Robust allele-based association analyses of complex genetic data

关联分析是当前遗传和复杂人类特征基因研究的核心。这些研究通常是大规模的，分析成千上万或更多的个体，通常通过全球联盟的努力，通过扫描基因组中数百万或更多的遗传标记来寻找与感兴趣的特征相关的遗传标记。这项工作本质上是跨学科的，但研究的一个重要部分是开发强大的统计和计算效率的方法，为后续的生物学研究和解释选择候选人。在这种情况下，基于等位基因的关联测试，比较等位基因频率在病例和对照组之间的差异，是一种直观，易于实现和局部最强大的方法。但是Sasieni（1997，生物计量学）的开创性工作强调了Hardy-Weinberg平衡（HWE）假设的重要性，从而巩固了当前基于基因型的关联测试的使用。由于频繁使用和严格审查，在分析日益复杂的数据时发现了无数的理论缺陷和分析困难。*** ***在这项研究中，我们提出了一个潜在的范式转换的新的基于等位基因的回归框架。基本公式看似简单，但我们表明，新框架包含了许多现有的方法作为特殊情况，它可以推广到更复杂的数据，并且可以灵活地用于关联之外的其他推断目的。具体而言，本研究将HWE的经典Pearson卡方检验重新表述为来自两个不同回归模型的分数检验。在此基础上，我们将推导出一个通用的健壮的基于等位基因的关联测试，我们将研究它与现有的基于基因型的对应物的联系，包括一类准似然评分测试。我们还将扩展统计模型，并将其定制为许多其他遗传研究，包括印迹和数据集成等。最后，提出的研究作为一个原则的证明，表明经典的统计技术，如回归，仍然与复杂的基因组数据科学相关和强大。**************

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.**************