# Enabling improved applicability and transferability of polygenic scores across diverse populations- a focus on South Asians

> **NIH NIH U01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $996,075

## Abstract

Polygenic scores – which quantify inherited risk by integrating information from many common sites of DNA
variation – hold considerable promise for enabling a tailored approach to clinical medicine. However, alongside
considerable (and warranted) enthusiasm, we and others have highlighted a crucial equity issue – current
polygenic scores have diminished predictive power in non-European ancestries. By assembling a team with
deep expertise in statistical genetics, clinical informatics, data sharing, and genomic medicine, we outline the
Functional and Fine-Mapping Approach to Improve Responsible Risk-modeling of Polygenic Risk
Scores (‘FFAIRR-PRS’) approach to systematically address the key factors driving diminished performance.
 To enable analysis by the NHGRI consortium within the ANVIL ecosytem, we will contribute genetic and
rich phenotype data from >57,136 individuals of South Asian ancestry from the Genes & Health and UK
Biobank Studies and whole genome sequencing data from 5,734 South Asians from the GenomeAsia Phase 2
to serve as an ancestry-matched reference panel. South Asian individuals are prioritized based on marked
under-representation in genome-wide association studies – accounting for 23% of the global population but
only 1.2% of individuals studied – and polygenic prediction efforts to date, as well as a key public health need
for enhanced risk stratification. Individual level data in ANVIL will be paired with summary association statistics
of >100,000 South Asians and individual >1 million individuals of other ancestries, which will enable enhanced
fine-mapping, sore weighting, and transethnic benchmarking activities.
 Our Study Site aims to (1) Aggregate and harmonize genotyping and phenotype data and deliver a
sharable and scalable end-to-end analytic pipeline that starts with genotyping array data and a phenotype file
and enables automated output of polygenic score benchmarking parameters.; (2) Develop and share the new
‘FFAIRR-PRS’ statistical genetics framework, leveraging: (i) fine-mapping to assign causal probabilities based
on >180 functional genomic annotations; (ii) incorporating correlations between effect sizes across traits; and
(iii) integration of South Asian and non-South Asian GWAS data; and (3) Benchmark FFAIRR-PRS scores for
27 important phenotypes in the South Asian datasets, and develop risk models that integrate genetic and
nongenetic factors. Performance will be benchmarked in accordance with ClinGen Complex Disease Working
Group recommendations and compared against individuals of European and other major ancestry groups.
Beyond enhanced polygenic scores – aware of an ultimate aim of clinical implementation – we will develop a
framework for integrated absolute risk models calibrated to the U.S. population that account for rare
monogenic variants of large effect, family history, lifestyle, and clinical risk factors by adapting the
Individualized Coherent Absolute Risk Estimator (iCARE) tool developed by co-I ...

## Key facts

- **NIH application ID:** 10424447
- **Project number:** 5U01HG011719-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Pradeep Natarajan
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $996,075
- **Award type:** 5
- **Project period:** 2021-06-08 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424447

## Citation

> US National Institutes of Health, RePORTER application 10424447, Enabling improved applicability and transferability of polygenic scores across diverse populations- a focus on South Asians (5U01HG011719-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10424447. Licensed CC0.

---

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