Queensland Brain Institute,University of Queensland,Brisbane,Queensland,Australia.
Institute for Molecular Bioscience,University of Queensland,Brisbane,Queensland,Australia.
Psychol Med. 2018 May;48(7):1055-1067. doi: 10.1017/S0033291717002318. Epub 2017 Aug 29.
The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.
数以十万计的人基因组范围内的遗传数据的可用性，使得可用于分析这些数据的方法也得到了同样迅速的发展。尽管进行全基因组关联研究（GWAS）的动机是确定与复杂性状相关的遗传标记，但一旦生成这些数据，就可以用于许多其他分析。GWAS 表明，复杂性状具有高度多基因遗传结构，通常在不同性状之间具有共同的遗传风险因素。用于分析 GWAS 数据的新方法越来越多地被用于解决关于复杂性状和疾病（包括精神障碍）病因的一系列不同问题。在这里，我们概述了其中的一些方法，并介绍了它们如何帮助我们理解精神障碍。我们考虑了：（i）估计遗传对性状的影响程度，（ii）揭示性状之间的共同遗传控制，（iii）个体遗传风险的预测，（iv）性状之间因果关系的揭示，（v）确定因果单核苷酸多态性和基因，或（vi）检测遗传异质性。这种分类有助于组织大量最近开发的方法，尽管有些方法可能属于不止一类。虽然有些方法需要个体的 GWAS 数据，但其他方法只需使用 GWAS 汇总统计数据，从而可以在低计算负担下进行新颖的、功能强大的分析。
