Statistical models and computational tools for gene-gene interaction analyses by utilizing multi-scale omics

了解表型变化的遗传基础和基于基因型预测表型是遗传学领域的长期目标。如今，在基因组大数据时代，可扩展的工具，即允许使用个人计算机有限内存快速分析非常大数据的软件，是一种新兴的需求。我的研究计划的长期目标是开发新的统计模型及其可扩展的实现，以促进基因型-表型映射和预测。背景。高通量测序技术的最新进展，包括全外显子组测序，RNA-Seq（转录组）和亚硫酸酯- seq（甲基组），已经在遗传学相关领域创造了一个兴奋。然而，缺乏工具可以无缝整合多尺度组学数据集，以便在生物学相关和有意义的背景下精确预测表型。特别是，基因-基因相互作用尚未被充分表征并用于预测。此外，在即将到来的大数据时代，缺乏可扩展的工具，可以在不需要具有非常大内存的机器的情况下对大型数据集进行有效的统计分析。在我之前鉴定基因相互作用和实施可扩展软件的工作的基础上，我将重点关注三个短期目标：(1)通过整合多尺度组学数据，利用基因型-表型数据鉴定基因相互作用；将贝叶斯网络和频繁项集挖掘相结合来实现这一目标。(2)构建整合基因相互作用的多基因表型预测因子；为此将实施LASSO小组的扩展。(3)创建可扩展的软件，使用基于磁盘的解决方案实现上述统计模型，即内存虚拟化和计算机科学中的大页面技术。它将大量数据存储在磁盘上，同时允许快速计算，就好像数据驻留在主存储器中一样。将使用1001拟南芥基因组计划的多尺度组学数据和阿尔伯塔省产生的多尺度组学植物数据。的影响。该研究不仅为统计遗传学提供了新的理论框架，而且为实验科学家开展基因定位项目提供了新的计算工具。此外，它将使农业和卫生从业人员能够改进表型预测，使加拿大食品生产和卫生系统受益。实际上，我的软件代表了一个可扩展的解决方案，用于大数据分析，内存使用最少。这对许多加拿大研究小组来说尤其重要，因为他们不能立即使用高性能计算设备。该项目将培训HQP进行生物信息学和生物统计学分析，以充分利用未来的基因组大数据。

Understanding the genetic basis of phenotypic changes and predicting phenotypes based on genotypes are long-standing goals of the field of genetics. Today, scalable tools, i.e., software that allows rapid analysis of very large data using limited memory in a personal computer, is an emerging requirement in the era of genomic big-data. The long-term goal of my research program is to develop novel statistical models and their scalable implementations to facilitate genotype-phenotype mappings and predictions. Background. Recent advances in high-throughput sequencing technologies including whole-exome sequencing, RNA-Seq (for transcriptome) and Bisulfite-Seq (for methylome) have created an excitement in genetics-related areas. However, there is a lack of tools that allow the seamless integration of multi-scale omics datasets for the precise prediction of phenotypes in a biologically relevant and meaningful context. In particular, gene-gene interactions have not been fully characterized and utilized in predictors. Moreover, in the coming big-data era, there is a lack of scalable tools that permit the effective statistical analysis of large datasets without requiring a machine with very large memory.Objectives. Building upon my previous work of identifying gene-gene interactions and implementing scalable software, I will focus on three short-term objectives: (1) Identify gene interactions using genotype-phenotype data by integrating multi-scale omics data; Bayesian Network and Frequent Itemset Mining will be integrated to achieve this goal. (2) Build a polygenic phenotype predictor that integrates gene interactions; an extension of Group LASSO will be implemented for this. (3) Create scalable software implementing the aforementioned statistical models using disk-based solutions, i.e., memory virtualization and huge-page techniques in computer science. It will store large data on disk while allowing rapid calculation as if the data resided in main memory. The multi-scale omics data from the 1,001 Arabidopsis Genomes Project and the multi-scale omics plant data generated in Alberta will be used. Impact. The proposed research will not only provide a new theoretical framework of statistical genetics, but will also furnish novel computational tools to assist experimental scientists carrying out gene mapping projects. Moreover, it will enable practitioners in agriculture and health to improve predictions of phenotype, benefiting Canadian food productions and the health system. Practically, my software represents a scalable solution for big-data analyses with minimum memory usage. This will be particularly relevant to many Canadian research groups that do not have immediate access to high-performance computing facilities. This program will train HQP to carry out bioinformatics and biostatistics analyses to fully utilize the future genomic big-data.