# Towards an integrated map of causal connections for common, complex diseases

> **NIH NIH R35** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $413,105

## Abstract

PROJECT SUMMARY / ABSTRACT
 Research in my laboratory aims to understand the genetic and biological links between different complex
traits and diseases. We plan to use computational approaches from several different disciplines, including
human genetics, statistical genetics, epigenetics and population genetics to map out the genetic, functional
and evolutionary links between hundreds of traits and diseases simultaneously.
 Genetic studies have inferred causal connections among numerous traits (re. measurable indicators of the
severity or presence of a disease state) and disease. However, almost all of these studies have tested one trait
with one disease at a time. While important for testing specific hypotheses about specific relationships
obtained from epidemiological studies, these studies, by nature, tend to miss unforeseen and unexpected
causal connections with other traits or disease. Furthermore, complex patterns across several traits and
diseases would be missed. For these reasons, there is a need to consider an approach that incorporates all
traits and disease links in a single, unified framework (the `phenome-wide map').
 To this end, over the next five years, we plan to embark on a series of studies to first, 1) build a phenome-
wide map of causal connections between a multitude of common human traits and diseases. This map
requires individual single nucleotide variant (SNV) level association results from genome-wide association
data. As a result, I have begun to build a comprehensive repository of genome-wide association data for
millions of SNVs and hundreds of different traits, biomarkers and diseases from several studies. This data
spans a wide spectrum of common diseases, including cardiovascular disease, cardiometabolic conditions,
inflammatory diseases, psychiatric disorders, renal function, amongst others. We will infer causal connections
using this repository of data between all combinations of associated SNVs, traits and diseases to generate the
phenome-wide map.
 Next, we will add biological links to the map by incorporating information related to 2) molecular function via
gene regulation. We will infer links to each SNV in the phenome-wide map with regulatory elements, cell types,
and expression of genes. Third, we will incorporate 3) natural selection metrics at the per gene level into our
phenome-wide map. We will develop an approach to make predictions on the strength and mode of natural
selection at the per gene level, and then add this to our map of causal connections. Finally, we expect to use
the phenome-wide map to explore similarities and differences across the different links observed between the
traits and diseases.
 Our proposed research program can provide insights into new biological mechanisms behind the shared
etiology of traits and diseases. Importantly, our research also has direction precision medicine applications as
it can inform about prioritization of new gene targets for drug discovery efforts.

## Key facts

- **NIH application ID:** 10004664
- **Project number:** 5R35GM124836-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Ron Do
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $413,105
- **Award type:** 5
- **Project period:** 2017-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10004664

## Citation

> US National Institutes of Health, RePORTER application 10004664, Towards an integrated map of causal connections for common, complex diseases (5R35GM124836-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10004664. Licensed CC0.

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