Principal Investigator: Dr Hang Zhou
Yale University, New Haven, Connecticut, USATags: 41910, comorbidities, Complex Traits, GWAS, multi-ethnic study, polygenic risk score, psychiatric disorders
Genetics of complex diseases, comorbidities, and other traits in multi-ethnic samples
1a: According to the Global Burden of Disease Study 2016, many genetically complex diseases, including psychiatric disorders, are among the top diseases that cause morbidity and mortality. Genetic factors contribute substantial risks to the etiology of those diseases, and the genetic heritability was estimated to be moderate to high for many of these deleterious traits. Genetic studies, including genome-wide association studies, have identified risk genes in global populations, but mostly in European populations. However, considerable work still needs to be done, and the availability of very large datasets is greatly increasing opportunities to do this work. Some of the most important
work requires us to address the lack of study in populations except Europeans. Many traits may share, to some extent, risk variants and biological pathways. Only a few studies investigated the shared genetic factors for comorbid diseases. There is much more to do to improve our understanding of the complex genetic architecture of different disorders and the comorbidity in different populations.
1b: The purpose of the proposal is to map novel genetic risk variants to diseases and traits, identify brain areas that mediate the genetic risk, and detect differential brain connectivity patterns by investigating the UK Biobank data, and by meta-analyzing the UK Biobank data with other resources to increase power, in different populations.
1c: We will conduct genome-wide association studies using genetic data collected from UK Biobank, our datasets, and other publicly available resources, to identify genetic variants associated with each relevant disease or trait in different populations. Then we will meta-analyze all the available datasets to improve power. Brain image data will be used to map the brain neural activities, connectivity, and structural regions associated with the genetic profiles for some diseases, especially psychiatric ones. Polygenic risk scores will be used to predict genetic risks for related diseases. The heritability of diseases in different populations will be estimated, and genetic correlations with other traits will be analyzed. Functional enrichments for the genetic variants will also be investigated.
1d: This study will extend our understanding of the genetic etiology of many diseases and comorbid diseases, as well as other complex traits, which will benefit the downstream intervention or treatments.
Project extension – March 2020
What’s the genetic etiology for complex diseases (e.g., psychiatric disorders) and comorbid diseases? What are the differences between population (ethnic) groups regarding the genetic heritability, associated variants, and genetic risk profiles? What’s the difference in brain activity between people with or without the risk genetic variants associated with a specific complex trait? What are the brain areas or connectivity subnetwork that mediate the susceptible genetic risk of diseases? To answer those questions, we propose this integrated genetic study using genetic data and brain image data in UK Biobank which contains full information of genetic diseases and other traits in multi-ethnic groups. We aim to identified novel signals associated with diseases/traits such as substance use disorders (SUD), and other psychiatric traits including depression, anxiety, personality traits, and general behaviors; and comorbid diseases, and other complex traits. We wish to investigate the functional or heritability enrichments for the genetic variants and develop brain systems-based polygenic risk scores to determine how behavioral phenotypes relate to the brain imaging data. This dataset will be combined with our datasets and other resources we can access to increase power.
Furthermore, we will develop new feature selection methods for GWAS studies utilizing the UK biobank database to predict body size measures.
Last updated Mar 26, 2020