Kazma Rémi, Hoffmann Thomas J, Witte John S
Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California San Francisco, 1450 Third Street, Box 3110, San Francisco, CA 94148-3110, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S29. doi: 10.1186/1753-6561-5-S9-S29.
Rare variants may help to explain some of the missing heritability of complex diseases. Technological advances in next-generation sequencing give us the opportunity to test this hypothesis. We propose two new methods (one for case-control studies and one for family-based studies) that combine aggregated rare variants and common variants located within a region through principal components analysis and allow for covariate adjustment. We analyzed 200 replicates consisting of 209 case subjects and 488 control subjects and compared the results to weight-based and step-up aggregation methods. The principal components and collapsing method showed an association between the gene FLT1 and the quantitative trait Q1 (P<10-30) in a fraction of the computation time of the other methods. The proposed family-based test has inconclusive results. The two methods provide a fast way to analyze simultaneously rare and common variants at the gene level while adjusting for covariates. However, further evaluation of the statistical efficiency of this approach is warranted.
罕见变异可能有助于解释复杂疾病中一些缺失的遗传度。新一代测序技术的进步使我们有机会检验这一假设。我们提出了两种新方法（一种用于病例对照研究，一种用于基于家系的研究），通过主成分分析将一个区域内的聚集罕见变异和常见变异结合起来，并允许进行协变量调整。我们分析了由209例病例受试者和488例对照受试者组成的200个重复样本，并将结果与基于权重和逐步聚集方法进行比较。主成分和压缩方法在其他方法计算时间的一小部分内显示出基因FLT1与数量性状Q1之间的关联（P<10-30）。所提出的基于家系的检验结果尚无定论。这两种方法提供了一种在调整协变量的同时在基因水平上同时分析罕见和常见变异的快速方法。然而，有必要对该方法的统计效率进行进一步评估。
