The graduate groups in Epidemiology and Biostatistics (EB) and Genomics &Computational Biology (GCB), in conjunction with the Department of Ophthalmology, all of the University of Pennsylvania (Penn) School of Medicine, propose to develop a pre- and post-doctoral training program in ophthalmic statistical genetics and bioinformatics. Penn promotes an academic environment in which basic and clinical research are encouraged and viewed as attractive career paths for trainees. This program will attract trainees nationwide;graduates will be placed in other institutions nationwide, resulting in a high impact program. The training program will consist of core courses in statistical theory, statistical genetics, genomics, computational biology, and ophthalmic disorders;extensive independent readings;participation in journal clubs and research conferences focusing on ophthalmic statistical genetics and bioinformatics;research seminars in the EB, GCB, and Department of Ophthalmology, and closely mentored experiences in translational ophthalmic research. The program for pre-doctoral trainees will be four to five years in duration, with structure provided by the PhD degree program in Biostatistics managed by the EB and the PhD degree program in Genomics &Computational Biology managed by GCB;pre-doctoral trainees will enroll in one of these PhD programs. The two- to three-year post-doctoral program is designed for scientists with PhD-level training in statistical genetics, bioinformatics, and/or computational biology who seek training in the application of their discipline to ophthalmic diseases and for physicians with specialty training in ophthalmology who seek training in statistical genetics, bioinformatics, and/or computational biology. The program is designed to: 1) train rigorous and independent academic investigators able to use the range of approaches available in statistics, statistical genetics, computational biology, and bioinformatics to conduct translational research in ophthalmic disorders;2) provide intensive, supervised research experiences with mentors in ophthalmic statistical genetics and bioinformatics;3) strengthen the links between biostatistics, genomics, computational biology, bioinformatics, and ophthalmology: 4) and encourage translational ophthalmic research. When fully operational (year three and beyond of this training program), four fellowship slots will be awarded annually. Three fellowships will be awarded annually to pre-doctoral trainees and one fellowship will be awarded annually to a post-doctoral trainee. Strengths of the proposed program are: 1) the successful research training programs in the EB and GCB;2) the availability of participating faculty in the EB, GCB, and Department of Ophthalmology, who provide expertise in a wide range of disciplines: 3) numerous research projects conducted within centers and institutes at Penn with which participating faculty are affiliated;and 4) the faculties'commitment to collaborative and multidisciplinary research and training.

Public Health Relevance

There is a major national shortage of qualified scientists with the training needed to conduct rigorous research in ophthalmic statistical genetics and bioinformatics. As the genomic data become more complex, and as the questions become more sophisticated with the help of new technologies, multidisciplinary teams consisting of statistical geneticists, bioinformaticians, and ophthalmologists are needed to study eye-related complex traits. Such teams will be well-positioned to identify genetic variants and biological pathways for complex diseases such as diabetic retinopathy, glaucoma, macular degeneration, and refractive error. Additionally, new technologies such as next generation sequencing will require bioinformatics expertise to interpret the vast amount of information relevant for eye diseases.

National Institute of Health (NIH)
National Eye Institute (NEI)
Institutional National Research Service Award (T32)
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Special Emphasis Panel (ZEY1-VSN (01))
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Agarwal, Neeraj
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University of Pennsylvania
Biostatistics & Other Math Sci
Schools of Medicine
United States
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Vardhanabhuti, Saran; Jeng, X Jessie; Wu, Yinghua et al. (2014) Parametric modeling of whole-genome sequencing data for CNV identification. Biostatistics 15:427-41