# A method to improve capture of causal genetics and by extension, cross-population portability when constructing polygenic scores

> **NIH NIH F31** · UNIVERSITY OF CHICAGO · 2023 · $47,694

## Abstract

PROJECT SUMMARY/ABSTRACT
Polygenic scores (PGS) can predict disease risk in a population or an individual using genetic data and are
poised to improve clinical care by making personalized preventive medicine a reality. Unfortunately, current
methods of PGS are less accurate predictors across populations of non-European ancestry as a consequence
of Eurocentric biases in genome-wide association studies (GWAS). PGS prediction accuracy can also vary
within just a single population due to differences in the environment experienced by individuals. These biases
in prediction both within and between populations severely limit the applicability of PGS. Unlike other forms of
medical inequity which benefit one population while harming or neglecting another (e.g., prescription drugs
such as some long-acting β2-agonist asthma treatments which exacerbate illness in African-ancestry
populations), PGS perform ubiquitously better across European populations, leading clinical applications to
systematically benefit individuals of European descent and neglecting the rest of the world. Despite this
systematic bias, clinical trials of PGS are underway with applications in breast and prostate cancers, type I
diabetes, and cardiovascular disease. Interest in PGS is only growing despite its limitations, so I propose to
develop methods that can aid in mitigating some of the harm caused by premature applications of PGS. In Aim
1 I will build and apply PGS(C) a method of PGS which can account for the effects of a binarized context (e.g.,
sex) on a trait by incorporating gene by context interactions (GxC) into my PGS model. I will apply this model
to improving prediction of sexually dimorphic traits such as major depression and Alzheimer’s disease witin
multiple diverse datasets. This will yield a more portable PGS better able to predict disease risk in varied
populations, incorporating biological variability, and gene by environment (GxE) interactions into prediction. In
Aim 2 I will extend this method to incorporate continuous contexts (e.g., ancestry, environment, age, etc.) into
prediction. Additionally, I will compare my novel PGS(C) method to existing state-of-the-art PGS methods to
itdentify when each method mostly accurately predicts a trait while minimizing loss in portability. This work is a
concerted effort to improve PGS portability, a crucial step in constructing a score that can bridge existing gaps
in genetic medicine negatively impacting diverse and underrepresented study populations.

## Key facts

- **NIH application ID:** 10679656
- **Project number:** 1F31HG013059-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Renee Fonseca
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $47,694
- **Award type:** 1
- **Project period:** 2023-11-01 → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10679656

## Citation

> US National Institutes of Health, RePORTER application 10679656, A method to improve capture of causal genetics and by extension, cross-population portability when constructing polygenic scores (1F31HG013059-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10679656. Licensed CC0.

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