Statistical Methods for Association Studies of Complex Genetic Disorders

Award Month: 
May - July 2013

A single gene can be solely responsible for certain genetic disorders. For example, only people who carry two defective copies of the CFTR gene develop cystic fibrosis. By contrast, complex genetic disorders such as diabetes and cancer likely involve a number of genes that increase susceptibility, and act in conjunction with lifestyle and environmental exposures to increase risk for developing disease. To tackle complex disorders, researchers have turned from studies of families to studies of populations. Among other goals, this project aims to develope improved biostatistical methods. The new techniques will reduce inaccuracies associated with the existing methods and will be applied to data from ongoing studies with collaborators. The analytic tools that are being developed should enable researchers to better evaluate genetic and environmental risks for conditions such as diabetes, cancer and asthma, and find the underlying genes. The knowledge gained can help with devising more effective treatment and prevention strategies for these conditions.

About Project Leader: Drs. Jinko Graham and Brad McNeney

Drs. Graham and McNeney work on problems at the interface of statistics and genetics. They have a long-standing interest in developing statistical tools that use the molecular genetic or genomic data of individuals to inform about their ancestral relationships. Another Dr. Graham's interest is exploratory visualization tools to understand how the health effect of an environmental factor can be modified by an individual's genetic background, using data from case-parent trios. In Dr. Graham's words: "As a statistician, my focus is on developing analysis tools that can uncover patterns in data while accounting for random variation. Much of the research involves complex data structures and so has a strong computational component."