Truong, Buu

Zhou, Xuan

Shin, Jisu

Li, Jiuyong

Van Der Werf, Julius H J

Le, Thuc D

Lee, S Hong

2020

<p> 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. </p>

Nature Communications, v.11, p. 1-11

2041-1723

32555176

https://hdl.handle.net/1959.11/51468

Nature Publishing Group

Attribution 4.0 International

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

Journal Article

Publication