TL;DR
It is shown that SPLENDID significantly improved prediction accuracy over existing methods, particularly for non-European and admixed ancestries, particularly for non-European and admixed ancestries.
Polygenic risk scores (PRSs) trained on multiancestry data can improve prediction in under-represented groups, but large linked genetic and health datasets capturing broad human diversity remain limited. Using 245,388 whole-genome sequences from the All of Us research program (AoU) together with UK Biobank data, we dev...
Biobanks increasingly include individuals with admixed genomes, yet conventional genome-wide association study frameworks either exclude participants who cannot be confidently assigned to a discrete ancestry group or ignore ancestry-specific effects. We present FELIX, a scalable framework for local-ancestry-aware genet...
This work performs a large-scale, multi-ancestry admixture mapping study across 415,792 unrelated individuals in the UK Biobank, examining associations between local haplotype ancestry and 108 phenotypes, demonstrating striking genetic heterogeneity.
Introduction Ischemic stroke (IS) is a leading cause of morbidity and mortality, and predicting events remains challenging. Polygenic risk scores (PRSs) aggregate genetic variants, but performance varies across ancestries and remains modest, particularly in non-European populations. Methods We compared Bayesian PRS met...
Polygenic risk scores (PRS) offer considerable potential for precision medicine. How ever, their predictive performance often attenuates when applied to populations that differ from the genome-wide association study (GWAS) training population. There are many potential sources of this portability problem, and one relati...
Human populations differ in disease prevalence and phenotypes, but the extent to which differences are caused by genetic factors is unknown for most complex traits. Comparing phenotypic means across populations is confounded by environmental differences and using polygenic predictors can lead to biased inference1,2. Fa...
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