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James A. Perry, PhD

Academic Title:

Assistant Professor

Primary Appointment:



HSF-III, Room 4053 670 W. Baltimore St Baltimore, MD 21201

Education and Training

1973 B.S. Chemistry, Kansas State University (Magna Cum Laude)
1976 M.S. Chemistry, University of Illinois, Urbana-Champaign
1977 Ph.D. Analytical Chemistry, University of Illinois, Urbana-Champaign
2016 M.S. Bioinformatics, Johns Hopkins University, Baltimore


             My work in the Department of Medicine is focused on enabling the process of biological discovery by developing high-speed, automated approaches for analyzing, searching, visualizing and understanding associations between omics data (genomics, metabolomics, transcriptomics, methylomics) and medical phenotypes. The techniques include developing and running computerized “pipelines” for large-scale association analysis as well as collecting and integrating a full spectrum of annotation for all genetic variants.  Annotation includes basic information about each variant, allele frequencies in various populations, predictive scores for potential biological effects and customized “link-outs”, which send queries with specific information for each variant or gene, to public resources such dbSNP, GTEx, Roadmap Epigenome Browser, UCSC Genome Browser, GWAS Catalog.

             The association analysis results (pValues, effect size, etc.) are stored in a database system called OASIS (Omics Analysis Search and Information System) which I developed for searching and filtering the results along with “on-demand, real-time” generation of boxplots to aid in the understanding of the effect size and the variability of the data.  OASIS directly calculates linkage disequilibrium (LD) from the population being studied to identify multiple, unique association signals for a genomic region.  The system is integrated with graphical tools (e.g. LocusZoom, Haploview) for quick LD visualizations.  Additionally, “on-demand, real-time” multi-covariate analysis is available for customized analysis conditioned on other phenotypes and/or on multiple genomic variants.

             OASIS currently allows University of Maryland investigators studying the Old Order Amish to query genetic associations for over 1200 cardiometabolic and other health-related traits for 6136 subjects. The fully annotated genotype database in OASIS is populated with 27 million exonic and noncoding variants from genotyping and whole exome sequencing. OASIS currently contains genomic, metabolomic, transcriptomic and methylomic association results.  In a recent 12 month period, 30,000 OASIS queries were performed by 35 University of Maryland users.

             A version of OASIS for UK Biobank (UKB) data was developed during 2020. This system contains full annotation on the 92 million imputed variants from the UKB and allows LD to be computed directly from the individual-level data on 502,520 subjects.  The “UKB OASIS” system contains association results on over 3300 phenotypes from our own analysis and from summary statistics made available on public websites. Thus, when an OASIS search identifies one or more variants of interest, extensive PheWAS information is also available.

             I am a member of 10 Working Groups in NHLBI’s TOPMed Program (Trans-Omics for Precision Medicine) and developed a separate “TOPMed OASIS” website in 2018 as a contribution to that program. The original OASIS for the Amish design scaled up nicely to handle TOPMed’s one billion variants for 140,000 subjects in TOPMed Freeze 8. As of September 2020, TOPMed OASIS contains 384 datasets contributed by 80 users from 12 TOPMed Working Groups (Diabetes, Kidney Function, Hematology & Hemostasis, Lipids, Anthropometry & Adiposity, Bone Mineralization, Population Genetics and Coronary Artery Calcification, Blood Pressure, Platelet Aggregation, Telomere Length, Structural Variation).  OASIS users include MD’s, PhDs, Post Docs, Graduate Students and Medical Students.

             I am also a member of the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology) and participate in the Analysis and Diabetes Working Groups.  Plans are being developed to add OASIS genotypes platforms for HRC and 1000Genomes so that OASIS can be used to examine the results of meta-analysis performed by CHARGE Working Groups using imputed genotypes.

Highlighted Publications

Genetic association studies:  I have worked extensively with genomic data from the Old Order Amish, performing association analysis to link genotypes to a broad range of phenotype data collected over multiple decades. The work has involved working with public databases as well as the development of unique analysis tools for searching, filtering, annotating and visualizing both genetic variants and association results. I have also worked extensively with statistical genetics tools, including SAS, Plink and MMAP, to identify statistically significant genetic associations in the Amish, a founder population with a unique structure.

  1. Tise CG, Perry JA, Anforth LE, Pavlovich MA, Backman JD, Ryan KA, Lewis JP, O'Connell JR, Yerges-Armstrong LM, Shuldiner AR. From Genotype to Phenotype: Nonsense Variants in SLC13A1 Are Associated with Decreased Serum Sulfate and Increased Serum Aminotransferases. G3 (Bethesda). 2016 Jul 13. pii: g3.116.032979. doi: 10.1534/g3.116.032979. PMID:27412988
  2. James A Perry, Brady J Gaynor, Braxton D Mitchell, Jeffrey R O’Connell, An Omics Analysis, Search and Information System (OASIS) for Enabling Biological Discovery in the Old Order Amish.
    bioRxiv 2021.05.02.442370; doi:
  3. Streeten EA, See VY, Jeng LBJ, Maloney KA, Lynch M, Glazer AM, Yang T, Roden D, Pollin TI, Daue M, Ryan KA, Van Hout C, Gosalia N, Gonzaga-Jauregui C, Economides A, Perry JA, O'Connell J, Beitelshees A, Palmer K, Mitchell BD, Shuldiner AR; Regeneron Genetics Center*. KCNQ1and Long QT Syndrome in 1/45 Amish: The Road From Identification to Implementation of Culturally Appropriate Precision Medicine. Circ Genom Precis Med. 2020 Dec;13(6):e003133. doi: 10.1161/CIRCGEN.120.003133. Epub 2020 Nov 3. PMID: 33141630; PMCID: PMC7748050.
  4. May E Montasser, Cristopher Van Hout, … James A Perry, …Jeffrey R O’Connell, …Alan Shuldiner. Genetic and functional evidence links a missense variant in B4GALT1 to lower LDL and fibrinogen. Science. 2021 Dec 3; 374(6572): 1221-1227. doi: 10.1126/science.abe0348 PMID: 34855475

UK Biobank Association Studies:  I have constructed a unique analysis pipeline for UK Biobank studies that includes both genetic and non-genetic analysis.

  1. Scalsky RJ, Chen YJ, Desai K, O’Connell JR, Perry JA*, Hong CC*. (*Co-Senior Authors) Baseline cardiometabolic profiles and SARS-CoV-2 infection in the UK Biobank. PLoS ONE 2021; 16: 30248602.
    Subject of coverage by numerous media outlets, including Fox News, Times of India, The Tribune India, Hindustan Times, and Verywell Health, among others.
  2. Tamara Ashvetiya, Sherry X Fan, Yi-Ju Chen, Charles H Williams, Jeffery R. O’Connell, James A Perry*, Charles C. Hong*. (*Co-Senior Authors) Identification of novel genetic susceptibility loci for thoracic and abdominal aortic aneurysms via genome-wide association study using the UK Biobank Cohort. PLoS One. 2021 Sep 1;16(9):e0247287. doi: 10.1371/journal.pone.0247287. PMID: 34469433
  3. Ryan J. Scalsky, Yi-Ju Chen, Zhekang Ying, James A. Perry*, Charles C. Hong*. (*Co-Senior Authors) The Social and Natural Environment’s Impact on SARS-CoV-2 Infections in the UK Biobank. J. Environ. Res. Public Health 2022, 19(1), 533;
  4. Alex Gyftopoulos, Yi-Ju Chen, Libin Wang, Charles H. Williams, Young Wook Chun, Jeffery R. O’Connell, James A. Perry*, Charles C. Hong*. (*Co-Senior Authors) Identification of Novel Genetic Variants and Comorbidities Associated with ICD-10-based Diagnosis of Hypertrophic Cardiomyopathy using the UK Biobank Cohort.  In final review, March 2022, Frontiers in Genetics.
  5. Fahad Alkhalfan, Alex Gyftopoulos, Yi-Ju Chen, Charles H. Williams, James A. Perry*, Charles C. Hong*. (*Co-Senior Authors) Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database. Submitted and in review, March 2022, PloS One.
  6. Mehmet Hocaoglu, Jamal Mikdashi, Yash Vinod Bhagat, Yi-Ju Chen, James A. Perry*, Charles C. Hong*. (*Co-Senior Authors) STEAP3, FZD2 and EGFLAM Are Novel Genetic Risk Loci for Granulomatosis with Polyangitis: A Genome Wide Association Study from UK Biobank. Submitted and in review, March 2022, Rheumatology (Oxford).

OASIS-TOPMed Working Group Collaborations:  I have worked with many Trans-omics for Precision Medicine (TOPMed) Working Groups to mine and fine-map genetic analysis results using OASIS (see list of Working Groups in Personal Statement).

  1. Natarajan P, Peloso GM, Zekavat SM, Montasser M, Ganna A, Chaffin M, Khera AV, Zhou W, Bloom JM, Engreitz JM, Ernst J, O'Connell JR, Ruotsalainen SE, Alver M, Manichaikul A, Johnson WC, Perry JA, Poterba T, Seed C, Surakka IL, Esko T, Ripatti S, Salomaa V, Correa A, Vasan RS, Kellis M, Neale BM, Lander ES, Abecasis G, Mitchell B, Rich SS, Wilson JG, Cupples LA, Rotter JI, Willer CJ, Kathiresan S; NHLBI TOPMed Lipids Working Group. Deep-coverage whole genome sequences and blood lipids among 16,324 individuals. Nat Commun. 2018 Aug 23;9(1):3391. doi: 10.1038/s41467-018-05747-8. PubMed PMID: 30140000; PubMed Central PMCID: PMC6107638.
  2. Sarnowski C, Leong A, Raffield LM, Wu P, de Vries PS, DiCorpo D, Guo X, Xu H, Liu Y, Zheng X, Hu Y, Brody JA, Goodarzi MO, Hidalgo BA, Highland HM, Jain D, Liu CT, Naik RP, O'Connell JR, Perry JA, Porneala BC, Selvin E, Wessel J, Psaty BM, Curran JE, Peralta JM, Blangero J, Kooperberg C, Mathias R, Johnson AD, Reiner AP, Mitchell BD, Cupples LA, Vasan RS, Correa A, Morrison AC, Boerwinkle E, Rotter JI, Rich SS, Manning AK, Dupuis J, Meigs JB; TOPMed Diabetes Working Group; TOPMed Hematology Working Group; TOPMed Hemostasis Working Group; National Heart, Lung, and Blood Institute TOPMed Consortium. Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program. Am J Hum Genet. 2019 Oct 3;105(4):706-718. doi: 10.1016/j.ajhg.2019.08.010. Epub 2019 Sep 26. PMID: 31564435.
  3. Michael D. Kessler, Douglas P. Loesch, James A. Perry, Nancy L. Heard-Costa, Brian E. Cade, Heming Wang, Michelle Daya, John Ziniti, Soma Datta, Juan C. Celedon, Manuel E. Soto-Quiros, Lydiana Avila, Scott T. Weiss, Kathleen Barnes, Susan S. Redline, Ramachandran Vasan, Andrew D. Johnson, Rasika A. Mathias, Ryan Hernandez, James G. Wilson, Deborah A. Nickerson, Goncalo Abecasis, Sharon R. Browning, Sebastian Sebastian Zoellner, Jeffrey R. O'Connell, Braxton D. Mitchell, Timothy D. O'Connor De novo mutations across 1,465 diverse genomes reveal novel mutational insights and reductions in the Amish founder population. Proc Natl Acad Sci U S A. 2020 Feb 4;117(5):2560-2569. doi: 10.1073/pnas.1902766117.
  4. Bridget Lin, Kelsey E Grinde, Jennifer Brody, Charles E Breeze, Laura M Raffield, Tim Thornton, Joe Mychaleckyj, James A Perry, Stephen S Rich, Dan-Yu Lin, Sharon Browning, Nora Franceschini Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium. EBioMedicine, Volume 63, 2021, 103157, ISSN 2352-3964, 

Complete List of Published Work in MyBibliography: