Collaborative Research: Development of New Statistical Methods for Genome-Wide Association Studies

高通量测序技术的进步现在使整个基因组的成本效益分析成为可能。任何两个人类的基因组都是99.9%相同的，其余0.1%的差异决定了人类特征的多样性。例如，DNA序列差异占人类身高变异的80%。目前的技术允许在个体之间识别这些序列多态，然后可以将其与给定特征的差异相关联。当在全基因组水平上对大量个体进行研究时，这种全基因组关联研究(GWASES)可以成为识别控制特定性状的关键基因的有用工具。然而，这种方法的一个要求是有强大而准确的统计和计算方法来搜索大量的测序数据，以正确地识别与感兴趣的表型性状相关的DNA差异。该项目的成果将(1)提供统计方法，以了解DNA序列差异与在种群中观察到的各种多样性之间的关系，以及(2)提供适合生物学家和生物医学专家用于其特定种群研究的相应计算工具。这一研究项目将产生中间的方法和理论结果，为最终产出奠定基础。该项目还将把开发的方法应用到真实的实验数据中，以演示它们的实用性。除这些研究成果外，该项目还将支持对该领域学生的培训，包括妇女和代表性不足的少数群体。Gwas估计表型性状和序列多态之间的相关性，以确定与特定性状高度相关的遗传变异。单核苷酸多态(SNPs)是最常见的遗传变异类型，测序技术允许大规模收集SNP信息。该项目团队将开发新的GWAS模型和方法，以更强大和更准确的方式找到影响性状的变异。具体地说，本研究项目中开发的新方法将通过允许对指数族中任何概率分布的观察到的特征进行建模来改进现有方法。这一扩展确保了统计模型在生物学上有意义和可解释。其次，新方法将利用不同的贝叶斯先验，特别是当代贝叶斯先验选择超高维模型，这些先验将在整个基因组中共享信息，以获得稳定的统计推断。这些新方法中贝叶斯先验的理论结果也将得到发展。第三，开发了一种随机搜索算法，在海量的模型空间中进行高效的搜索，以进行模型选择。这确保了新的方法是实用和有用的，因为分析可以在相当短的时间内完成。同时，这也消除了主观重要性阈值的使用，这些阈值现在在GWAS中普遍使用，但却是一种令人尴尬的做法，没有理论支持。方法将被实施到软件工具中，并将免费提供给统计学家、生物学家和生物医学研究人员。该项目由数学科学部、数学生物学计划和统计计划共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Advances in high-throughput sequencing technologies now make possible cost-effective analysis of whole genomes. The genomes of any two humans are 99.9% identical, with differences in the remaining 0.1% determining the diversity of human traits. For example, DNA sequence differences account for 80% of the variability in human height. Current technology allows the identification of these sequence polymorphisms between individuals, which can then be correlated to differences in a given trait. When done on a genome wide level with a large population of individuals, such genome wide association studies (GWASes) can be a useful tool for the identification of key genes controlling specific traits. However, a requirement for this approach is the availability of powerful and accurate statistical and computational methods to search through a massive amount of sequencing data to correctly identify DNA differences associated with the phenotypic trait of interest. The outcome of the project will (1) provide statistical methods to understand relationships between DNA sequence differences and the full range of diversity observed in a population, and (2) provide corresponding computational tools suitable for use by biologists and biomedical specialists for their specific population studies. This research project will produce intermediate methodological and theoretical results that lay the foundation for the final output. This project will also apply the developed methods to real, experimental data to demonstrate their utility. In addition to these research outcomes, the project will support the training of students in the field, including women and underrepresented minorities. GWAS estimates the correlation between phenotypic traits and sequence polymorphisms to identify genetic variants highly associated with specific traits. Single nucleotide polymorphisms (SNPs) are the most common type of genetic variant, and sequencing technologies allow for large-scale collection of SNP information. The project team will develop new GWAS models and methods to find trait-affecting variants with more power and accuracy. Specifically, the new methods developed in this research project will improve existing approaches by allowing modeling of observed traits from any probabilistic distribution in the exponential family. This extension ensures statistical models are biologically meaningful and interpretable. Second, the new methods will exploit different Bayesian priors, especially contemporary Bayesian priors for ultra-high dimensional model selection, that will share information across the entire genome for stable statistical inferences. Theoretical results of Bayesian priors in these new methods will also be developed. Third, a stochastic search algorithm will be developed to efficiently search through the massively large model space for model selection. This ensures that new methods are practical and useful since analysis can be done within a reasonably short time frame. Meanwhile, this also eliminates the use of subjective thresholds of significance that are now commonly used but an embarrassing practice in GWAS, having no theoretical support. Methods will be implemented into software tools and will be freely available for statisticians, biologists, and biomedical researchers. This project is funded jointly by the Division of Mathematical Sciences Mathematical Biology Program and the Statistics Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.