High Throughput Sequencing and Copy Number Variation in Schizophrenia

候选人：Jin P. Szatkiewicz博士，北卡罗来纳大学教堂山分校博士后实习生，在生物学和统计学方面都有很强的背景，在她的培训期间一直从事生物医学研究。受到患有严重精神疾病的家庭成员的启发，她希望将自己的一生奉献给有利于精神疾病患者的基因研究。长期职业目标：成为一名独立的学术研究人员，专注于拷贝数变异（CNV）对精神分裂症和其他精神疾病风险的影响。培养目标：候选人计划进一步发展必要的技术和专业技能，以建立未来CNV和精神分裂症的独立研究项目，并领导未来的多学科研究。候选人计划提供大量的初步数据和出版物，以支持R01拨款申请。职业发展活动、提出的研究计划、指导团队和机构环境都非常适合帮助申请人实现这些目标。职业发展：建议职业发展的一个关键要素是同时进行计算生物学、统计遗传学、精神遗传学和计算机科学方面的培训。研究研究：本研究的目标是开发从高通量测序（HTS）数据中检测和分析CNVs的最佳方案和软件工具。这些工具将以用户友好的实现方式公开提供。最佳方案将在多个数据集中全面实施，以了解CNVs在精神分裂症病因学中的作用。指导团队：专门的指导团队包括国际公认的，独立资助的研究人员，他们在精神病学遗传学（Sullivan），统计遗传学（Lin），计算生物学（Sun）和计算机科学（Wang）方面具有专业知识。支持顾问/顾问团队包括领先的专家，博士。Purcell， Sebat和Li，具有适合所提议的研究和职业发展的专业知识。环境：北卡罗来纳大学提供了一个富有成效的、合议的、合作的氛围，以追求上述研究和培训目标。影响：拟议研究的完成将通过提供使用HTS进行CNV分析的最佳方案和用户友好的工具，以及通过识别与精神分裂症相关的CNV，对该领域产生重大影响。在完成本申请的培训和研究计划后，PI将很好地定位为对精神分裂症有深刻理解的独立研究者，并有能力领导未来的多学科研究。

DESCRIPTION (provided by applicant): Candidate: Dr. Jin P. Szatkiewicz, a postdoctoral trainee at the University of North Carolina at Chapel Hill, has a very strong background in both biology and statistics and a track record of biomedical research throughout her training. Inspired by family members who suffer from severe mental illness, she wishes to devote her life to genetic research that benefits the mentally ill. Long-term career goal: To become an independent academic researcher focused on the impact of copy number variation (CNV) on risk for schizophrenia and other psychiatric disorders. Training objectives: The candidate plans to further develop the technical and professional skills necessary to establish an independent program of future research in CNV and schizophrenia, and to lead future multidisciplinary studies. The candidate plans to produce a critical mass of preliminary data and publications to support an R01 grant application. The career development activities, proposed research plan, mentorship team, and institutional environment are all uniquely suited to assist the applicant in achieving these goals. Career development: A key element of the proposed career development is simultaneous training in computational biology, statistical genetics, psychiatric genetics, and computer science. Research Study: The objectives of the proposed research are to develop optimal protocols and software tools for detecting and analyzing CNVs from high-throughput sequencing (HTS) data. These tools will be made publicly available in user-friendly implementations. The optimal protocols will be fully implemented in multiple datasets to understand the role of CNVs in the etiology of schizophrenia. Mentorship team: The dedicated mentorship team includes internationally recognized, independently funded investigators with expertise in psychiatric genetics (Sullivan), statistical genetics (Lin), computational biology (Sun), and computer science (Wang). The supportive consultant/advisor team includes leading experts Drs. Purcell, Sebat, and Li, with expertise appropriate for the proposed research and career development. Environment: The University of North Carolina provides a productive, collegial, and collaborative atmosphere in which to pursue the above research and training goals. Impact: Completion of the proposed research will significantly impact the field by providing optimal protocols and user-friendly tools for CNV analysis using HTS and by identifying CNVs associated with schizophrenia. Upon completing the training and research plans in this application, the PI will be well positioned as an independent investigator with a deep understanding of schizophrenia and the capability to lead multidisciplinary future studies. PUBLIC HEALTH RELEVANCE: Schizophrenia is a global public health problem and an often devastating and complex brain disorder affecting thoughts and perceptions. The proposed research seeks to increase our understanding of the genetic basis of this disease by discovering how variation in the number of copies of a gene is associated with the susceptibility to schizophrenia. This project will use high throughput sequencing technology and will develop analysis methods and computer software tools to aid the discovery. Our analysis methods, tools, and results will be made publically available, and will facilitate future discovery of improved clinical treatment protocols and preventive methods for schizophrenia.