Honors & Awards

  • Clinical Translational Science Award, Spectrum (Summer 2010)
  • HHMI Medical Fellows Fellowship, Howard Hughes Medical Institute (2011-2012)
  • Paul and Daisy Soros Fellowship for New Americans, Paul and Daisy Soros Fellowship for New Americans

Education & Certifications

  • Bachelor of Science, Rice University, Bioengineering (2009)

Service, Volunteer and Community Work

  • SMSA Recruitment Chair, Stanford University (10/1/2009 - 3/30/2010)

    Worked with the admissions office to enhance student recruitment to the School of Medicine.


    Stanford, CA

  • Stanford Medical Student Association Vice President of Advocacy, Stanford University (3/30/2011)


    Stanford, CA

  • Stanford Medical Student Association President, Stanford School of Medicine (4/30/2012 - 4/30/2013)

    Medical student body president


    Stanford, CA


All Publications

  • PharmGKB summary: very important pharmacogene information for CYP4F2 PHARMACOGENETICS AND GENOMICS Alvarellos, M. L., Sangkuhl, K., Daneshjou, R., Whirl-Carrillo, M., Altman, R. B., Klein, T. E. 2015; 25 (1): 41-47
  • Genetic variant in folate homeostasis is associated with lower warfarin dose in African Americans BLOOD Daneshjou, R., Gamazon, E. R., Burkley, B., Cavallari, L. H., Johnson, J. A., Klein, T. E., Limdi, N., Hillenmeyer, S., Percha, B., Karczewski, K. J., Langaee, T., Patel, S. R., Bustamante, C. D., Altman, R. B., Perera, M. A. 2014; 124 (14): 2298-2305
  • Targeted Exon Capture and Sequencing in Sporadic Amyotrophic Lateral Sclerosis PLOS GENETICS Couthouis, J., Raphael, A. R., Daneshjou, R., Gitler, A. D. 2014; 10 (10)
  • Path-scan: a reporting tool for identifying clinically actionable variants. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Daneshjou, R., Zappala, Z., Kukurba, K., Boyle, S. M., Ormond, K. E., Klein, T. E., Snyder, M., Bustamante, C. D., Altman, R. B., Montgomery, S. B. 2014; 19: 229-240


    The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.

    View details for PubMedID 24297550

  • Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study LANCET Perera, M. A., Cavallari, L. H., Limdi, N. A., Gamazon, E. R., Konkashbaev, A., Daneshjou, R., Pluzhnikov, A., Crawford, D. C., Wang, J., Liu, N., Tatonetti, N., Bourgeois, S., Takahashi, H., Bradford, Y., Burkley, B. M., Desnick, R. J., Halperin, J. L., Khalifa, S. I., Langaee, T. Y., Lubitz, S. A., Nutescu, E. A., Oetjens, M., Shahin, M. H., Patel, S. R., Sagreiya, H., Tector, M., Weck, K. E., Rieder, M. J., Scott, S. A., Wu, A. H., Burmester, J. K., Wadelius, M., Deloukas, P., Wagner, M. J., Mushiroda, T., Kubo, M., Roden, D. M., Cox, N. J., Altman, R. B., Klein, T. E., Nakamura, Y., Johnson, J. A. 2013; 382 (9894): 790-796


    BACKGROUND: VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. METHODS: We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ?18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 -1639G?A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<510(-8) in the discovery cohort and p<00038 in the replication cohort. FINDINGS: The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=15110(-8)). This association was confirmed in the replication cohort (p=50410(-5)); analysis of the two cohorts together produced a p value of 4510(-12). Individuals heterozygous for the rs12777823 A allele need a dose reduction of 692 mg/week and those homozygous 934 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement). INTERPRETATION: A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population. FUNDING: National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.

    View details for DOI 10.1016/S0140-6736(13)60681-9

    View details for Web of Science ID 000324239200029

  • Pathway analysis of genome-wide data improves warfarin dose prediction BMC GENOMICS Daneshjou, R., Tatonetti, N. P., Karczewski, K. J., Sagreiya, H., Bourgeois, S., Drozda, K., Burmester, J. K., Tsunoda, T., Nakamura, Y., Kubo, M., Tector, M., Limdi, N. A., Cavallari, L. H., Perera, M., Johnson, J. A., Klein, T. E., Altman, R. B. 2013; 14
  • Chapter 7: Pharmacogenomics PLOS COMPUTATIONAL BIOLOGY Karczewski, K. J., Daneshjou, R., Altman, R. B. 2012; 8 (12)


    There is great variation in drug-response phenotypes, and a "one size fits all" paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

    View details for DOI 10.1371/journal.pcbi.1002817

    View details for Web of Science ID 000312901500023

    View details for PubMedID 23300409

  • Data-Driven Prediction of Drug Effects and Interactions SCIENCE TRANSLATIONAL MEDICINE Tatonetti, N. P., Ye, P. P., Daneshjou, R., Altman, R. B. 2012; 4 (125)


    Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.

    View details for DOI 10.1126/scitranslmed.3003377

    View details for Web of Science ID 000301538300005

    View details for PubMedID 22422992

  • Bioinformatics challenges for personalized medicine BIOINFORMATICS Fernald, G. H., Capriotti, E., Daneshjou, R., Karczewski, K. J., Altman, R. B. 2011; 27 (13): 1741-1748


    Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical

    View details for DOI 10.1093/bioinformatics/btr295

    View details for Web of Science ID 000291752600050

    View details for PubMedID 21596790

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