CAREER: Integrative Approaches to Uncovering Complex Genotype-Phenotype Relationships in High Dimensional Genomics Data

基因组数据史无前例的积累为深入了解生物学提供了一个独特的机会，只要有适当的工具来挖掘这些数据。这项研究将使和加快实现大数据基因组学所设想的承诺所需的能力，并通过充分利用基因组数据集的范围来更好地理解基础生物学来建立基因组学的新范式。具体地说，该项目将结合稳健的统计建模和严格的计算方法，对基因组数据进行预测建模。该项目的成功完成将带来新的知识、新的工具，最重要的是，基因组数据的可用性和重要性将得到持久的变革性的加强。该项目将对本科生和研究生的基因组学和生物信息学教育产生影响，并将推广到K-12学生和代表性不足的群体。为了利用基因组数据来更好地理解生物系统，社区迫切需要准确、健壮、可扩展和有效的方法来解释这些数据，以预测各种表型的模型。这项研究呼应了PI的首要职业目标，即为生命科学中的计算和实验科学家提供易于使用的数据分析和软件工具，这项研究将产生一套工具，使生物学家能够在阐明基因-表型关系的图景方面进行新颖的科学研究。该项目将通过1)结合了领域知识的新型贝叶斯分层模型来预测基因型的表型；2)迭代管道以利用新的模型来揭示基因型和表型之间的复杂关系；以及3)与现有的数据科学基础设施相结合的新的软件模块，用于大规模和高维基因组数据的可伸缩建模和可视化。更多信息可以在https://shilab.uncc.edu.This上找到，该奖项反映了国家科学基金会的法定使命，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。

The unprecedented accumulation of genomic data offers a unique opportunity to dive deep into the understanding of biology given appropriate tools to mine such data. This research will enable and accelerate the capabilities needed to realize the promise envisioned for big data genomics, and establish a new paradigm for genomics by fully exploiting the gamut of genomic datasets to better understand basis biology. Specifically, this project will combine robust statistical modeling and rigorous computational approaches toward predictive modeling of genomics data. Successful completion of the project will result in new knowledge, new tools, and most importantly long-lasting transformative enhancement of the usability and significance of genomic data. This project will have impact on education in genomics and bioinformatics at undergraduate and graduate levels and will outreach to K-12 students and underrepresented groups. To capitalize on the gamut of genomic data toward better understanding of biological systems, the community is in dire need of accurate, robust, scalable, and efficient methods to interpret such data toward predictive modeling of various phenotypes. Echoing the PI's overarching career goal of providing easy-to-use data analytics and software tools to computational and experimental scientists in life sciences, this research will result in a suite of tools that allow biologists to conduct novel scientific research in elucidating the landscape of genotype-phenotype relationships. The project will advance science through 1) novel Bayesian hierarchical models that incorporate domain knowledge to predict phenotypes from genotypes; 2) iterative pipelines to capitalize on the new models for uncovering the complex relationships between genotypes and phenotypes; and 3) new software modules integrated with existing data science infrastructure for scalable modeling and visualization of large-scale and high-dimensional genomic data. Further information may be found at https://shilab.uncc.edu.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.