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. Werf, Thuc D. Le () and S. Hong Lee ()
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Buu Truong: UniSA STEM, University of South Australia
Xuan Zhou: University of South Australia Cancer Research Institute, University of South Australia
Jisu Shin: University of South Australia Cancer Research Institute, University of South Australia
Jiuyong Li: UniSA STEM, University of South Australia
Julius H. J. Werf: University of New England
Thuc D. Le: UniSA STEM, University of South Australia
S. Hong Lee: University of South Australia Cancer Research Institute, University of South Australia

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-16829-x

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