The Center for Human Genetic Research, Massachusetts General Hospital, Boston, United States of America.
PLoS Genet. 2011 Mar;7(3):e1001322. doi: 10.1371/journal.pgen.1001322. Epub 2011 Mar 3.
Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals.
技术进步使得高通量测序成为医学遗传学的主要发现工具，特别是用于检测罕见变异。然而，这种方法面临着分析上的挑战，即非常罕见的变异的影响只能作为一个整体来有效地评估。进一步的复杂性在于，任何给定的罕见变异都可能没有影响，可能增加风险，也可能具有保护作用。我们在这里提出 C-alpha 检验统计量作为一种新的方法，用于检验一组罕见变异中是否存在这种混合效应。与现有的负担检验不同，C-alpha 通过检验方差而不是均值，在目标集包含风险和保护变异时保持一致的功效。通过模拟和病例/对照数据的分析，我们证明了相对于评估个体中罕见变异负担的现有方法具有良好的功效。
