Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America.
Department of Mathematics and Computer Science, John Carroll University, University Heights, Ohio, United States of America.
PLoS One. 2018 Jul 26;13(7):e0201186. doi: 10.1371/journal.pone.0201186. eCollection 2018.
Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.
最近，多性状联合分析变得流行起来，因为它可以提高识别与复杂疾病相关遗传变异的统计能力。此外，越来越多的证据表明，多效性是复杂疾病中的一种普遍现象。目前，大多数现有的方法都是测试多个性状与单个遗传变异之间的关联。然而，这些方法一次分析一个变体，对于罕见变异关联研究可能并不理想，因为等位基因异质性以及罕见变异的极端稀有性。在本文中，我们开发了一种统计方法，通过测试多个性状与变体的最优加权组合（TOWmuT）来测试多个性状与基因组区域中变体（罕见和/或常见）的加权组合之间的关联。TOWmuT 对因果变异的作用方向具有稳健性，适用于不同类型的性状。通过广泛的模拟研究，我们将 TOWmuT 的性能与以下五种现有方法进行了比较：多性状基因关联（GAMuT）、多序列核关联测试（MSKAT）、自适应加权反向回归（AWRR）、单一 TOW 和 MANOVA。我们的结果表明，在所有模拟场景中，TOWmuT 具有正确的 I 型错误率，并且始终比其他五种测试更有效。我们还通过分析来自肺功能研究的全基因组基因分型数据说明了 TOWmuT 的有用性。
