Predictive accuracy of combined genetic and environmental risk scores.
Predictive accuracy of combined genetic and environmental risk scores.
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DOI：
10.1002/gepi.22092
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发表时间：
2018-03
影响因子：
2.1
通讯作者：
Yang J
中科院分区：
文献类型：
--
作者：
Dudbridge F;Pashayan N;Yang J
大多数复杂疾病的巨大遗传性表明，遗传数据可以提供有用的风险预测。到目前为止，遗传风险评分的表现低于遗传力所暗示的潜力，但这可以解释为估计高度多基因模型的样本量不足。当基于环境或生活方式的风险预测因子已经存在时，两个关键问题是通过添加遗传信息可以在多大程度上改善它们，以及遗传和环境风险评分组合的最终潜力是什么？在这里，我们扩展了以前的多基因评分的预测准确性的工作，以允许环境评分，可能与多基因评分相关，例如当环境因素介导的遗传风险。我们得出的预测准确性和改善的功能的训练样本量，芯片遗传疾病和环境评分，疾病和环境风险因素之间的遗传相关性的共同措施。我们考虑两个分数的简单相加和考虑它们的相关性的加权和。使用心血管疾病和乳腺癌研究的例子，我们表明，改善歧视一般是小的，但合理的程度的重新分类，可以获得与目前的样本量。遗传和环境分数之间的相关性只有轻微的影响，在现实的情况下的数值结果。从长远来看，随着多基因评分准确性的提高，与环境评分相比，它们将主导预测准确性。
The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.
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影响因子：
2.1
作者：
Dudbridge F
通讯作者：
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