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A general statistic to test an optimally weighted combination of common and/or rare variants.
Genetic Epidemiology ( IF 1.7 ) Pub Date : 2019-09-09 , DOI: 10.1002/gepi.22255
Jianjun Zhang 1 , Baolin Wu 2 , Qiuying Sha 3 , Shuanglin Zhang 3 , Xuexia Wang 1
Affiliation  

Both genome-wide association study and next-generation sequencing data analyses are widely employed to identify disease susceptible common and/or rare genetic variants. Rare variants generally have large effects though they are hard to detect due to their low frequencies. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some adhoc assumptions (e.g., ignoring dependence between rare variants). In this study, we analytically derived optimal weights for both common and rare variants and proposed a general and novel approach to test association between an optimally weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. Additionally, we applied G-TOW and other competitive methods to test disease-associated genes in real data of schizophrenia. The G-TOW has successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic and the sequence kernel association test. Simulation study and real data analysis indicate that G-TOW is a powerful test.

中文翻译:

用于测试常见变体和/或稀有变体的最佳加权组合的一般统计量。

全基因组关联研究和下一代测序数据分析均广泛用于鉴定易受疾病侵害的常见和/或稀有遗传变异。稀有变体通常会产生很大的影响,尽管由于频率低而很难检测到。当前,用于稀有变体关联研究的许多现有统计方法都采用加权组合方案,该方案通常基于一些特殊假设(例如,忽略稀有变体之间的依赖性)来施加主观权重或次优权重。在这项研究中,我们通过分析得出了常见变体和稀有变体的最佳权重,并提出了一种通用且新颖的方法来测试连续或二分性状的基因或途径中变体的最佳加权组合(G-TOW)之间的关联调整协变量。仿真研究的结果表明,G-TOW具有适当控制的I型错误率,并且在测试稀有和常见变体或仅稀有变体的效果时,它是我们比较的方法中最强大的测试。我们还使用遗传分析研讨会17(GAW17)数据说明了G-TOW的有效性。此外,我们应用了G-TOW和其他竞争性方法来在精神分裂症的真实数据中测试疾病相关基因。G-TOW已成功验证了与现有出版物中报道的精神分裂症相关的基因FYN和VPS39。加权和统计和序列核关联检验都错过了这两个基因。仿真研究和实际数据分析表明,G-TOW是一项功能强大的测试。
更新日期:2019-11-01
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