Candidate: I am a board-certified neurologist at the Brigham and Women's Hospital (BWH) and instructor at the Harvard Medical School (HMS). My research interest lies in the translation of discoveries in human genetics to clinical application n multiple sclerosis (MS) and related neuroimmunological and neurodegerative disorders. Leveraging my PhD thesis work in basic cellular and molecular neuroscience and ongoing post- doctoral training in statistical genetics and translational genomics, I will gain proficiency in th following new research areas through mentored project and structured educational activities: (1) lead a team of researchers and direct patient-oriented research;(2) design and implement studies that incorporate innovative biomarkers and neuroimaging outcomes of neuroinflammation and neurodegeneration;(3) devise clinically relevant applications of genetic information such as a genetic risk score for biomarker selection, disease prediction, and risk stratification;(4) pursue translational genetics research using Electronic Medical Record (EMR)-derived data and bioinformatics tools. These new translational research skills will enable me to achieve the career goal of making the transition to an independent investigator. Environment: I have assembled a multidisciplinary team of mentor (Dr. Philip De Jager, an expert in incorporating human genomics techniques into the study of complex neurologic disorders) and co-mentors (Dr. Issac Kohane, an expert in bioinformatics and predictive modeling;Dr. Daniel Reich, an expert in MS neuroimaging), consultants and senior faculty members with complementary expertise who will guide my research and promote my career development. This multi-layered mentor structure is embedded in a highly collaborative environment of unparalleled intellectual caliber that is part of Harvard-affiliated institutions: BWH Department o Neurology, BWH Institute of Neurosciences, Broad Institute, and HMS. Dr. Martin Samuels (Chairman of BWH Department of Neurology) and Dr. David Silbersweig (Chairman of BWH Institute of Neurosciences) are both supportive of my career plan. Research: An important challenge facing clinical care in multiple sclerosis (MS) is the lack of robust predictive tools to guide individualized risk stratification for subjects at risk of developing MS. The overall goal of the study is to test the efficacy of an algorithm that integrates existing genetic and environmental data into a single, individual estimate of the risk of developing MS. We hypothesize that such an algorithm can provide MS risk stratification for asymptomatic subjects at risk of MS, such as family members or patients with Radiologically Isolated Syndrome (RIS: asymptomatic individuals with incidental findings of MS-like lesions on brain magnetic resonance imaging, MRI). First, we will calculate our MS genetic and environmental risk score (GERSMS) in a cohort of 500 neurologically asymptomatic first-degree relatives of MS patients and assess whether the 100 subjects with the highest GERSMS exhibit an MS-associated peripheral blood biomarker profile when compared to the 100 subjects with the lowest GERSMS. Second, we will assess the efficacy of GERSMS in predicting the presence of MRI-defined MS lesions and other MS-related MRI outcomes in neurologically asymptomatic first- degree relatives and assess whether the 50 subjects with the highest GERSMS have more MS-like lesions, smaller brain volume or greater diffusion tensor imaging (DTI) changes that suggest loss of white matter tract integrity when compared to the 50 subjects with the lowest GERSMS. Finally, we will assess the efficacy of GERSMS in predicting conversion to clinical MS in RIS patients identified from existing EMR-derived data. Innovation: This proposal is innovative since it applies a novel approach to address an understudied question: how do we identify asymptomatic individuals who are at the highest risk of developing multiple sclerosis? The proposed study leverages a recently published, robust analytic method that is further enhanced by the most up-to-date genetic information and validated epidemiological data to produce a novel single estimate of risk for MS. We will test the method in two unique sample collections of asymptomatic subjects: (a) asymptomatic first-degree relatives of MS patients, (b) RIS patients identified from one of the largest EMR systems Specifically, we will assess the efficacy of this method in the selection of cutting-edge blood biomarkers and sensitive neuroimaging measures. If validated, our approach has the potential to make the study of asymptomatic subjects feasible by identifying the subset of subjects at the highest risk of transitioning from health to MS, thus opening up a whole new area of investigation in MS that could ultimately shed light on the design of strategies to prevent the onset of MS. Finally, the utilization of an EMR-derived virtual cohort for translational genetics research opens a rich resource for tackling challenging translational research questions that cannot be easily addressed by traditional cohort studies: (1) unforeseen co-morbidities of neurological disorders, (2) unrecognized neurological complications of medications on the market, particularly new immune modulating biologic agents. These future studies are not restricted to the field of MS and create future areas to develop independent investigation.

Public Health Relevance

Multiple sclerosis (MS) is a progressive neurodegenerative disease that causes a substantial socioeconomic burden to society in addition to its effects on affected individuals who are often in the prime of their life and their families. A central challene facing MS is the lack of reliable tools to provide individual estimates of the risk of developing M for family members of MS patients, who are at increased risk for the disease. The proposed project develops and tests an individualized risk prediction tool that incorporates the latest knowledge in MS genetics, environmental exposures, and blood tests to identify those individuals who are at the highest risk for developing the disease.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Clinical Investigator Award (CIA) (K08)
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NST-2 Subcommittee (NST)
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Utz, Ursula
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Brigham and Women's Hospital
United States
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Dhand, Amar; White, Charles C; Johnson, Catherine et al. (2018) A scalable online tool for quantitative social network assessment reveals potentially modifiable social environmental risks. Nat Commun 9:3930
Bargiela, David; Bianchi, Matthew T; Westover, M Brandon et al. (2017) Selection of first-line therapy in multiple sclerosis using risk-benefit decision analysis. Neurology 88:677-684
Xia, Zongqi; Steele, Sonya U; Bakshi, Anshika et al. (2017) Assessment of Early Evidence of Multiple Sclerosis in a Prospective Study of Asymptomatic High-Risk Family Members. JAMA Neurol 74:293-300
Bove, Riley; Chua, Alicia S; Xia, Zongqi et al. (2016) Complex relation of HLA-DRB1*1501, age at menarche, and age at multiple sclerosis onset. Neurol Genet 2:e88
Xia, Zongqi; White, Charles C; Owen, Emily K et al. (2016) Genes and Environment in Multiple Sclerosis project: A platform to investigate multiple sclerosis risk. Ann Neurol 79:178-89
Liao, Katherine P; Cai, Tianxi; Savova, Guergana K et al. (2015) Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ 350:h1885
Liao, Katherine P; Ananthakrishnan, Ashwin N; Kumar, Vishesh et al. (2015) Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts. PLoS One 10:e0136651
Ottoboni, Linda; Frohlich, Irene Y; Lee, Michelle et al. (2013) Clinical relevance and functional consequences of the TNFRSF1A multiple sclerosis locus. Neurology 81:1891-9
Xia, Zongqi; Secor, Elizabeth; Chibnik, Lori B et al. (2013) Modeling disease severity in multiple sclerosis using electronic health records. PLoS One 8:e78927
Ananthakrishnan, Ashwin N; Cai, Tianxi; Savova, Guergana et al. (2013) Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach. Inflamm Bowel Dis 19:1411-20

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