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• W3035930969 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." @default.
• W3035930969 created "2020-06-25" @default.
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• W3035930969 date "2020-06-17" @default.
• W3035930969 modified "2023-10-14" @default.
• W3035930969 title "Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives" @default.
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• W3035930969 doi "https://doi.org/10.1038/s41467-020-16829-x" @default.
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