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

serial: '3'
sid: '180236'
uuid: e9cba995-ccab-4c19-9d83-2fe73c86a2bf
uri: /egrp/seqspaceabstracts
created: '1779372946'
completed: '1779372946'
changed: '1779372946'
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langcode: en
webform_id: seqspace_abstracts_
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metatag: meta
data:
  degree_s_: PhD
  email: chentony@broadinstitute.org
  first_name: Tony
  last_name: Chen
  middle_initial: ''
  organization: 'Massachusetts General Hospital'
  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.'
  title: 'Postdoctoral Research Fellow'
  ttile: 'SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations'