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Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Wang, Ying (author)
Namba, Shinichi (author)
Lopera, Esteban (author)
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Kerminen, Sini (author)
Tsuo, Kristin (author)
Läll, Kristi (author)
Kanai, Masahiro (author)
Zhou, Wei (author)
Wu, Kuan-Han (author)
Favé, Marie-Julie (author)
Bhatta, Laxmi (author)
Awadalla, Philip (author)
Brumpton, Ben (author)
Deelen, Patrick (author)
Hveem, Kristian (author)
Lo Faro, Valeria, Postdoc (author)
Uppsala universitet,Genomik och neurobiologi,Science for Life Laboratory, SciLifeLab,Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Department of Clinical Genetics, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands
Mägi, Reedik (author)
Murakami, Yoshinori (author)
Sanna, Serena (author)
Smoller, Jordan W. (author)
Uzunovic, Jasmina (author)
Wolford, Brooke N. (author)
Willer, Cristen (author)
Gamazon, Eric R. (author)
Cox, Nancy J. (author)
Surakka, Ida (author)
Okada, Yukinori (author)
Martin, Alicia R. (author)
Hirbo, Jibril (author)
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 (creator_code:org_t)
Elsevier, 2023
2023
English.
In: Cell Genomics. - : Elsevier. - 2666-979X. ; 3:1
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Medical Genetics (hsv//eng)

Keyword

Global-Biobank Meta-analysis Initiative
polygenic risk scores
multi-ancestry genetic prediction
accuracy heterogeneity

Publication and Content Type

ref (subject category)
art (subject category)

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