction Diabetic kidney disease (DKD) is a major complication of diabetes, with genetic factors contributing to its progression.While genome-wide association studies have identified common variants, the role of low-frequency and rare coding variants remains underexplored. MethodsWe performed exome-wide meta-analysis of up to 10,312 individuals with type 1 diabetes (T1D) genotyped using genome arrays with focused exome content.We included ten DKD definitions based on albuminuria, eGFR, or both.We analyzed non-synonymous variants individually, and using gene-level analyses for low-frequency (minor allele frequency <5%) and rare (<1%) variants.Replication was performed in 10,066 participants with T1D and in UK Biobank participants with type 2 diabetes.Gene expression was assessed in cultured human podocytes. ResultsIn addition to the known COL4A3 variant, a novel rare missense variant in EXD3 (p.Asp555Asn, rs200080727, MAF=0.4%) was associated with DKD (OR=8.7,p=4.510 -9 ).The variant was predicted to be deleterious and EXD3 was downregulated in DKD in kidney expression datasets.EXD3 knockdown in a cultured human podocyte cell line reduced nephrin gene expression, suggesting a functional role in podocyte biology.Gene-level analyses identified seven DKD-associated genes (p<3.410 -6 ), including MUC5B, which harbored multiple low-frequency missense variants and with evidence of replication.Replication in UK Biobank supported the association of EXD3 rs200080727 with albuminuria (p=0.014). ConclusionThis study identified a rare EXD3 variant with a strong effect on DKD risk in T1D.Functional data support a role for EXD3 in podocyte integrity and DKD pathogenesis.However, further functional investigations are necessary to understand the underlying molecular mechanisms.

  PublisherOA PDFDOI

• cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions

  Nature Methods · 2025-12-31

  article
  PublisherDOI

• cellSTAAR: Incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of non-coding regions

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-26 · 2 citations

  preprintOpen access

  Abstract Whole genome sequencing (WGS) studies have identified hundreds of millions of rare variants (RVs) and have enabled RV association tests (RVATs) of these variants with complex traits and diseases. Analysis of non-coding variants is challenged by the considerable variability in regulatory function which candidate Cis-Regulatory Elements (cCREs) exhibit across cell types. We propose cellSTAAR, which integrates WGS data with single-cell ATAC-seq data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and variant sets. To reflect the uncertainty in cCRE-gene linking, cellSTAAR also links cCREs to their target genes using an omnibus framework which aggregates results from a variety of popular linking approaches. We applied cellSTAAR on Freeze 8 (N = 60,000) of the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium data to four lipids phenotypes: LDL cholesterol, a binary variable corresponding to high LDL cholesterol, HDL cholesterol, and triglycerides. We also provide replication results for all four phenotypes using UK Biobank (N = 190,000). Evidence from simulation studies and our real data analysis demonstrates that cellSTAAR boosts power and improves interpretation of RVATs of cCREs.

  PublisherOA PDFDOI

• Cross-Ancestry Comparison of Aptamer and Antibody Proteomics Measures

  Research Square · 2025-02-12 · 1 citations

  preprintOpen access
  PublisherOA PDFDOI

• Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood

  UNC Libraries · 2025-10-22

  articleOpen access
  PublisherDOI

• Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood

  The American Journal of Human Genetics · 2025-01-13 · 9 citations

  articleOpen access
  PublisherOA PDFDOI

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