Date Available

4-28-2017

Year of Publication

2017

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Arts and Sciences

Department/School/Program

Biology

Advisor

Dr. Bruce F. O'Hara

Abstract

Humans spend a third of their lives sleeping but very little is known about the physiological and genetic mechanisms controlling sleep. Increased data from sleep phenotyping studies in mouse and other species, genetic crosses, and gene expression databases can all help improve our understanding of the process. Here, we present analysis of our own sleep data from the large-scale phenotyping program at The Jackson Laboratory (JAX), to identify the best gene candidates and phenotype predictors for influencing sleep traits.

The original knockout mouse project (KOMP) was a worldwide collaborative effort to produce embryonic stem (ES) cell lines with one of mouse’s 21,000 protein coding genes knocked out. The objective of KOMP2 is to phenotype as many as of these lines as feasible, with each mouse studied over a ten-week period (www.mousephenotype.org). The phenotyping for sleep behavior is done using our non-invasive Piezo system for mouse activity monitoring. Thus far, sleep behavior has been recorded in more than 6000 mice representing 343 knockout lines and nearly 2000 control mice. Control and KO mice have been compared using multivariate statistical approaches to identify genes that exhibit significant effects on sleep variables from Piezo data. Using these statistical approaches, significant genes affecting sleep have been identified. Genes affecting sleep in a specific sex and that specifically affect sleep during daytime and/or night have also been identified and reported.

The KOMP2 consists of a broad-based phenotyping pipeline that consists of collection of physiological and biochemical parameters through a variety of assays. Mice enter the pipeline at 4 weeks of age and leave at 18 weeks. Currently, the IMPC (International Mouse Phenotyping Consortium) database consists of more than 33 million observations. Our final dataset prepared by extracting biological sample data for whom sleep recordings are available consists of nearly 1.5 million observations from multitude of phenotyping assays. Through big data analytics and sophisticated machine learning approaches, we have been able to identify predictor phenotypes that affect sleep in mice. The phenotypes thus identified can play a key role in developing our understanding of mechanism of sleep regulation.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2017.159

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