Incorporating Functional Information in Tests of Excess De Novo Mutational Load.

TitleIncorporating Functional Information in Tests of Excess De Novo Mutational Load.
Publication TypeJournal Article
Year of Publication2015
AuthorsJiang, Yu, Yujun Han, Slavé Petrovski, Kouros Owzar, David B. Goldstein, and Andrew S. Allen
JournalAm J Hum Genet
Date Published2015 Aug 06
KeywordsComputer Simulation, Genetic Diseases, Inborn, Guanine Nucleotide Exchange Factors, Histone-Lysine N-Methyltransferase, Humans, Likelihood Functions, Models, Genetic, Mutation, Nervous System Diseases, Poisson Distribution, Protein Serine-Threonine Kinases, Retrospective Studies

A number of recent studies have investigated the role of de novo mutations in various neurodevelopmental and neuropsychiatric disorders. 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 risk. Here, we propose a method, fitDNM, that incorporates functional information in a test of excess de novo mutational load. Specifically, we derive score statistics from a retrospective likelihood that incorporates the probability of a mutation being damaging 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 approximation of this distribution to obtain accurate p values. Using simulation, we have shown that our method outperforms current methods in terms of statistical power while maintaining validity. We have applied this approach to four de novo mutation datasets of neurodevelopmental and neuropsychiatric disorders: autism spectrum disorder, epileptic encephalopathy, schizophrenia, and severe intellectual disability. Our approach also implicates genes that have been implicated by existing methods. Furthermore, our approach provides strong statistical evidence supporting two potentially causal genes: SUV420H1 in autism spectrum disorder and TRIO in a combined analysis of the four neurodevelopmental and neuropsychiatric disorders investigated here.

Alternate JournalAm J Hum Genet
Original PublicationIncorporating functional information in tests of excess De Novo mutational load.
PubMed ID26235986
PubMed Central IDPMC4573447
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
U01 NS077303 / NS / NINDS NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States