Comparison of Family History and SNPs for Predicting Risk of Complex Disease
Comparison of Family History and SNPs for Predicting Risk of Complex Disease
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DOI：
10.1371/journal.pgen.1002973
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发表时间：
2012-10-01
期刊：
影响因子：
4.5
通讯作者：
Eriksson, Nicholas
中科院分区：
文献类型：
--
作者：
Do, Chuong B.;Hinds, David A.;Eriksson, Nicholas
家族史和基因检测的临床效用通常被很好地理解为简单的孟德尔疾病和复杂疾病的罕见亚型，这些疾病直接归因于高度外显的遗传变异。然而，目前很少有人知道这些方法在疾病易感性取决于中等或低遗传率的多个遗传因素的累积贡献的情况下的性能。利用数量遗传学理论，我们开发了一个模型，用于研究家族史的预测能力和基于单核苷酸多态性（SNP）的方法，用于评估多基因疾病的风险。我们表明，家族史是最有用的高度共同的，遗传条件（e。例如，在一个实施例中，冠状动脉疾病），其中它解释了大约20%-30%的疾病遗传性，与迄今为止发现的基于关联的最成功的SNP模型相当。相比之下，我们发现，对于中度或低频率的疾病（e。例如，在一个实施例中，克罗恩病）家族史占疾病遗传力的不到4%，在几乎所有情况下都大大落后于SNP。这些结果表明，对于广泛的疾病，已经确定的SNP关联可能比基于家族史的对应物更好地预测风险，尽管大部分缺失的遗传性仍有待解释。我们的模型说明了使用家族史或SNP进行独立疾病预测的困难。另一方面，我们表明，与家族史不同，基于SNP的测试可以揭示相对较大比例的个体的极端似然比，从而在鉴别诊断中提供潜在的有价值的证据。
The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)-based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e. g., coronary artery disease), where it explains roughly 20%-30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e. g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history-based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP-based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.