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

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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 Chatterjee.