A Gene Network Model for Combining De Novo Mutations and Inherited Variations to Identify Factors for Autism

• Activity Number: 460
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Kathryn Roeder*+ and Xin He and Li Liu and Jing Lei
• Companies: CMU and Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
• Keywords: genetic association ; DNA sequence ; gene expression ; genes ; autism ; mutation

Abstract

A powerful new design for complex gene mapping is based on de novo mutation. These mutations, which are not inherited from parents, affect risk for many diseases. This strategy has lead to remarkable breakthroughs in understanding the genetic basis of autism spectrum disorder (ASD). We develop statistical methods that increase the utility of de novo, mutations by incorporating additional information concerning transmitted variation. The Transmission And De novo Association test (TADA) relates distinct types of data through a set of genetic parameters such as mutation rate and relative risk, facilitating analysis in an integrated fashion. Inference is based on a hierarchical Bayes strategy that allows us to borrow information across all genes to infer parameters that would be difficult to estimate from individual genes. To increase power and interpretability we use a hidden Markov random field based on gene expression networks. We illustrate our strategy in the context of ASD where we identify several promising risk genes and gene networks.