Polygenic Epidemiology.
Polygenic Epidemiology.
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DOI：
10.1002/gepi.21966
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发表时间：
2016-05
影响因子：
2.1
通讯作者：
Dudbridge F
中科院分区：
文献类型：
--
作者：
Dudbridge F
复杂性状的大部分遗传基础存在于当前的基因分型产品中，但影响性状的个体变异在很大程度上尚未被鉴定。遗传流行病学中的几个传统问题最近已经通过假设疾病的多基因基础并将其视为单一实体来解决。在这里，我简要地回顾了其中的一些应用，这些应用统称为多基因流行病学。该领域的方法包括多基因评分、线性混合模型和连锁不平衡评分。它们已被用于建立多基因效应，估计性状之间的遗传相关性，估计有多少变体影响性状，将病例分层为亚表型，预测个体疾病风险，以及使用孟德尔随机化推断因果效应。多基因流行病学将继续产生有用的应用，即使许多复杂性状下的特定变异仍然没有被发现。
Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.