Marylyn D Ritchie, PhD

faculty photo
Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor
Associate Director, Center for Precision Medicine, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania
Director, Center for Translational Bioinformatics Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania
Director, Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania
Department: Genetics

Contact information
A301 Richards Building
3700 Hamilton Walk
Philadelphia, PA 19104
Office: 215-573-2438
Education:
BS (Biology)
University of Pittsburgh at Johnstown, 1999.
MS (Applied Statistics)
Vanderbilt University, 2002.
PhD (Statistical Genetics)
Vanderbilt University, 2004.
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Description of Research Expertise

Computational Genomics
Bioinformatics
Epistasis
Pharmacogenomics
Big Data
Evolutionary Computation
Genetic Epidemiology
Statistical Genetics
Systems Genomics
Computational Biology
Translational Informatics
Cardiovascular Disease

Selected Publications

Li B, Verma SS, Veturi YC, Verma A, Bradford Y, Haas DW, Ritchie MD: Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression. Pac Symp Biocompu 2018.

Li R, Kim D, Wheeler HE, Dudek SM, Dolan ME, Ritchie MD. : Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity. Pharmacogenomics J. 2018.

Haas DW, Bradford Y, Verma A, Verma SS, Eron JJ, Gulick RM, Riddler SA, Sax PE, Daar ES, Morse GD, Acosta EP, Ritchie MD: Brain neurotransmitter transporter/receptor genomics and efavirenz central nervous system adverse events. Pharmacogenet Genomics 2018.

Gusarova V, O'Dushlaine C, Teslovich TM, Benotti PN, Mirshahi T, Gottesman O, Van Hout CV, Murray MF, Mahajan A, Nielsen JB, Fritsche L, Wulff AB, Gudbjartsson DF, Sjögren M, Emdin CA, Scott RA, Lee WJ, Small A, Kwee LC, Dwivedi OP, Prasad RB, Bruse S, Lopez AE, Penn J, Marcketta A, Leader JB, Still CD, Kirchner HL, Mirshahi UL, Wardeh AH, Hartle CM, Habegger L, Fetterolf SN, Tusie-Luna T, Morris AP, Holm H, Steinthorsdottir V, Sulem P, Thorsteinsdottir U, Rotter JI, Chuang LM, Damrauer S, Birtwell D, Brummett CM, Khera AV, Natarajan P, Orho-Melander M, Flannick J, Lotta LA, Willer CJ, Holmen OL, Ritchie MD, Ledbetter DH, Murphy AJ, Borecki IB, Reid JG, Overton JD, Hansson O, Groop L, Shah SH, Kraus WE, Rader DJ, Chen YI, Hveem K, Wareham NJ, Kathiresan S, Melander O, Stefansson K, Nordestgaard BG, Tybjærg-Hansen A, Abecasis GR, Altshuler D, Florez JC, Boehnke M, McCarthy MI, Yancopoulos GD, Carey DJ, Shuldiner AR, Baras A, Dewey FE, Gromada J.: Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes. Nat Commun 2018.

Basile AO, Ritchie MD.: Informatics and machine learning to define the phenotype. Expert Rev Mol Diagn. 2018.

van Setten J, Brody JA, Jamshidi Y, Swenson BR, Butler AM, Campbell H, Del Greco FM, Evans DS, Gibson Q, Gudbjartsson DF, Kerr KF, Krijthe BP, Lyytikäinen LP, Müller C, Müller- Nurasyid M, Nolte IM, Padmanabhan S, Ritchie MD, Robino A, Smith AV, Steri M, Tanaka T, Teumer A, Trompet S, Ulivi S, Verweij N, Yin X, Arnar DO, Asselbergs FW, Bader JS, Barnard J, Bis J, Blankenberg S, Boerwinkle E, Bradford Y, Buckley BM, Chung MK, Crawford D, den Hoed M, Denny JC, Dominiczak AF, Ehret GB, Eijgelsheim M, Ellinor PT, Felix SB, Franco OH, Franke L, Harris TB, Holm H, Ilaria G, Iorio A, Kähönen M, Kolcic I, Kors JA, Lakatta EG, Launer LJ, Lin H, Lin HJ, Loos RJF, Lubitz SA, Macfarlane PW, Magnani JW, Leach IM, Meitinger T, Mitchell BD, Munzel T, Papanicolaou GJ, Peters A, Pfeufer A, Pramstaller PP, Raitakari OT, Rotter JI, Rudan I, Samani NJ, Schlessinger D, Silva Aldana CT, Sinner MF, Smith JD, Snieder H, Soliman EZ, Spector TD, Stott DJ, Strauch K, Tarasov KV, Thorsteinsdottir U, Uitterlinden AG, Van Wagoner DR, Völker U, Völzke H, Waldenberger M, Jan Westra H, Wild PS, Zeller T, Alonso A, Avery CL, Bandinelli S, Benjamin EJ, Cucca F, Dörr M, Ferrucci L, Gasparini P, Gudnason V, Hayward C, Heckbert SR, Hicks AA, Jukema JW, Kääb S, Lehtimäki T, Liu Y, Munroe PB, Parsa A, Polasek O, Psaty BM, Roden DM, Schnabel RB, Sinagra G, Stefansson K, Stricker BH, van der Harst P, van Duijn CM, Wilson JF, Gharib SA, de Bakker PIW, Isaacs A, Arking DE, Sotoodehnia N.: R interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity. Nat Commun 2018.

Verma A, Bradford Y, Dudek SM, Verma SS, Pendergrass SA, Ritchie MD: A simulation study investigating power of Phenome-Wide Association Studies. BMC Bioinformatics 2018.

Verma A., Lucas A., Verma SS, Zhang Y, Josyula N. Khan A, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA: PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger. Am J Hum Genet. 2018.

Nielsen JB, Fritsche LG, Zhou W, Teslovich TM, Holmen OL, Gustafsson S, Gabrielsen ME, Schmidt EM, Beaumont R, Wolford BN, Lin M, Brummett CM, Preuss MH, Refsgaard L, Bottinger EP, Graham SE, Surakka I, Chu Y, Skogholt AH, Dalen H, Boyle AP, Oral H, Herron TJ, Kitzman J, Jalife J, Svendsen JH, Olesen MS, Njølstad I, Løchen ML, Baras A, Gottesman O, Marcketta A, O'Dushlaine C, Ritchie MD, Wilsgaard T, Loos RJF, Frayling TM, Boehnke M, Ingelsson E, Carey DJ, Dewey FE, Kang HM, Abecasis GR, Hveem K, Willer CJ.: Genome- wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development. Am J Hum Genet 2018.

Ritchie MD, Van Steen K.: The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation. Ann Transl Med 2018.

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Last updated: 09/01/2022
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