Nathan Pankratz


Research Summary

Dr. Pankratz is director of the Division of Computational Pathology and a member of the Division of Molecular Pathology and Genomics who conducts research at the intersection of genetics, epidemiology, statistics, and computer science in the context of population studies. After a decade of focus on the neurogenetics of Parkinson and Alzheimer disease, Pankratz has expanded his research efforts to investigate genes and biomarkers associated with cardiovascular disease and cancer. He conducts meta-analyses of data sets from large-scale studies of these diseases using linkage analysis, genome-wide association studies (GWAS), analysis of copy number variation (CNV), and analysis of targeted whole-exome and whole-genome sequencing data produced by next-generation sequencing (NGS) technologies.NIH-sponsored cohort studies such as the Atherosclerosis Risk in Communities (ARIC) study and the Coronary Artery Risk Development in Young Adults (CARDIA) study give Pankratz and his fellow collaborators large data sets to analyze in their search for genetic factors involved in cardiovascular disease (CVD). These meta-analyses usually involve multiple institutions and collaborators, often under the banner of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Pankratz has led exome-wide meta-analyses for the CHARGE working groups focused on hemostatic factors, hematology markers, and inflammatory biomarkers of CVD, using data from as many as a hundred thousand research participants. Advanced software tools are required to manage the resulting large datasets and perform genetic analyses, and developing such software is a major focus of the Pankratz lab.One of the Java software packages that the lab is actively developing is designed to more reliably identify copy number variants (CNVs) in the human genome. CNVs are gains or losses of large DNA segments that vary from one individual to the next. CNVs are associated with many common diseases, but they can be difficult to detect and measure with the most commonly used genomic platforms. Software that is easy to use, automatically implements best practices, and allows for the meta-analysis of multiple datasets will allow investigators to identify CNVs associated with complex diseases, such as those being studied by the CHARGE consortium. Large-scale genome-wide association studies focused on CNVs will go a long way toward clarifying their role in health and disease.


  • Sullivan SM, Cole B, Lane J, Meredith JJ, Langer E, Hooten AJ, Roesler M, McGraw KL, Pankratz N, Poynter JN. Predicted leukocyte telomere length and risk of myeloid neoplasmsHum Mol Genet. 2023 Aug 2:ddad126. doi: 10.1093/hmg/ddad126.
  • Thibord F, Klarin D, Brody JA, Chen MH, Levin MG, Chasman DI, Goode EL, Hveem K, Teder-Laving M, Martinez-Perez A, Aïssi D, Daian-Bacq D, Ito K, Natarajan P, Lutsey PL, Nadkarni GN, de Vries PS, Cuellar-Partida G, Wolford BN, Pattee JW, Kooperberg C, Braekkan SK, Li-Gao R, Saut N, Sept C, Germain M, Judy RL, Wiggins KL, Ko D, O'Donnell CJ, Taylor KD, Giulianini F, De Andrade M, Nøst TH, Boland A, Empana JP, Koyama S, Gilliland T, Do R, Huffman JE, Wang X, Zhou W, Manuel Soria J, Carlos Souto J, Pankratz N, Haessler J, Hindberg K, Rosendaal FR, Turman C, Olaso R, Kember RL, Bartz TM, Lynch JA, Heckbert SR, Armasu SM, Brumpton B, Smadja DM, Jouven X, Komuro I, Clapham KR, Loos RJF, Willer CJ, Sabater-Lleal M, Pankow JS, Reiner AP, Morelli VM, Ridker PM, Vlieg AVH, Deleuze JF, Kraft P, Rader DJ; Global Biobank Meta-Analysis Initiative; Estonian Biobank Research Team; 23andMe Research Team; Biobank Japan; CHARGE Hemostasis Working Group; Min Lee K, Psaty BM, Heidi Skogholt A, Emmerich J, Suchon P, Rich SS, Vy HMT, Tang W, Jackson RD, Hansen JB, Morange PE, Kabrhel C, Trégouët DA, Damrauer SM, Johnson AD, Smith NL. Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors. Circulation. 2022 Oct 18;146(16):1225-1242. doi: 10.1161/CIRCULATIONAHA.122.059675
  • Martinez RJ, Pankratz N, Schomaker M, Daniel J, Beckman K, Karger AB, Thyagarajan B, Ferreri P, Yohe SL, Nelson AC. Prediction of false positive SARS-CoV-2 molecular results in a high-throughput open platform system. J Mol Diagn. 2021 Jun 8:S1525-1578(21)00166-5. doi: 10.1016/j.jmoldx.2021.05.015
  • Tang W, Stimson MR, Basu S, Heckbert SR, Cushman M, Pankow JS, Folsom AR, Pankratz N. Burden of rare exome sequence variants in PROC gene is associated with venous thromboembolism: a population-based study. J Thromb Haemost. 2019 Nov 4. doi: 10.1111/jth.14676.
  • Pankratz N, Schick UM, Zhou Y, Zhou W, Ahluwalia TS, Allende ML, Auer PL, Bork-Jensen J, Brody JA, Chen MH, Clavo V, Eicher JD, Grarup N, Hagedorn EJ, Hu B, Hunker K, Johnson AD, Leusink M, Lu Y, Lyytikäinen LP, Manichaikul A, Marioni RE, Nalls MA, Pazoki R, Smith AV, van Rooij FJ, Yang ML, Zhang X, Zhang Y, Asselbergs FW, Boerwinkle E, Borecki IB, Bottinger EP, Cushman M, de Bakker PI, Deary IJ, Dong L, Feitosa MF, Floyd JS, Franceschini N, Franco OH, Garcia ME, Grove ML, Gudnason V, Hansen T, Harris TB, Hofman A, Jackson RD, Jia J, Kähönen M, Launer LJ, Lehtimäki T, Liewald DC, Linneberg A, Liu Y, Loos RJ, Nguyen VM, Numans ME, Pedersen O, Psaty BM, Raitakari OT, Rich SS, Rivadeneira F, Di Sant AM, Rotter JI, Starr JM, Taylor KD, Thuesen BH, Tracy RP, Uitterlinden AG, Wang J, Wang J, Dehghan A, Huo Y, Cupples LA, Wilson JG, Proia RL, Zon LI, O'Donnell CJ, Reiner AP, Ganesh SK. Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits. Nat Genet. 2016 Aug;48(8):867-76. doi: 10.1038/ng.3607


PhD, Indiana University (Medical and Molecular Genetics), 2003



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