Approved research

Detecting pleiotropic effects through integration of omics data

Lay summary

Using omics data, we will attempt to identify shared genetic variants that play a role in a number of common traits and diseases that have a high public health significance that include: asthma, obesity, type 2 diabetes, blood pressure and blood lipid profiles. We will accomplish these goals by using the UK Biobank data and a second dataset with gene expression and genome sequence data on a small set of subjects. We will use statistical methods to detect shared genetic effects and perform biological validation in order to bring about a better understanding of the role shared genetics plays in complex disease. The co-occurrence of common traits and diseases pose a critical public health challenge; this co-occurrence may be explained, in part, by genetic loci shared between these traits and diseases. Identifying shared genetic loci for pairs of traits/diseases will advance our understanding of the mechanistic links between them. These loci have the potential to serve as targets for a single intervention that simultaneously treats both diseases. We will implement previously developed statistical methods and extend existing methods to analyze imputed and rare genetic variants to identify variants associated with two of the diseases/traits listed in 1a. All methods will be implemented in software developed by one of the investigators on this proposal, Dr. Suzanne Leal, which uses parallel processing to make it feasible to analyze hundreds of thousands of samples efficiently and quickly. We plan to analyze the full cohort of approximately 500,000 subjects with genotype and phenotype data.

Scope extension, May 2024: 

Using omics data, we will elucidate pleiotropic variants that play a role in a number of common traits and diseases that have a high public health significance that include: asthma, obesity, type 2 diabetes, blood pressure and blood lipid profiles. We will accomplish these goals by using the UK Biobank data and a second dataset with gene expression and genome sequence data on a small set of subjects. We will use statistical methods to detect pleiotropic effects and perform biological validation in order to bring about a better understanding of the role pleiotropy plays in complex disease etiology.

We now plan to expand the scope of our project in two ways: by additional phenotypes in which to look for pleiotropy - age-related hearing loss and tinnitus; and to incorporate some statistical methods development work, mainly methods for identifying gene-gene and gene-environment interactions. The interaction methodology work will potentially allow us a new set of variants in which to look for pleiotropy using the same set of phenotypes for which we already have approval for in our project.

We plan an additional expansion of our set of phenotypes to include ICD10 defined sepsis and ICD10 defined skin conditions including Hidradenitis suppurative. We also plan to incorporate an investigation of telomere length into our analysis of pleiotropy in additional to the genetic and sequence data.

We plan an additional expansion of our set of phenotypes to include ICD/self-report defined depression, gluacoma, peripheral artery disease, COPD, colorectoal cancer and sleep traits. We will also be incorporating the pre-defined polygenic scores that are available to examine how these can be used to predict comorbidity and co-heritability.