Tang Yuan, Zhou Yajing, Bao Yunlong
Department of Statistics, School of Mathematical Sciences, Heilongjiang University, Harbin 150080, People's Republic of China.
J Genet. 2022;101.
In sequence study, set-based analysis has been developed as a popular tool for analysing the association of a group rare variant with disease. However, most of the methods are sensitive to the genetic architecture. Besides, by directly combining the association signals of multiple markers within a genomic region can inevitably include a large proportion of noises. To address this issue, we extend the aggregated Cauchy association test (ACAT; Liu . 2019) and propose an adaptive Cauchy-variable combination method (ACC). Our proposed method combines Cauchy-variables, which are transformed from variant-level -values in a given genomic region and adaptively truncate noises by choosing an optimal truncation threshold of the variant-level -value that is determined by the data. Besides, the ACC method can use summary statistics obtained from open access database when the original genotype and phenotype data are unavailable. Extensive simulation studies and Genetic Analysis Workshop 19 real data analysis show that ACC is more powerful than the other comparative methods when only a small proportion of variants are causal, and ACC is robust to the varied genetic architecture.
在序列研究中，基于集合的分析已发展成为一种用于分析一组罕见变异与疾病关联的常用工具。然而，大多数方法对遗传结构敏感。此外，通过直接组合基因组区域内多个标记的关联信号不可避免地会包含很大比例的噪声。为了解决这个问题，我们扩展了聚合柯西关联检验（ACAT；Liu，2019）并提出了一种自适应柯西变量组合方法（ACC）。我们提出的方法结合了柯西变量，这些变量是从给定基因组区域内的变异水平值转换而来的，并通过选择由数据确定的变异水平值的最佳截断阈值来自适应地截断噪声。此外，当原始基因型和表型数据不可用时，ACC方法可以使用从开放获取数据库中获得的汇总统计信息。广泛的模拟研究和遗传分析研讨会19的真实数据分析表明，当只有一小部分变异是因果性的时，ACC比其他比较方法更具效力，并且ACC对不同的遗传结构具有稳健性。
