Robust allele-based association analyses of complex genetic data

关联分析是目前对可遗传和复杂人类特征的遗传研究的核心。这些研究通常是大规模的，分析数以万计或更多的个体，通常通过全球联盟的努力，扫描整个基因组中数百万或更多的遗传标记，寻找与感兴趣的性状(S)相关的遗传标记。这项工作本质上是跨学科的，但研究的重要部分是开发统计上强大的和计算效率高的方法，为后续的生物学研究和解释选择候选者。在这种背景下，基于等位基因的关联检验，比较病例和对照之间等位基因频率的差异，是一种直观、易于实施和局部最有效的方法。但Sasieni(1997，Biometrics)的开创性工作强调了Hardy-Weinberg平衡(HWE)假设的重要性，从而巩固了基于基因的关联检验的当前使用。在频繁的使用和严格的审查下，人们注意到了无数的理论缺陷，在分析越来越复杂的数据时遇到了分析困难。*在这项研究中，我们提出了一个潜在的改变范式的新的基于等位基因的回归框架。基本公式看起来很简单，但我们表明，新的框架包含了许多现有方法作为特例，它可以推广到更复杂的数据，并灵活地用于关联以外的其他推理目的。具体地说，该研究将HWE的经典皮尔逊卡方检验重新表述为源自两个不同回归模型的得分检验。在此基础上，我们将推导出一种基于等位基因的广义稳健关联检验，并将研究其与现有的基于基因型的关联检验的联系，包括一类准似然得分检验。我们还将扩展统计模型，并将其调整为其他一些基因研究，包括印记和数据整合等。最后，这项拟议的研究作为原则的证明，证明了经典的统计技术，如回归，对于复杂的基因组数据科学仍然是相关的和强大的。

Association analysis is at the heart of current genetic studies of heritable and complex human traits. These studies are typically large-scale, analyzing tens of thousands or more individuals, often through worldwide consortium efforts, by scanning through millions or more genetic markers across the genome for the ones associated with trait(s) of interest. The work is interdisciplinary in nature, but a significant part of the research is developing statistically powerful and computationally efficient methods that select candidates for follow-up biological study and interpretation. In this context, the allele-based association test, comparing difference in allele frequency between cases and controls, is an intuitive, easy-to-implement and locally most powerful method. But the seminal work of Sasieni (1997, Biometrics) highlighted the importance of the Hardy-Weinberg equilibrium (HWE) assumption, thus cementing the current use of genotype-based association test. Subjected to frequent use and intense scrutiny, a myriad of theoretical deficiencies are being noted and analytical difficulties are being encountered in analyzing increasingly more complex data.*** ***In this research, we propose a potentially paradigm-shifting new allele-based regression framework. The foundational formulation is deceptively simple, but we show that the new framework encompasses many existing methods as special cases, and it is generalizable for more complex data and flexible for other inferential purposes beyond association. Specifically, the proposed research reformulates the classical Pearson chi-square test of HWE as score tests derived from two distinct regression models. Building upon this, we will derive a generalized robust allele-based association test, and we will investigate its connections with exiting genotype-based counterparts including a class of quasi-likelihood score tests. We will also expand the statistical model and tailor it to a number of other genetic studies including imprinting and data-integration, among others. Finally, the proposed research serves as a proof of principle, demonstrating that classical statistical techniques, such as regression, remain relevant and powerful for complex genome data science.**************