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.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Scientist Development Award - Research & Training (K01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Rosemond, Erica K
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University of North Carolina Chapel Hill
Schools of Medicine
Chapel Hill
United States
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Yilmaz, Zeynep; Szatkiewicz, Jin P; Crowley, James J et al. (2017) Exploration of large, rare copy number variants associated with psychiatric and neurodevelopmental disorders in individuals with anorexia nervosa. Psychiatr Genet 27:152-158
Tzeng, Jung-Ying; Magnusson, Patrik K E; Sullivan, Patrick F et al. (2015) A New Method for Detecting Associations with Rare Copy-Number Variants. PLoS Genet 11:e1005403
Wang, WeiBo; Wang, Wei; Sun, Wei et al. (2015) Allele-specific copy-number discovery from whole-genome and whole-exome sequencing. Nucleic Acids Res 43:e90
Rees, Elliott; Walters, James T R; Chambert, Kimberly D et al. (2014) CNV analysis in a large schizophrenia sample implicates deletions at 16p12.1 and SLC1A1 and duplications at 1p36.33 and CGNL1. Hum Mol Genet 23:1669-76
Szatkiewicz, J P; O'Dushlaine, C; Chen, G et al. (2014) Copy number variation in schizophrenia in Sweden. Mol Psychiatry 19:762-73
Rees, E; Kirov, G; Sanders, A et al. (2014) Evidence that duplications of 22q11.2 protect against schizophrenia. Mol Psychiatry 19:37-40
Collins, A L; Kim, Y; Szatkiewicz, J P et al. (2013) Identifying bipolar disorder susceptibility loci in a densely affected pedigree. Mol Psychiatry 18:1245-6
Szatkiewicz, J P; Neale, B M; O'Dushlaine, C et al. (2013) Detecting large copy number variants using exome genotyping arrays in a large Swedish schizophrenia sample. Mol Psychiatry 18:1178-84
Szatkiewicz, Jin P; Wang, WeiBo; Sullivan, Patrick F et al. (2013) Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation. Nucleic Acids Res 41:1519-32