Using Random Forest to Uncover Missing Heritability

Abstract

Genetic sequencing technology has provided us with the information to show a correlation between some genetic variants and diseases. However, single genetic variations cannot account for much of the heritability of diseases, behaviors, and other phenotypes. This large amount of our genetic makeup that is unaccounted for is a problem known as missing heritability. Knowing what gene is correlated with a disease can help in both treatment and prevention of disease. For example, Leber’s Congenital Amaurosis is an eye disorder that can cause visual impairment. If this disease was caused by the gene RPE65, this disorder can be treated through gene therapy. However, if the disease was caused by any of the 13 other genes known to be associated with the disease, this treatment would be ineffective. Our goal is to develop statistical methods to condition our analysis on genes already known to be associated with a disease. Achieving this will allow us to focus on identifying genetic variants not already known to be correlated with the disease - helping to piece together the puzzle of missing heritability.