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Phenome-wide analysis of genome-wide polygenic scores

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  • E. Krapohl
  • J. Euesden
  • D. Zabaneh
  • J. B. Pingault
  • K. Rimfeld
  • S. Von Stumm
  • P. S. Dale
  • G. Breen
  • P. F. O'Reilly
  • R. Plomin


Publication details

JournalMolecular psychiatry
DateAccepted/In press - 14 Jul 2015
DateE-pub ahead of print (current) - 25 Aug 2015
Issue number9
Number of pages6
Pages (from-to)1188-1193
Early online date25/08/15
Original languageEnglish


Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, the behavioral phenome. For 3152 unrelated 16-year-old individuals representative of the United Kingdom, we created 13 GPS from the largest GWAS for psychiatric disorders (for example, schizophrenia, depression and dementia) and cognitive traits (for example, intelligence, educational attainment and intracranial volume). The behavioral phenome included 50 traits from the domains of psychopathology, personality, cognitive abilities and educational achievement. We examined phenome-wide profiles of associations for the entire distribution of each GPS and for the extremes of the GPS distributions. The cognitive GPS yielded stronger predictive power than the psychiatric GPS in our UK-representative sample of adolescents. For example, education GPS explained variation in adolescents' behavior problems (∼0.6%) and in educational achievement (∼2%) but psychiatric GPS were associated with neither. Despite the modest effect sizes of current GPS, quantile analyses illustrate the ability to stratify individuals by GPS and opportunities for research. For example, the highest and lowest septiles for the education GPS yielded a 0.5 s.d. difference in mean math grade and a 0.25 s.d. difference in mean behavior problems. We discuss the usefulness and limitations of GPS based on adult GWAS to predict genetic propensities earlier in development.

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© The Author(s) 2016.

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