Wohland Tobias, Schleinitz Dorit
IFB AdiposityDiseases, Leipzig University Medical Center, University of Leipzig - Medical Faculty, Liebigstrasse 19-21, 04103, Leipzig, Germany.
Clinic and Policlinic for Endocrinology and Nephrology, Leipzig University Medical Center, Leipzig, Germany.
Methods Mol Biol. 2018;1706:113-150. doi: 10.1007/978-1-4939-7471-9_7.
Genome-wide association studies (GWAS) provide a hypothesis-free approach to discover genetic variants contributing to the risk of a certain disease or disease-related trait. Ongoing efforts to annotate the human genome have helped to localize disease-causing variants and point to mechanisms by which genetic variants might exert functional effects. By integrating bioinformatics approaches with in vivo and in vitro genomic strategies to predict and subsequently validate the functional roles of GWAS-identified variants, disease-related pathways can be characterized, providing new possibilities for therapeutic intervention. Here, we describe a basic workflow, from sample preparation to data analysis, for performing a GWAS to identify disease genes. We also discuss resources for the annotation and interpretation of GWAS results.
全基因组关联研究（GWAS）提供了一种无假设的方法，用于发现导致某种疾病或疾病相关性状风险的遗传变异。目前对人类基因组进行注释的工作有助于定位致病变异，并指出遗传变异可能发挥功能作用的机制。通过将生物信息学方法与体内和体外基因组策略相结合，以预测并随后验证GWAS识别出的变异的功能作用，可以对疾病相关途径进行表征，为治疗干预提供新的可能性。在此，我们描述了一个从样本制备到数据分析的基本工作流程，用于进行GWAS以识别疾病基因。我们还讨论了用于注释和解释GWAS结果的资源。
