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全基因组的成本效益分析成为可能。任何两个人的基因组有99.9%是相同的，剩下的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%。目前的技术允许识别个体之间的这些序列多态性，然后可以将其与给定性状的差异相关联。当在全基因组水平上对大量个体进行时，这种全基因组关联研究（GWASes）可以成为鉴定控制特定性状的关键基因的有用工具。然而，这种方法的要求是强大且准确的统计和计算方法的可用性，以搜索大量的测序数据，从而正确地鉴定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序列差异与种群中观察到的全部多样性之间的关系，（2）提供相应的计算工具，适用于生物学家和生物医学专家进行特定的种群研究。本研究项目将产生中间方法和理论成果，为最终产出奠定基础。本项目还将把开发的方法应用于真实的实验数据，以证明其实用性。除了这些研究成果外，该项目还将支持培训该领域的学生，包括妇女和代表性不足的少数民族。GWAS估计表型性状和序列多态性之间的相关性，以识别与特定性状高度相关的遗传变异。单核苷酸多态性（SNP）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。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。具体来说，在这个研究项目中开发的新方法将通过允许从指数家族中的任何概率分布对观察到的性状进行建模来改进现有方法。这种扩展确保统计模型具有生物学意义和可解释性。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进行超高维模型选择，这将在整个基因组中共享信息，以实现稳定的统计推断。贝叶斯先验在这些新方法的理论结果也将被开发。第三，将开发一种随机搜索算法，以有效地搜索用于模型选择的巨大模型空间。这确保了新方法的实用性和有用性，因为分析可以在相当短的时间内完成。同时，这也消除了现在普遍使用的主观显著性阈值的使用，但这在GWAS中是一种尴尬的做法，没有理论支持。这些方法将被应用到软件工具中，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知识价值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.