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全基因组关联数据的分析

Analysis of Genome-Wide Association Data.

作者信息

McRae Allan F

机构信息

Centre for Neurogenetics and Statistical Genomics, Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia.

出版信息

Methods Mol Biol. 2017;1526:161-173. doi: 10.1007/978-1-4939-6613-4_9.

Abstract

The last decade has seen substantial advances in the understanding of the genetics of complex traits and disease. This has been largely driven by genome-wide association studies (GWAS), which have identified thousands of genetic loci associated with these traits and disease. This chapter provides a guide on how to perform GWAS on both binary (case-control) and quantitative traits. As poor data quality, through both genotyping failures and unobserved population structure, is a major cause of false-positive genetic associations, there is a particular focus on the crucial steps required to prepare the SNP data prior to analysis. This is followed by the methods used to perform the actual GWAS and visualization of the results.

摘要

在过去十年中,人们对复杂性状和疾病的遗传学理解取得了重大进展。这在很大程度上是由全基因组关联研究(GWAS)推动的,该研究已经确定了数千个与这些性状和疾病相关的基因座。本章提供了关于如何对二元(病例对照)性状和定量性状进行GWAS的指南。由于数据质量差,包括基因分型失败和未观察到的群体结构,是导致假阳性遗传关联的主要原因,因此特别关注分析前准备SNP数据所需的关键步骤。接下来是用于进行实际GWAS和结果可视化的方法。

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