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James A. Perry, PhD

Academic Title:

Assistant Professor

Primary Appointment:

Medicine

Location:

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

Education and Training

Education
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
 
Post Graduate Education and Training
 
Training while employed by Industry
1978 Computer Languages: Fortran, Basic, PL/M, APL, SAIL
1979 People-to-people communications
1979 Burger Writing Course, Marketing Management
1980 Supervisory Training & Supervisory Management Workshops
1981 Database Management: MySQL, VAX/VMS DBMS
1983 Artificial Intelligence & Expert Systems Workshop
1984 Consultative Workshop
1985 Career Management Concepts
1987 Exceptional Management Practices
2011 Cultural Diversity Workshop, Respect: The Source of our Strength
 
Training while managing private business
2001 Computer Languages: Perl
2001 Web Graphics and Site Design (HTML, CSS, JavaScript, Flash)
2001 Strategic Marketing Planning
 
Training while postdoctoral fellow at University of Maryland, School of Medicine
2013 Biochemistry via the Open Learning Initiative, Carnegie Mellon University
2014 USCS Genome Browser, USCS Workshop
2015 Integrative Genomics Viewer, Broad Institute Workshop
2015 Genetic Epidemiology, University of Maryland
2015 Computer Languages: R, Python, SAS
  

Biosketch

Overview
My work at the Department of Medicine is focused on enabling the process of gene discovery by developing high-speed, automated approaches for analyzing genotype and phenotype data combined with tools for searching, visualizing and understanding the results. The techniques include developing and running computerized “pipelines” for large-scale association analysis as well as developing full annotation of all genetic variants.  Annotation includes basic information about each variant, allele frequencies in various populations, predictive scores for potential biological effects and “link-outs” to public databases such dbSNP, Gene, genomic viewers, Roadmap Epigenome Brower, GWAS Catalog and many more. The association analysis results (pValues, effect size, etc.) are stored in a database for searching and filtering and are augmented with “on-demand, real-time” generation of boxplots to aid in the understanding of the effect size and the variability of the data.  The database is also integrated with tools (LocusZoom, Haploview, Plink) for quickly understanding and visualizing linkage disequilibrium (LD).

Automated spectrometric analysis
My early research involved development and construction of computerized analytical instrumentation for spectrometric analysis. State-of-the-art microprocessor technology and newly-discovered laser systems were integrated to provide a flexible analysis system.

Genetic association studies
As a postdoctoral fellow, I 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.

Research/Clinical Keywords

Bioinformatics, Amish, GWAS, Association Studies, Amish Results Database

Highlighted Publications

Automated spectrometric analysis

  • Perry, JA., Bryant, MF., Malmstadt, HV. Microprocessor-Controlled, Scanning Dye Laser for Spectrometric Analytical Systems, Anal. Chem. 1977, 49(12) 1702-1710


Genetic association studies

  • O’Hare EA, Yerges-Armstrong LM, Perry, JA, Shuldiner AR, Zaghloul NA. Assignment of Functional Relevance to Genes at Type 2 Diabetes-Associated Loci Through Investigation of β-Cell Mass Deficits, Mol. Endocrinol. 2016 Apr;30(4):429-45. Epub 2016 Mar 10. PMID:26963759

  • 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


Complete List of Published Work in MyBibliography:

http://www.ncbi.nlm.nih.gov/sites/myncbi/10gcdhWtvJcAa/bibliography/44511503/public/?sort=date&direction=descending