Weissenkampen J Dylan, Jiang Yu, Eckert Scott, Jiang Bibo, Li Bingshan, Liu Dajiang J
Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania.
Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee.
Curr Protoc Hum Genet. 2019 Apr;101(1):e83. doi: 10.1002/cphg.83. Epub 2019 Mar 8.
With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.
随着下一代测序(NGS)技术的出现,全基因组和全外显子组DNA测序对于常规遗传学研究而言已变得经济实惠。再加上改进的基因分型阵列和基因型填充方法,在大型数据集中获取罕见遗传变异信息变得越来越可行。此类数据集使研究人员能够更全面地了解由罕见变异引起的复杂性状的遗传结构。本文综述了用于基于序列的关联统计遗传学分析的先进统计方法,包括生物样本库规模数据集中关联分析的高效算法、基因关联测试、荟萃分析、整合功能基因组数据集的精细定位方法以及全表型组关联研究(PheWAS)。这些方法有望在精准医学时代对下一代统计遗传学分析非常有用。© 2019约翰威立父子公司版权所有