Recently we have produced the first genetic atlas of the human cortex based on magnetic resonance imaging (MRI) data of twins using fuzzy clustering. This finding not only confirms that human brain phenotypes are heritable traits but also demonstrates a very clear region-specific genetic pattern in which the cortex can be subdivided into various pleiotropic regions. In this project, we will extend this approach further and apply it to a uniquely large in-house sample with both MRI and single nucleotide polymorphism (SNP) data, in order to generate SNP-based atlases. We will adopt a statistical framework developed by Visscher and colleagues (Yang et al. 2010, Deary et al. 2012) to examine the contribution of all SNPs to phenotypic variation in aggregate. This approach is analogous to twin modeling which examines aggregate genetic influences on a trait, and captures a much larger portion of heritability than the conventional gene-wide association study (GWAS) approach. However, neither the twin nor the Visscher methods is informative about specific genetic variants underlying each genetic division. Next, we will take the genetic maps and find SNPs associated with each genetic division using new statistical methods to establish a database tool that has information about contribution of specific genetic variants to phenotypic variation in different parts of the brain. Finally, it is well-recognized that psychiatric and substance use disorders affect both brain and behavior. We will study the genetic variants and mechanisms that are involved in disease or symptom pathogenesis with benefit from incorporating the knowledge of polygenic basis of the brain.
The Specific Aims of the proposal are:
Aim 1 : Generate genetic atlases of the human cortex based on MRI and SNP data. We will take a novel approach of implementing a method developed by Yang et al, with its strength for estimating aggregate genetic effects of all SNPs, to generate genetic maps of cortical surface area and thickness by fuzzy clustering.
Aim 2 : Discover SNPs associated with individual genetic divisions of the cortical cluster maps and individual subcortical structures. We will parcel the cortex into genetic brain regions defined by the twin-based genetic atlases and find SNPs or genes associated with each brain region.
Aim 3 : Identify genetic basis of psychiatric and substance use disorders with a focus on SNPs associated with neural systems implicated in the disorders. The information regarding SNPs associated with brain phenotypes will be further applied to enhance our ability in identifying disease- related SNPs in individuals with psychiatric disorders (including schizophrenia, and bipolar and unipolar disorders) and substance dependence (including alcohol and nicotine dependence).

Public Health Relevance

Many devastating human diseases, such as schizophrenia and bipolar disorder are heritable, but the genetic basis of these disorders is not well understood. Through application of genetically-based novel brain phenotypes and state-of-the-art statistical approaches in a large sample with brain imaging and genotyping data, we will increase understanding of the genetic basis of the human brain and psychiatric disorders, which could lead to new insight into disease mechanisms and identify novel therapeutic targets. The information of the genetic basis of these disorders can be used for generalization performance of predictive models for patient stratification with potential usage in clinical medicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH100351-02
Application #
8690981
Study Section
Molecular Neurogenetics Study Section (MNG)
Program Officer
Addington, Anjene M
Project Start
2013-07-01
Project End
2018-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Psychiatry
Type
Schools of Medicine
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093
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