Thesis Defense: Dr. Nick Furlotte

Nick Furlotte’s thesis defense talk is available on our newly created YouTube channel ZarLabUCLA.  This talk gives a great summary of Nick’s research over the course his his Ph.D. and an overview of the types of problems that our lab works on.  Note that for the record, students in the lab do not dress as well as Nick is dressed in the video.  Nick actually bought those clothes the day before especially for his defense.  Today was Nick’s last day in the lab and he is now on his way to start the next chapter in his career at 23andMe.

His thesis title and abstract are:

Nick Furlotte Thesis Defense
“Computational Genetic Approaches for the Dissection of Complex Traits”

University of California, Los Angeles
May 15 at 2:30 pm

Eleazar Eskin (Chair)
David Heckerman
Christopher Lee
A. Jake Lusis
Amit Sahai
Over the past two decades, major technological innovations have transformed the field of genetics allowing researchers to examine the relationship between genetic and phenotypic variation at an unprecedented level of granularity. As a result, genetics has increasingly become a data-driven science, demanding effective statistical procedures and efficient computational methods and necessitating a new interface that some refer to as computational genetics. This talk will focus on a few problems existing within this interface. First, I will introduce a statistical and computational construct called the matrix-variate linear mixed-model (mvLMM), which is used for multiple phenotype genome-wide association. I show how the application of this method results in increased association power over single trait mapping and leads to a dramatic reduction in computational time over classical multiple phenotype optimization procedures. For example, where a classically-based approach takes hours to perform parameter optimization for moderate sample sizes mvLMM takes minutes. Next, I introduce a meta-analysis technique that allows for genome-wide association studies to be combined across populations that are known to contain population structure. This development was motivated by a specific problem in mouse genetics, the aim of which is to utilize multiple mouse association studies jointly. I show that by combining the studies using meta-analysis, while accounting for population structure, the proposed method achieves increased statistical power and increased association resolution. Finally, I will introduce a method for calculating gene coexpression in a way that is robust to statistical confounding introduced through expression heterogeneity.