Moore Jason H, Williams Scott M
Computational Genetics Laboratory, Department of Genetics and Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, NH 03756, USA.
Am J Hum Genet. 2009 Sep;85(3):309-20. doi: 10.1016/j.ajhg.2009.08.006.
The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.
高通量基因分型技术的广泛应用开启了个人遗传学时代,这为消费者带来了利用基因变异预测个体对常见疾病易感性的希望。尽管商业个人遗传学服务唾手可得,但我们对常见疾病遗传结构的了解仍然非常有限,尚未实现准确预测大多数患病风险人群的承诺。部分原因在于基因型与表型之间的映射关系复杂,这是由上位性(基因-基因相互作用)以及其他现象如基因-环境相互作用和基因座异质性导致的。不幸的是,在为解释大规模基因关联结果提供知识基础的大多数基因关联研究中,尚未涉及遗传结构的这些方面。在此,我们对上位性如何影响人类健康和疾病以及如何在基于人群的研究中检测上位性进行了介绍性综述。鉴于这种复杂性,我们对上位性对个人遗传学的影响提出了一些看法,并对改进个人遗传学提出了一些建议。