• Robert Sebra, Eric Schadt, et al. | Nature Methods | 29 June 2015

      Putting Genomes Together to Understand the Complexity of Life

      Assembly and diploid architecture of an individual human genome via single-molecule technologies

      We carried out the first study to sequence a human diploid genome using what are known as long-read DNA sequencing technologies, delivering the highest quality reference genome to date. Assembling a more accurate and complete genome is critical to our understanding of complex phenotypes such as human disease that have a genetic component.


    • Robert Sebra, Eric Schadt, et al. | Nature Biotechnology | 1 July 2015

      Pioneering a Novel Genome Assembly Approach

      A hybrid approach for the automated finishing of bacterial genomes

      One of the challenges in the field of genomics today is assembling complete genomes de novo, as opposed to using existing reference genomes to put a given genome together. By forcing the use of a reference genome, structural features in a given genome can be missed if they are absent in the reference, so the potential for discovery and clinical impact is reduced. We pioneered the first de novo assembly approach to assembling genomes using a combination of long and short read DNA sequencing technologies, demonstrating the ability to complete genomes de novo in an automated fashion, a necessary first step to achieve the vision of more comprehensive precision medicine


    • Andrew V. Uzilov, Marc Fink, Yeygeniy Antipin, Katie Raustad, Robert Sebra, Shuyu Dan Li, Eric Schadt, Rong Chen, et al. | Genome Medicine | 1 June 2016

      The Clinical Utility of Understanding Complex Genomes

      Development and clinical application of an integrative genomic approach to personalized cancer therapy

      Personalized therapy provides the best outcome of cancer care and its implementation in the clinic has been greatly facilitated by recent convergence of enormous progress in basic cancer research, rapid advancement of new tumor profiling technologies, and an expanding compendium of targeted cancer therapeutics. We developed and carried out a personalized cancer therapy study, generating extensive DNA and RNA data on cancer patients to molecularly characterize the makeup of a patient’s tumor, what the potential drivers of the tumor are, and what existing therapies, clinical trials, or de novo treatment strategies may apply. From our study, we validated the utility of such a program by demonstrating therapeutic recommendations for 91% of patients in the study.


    • Eric Schadt, et al. | Cell | 25 April 2013

      Modeling the Complexity of Human Disease

      Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease

      Complex human diseases such as Alzheimer’s disease (AD) are not the result of defects in a single gene or the result of a single environmental factor, but rather are the result of many (even hundreds or thousands) genes interacting in complex ways with many environmental factors that result in the development, progression and severity of disease. Thus, just as weather systems, financial markets, and solar systems are modeled using advanced mathematical modeling techniques to understand the interactions among the many thousands of variables that define these complex systems, living systems must be similarly modeled to achieve an understanding of disease. Here we presented one of the first holistic molecular models for AD, elucidating the complexity of this disease at the molecular and cellular levels. By taking this objective, data driven approach to understanding AD, we were able to uncover that immune cells in the brain (microglia) were a key causal determinant for AD.


  • Rong Chen, Lisa Edelmann, Eric Schadt, et al. | Nature Biotechnology | 11 April 2016

    Investigating Resilience to Disease Through Population Genomics

    Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases

    Genetic studies of human disease have traditionally focused on the detection of disease-causing mutations in afflicted individuals. Here we described a complementary approach that seeks to identify healthy individuals resilient to highly penetrant forms of genetic childhood disorders. To accomplish this, we carried out the largest genomics study carried out at the time, analyzing over 589,000 genomes in search of individuals harboring highly penetrant, deleterious mutations in their DNA that should have caused catastrophic illness in childhood, but these individuals never manifested clinical disease and thus are protected.The protecting mechanism, if uncovered, offers a direct therapeutic path for disease. In our search we identified 13 individuals resilient to severe Mendelian childhood diseases, including diseases such as cystic fibrosis, where we identified individuals harboring the most damaging cystic fibrosis mutations, but never having manifested disease into their 40’s and 50’s.


  • Eric Schadt, Erick Scott, Samantha Violante, et al. | Nature Biotechnology | 13 March 2017

    Using Mobile Health Apps to Conduct Clinical Research

    The Asthma Mobile Health Study, A Large-Scale Clinical Observational Study Using ResearchKit

    The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. The Asthma Mobile Health Study (AMHS) is one of the few studies to examine the value and validity of the novel mobile health research platform, ResearchKit. We demonstrated that a broad-scale study can be conducted in its entirety via a smartphone application, including remote recruitment, consent, enrollment, and secure bi-directional data flow between investigators and participants. For AMHS, we prospectively collected detailed, multi-dimensional, longitudinal data on an asthma cohort more efficiently than traditional epidemiological studies by automating, standardizing, and accelerating various costly and time-consuming processes. Our study’s rapid recruitment and participants’ willingness to share de-identified data broadly highlight users’ acceptance of this methodology for low-risk health studies.