Robust allele-based association analyses of complex genetic data

关联分析是目前遗传和复杂人类特征遗传研究的核心。这些研究通常是大规模的，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分析数万或更多的个体，以寻找与感兴趣的性状相关的遗传标记。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统计功能强大和计算效率高的方法，为后续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，易于实施和局部最强大的方法。但是Sasieni（1997，Biometrics）的开创性工作强调了Hardy-Weinberg平衡（HWE）假设的重要性，从而巩固了目前使用的基于基因型的关联测试。由于经常使用和严格审查，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范式转移新的基于等位基因的回归框架。基本配方是看似简单，但我们表明，新的框架包括许多现有的方法作为特例，它是可推广的更复杂的数据和灵活的其他推理目的超越协会。 具体而言，拟议的研究重新制定了经典的皮尔逊卡方检验HWE得分测试来自两个不同的回归模型。在此基础上，我们将得到一个广义的强大的等位基因为基础的关联测试，我们将调查其与现有的基于基因型的同行，包括一类准似然得分测试的连接。我们还将扩展统计模型，并将其调整到其他一些遗传研究，包括印记和数据整合等。最后，拟议的研究作为一个原则的证明，表明经典的统计技术，如回归，仍然是相关的和强大的复杂的基因组数据科学。

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