Statistical methods for genetic and bioinformatic studies

圭尔夫大学以其在农业、动物、生物和食品科学方面的悠久传统而闻名。统计和计算工具对于这些学科的研究是不可或缺的。我的长期目标是在校园里建立一个统计和计算实验室，以解决这些领域的尖端研究产生的问题和分析数据。具体地说，我的研究项目主要包括两个主题：统计生物信息学和统计遗传学。下一代测序(NGS)技术能够产生大量的元基因组数据，用于探索和检测生物/环境因素与微生物组组成之间的关系，从而对人类健康产生影响。我的研究计划的第一个主题是统计生物信息学，研究项目包括：1)基于模型的人体肠道细菌微生物群落的聚类以及混杂协变量与潜在集群结构之间的关联；2)细菌关联分析，并基于计数数据和成分数据检测差异丰富的细菌；以及3)噬菌体病毒测序数据的预处理管道开发和用于下游分析的统计方法开发。我们的研究将产生有用的统计和计算工具，用于单独或综合分析高通量细菌和噬菌体的数据。在统计遗传学方面，我的研究兴趣集中在单核苷酸多态(SNP)数据分析的统计方法和软件工具开发上。我的研究项目包括：1)使用缺失表型数据的纵向数据进行全基因组关联分析；2)通过纵向研究检测基因与环境的相互作用和时变基因；3)利用基因组信息进行动物选择和动物交配的优化设计。在1)中，我们研究了缺失数据对参数估计的影响，以及随后它们对遗传关联检验的影响。我们提出了一种基于似然的方法，通过蒙特卡洛期望最大化(MCEM)算法进行模型拟合，从而产生稳健的下游关联分析。在2)中，我们建议通过在混合效应模型中引入随机斜率来检测时变基因的基因-环境交互作用，当受试者内的重复测量允许预测随机斜率时，通过协变量来捕捉特定于受试者的遗传交互效应。在3)中，最新的技术允许饲养者使用高密度SNP面板(60K)基于基因组评估来选择动物。下一步是分配交配动物，在一定的约束条件下，最大化其预期后代的重要经济性状的基因组繁育价值。我们的研究将集中在不同类型的畜群和阶段的动物选择和交配的最佳策略上。

The University of Guelph is well-known for its longstanding tradition of excellence in agricultural, animal, biological, and food science. Statistical and computational tools are indispensable for research in these disciplines.  My long-term goal is to develop a statistical and computational lab on campus to address issues and analyze data arising from cutting-edge research in these areas. Specifically, my research program mainly comprises two themes: statistical bioinformatics and statistical genetics. The next generation sequencing (NGS) technique enables the generation of  a massive amount of metagenomic data for the exploration and detection of relationships between biological/environmental factors and microbiome composition, and thus their impacts on human health. Research projects in the first theme, statistical bioinformatics, of my research program include: 1) model-based clustering of human gut bacterial microbiome communities and the association between confounding covariates and the underlying cluster structure.2) bacterial association analysis and detecting differential abundant bacteria based on count data and compositional data; and 3) phage virome sequencing data preprocessing pipeline development and statistical methodology development for downstream analysis. Our research will generate useful statistical and computational tools for analyzing the high throughput bacterial and phage "omic" data separately or integratedly.  In statistical genetics, my research interests focus on statistical methodology and software tool development for single nucleotide polymorphism (SNP) data analysis. My research projects include: 1) genome-wide association analysis using longitudinal data with missing phenotype data; 2) detecting gene-environmental interactions and time-varying genes via longitudinal studies; and 3) optimal design for animal selection and animal mating using genomic information. In 1), we study the impact of missing data on the parameter estimates and, subsequently, their impacts on genetic association tests. We propose a likelihood-based approach via the Monte-Carlo expectation-maximization (MCEM) algorithm for model fitting that leads to a robust downstream association analysis. In 2), we propose to detect the gene-environmental interactions of time-varying genes by introducing a random slope in a mixed effects model to capture the subject-specific genetic interaction effect with a covariate when repeated measurements within subjects allow for the prediction of the random slope.  In 3), recent technology allows breeders to select animals based on genomic evaluation using a high-density SNP panel (60K). The next step is assigning animals for mating to maximize their expected progenies' genomic breeding values of important economic traits subject to some constraints. Our research will focus on optimal strategies for animal selection and animal mating in different types of herds and stages.