Incorporating Functional Information in Tests of Excess De Novo Load

• Activity Number: 291
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: International Chinese Statistical Association
• Author(s): Andrew Allen* and Yu Jiang and Slave Petrovski and Kouros Owzar and David Goldstein
• Companies: Duke University and Duke University and Duke University and Duke University and Duke University
• Keywords: genetic association ; de novo mutations ; saddle-point approximation ; neuropsychiatric disorders

Abstract

A number of recent studies have investigated the role of de novo mutations in various neuropsychiatric disorders including autism, epilepsy, intellectual disability and schizophrenia. These studies attempt to implicate causal genes by looking for an excess load of de novo mutations within those genes. Current statistical methods for assessing this excess are based on the implicit assumption that all qualifying mutations in a gene contribute equally to disease. However, it is well established that different mutations can have radically different effects on the ultimate protein product and, as a result, on disease. Here we derive score statistics from a retrospective likelihood that incorporates the probability of a mutation being deleterious to gene function. We show that, under the null, the resulting test statistic is distributed as a weighted sum of Poisson random variables and we implement a saddlepoint approach to accurately approximate this distribution. We evaluate our approach using simulation and apply it to four, currently available, de novo mutation datasets of neurodevelopmental/neuropsychiatric disorders.