Webcast: On the Analysis of Copy-Number Variations in Genome-Wide Association Studies: A Translation of the Family-Based Association Test

Presenter: Christoph Lange, PhD., Harvard University School of Public Health

Date: March 18, 2008

Duration: 60 Minutes

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Abstract

Though there is an increasing support for an important contribution of copy number variation (CNV) to the genetic architecture of complex disease, few methods have been developed for the analysis of such variation in the context of genetic association studies. We propose a generalization of family-based association tests (FBATs) to allow for the analysis of CNVs at a genome-wide level. We translate the popular FBAT approach so that, instead of genotypes, raw intensity values that reflect copy number are used directly in the test statistic, thereby bypassing the need for a CNV genotyping algorithm. Moreover, both inherited and de novo CNVs can be analyzed without any prior knowledge about the type of CNV, making it easily applicable to large-scale association studies. All robustness properties of the genotype FBAT approach are maintained and all previously developed FBAT extensions, including FBATs for time-to-onset, multivariate FBATs, and FBAT-testing strategies, can be directly transferred to the analysis of CNVs. Using simulation studies, we evaluate the power and the robustness of the new approach. Furthermore, for those CNVs that can be genotyped, we compare FBATs based on genotype calls with FBATs that are directly based on the intensity data. An application to one of the first CNV genome-wide-association studies of asthma identifies a very plausible candidate gene.

About the Presenter

Dr. Christoph Lange is an Associate Professor of Biostatistics at Harvard University's School of Public Health. His current research interests fall into the broad areas of statistical genetics and generalized linear models. Recent topics in statistical genetics include family-based association tests, meta-analysis of linkage studies, GEE-methods in linkage analysis and marker-assisted selection. In the area of family-based association tests (FBATs), he has focused on the development of power calculations for family-based association studies, multivariate extensions of the FBAT statistic, FBATs for time-to-onset data and approaches to handle the multiple testing problem in family-based studies. Dr. Lange is also involved in collaborative research in asthma genetics and COPD genetics.