Approved Research

Genetic contributors to complex human traits: emphasis on neurological and cardiovascular-related diseases and the interplay between these two systems

Lay summary

Project aims: We aim to identify and better understand the genetic contributors underlying human diseases in order to better predict who will develop disease and how we can more effectively treat different clinical presentations.

Background/scientific rationale: Using large collections of genetic information from hundreds of thousands of individuals and comparing them to individuals that do not have disease can enable the association of disease with previously unknown genes and genomic regions. The identification of new genetic contributors can provide new targets for researchers to investigate in order to ultimately develop new treatment approaches. Our focus will be on cardiovascular-related and neurological diseases, two of the leading causes of death or disability as the population ages, as well as the interplay between these two biological systems.

Anticipated project duration: 36 months

Potential public health impact: As genetic data becomes more readily available in the clinic, it will be increasingly important to accurately predict disease risk to more accurately treat and follow patients that are predisposed for disease. By identifying new genes and biological mechanisms involved in disease onset or progression, this project has the potential to provide novel avenues for advancing knowledge on cardiovascular-related and neurological diseases, and to open up previously undiscovered treatment possibilities.

Scope extension:

Research questions: What are the characteristics of genetic variants associated with cardiovascular-related and neurological traits, and how can we use this information to better understand the interplay between these two biological systems? Can we use genetic and non-genetic data to predict individual-level disease risk?

Aim 1a) Assess which types of genetic variant categories are enriched for a specific trait/category of traits (correlated and non-correlated traits). Our focus will be on cardiovascular-related and neurological traits, and the interplay between these two systems. We will assess which classes (for instance, single nucleotide polymorphism or short insertions or deletions) and categories (for instance, missense, intergenic, variants in promoters) of variants contribute to complex human traits.

Aim 1b) Repeat Aim 1a) using genome-wide association results using subsets of individuals split by genetic ancestry to assess ancestry-specific differences in enrichment.

Aim 1c) Repeat Aim 1a) using genome-wide association results from female-specific and male-specific analyses to assess sex-specific differences in enrichment for females and males, respectively.

Aim 2. Train machine learning models in the UK Biobank to predict disease risk for cardiovascular-related and neurological traits (exposures: genetic variation and non-genetic factors; outcome: disease, yes or no) and test predictions in in-house cohorts.

In addition to cardiovascular-related and neurological traits, we will also explore the genetic contributors to other systems, including cardiometabolic traits more broadly (including diabetes), as well as kidney-related traits (hematuria, eGFR, albumin-creatinine ratio).