Enhancing Our Genetic Knowledge of Human Iris Pigmentation and Facial Morphology

Abstract

The biological underpinnings that control iris pigmentation and facial morphology are two areas of research that over the last decade are becoming more thoroughly investigated due to the increased affordability of genotyping and advances in technology allowing for more advanced analysis techniques. Despite the ease of access to the data and the tools required to perform iris pigmentation and facial morphological studies, there are still numerous challenges researchers must overcome when exploring the genetics of these complex phenotypes. Some of these challenges include difficulty in working with the bioinformatic programs designed to analyze genetic associations, the inability to define a phenotype that captures the true nature of these traits, and analysis techniques that fail to model complex gene-gene interactions and their effect on a phenotype or phenotypes of interest. In this body of work, I attempted to address these challenges by designing a bioinformatic pipeline, Odyssey, that bridges the communication gaps between various data preparation programs and the programs that analyze genomic data. With this program, genome-wide association studies (GWAS) could be conducted in a quicker, more efficient, and easier manner. I also redefined iris color as a quantitative measurement of pre-defined color classes. In this way it is possible to define and quantify the unique and intricate mixtures of color, which allows for the identification of known and novel variants that affect individual iris color. I also improved upon current prediction models by developing a neural network model capable of predicting a quantitative output to four pre-defined classes; blue/grey, light brown (hazel), perceived green, and dark brown. I examined the effects of defining a simple facial morphology phenotype that more accurately captures the lower face and jaw shape. I then analyzed this phenotype via a GWAS and found several novel variants that may be associated with a square and diamond shaped face. Lastly, I demonstrated that structural equation modeling can be used in combination with traditional GWAS to examine interactions amongst associated variants, which unearths potential biological relationships that impact the multifaceted phenotype of facial morphology.