Yan Qi
Division of Pulmonary Medicine, Allergy and Immunology; Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA.
Methods Mol Biol. 2018;1793:135-144. doi: 10.1007/978-1-4939-7868-7_9.
The recent development of microarray and sequencing technology allows identification of disease susceptibility genes. Although the genome-wide association studies (GWAS) have successfully identified many genetic markers related to human diseases, the traditional statistical methods are not powerful to detect rare genetic markers. The rare genetic markers are usually grouped together and tested at the set level. One of such methods is the sequence kernel association test (SKAT), which has been commonly used in the rare genetic marker analysis. In recent publications, SKAT has been extended to be applicable for family-based rare variant analysis. Here, I present three published statistical approaches for family-based rare variant analysis for: 1. continuous traits, 2. binary traits, and 3. multiple correlated traits.
微阵列和测序技术的最新发展使得疾病易感基因的识别成为可能。尽管全基因组关联研究(GWAS)已成功识别出许多与人类疾病相关的遗传标记,但传统统计方法在检测罕见遗传标记方面能力不足。罕见遗传标记通常被归为一组并在集合水平上进行检验。序列核关联检验(SKAT)就是这样一种方法,它在罕见遗传标记分析中被广泛使用。在最近的出版物中,SKAT已扩展到适用于基于家系的罕见变异分析。在此,我介绍三种已发表的用于基于家系的罕见变异分析的统计方法,分别针对:1. 连续性性状,2. 二元性状,以及3. 多个相关性状。