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

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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.