This is a proposal to continue a successful research training program in the genetic epidemiology and statistical analysis of psychiatric and other complex diseases. The field of psychiatric genetics is changing rapidly, and successful investigators must be competent in a broad array of techniques, must be able to speak the languages of fields outside their own, and must be able to collaborate effectively with scientists in other fields. The program trains postdoctoral (M.D., Ph.D., and Dr.P.H.) and predoctoral Fellows. Training has both (a) didactic and (b) research components. (a) The didactic component is further broken down into an academic program and a series of practical laboratory rotations. The academic program includes a series of academic courses in human genetics, epidemiology, statistical genetics, computer simulations, research communication skills, and responsible conduct of research. The laboratory rotations take place in a number of laboratories at Columbia University, where a rich and broad variety of genetic studies are being carried out. (b) In the research component each Fellow works closely with a Preceptor on an independent research project of the Fellow's choosing;the Fellow prepares a clearly written research proposal, carries out the proposal, prepares an oral description of the study and its results, and prepares a publishable manuscript based on the completed study. At the end of training, Fellows understand: the biological underpinnings of genetic influences on disease risk;how to formulate testable hypotheses in human genetics and design studies to test those hypotheses;the critical importance of phenotype definition;how to design data collection strategies for genetic studies;the factors that go into selecting appropriate samples;issues of responsible conduct of research and Good Clinical Practice;the mathematical underpinnings of genetic analysis, including familial aggregation studies, twin studies, and segregation, linkage, and association analysis;laboratory techniques such as genotyping and sequencing, extracting DMA from blood, PCR, etc.;proper data management of genetic and clinical data through the use of a data base management system;how to use current genetic analysis programs, to interpret the results, and to test and evaluate new methods of genetic analysis as they become available;and microarray technology and other current molecular-biological techniques.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Special Emphasis Panel (ZMH1-ERB-Y (02))
Program Officer
Desmond, Nancy L
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Columbia University (N.Y.)
Schools of Medicine
New York
United States
Zip Code
Subaran, Ryan L; Odgerel, Zagaa; Swaminathan, Rajeswari et al. (2016) Novel variants in ZNF34 and other brain-expressed transcription factors are shared among early-onset MDD relatives. Am J Med Genet B Neuropsychiatr Genet 171B:333-41
Corso, Barbara; Greenberg, David A (2014) Using linkage analysis to detect gene-gene interaction by stratifying family data on known disease, or disease-associated, alleles. PLoS One 9:e93398
Lipner, E M; Tomer, Y; Noble, J A et al. (2013) HLA class I and II alleles are associated with microvascular complications of type 1 diabetes. Hum Immunol 74:538-44
Hodge, Susan E; Subaran, Ryan L; Weissman, Myrna M et al. (2012) Designing case-control studies: decisions about the controls. Am J Psychiatry 169:785-9
Subaran, Ryan L; Talati, Ardesheer; Hamilton, Steven P et al. (2012) A survey of putative anxiety-associated genes in panic disorder patients with and without bladder symptoms. Psychiatr Genet 22:271-8
Stewart, William C L; Subaran, Ryan L (2012) Obtaining accurate p values from a dense SNP linkage scan. Hum Hered 74:12-6
Fung, Eva Lai-wah; Ho, Yuan Yuan; Hui, Joannie et al. (2011) First report of GLUT1 deficiency syndrome in Chinese patients with novel and hot spot mutations in SLC2A1 gene. Brain Dev 33:170-3
Zhou, Jun-Wei; Tsui, Stephen K W; Ng, Maggie C Y et al. (2011) Apolipoprotein M gene (APOM) polymorphism modifies metabolic and disease traits in type 2 diabetes. PLoS One 6:e17324
Geng, Hua; Law, Peggy P Y; Ng, Maggie C Y et al. (2011) APOE genotype-function relationship: evidence of -491 A/T promoter polymorphism modifying transcription control but not type 2 diabetes risk. PLoS One 6:e24669
Madsen, Ann M; Ottman, Ruth; Hodge, Susan E (2011) Causal models for investigating complex genetic disease: II. what causal models can tell us about penetrance for additive, heterogeneity, and multiplicative two-locus models. Hum Hered 72:63-72

Showing the most recent 10 out of 22 publications