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Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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  • Clara Albiñana
  • Jakob Grove
  • John J McGrath
  • Esben Agerbo
  • Naomi R Wray
  • Cynthia M Bulik
  • Merete Nordentoft
  • David M Hougaard
  • Thomas Werge
  • Anders D Børglum
  • Preben Bo Mortensen
  • Florian Privé
  • Bjarni J Vilhjálmsson
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The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.

OriginalsprogEngelsk
TidsskriftAmerican Journal of Human Genetics
Vol/bind108
Udgave nummer6
Sider (fra-til)1001-1011
Antal sider11
ISSN0002-9297
DOI
StatusUdgivet - 3 jun. 2021

ID: 65785891