Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives
Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives
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DOI：
10.1038/s41467-020-16829-x
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发表时间：
2020-06-17
影响因子：
16.6
通讯作者：
Lee, S. Hong
中科院分区：
文献类型：
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作者：
Buu Truong;Zhou, Xuan;Lee, S. Hong
多基因风险评分正在成为预测目标个体未来表型的潜在强大工具，通常使用无血缘关系的个体，从而降低来自亲属的信息的价值。在这里，对于来自UK Biobank数据的50个性状，我们表明，尽管样本量相差44倍，但5000个具有目标个体一级亲属的个体的设计可以实现与约22万个无亲缘关系个体相似的预测精度（平均预测精度= 0.26 vs. 0.24，平均fold-change = 1.06 (95% CI: 0.99-1.13)， p值= 0.08）。对于生活方式性状，包括目标个体一级亲属在内的5000个体的预测准确率显著高于无血缘关系个体22万个体的预测准确率（平均预测准确率= 0.22 vs. 0.16，平均fold-change = 1.40 (1.17 ~ 1.62)， p值= 0.025）。我们的研究结果表明，整合家庭信息的多基因预测可能有助于加速精准健康和临床干预。来自大量不相关个体的遗传数据可用于创建多基因风险评分，可用于预测个体患特定疾病的风险。在这里，作者表明，较小的相关个体群体可以提供同样强大的预测能力。
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.