# Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk Prediction

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $558,315

## Abstract

Abstract
Modern genome-wide association studies have unequivocally demonstrated that complex traits are extremely
polygenic, with each individual trait potentially involving thousands to tens of thousands of genetic variants. In
this project, we will develop a series of novel methods to harness the power of polygenic signals in large
GWAS to inform disease etiology and improve models for risk prediction. In (Aim 1), we will develop methods
for conducting enrichment analysis of association signals in GWAS in relationship to various population genetic
and functional genomic characteristics of the genome. We propose to model effect-size distributions
associated with whole genome panel of markers using flexible normal-mixture models, where class
memberships of the markers are modelled probabilistically in terms of various genomic “covariates”. Inferred
models and underlying parameters will be further utilized in an empirical-Bayes framework to derive polygenic
risk-scores (PRS) for genetic risk prediction. In (Aim 2), we will develop novel methods for Mendelian
randomization analysis, a form of instrumental variable analysis, for the investigation of causal relationships
between risk-factors and health outcomes. We will utilize flexible models for bivariate effect-size distributions
across pairs of traits, allowing for genetic correlation to arise from both causal and non-causal relationships.
We propose a solution to the complex problem of estimation of causal effects under the proposed framework
using an innovative method for “spike detection” in the distribution of certain types of residuals. In (Aim 3), we
will develop novel methods to enhance the power of gene-environment interaction analysis using PRS in case-
control studies. We will develop retrospective methods that can take advantage of various natural assumptions
about the distribution of PRS, including normality and its independence from environmental exposures,
possibly conditional on other factors, in the underlying population. We will apply the proposed methods to
conduct large scale analysis of existing GWAS datasets for a wide variety of traits and expect to make novel
scientific observations regarding mechanisms of genetic susceptibility, causal basis for epidemiologic
associations, nature of gene-environment interactions and utility of genetic risk prediction.

## Key facts

- **NIH application ID:** 10112944
- **Project number:** 5R01HG010480-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Nilanjan Chatterjee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $558,315
- **Award type:** 5
- **Project period:** 2019-05-01 → 2024-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10112944

## Citation

> US National Institutes of Health, RePORTER application 10112944, Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk Prediction (5R01HG010480-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10112944. Licensed CC0.

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