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Rutgers University

1. Diao, Liyang, 1986-. Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery.

Degree: PhD, Computational Biology and Molecular Biophysics, 2014, Rutgers University

If sheer number of papers published is indicative of anything, it suggests that the age of genome-wide association studies, or GWAS, is here to stay. However, in spite of the influx of data, several issues remain, one of which is the presence of confounding factors caused by relatedness within the study sample. This can cause many false positive results. In recent years, the use of mixed linear models to correct for unknown types of relatedness, i.e. "cryptic relatedness", has been very popular. While this model has been shown to be successful in some cases, here we address the feasibility of performing GWAS in a highly structured population such as Saccharomyces cerevisiae, and find that the inclusion of fixed local ancestry covariates can sometimes lend a study more power. Furthermore, we explore the application of mixed linear models in a different type of biological problem of discovering motifs associated with active microRNAs. While there exist several algorithms for miRNA motif discovery, only a few consider background sequence composition of the 3' UTR binding site in addition to seed sequence motif enrichment, which is known to factor into miNRA binding efficacy. The methods that do account for 3' UTR sequence composition do so by rescoring motif counts based on the background UTR sequence in which it appears. Though computationally efficient, these methods are unable to simultaneously compare both gene expression values and UTR sequence, which our method, named MixMir, is able to do, with favorable results. When compared to the simple linear model, as well as existing motif discovery algorithms, MixMir is able to rank true motifs more highly in multiple data sets. Such computational methods are biologically significant because although it is possible to sequence small RNAs in a sample, their expression may not be perfectly correlated with the size of their effect, which is what we observed.

Advisors/Committee Members: Chen, Kevin C. (chair), Mischaikow, Konstantin (internal member), Olson, Wilma (internal member), Xing, Jinchuan (outside member).

Subjects/Keywords: Linear models (Statistics); Genomes – Analysis

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APA (6th Edition):

Diao, Liyang, 1. (2014). Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/45229/

Chicago Manual of Style (16th Edition):

Diao, Liyang, 1986-. “Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery.” 2014. Doctoral Dissertation, Rutgers University. Accessed November 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/45229/.

MLA Handbook (7th Edition):

Diao, Liyang, 1986-. “Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery.” 2014. Web. 29 Nov 2020.

Vancouver:

Diao, Liyang 1. Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery. [Internet] [Doctoral dissertation]. Rutgers University; 2014. [cited 2020 Nov 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45229/.

Council of Science Editors:

Diao, Liyang 1. Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery. [Doctoral Dissertation]. Rutgers University; 2014. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45229/

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