2026 Sequencing Strategies for Population and Cancer Epidemiology Studies (SeqSPACE) : Submission #3

Submission information
Submission Number: 3
Submission ID: 180236
Submission UUID: e9cba995-ccab-4c19-9d83-2fe73c86a2bf
Submission URI: /egrp/seqspaceabstracts

Created: Thu, 05/21/2026 - 10:15
Completed: Thu, 05/21/2026 - 10:15
Changed: Thu, 05/21/2026 - 10:15

Remote IP address: 10.208.28.250
Submitted by: Anonymous
Language: English

Is draft: No
First Name Tony
Middle Initial
Last Name Chen
Degree(s) PhD
Position/Title/Career Status Postdoctoral Research Fellow
Organization Massachusetts General Hospital
Email chentony@broadinstitute.org
Abstract Title SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations
Abstract Summary Polygenic risk scores are widely used in disease risk stratification, but their accuracy varies across different ancestries. Recent methods leverage multi-ancestry data to improve accuracy in under-represented populations but require labelling individuals by ancestry. This poses practical challenges, as clinical decisions are typically not based on ancestry, and many individuals may not fit into a pre-specified ancestry group. We propose SPLENDID, a penalized regression framework for large-scale individual-level data that models genetic ancestry as a continuum to produce a unified prediction model without any ancestry labels. In extensive simulations and analysis in the All of Us Research Program (N=224,364) and UK Biobank (N=340,140), we show that SPLENDID significantly improved prediction accuracy over existing methods, particularly in non-European and admixed ancestries. By modeling genetic interactions with continuous ancestry, we further identified ancestry-differential effects in lipid and blood cell phenotypes that may explain limited transferability of existing PRS methods across ancestry groups. Finally, using a logistic regression extension of SPLENDID improved prediction of breast and prostate cancer by 6% and 9%, respectively, compared to current state-of-the-art PRS. Altogether, SPLENDID stands as a valuable tool for robust risk prediction across diverse populations, reduced health disparities in genetic research, and fairer clinical implementation.