Dr. Laura Kubatko, Department of Statistics
Rank at time of award: Associate Professor
Abstract
Significance to Population and Health Research: This goal of this collaboration (described more fully below) is to provide new statistical techniques for the analysis of population-based samples of individuals affected with a particular disease under study. The result will be the development of more powerful methods for the identification of putative genetic regions at which disease-causing mutations might be found. These regions can then be studied with the goal of understanding (and in the long-term, treating) the mechanisms that cause the disease. Our methods will be tested using two sets of samples. The frrst comes from Dr. Bartlett and his colleagues who are collecting cases and families with autism or other language impairments. These samples will be collected from central Ohio over the next 4 years with several clinical collection grants pending. The second set of data comes from the database core within the Battelle Center for Mathematical Medicine, which manages the genetic data for the international Autism Genome Project that will ultimately include 3,000-4,000 cases with very dense genetic data across the entire human genome. Use of this test data set will not only allow testing of our developed methodology, but may also result in increased understanding of the genetic basis of autism in Ohio's children.
Ending Synopsis/Findings
This funding was used to partially support Statistics Graduate Student Lori Hoffman as a Graduate Research Assistant, and to establish research collaboration with Dr. Veronica Vieland at the Battelle Center for Mathematical Medicine at Nationwide Children’s Hospital. Dr. Hoffman completed her Ph.D., titled “Disease Gene Mapping Under the Coalescent Model”, in Spring 2010. Her work developed new methodology for inferring the genomic location of disease-causing gene variants from large-scale genome-wide association data using a statistical modeling framework in which population-level relationships among individuals were accounted for. She found that her method led to improved power in the detection and localization of genes underlying discrete traits (e.g., presence or absence of a disease) when populations were structured. The method showed similar power to existing methods in other cases. Statistics Graduate Student Katie Thompson built on this work, extending the method to continuous traits (e.g., blood pressure) and incorporating models for the covariance between genes and the environment. Although Dr. Thompson’s work was not formally supported by this award, the supported work of Dr. Hoffman was of fundamental importance in the subsequent development. Dr. Thompson published two papers on her work listed below.