# Characterizing genetic signatures of natural selection to understand human diseases

> **NIH NIH R35** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $407,897

## Abstract

Abstract
We are now at a pivotal point of medical and population genetics where available genetic and genomic datasets
are powered to detect diverse signatures of natural selection on the human genome, and to investigate their
downstream effects on the genetic architecture of human diseases and complex traits. Characterizing these
signatures could enable us to improve our understanding of diseases, as well as their prevention, through
improved polygenic risk scores across diverse ancestry groups, and diagnosis, through improved variant
prioritization scores in clinical studies. However, methodological development and new analyses are still required
to make sense of these new disparate datasets.
In this proposal, we will develop models and apply methods aiming at investigate the downstream effects of
natural selection on human diseases by leveraging novel large genetic and genomic datasets. First, we will
characterize the genetic signatures of natural selection shaping the genetic architecture of human complex traits,
by leveraging polygenic methods, genome-wide association studies (GWAS) summary statistics for a hundred
of traits, and evolutionary simulations. Indeed, while many works have recently highlighted the action of negative
selection on human diseases, we still need methods to analyze low-prevalence diseases and to investigate
selection beyond the action of negative selection. Second, we will characterize the genetic signatures of recent
selection leading to different gene regulation and allele effect sizes across diverse ancestry groups by leveraging
single-cell RNA-seq and GWAS datasets from European and Asian ancestries. Developing methods to analyze
and interpret the recently released non-European genomic and genetic datasets has the premise to understand
recent human adaptation, and why allele effect sizes from GWAS differ across ancestry groups, which is
fundamental to improve polygenic risk scores transportability. Third, we will characterize the genetic signatures
of natural selection on genes at the exon and regulatory levels over millions of years of evolution by leveraging
sequencing data from 240 mammals and recent enhancer-gene maps. Including the base pair resolution of
constraint datasets to exon and regulatory scores will allow to improve our knowledge of gene evolution and
function, and ultimately the interpretation of rare genetic variants in diagnostic studies. Our methods and datasets
will be publicly available, deeply documented, and applicable to any heritable traits, maximizing their impact to
the community.

## Key facts

- **NIH application ID:** 10674983
- **Project number:** 5R35GM147789-02
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Steven Gazal
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $407,897
- **Award type:** 5
- **Project period:** 2022-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10674983

## Citation

> US National Institutes of Health, RePORTER application 10674983, Characterizing genetic signatures of natural selection to understand human diseases (5R35GM147789-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10674983. Licensed CC0.

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