Mehta Divya, Czamara Darina
School of Psychology and Counselling, Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Australia.
Department of Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
Curr Top Behav Neurosci. 2019;42:1-34. doi: 10.1007/7854_2019_105.
Over the past decade, genome-wide association studies (GWAS) have evolved into a powerful tool to investigate genetic risk factors for human diseases via a hypothesis-free scan of the genome. The success of GWAS for psychiatric disorders and behavioral traits have been somewhat mixed, partly owing to the complexity and heterogeneity of these traits. Significant progress has been made in the last few years in the development and implementation of complex statistical methods and algorithms incorporating GWAS. Such advanced statistical methods applied to GWAS hits in combination with incorporation of different layers of genomics data have catapulted the search for novel genes for behavioral traits and improved our understanding of the complex polygenic architecture of these traits.This chapter will give a brief overview on GWAS and statistical methods currently used in GWAS. The chapter will focus on reviewing the current literature and highlight some of the most important GWAS on psychiatric and other behavioral traits and will conclude with a discussion on future directions.
在过去十年中，全基因组关联研究（GWAS）已发展成为一种强大的工具，通过对基因组进行无假设扫描来研究人类疾病的遗传风险因素。GWAS在精神疾病和行为特征方面的成功程度参差不齐，部分原因在于这些特征的复杂性和异质性。在过去几年中，在开发和应用结合GWAS的复杂统计方法和算法方面取得了重大进展。将此类先进统计方法应用于GWAS结果，并结合不同层面的基因组数据，推动了对行为特征新基因的搜索，并增进了我们对这些特征复杂多基因结构的理解。本章将简要概述GWAS以及当前GWAS中使用的统计方法。本章将重点回顾当前文献，突出一些关于精神和其他行为特征的最重要的GWAS，并以对未来方向的讨论作为结尾。
