Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York 10032, USA.
Genet Epidemiol. 2012 Nov;36(7):675-85. doi: 10.1002/gepi.21662. Epub 2012 Aug 3.
Next generation sequencing technology has enabled the paradigm shift in genetic association studies from the common disease/common variant to common disease/rare-variant hypothesis. Analyzing individual rare variants is known to be underpowered; therefore association methods have been developed that aggregate variants across a genetic region, which for exome sequencing is usually a gene. The foreseeable widespread use of whole genome sequencing poses new challenges in statistical analysis. It calls for new rare-variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of noncausal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease-associated P-values of individual variants using a weight that is the inverse of the expected standard deviation of the allele frequencies under the null. This approach, dubbed as Sigma-P method, is extremely robust to the inclusion of a high proportion of noncausal variants and is also powerful when both detrimental and protective variants are present within a genetic region. The performance of the Sigma-P method was tested using simulated data based on realistic population demographic and disease models and its power was compared to several previously published methods. The results demonstrate that this method generally outperforms other rare-variant association methods over a wide range of models. Additionally, sequence data on the ANGPTL family of genes from the Dallas Heart Study were tested for associations with nine metabolic traits and both known and novel putative associations were uncovered using the Sigma-P method.
下一代测序技术使遗传关联研究从常见疾病/常见变异到常见疾病/罕见变异假说发生了范式转变。分析个体罕见变异的功效通常较低；因此，已经开发了一些关联方法，可以在遗传区域内聚集变异，对于外显子组测序来说，通常是一个基因。可预见的全基因组测序的广泛应用给统计分析带来了新的挑战。这需要新的罕见变异关联方法，这些方法在统计学上具有强大的功效，能够抵抗由于包含非因果变异而导致的高水平噪声，并且计算效率高。我们提出了一种简单而强大的统计方法，该方法使用一种权重来组合个体变异的疾病相关 P 值，权重为在零假设下等位基因频率的预期标准偏差的倒数。这种方法称为 Sigma-P 方法，即使包含大量非因果变异，也具有极强的稳健性，并且在遗传区域内存在有害和保护变异时也具有强大的功效。使用基于现实人口统计学和疾病模型的模拟数据测试了 Sigma-P 方法的性能，并将其功效与几种先前发表的方法进行了比较。结果表明，该方法在广泛的模型范围内通常优于其他罕见变异关联方法。此外，使用 Sigma-P 方法对来自达拉斯心脏研究的 ANGPTL 基因家族的序列数据进行了与九个代谢特征的关联测试，发现了已知和新的假定关联。
