Accuracy of predicting the genetic risk of disease using a genome-wide approach.
Accuracy of predicting the genetic risk of disease using a genome-wide approach.
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DOI：
10.1371/journal.pone.0003395
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发表时间：
2008
期刊：
影响因子：
3.7
通讯作者：
Woolliams JA
中科院分区：
文献类型：
--
作者：
Daetwyler HD;Villanueva B;Woolliams JA
预测个人的遗传疾病风险是一个强有力的公共卫生工具。虽然预测风险在遵循简单孟德尔遗传的疾病中是成功的，但在复杂的疾病中，它已被证明具有挑战性，因为大量的基因座有助于遗传变异。现在大量的单核苷酸多态性为高准确性地预测复杂疾病的遗传风险提供了新的机会。我们已经推导出简单的确定性公式来预测预测的准确性，预测遗传风险的人群或病例对照研究，使用全基因组的方法，并假设一个二分疾病表型与潜在的连续负债。我们表明，预测方程是特殊情况下的更一般的问题，预测的准确性估计的遗传值的连续表型。我们的预测方程是响应于所有的参数，影响精度，他们是独立的等位基因频率和效应分布。通过模拟测试时，确定性预测误差一般都很小。准确性表达之间的共同联系是，它们最好概括为表型记录数与风险位点数之比与观察到的遗传力的乘积。这项研究促进了对疾病病例控制和人群研究相对力量的理解。预测表示可以用改进的效果估计方法实现的准确度的上限。推导出的公式将有助于研究人员确定适当的样本量，以达到一定的精度预测遗传风险。
The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.
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