全基因组关联研究
遗传关联
遗传建筑学
特质
遗传力
数据科学
计算生物学
生物
计算机科学
数量性状位点
遗传学
单核苷酸多态性
基因
基因型
程序设计语言
作者
Emil Uffelmann,Qin Qin Huang,Nchangwi Syntia Munung,Jantina de Vries,Yukinori Okada,Alicia R. Martin,Hilary C. Martin,Tuuli Lappalainen,Daniëlle Posthuma
标识
DOI:10.1038/s43586-021-00056-9
摘要
Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants across many genomes to find those statistically associated with a specific trait or disease. This methodology has generated a myriad of robust associations for a range of traits and diseases, and the number of associated variants is expected to grow steadily as GWAS sample sizes increase. GWAS results have a range of applications, such as gaining insight into a phenotype’s underlying biology, estimating its heritability, calculating genetic correlations, making clinical risk predictions, informing drug development programmes and inferring potential causal relationships between risk factors and health outcomes. In this Primer, we provide the reader with an introduction to GWAS, explaining their statistical basis and how they are conducted, describe state-of-the art approaches and discuss limitations and challenges, concluding with an overview of the current and future applications for GWAS results. Uffelmann et al. describe the key considerations and best practices for conducting genome-wide association studies (GWAS), techniques for deriving functional inferences from the results and applications of GWAS in understanding disease risk and trait architecture. The Primer also provides information on the best practices for data sharing and discusses important ethical considerations when considering GWAS populations and data.
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