Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives
| Autor: | Julius H. J. van der Werf, Xuan Zhou, Jisu Shin, Jiuyong Li, Buu Truong, S. Hong Lee, Thuc Duy Le |
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| Přispěvatelé: | Truong, Buu, Zhou, Xuan, Shin, Jisu, Li, Jiuyong, van der Werf, Julius HJ, Le, Thuc D, Lee, S Hong |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
0301 basic medicine Multifactorial Inheritance General Physics and Astronomy Genome-wide association study predictive medicine 0302 clinical medicine lcsh:Science Biological Specimen Banks Multidisciplinary Life style food and beverages Biobank Phenotype Pedigree Experimental models of disease population screening Scale (social sciences) Female experimental models of disease Genotype Science Predictive medicine Biology Polymorphism Single Nucleotide Risk Assessment Article General Biochemistry Genetics and Molecular Biology Population screening 03 medical and health sciences Humans Genetic Predisposition to Disease Life Style Family Health Models Genetic Genome Human fungi Reproducibility of Results General Chemistry United Kingdom 030104 developmental biology Sample size determination lcsh:Q Polygenic risk score 030217 neurology & neurosurgery Genome-Wide Association Study Demography |
| Zdroj: | Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. Genetic data from large cohorts of unrelated individuals can be used to create polygenic risk scores, which could be used to predict individual risk of developing a specific disease. Here the authors show that smaller cohorts of related individuals can provide similarly powerful predictive ability. |
| Databáze: | OpenAIRE |
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