Functional Analysis of Variance for Association Studies
关联研究中的函数型方差分析AI 解读 ↓
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关联研究中的函数型方差分析
- 研究问题
- 如何利用基因区域内常见与罕见序列变异的联合信息，检验它们与定性性状的关联，并兼顾遗传位置和连锁不平衡结构？
- 方法与思路
- 作者提出函数型方差分析方法，整合区域内变异、位置与连锁不平衡信息，通过模拟比较 SKAT 和函数线性模型并分析测序数据。
- 材料陈述的贡献
- 摘要称方法允许保护性和风险增加型变异并存，在所述模拟场景中表现更好；达拉斯心脏研究中同时检出两个与肥胖关联的基因。
- 阅读关注点
- 应核对遗传序列的函数表示、检验统计量及校准方式，以及模拟比较的错误率控制、多重检验处理和实证关联的稳健性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未给出功效数值、模型条件或多重检验细节，基因关联不能直接解释为因果。
原文摘要
While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods - SKAT and a previously proposed method based on functional linear models (FLM), - especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.