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Title: Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives
Contributor(s): Truong, Buu (author); Zhou, Xuan (author); Shin, Jisu (author); Li, Jiuyong (author); Van Der Werf, Julius H J  (author)orcid ; Le, Thuc D (author); Lee, S Hong  (author)
Publication Date: 2020
Early Online Version: 2020-06-17
Open Access: Yes
DOI: 10.1038/s41467-020-16829-x
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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.

Publication Type: Journal Article
Grant Details: ARC/DP190100766
Source of Publication: Nature Communications, v.11, p. 1-11
Publisher: Nature Publishing Group
Place of Publication: United Kingdom
ISSN: 2041-1723
Fields of Research (FoR) 2020: 310207 Statistical and quantitative genetics
Socio-Economic Objective (SEO) 2020: 280102 Expanding knowledge in the biological sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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