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首页> 外文期刊>BMC Medical Genomics >Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets
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Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets

机译：自适应Fisher方法检测SNV集的关联分析中的密集和稀疏信号

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With the development of next generation sequencing (NGS) technology and genotype imputation methods, statistical methods have been proposed to test a set of genomic variants together to detect if any of them is associated with the phenotype or disease. In practice, within the set, there is an unknown proportion of variants truly causal or associated with the disease. There is a demand for statistical methods with high power in both dense and sparse scenarios, where the proportion of causal or associated variants is large or small respectively. We propose a new association test – weighted Adaptive Fisher (wAF) that can adapt to both dense and sparse scenarios by adding weights to the Adaptive Fisher (AF) method we developed before. Using simulation, we show that wAF enjoys comparable or better power to popular methods such as sequence kernel association tests (SKAT and SKAT-O) and adaptive SPU (aSPU) test. We apply wAF to a publicly available schizophrenia dataset, and successfully detect thirteen genes. Among them, three genes are supported by existing literature; six are plausible as they either relate to other neurological diseases or have relevant biological functions. The proposed wAF method is a powerful disease-variants association test in both dense and sparse scenarios. Both simulation studies and real data analysis indicate the potential of wAF for new biological findings.
机译：随着下一代测序（NGS）技术和基因型归零方法的发展，已经提出了统计方法以将一组基因组变体一起测试，以检测它们是否与表型或疾病相关。在实践中，在该组内，有一种不明的变体比例，真正因果或与疾病相关。有一种统计方法的需求，具有致密和稀疏场景的高功率，其中因果或相关变体的比例分别为大或小。我们提出了一种新的协会测试加权自适应Fisher（WAF），可以通过向我们之前开发的自适应Fisher（AF）方法添加重量来适应密集和稀疏场景。使用模拟，我们表明WAF对流行的方法享有相当或更好的功率，例如序列核关联测试（SKAT和SKAT-O）和Adaptive Spu（ASPU）测试。我们将WAF应用于公开的精神分裂症数据集，并成功检测了十三基因。其中，存在的三种基因是现有文献的支持;六是合理的，因为它们与其他神经疾病有关或具有相关的生物学功能。拟议的WAF方法是密集和稀疏场景中强大的疾病 - 变体关联测试。仿真研究和实际数据分析都表明WAF用于新的生物学发现的潜力。

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