# From common to rare variant functional architectures of human diseases

> **NIH NIH R00** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $237,087

## Abstract

Project Summary/Abstract
Large-scale genome-wide association studies (GWAS) have highlighted that heritability explained by common
variants is concentrated into non-coding functional annotations that are often cell-type or tissue specific.
However, the leveraging of non-coding regulatory variants to detect new disease genes or gene sets is largely
unknown. In this proposal, I will investigate the effects of non-coding variants in lower frequency
architecture by developing a new statistical method partitioning heritability of low-frequency variants.
Then, I will use this method to connect functional heritability to genes, in order to increase the
statistical power to detect genes and gene sets enriched in coding and non-coding disease variants.
My K99 training will be conducted at the Harvard T.H. Chan School of Public Health, as well as the Broad
Institute, under the mentorship of Dr. Alkes Price. The key areas of my training will be: development of models
for partitioning heritability explained by low-frequency variants across functional annotations (including gene
set annotations); analyses of large-scale GWAS and whole genome sequencing datasets; and joint analyses of
multiple large functional genomics datasets. The long-term goal of this research is to produce functional
annotations and software that will enable geneticists to analyze large GWAS and whole genome sequencing
datasets, in order to make discoveries that will improve our biological knowledge of human diseases.
The first aim of this proposal is to develop a method for partitioning the heritability of common and low-
frequency variants across functional annotations. I will apply this method on large GWAS data sets, and will
use the results to fit an evolutionary model that will predict the distribution of rare variant effect sizes for each
annotation. The second aim is to determine the best strategy to connect functional heritability to genes. I will
compare different strategies using Hi-C data, conserved annotations, and other functional data to connect
functional elements to genes and determine which strategy is maximally informative for trait heritability. Then, I
will use this strategy to identify gene sets enriched for heritability. The third aim will leverage insights from
common and low-frequency variant enrichments estimated from large GWAS data sets (Aim 1) as well as
insights on how to connect functional elements to a gene (Aim 2) to improve the statistical power of gene-
based rare variant association tests. The new annotations and computational tools developed in this research
proposal will be distributed to the scientific community.

## Key facts

- **NIH application ID:** 10408102
- **Project number:** 5R00HG010160-05
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Steven Gazal
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $237,087
- **Award type:** 5
- **Project period:** 2020-08-12 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10408102

## Citation

> US National Institutes of Health, RePORTER application 10408102, From common to rare variant functional architectures of human diseases (5R00HG010160-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10408102. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*