We propose to develop and apply state-of-the-art statistical methods to identify clusters of rare disease risk variants within large copy-number variable (CNV) regions previously implicated in autism spectrum disorders (ASD) and schizophrenia (SCZ). Although many large CNV regions have been implicated in risk to psychiatric disorders such as ASD and SCZ, the underlying disease genes in these regions are mostly unknown, because these CNVs are large and contain many genes. Furthermore, these CNVs have not been comprehensively investigated using the large whole-exome sequencing (WES) datasets that have become recently available for ASD and SCZ, with more than 20,000 WES samples combined. We propose to take advantage of these new WES data for ASD and SCZ and propose a systematic investigation of the CNVs implicated in these disorders to identify the underlying disease gene(s) within these CNVs. The problem of identifying rare disease risk variants within these CNVs is of great importance to the field, as rare and large CNVs are the most replicable association so far for these psychiatric disorders. Based on previous work from our group, and taking advantage of some of the largest WES studies for ASD and SCZ, the novel scan statistic approaches we propose to develop promise to help substantially in elucidating the disease genes in these CNV regions. In addition, software implementing these methods will be made publicly available for other researchers interested in pursuing similar work. 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 mental diseases.

Public Health Relevance

Autism Spectrum Disorders and Schizophrenia are major public health problems. The proposed statistical methodology and the direct application to copy-number variable regions, previously implicated in these mental diseases, will help in the identification of genetic variants influencing disease risk, with important implications for public health.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21MH106888-02
Application #
9039159
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Addington, Anjene M
Project Start
2015-04-01
Project End
2017-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
2
Fiscal Year
2016
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
Backenroth, Daniel; He, Zihuai; Kiryluk, Krzysztof et al. (2018) FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications. Am J Hum Genet 102:920-942
Liu, Yuwen; Liang, Yanyu; Cicek, A Ercument et al. (2018) A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies. Am J Hum Genet 102:1031-1047
Lim, Elaine T; Uddin, Mohammed; De Rubeis, Silvia et al. (2017) Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder. Nat Neurosci 20:1217-1224
Talati, Ardesheer; Guffanti, Guia; Odgerel, Zagaa et al. (2015) Genetic variants within the serotonin transporter associated with familial risk for major depression. Psychiatry Res 228:170-3
McCallum, Kenneth J; Ionita-Laza, Iuliana (2015) Empirical Bayes scan statistics for detecting clusters of disease risk variants in genetic studies. Biometrics 71:1111-20