# Genetics as a tool to improve phenotypes and associations with human disease

> **NIH NIH R35** · UNIVERSITY OF COLORADO DENVER · 2024 · $390,000

## Abstract

PROJECT ABSTRACT
The Cole Lab’s overarching vision is to harness unique characteristics of the human genome to gain deeper
insights into the impact of environmental risk factors on human disease. Our research focuses on two key areas.
First, we aim to develop methods that address gaps in the derivation, analysis, and interpretation of phenotypes
and their associations with markers of human health. Second, we utilize these and other approaches to identify
risk factors and mechanisms related to disease, consequently driving the evolution of novel methods. These two
pillars of my lab are synergistic, continually reinforce one another, and will support my lab’s sustainable growth.
Over the next five years, our primary goals are to develop methods and expand resources for the broader
scientific community that improve the derivation and utilization of environmental phenotypes for human disease
research. One, we will improve the curation and optimization of phenotype pre-processing decisions using
genetic heritability as an unbiased metric for phenotype precision. Two, we will use genetic correlation as a proxy
for phenotypic correlation in non-overlapping individuals to evaluate phenotype similarity and harmonize traits
across diverse groups of individuals. Third, to improve the accurate identification and interpretation of the role
environmental risk factors play in human disease, we will extend traditional genetic causal inference methods
(i.e. Mendelian randomization) to better handle excess cross-trait correlation and genetic instrument mis-
specification commonly seen with environmental risk factors. Furthermore, we will use comprehensive sets of
phenotypes in diverse, large-scale biobanks (UK Biobank and AllofUs) to develop and optimize these methods,
and will make the methods and results publicly available for a broad range of scientists to utilize.
Both in the short-term and long-term, we aim to expand public datasets with more comprehensive, meticulously
curated phenotypes. In the next five years, we will link clinical trials with high-quality longitudinal phenotypes at
the University of Colorado (CU) with data available through the Colorado Biobank, including genetics, health
records, surveys, and geocoded data. This will enhance this resource for CU investigators, and specifically
enable our group to investigate the effects of genetic risk on intervention response and clinical-grade phenotypes.
Our efforts in the next five years will support our research focus on applied statistical genetics to identify risk
factors and mechanisms related to disease, with an emphasis on metabolic disease as a model system for which
our group has expertise. In the long-term, this work allows our group to identify novel research gaps for new
method development. Furthermore, our applied research positions our team to best identify the challenges of
uniting scientists across disciplines and skillsets and the gaps in current resources. Together this research and
...

## Key facts

- **NIH application ID:** 10936970
- **Project number:** 1R35GM154792-01
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Joanne Burnette Cole
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $390,000
- **Award type:** 1
- **Project period:** 2024-09-10 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10936970

## Citation

> US National Institutes of Health, RePORTER application 10936970, Genetics as a tool to improve phenotypes and associations with human disease (1R35GM154792-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10936970. Licensed CC0.

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