Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives

Buu Truong, Xuan Zhou, Jisu Shin, Jiuyong Li, Julius H.J. van der Werf, Thuc D. Le, S. Hong Lee

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention.

Original languageEnglish
Article number3074
JournalNature Communications
Volume11
Issue number1
DOIs
Publication statusPublished or Issued - 1 Dec 2020
Externally publishedYes

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry,Genetics and Molecular Biology
  • General Physics and Astronomy

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