The tremendous progress in massively parallel sequencing technologies enables investigators to obtain genetic information down to single base resolution on a genome-wide scale in a rapid and cost efficient manner. Despite this progress in data generation, it remains very challenging to analyze and interpret these data. The resulting datasets are high dimensional and very sparse, with millions of genetic variants, the vast majority of which are rare in the population. Identifying which of the many genetic variants in a region of interest are true causal variants is very difficult. Indeed, despite enormous progress in identifying robust associations in genome-wide association studies (GWAS) studies, the underlying causal variants for the vast majority of GWAS loci are unknown. The problem of identifying the underlying causal variants is of fundamental importance for understanding precise biological mechanisms. While experimental functional studies are the gold standard, they are expensive and difficult to implement for a large number of variants. Here we propose to develop state of the art and powerful statistical methods that integrate genome-wide functional annotation data with genetic data on a large number of individuals from whole-exome sequencing and GWAS studies of autism and schizophrenia to help us identify the true causal variants among the abundant natural variation that occurs at a particular locus of interest. The proposed statistical methods will be implemented into a publicly available software package. We believe that the proposed research is very timely and has the potential to be of great public health importance through direct application to autism and schizophrenia, and more broadly to other psychiatric diseases.

Public Health Relevance

Autism Spectrum Disorders and Schizophrenia are common diseases with major impact on public health. The proposed integrative statistical methods and their direct applications to psychiatric diseases will lead to a better understanding of the biological mechanisms underlying these disorders, with important implications on disease treatment.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH106910-03
Application #
9414448
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Addington, Anjene M
Project Start
2016-04-14
Project End
2019-01-31
Budget Start
2018-02-01
Budget End
2019-01-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032