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Development of Computational Frameworks to Link Genomic/Transcriptomic Data to Phenotype in Plants
Abstract
Understanding the molecular mechanisms underlying complex phenotypes and even predicting phenotypes with identified genomic or transcriptomic candidates have been a subject of interest to scientists. To this end, we developed two computational frameworks, one focusing on the integrative analysis of transcriptomic data, and the other extending the marker used in genomic association studies from single nucleotide polymorphisms to microsatellites. With the first framework, the high-dimensional transcriptomics could be efficiently summarized with only a few expression patterns. Using a regularized linear regression, gene groups contributing to the phenotype of interest can be selected out with higher power compared with traditional methods. The second framework automates the genotyping of genomic microsatellites by constructing consensus genomes and performing PCR computationally. The average genotyping accuracy reached 95% using our framework which satisfies the requirements of downstream association studies. The computational frameworks developed in this study provide a great convenience for those who are interested in data mining and data integration. The knowledge acquired by implementing our frameworks would help build a more comprehensive understanding of the relationship between observed phenotype and genomics/transcriptomics.
Subject Area
Bioinformatics|Biostatistics|Statistics
Recommended Citation
Du, Qian, "Development of Computational Frameworks to Link Genomic/Transcriptomic Data to Phenotype in Plants" (2019). ETD collection for University of Nebraska-Lincoln. AAI27667865.
https://digitalcommons.unl.edu/dissertations/AAI27667865