Principal Investigator: Dr James Yurkovich
Institute for Systems Biology, Seattle, USATags: 48049, cancer, common disease, metabolites, microbiome, polygenic risk scores, proteomics
Personalized medicine aims to improve health outcomes in patients by incorporating information about the genetic makeup of the individual into treatment planning and disease prevention. Recent advances in the fields of human genetics have led to the development of polygenic risk scores that combine the effects of multiple genetic variants to determine whether an individual is at an increased risk for a particular disease. The mechanisms through which these polygenic risk scores increase disease risk are poorly understood. Incorporation of this information into routine clinical care has the potential to reduce unnecessary interventions, enable early disease detection, and develop novel preventative care strategies. Throughout the course of 2019, Arivale’s Research Team proposes to perform a set of studies using the UK BioBank data to build polygenic risk scores for a number of common diseases. We will then leverage the Arivale internal dataset that includes genetic, blood, stool, and health history data on more than 4,500 individuals to identify biological signatures of genetic risk for disease. The proposed research project has the potential to contribute to our understanding of the biological mechanisms that underlie genetic risk for common diseases by identifying the specific biological features characteristic of individuals with low and high estimated genetic risk. These biological signatures, in turn, may enable the discovery of early signs that a disease is developing, research on novel therapies, and the development of personalized strategies for disease prevention.
Scope extension – October 2019
We will characterize polygenic risk scores (PGS) for blood biomarkers derived from the UK BioBank dataset. This extension is aimed at providing a multi-omic characterization of PGS for all blood analyte assays. We will explicitly model properties of multi-omic networks in a cohort of healthy individuals who participated in the commercial wellness program with high-density personal data clouds that include genomic, clinical, metabolomic, proteomic, gut microbiome, and lifestyle data (N>4,500).
First, by simultaneously profiling and contrasting multi-omic changes across the continua of type 2 diabetes (T2D) and glycated hemoglobin (HbA1c) specific PGS, we aim to discover shared and unique disease pathways as indexed by disease- and biomarker-based population GWAS. We will then compare the multi-omic impact of changes along the T2D PGS continua with those associated with individual in HbA1c levels. Finally, this extension is motivated by our published studies of small cohorts of long-lived individuals (Gierman, 2014; Erikson, 2016) that we would like to extend to the UK Biobank. We will perform precise cohort design through genome fingerprint matching (Glusman 2017) and statistical analysis of common and rare variants and haplotypes compared with other datasets (e.g., matched controls) in the context of longevity research.
Last updated Oct 31, 2019