# Computational approaches to advance genomic, biological and clinical understandings of human disease

> **NIH NIH R35** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $453,075

## Abstract

PROJECT SUMMARY / ABSTRACT
 The NIGMS Established Investigator (EI) R35 Maximizing Investigators’ Research Award (MIRA)
proposal aims to use computational approaches to advance genomic, biological, and clinical understandings of
human disease. The research program is broadly focused on three areas of human genomics: genomic
association, biological mechanism and translational medicine. Within these areas, research in the laboratory
has focused on: 1) evaluation of disease risk of genetic variants; 2) development and application of Mendelian
randomization to infer causal relationships between complex traits and diseases; 3) evaluation of the complex
interplay between natural selection and human diseases; and 4) using human genomics to inform drug side
effect prediction. The proposed research program leverages large-scale genetic and clinical data resources,
combined with statistical methods development, building directly on our prior published research in each of the
research areas. Importantly, we have highlighted critical unmet needs, key knowledge gaps in our
understanding and important challenges to be addressed pertaining to general medical sciences research.
Over the next five years, we plan to embark on a series of studies designed to address these unmet needs and
overcome associated challenges. First, the disease risk of clinical variants at the variant level is uncertain. We
will quantify disease risk of clinical variants for human diseases by quantifying population-based penetrance in
the exome sequences of 510,000 individuals with linked electronic health record data. This research area can
refine variant interpretation. Second, little is known about the full spectrum of causal risk factors contributing to
complex diseases. We will dissect the phenotypic heterogeneity of complex diseases using a novel Mendelian
randomization framework. This research area can provide new insights into the heterogenous causes of
complex diseases. Third, little is known about the contribution of rare coding variants on deleterious load, and
its effect on human phenotypes. We will examine the interplay between fitness via the load, its constituents
and human phenotypes in a very large exome sequencing dataset (e.g. 510,000 individuals) that enables
capture of rare coding variants. This research area will provide insights into the bidirectional relationship
between deleterious load and human phenotypes, which can inform about the genetic architecture of human
phenotypes. Fourth, studies have shown that the side effects of drugs targeting genes are enriched for certain
human genomic features; however, these studies have not yet translated to useful prediction of drug side
effects. We will build a human genomics-guided priority index for drug side effect prediction using a drug side
effect dataset and a wide array of genetic features. This research area can potentially improve selection of
drug therapeutic candidates in drug development. Taken together, the res...

## Key facts

- **NIH application ID:** 10833465
- **Project number:** 5R35GM124836-07
- **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:** 2024
- **Award amount:** $453,075
- **Award type:** 5
- **Project period:** 2017-09-15 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833465

## Citation

> US National Institutes of Health, RePORTER application 10833465, Computational approaches to advance genomic, biological and clinical understandings of human disease (5R35GM124836-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10833465. Licensed CC0.

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