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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Goldstein, David B. Owzar, Kouros Petrovski, Slavé Allen, Andrew S. Jiang, Yu Han, Yujun |
| Description | Country affiliation: United States Author Affiliation: Jiang Y ( Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.); Han Y ( School of Medicine, Duke University, Durham, NC 27708, USA.); Petrovski S ( Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA.); Owzar K ( Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.); Goldstein DB ( Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA.); Allen AS ( Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA. Electronic address: andrew.s.allen@duke.edu.) |
| Abstract | 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. |
| ISSN | 00029297 |
| e-ISSN | 15376605 |
| DOI | 10.1016/j.ajhg.2015.06.013 |
| Journal | The American Journal of Human Genetics |
| Issue Number | 2 |
| Volume Number | 97 |
| Language | English |
| Publisher | Cell Press (on behalf of American Society of Human Genetics) |
| Publisher Date | 2015-08-06 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Genetic Diseases, Inborn Genetics Models, Genetic Mutation Nervous System Diseases Computer Simulation Guanine Nucleotide Exchange Factors Histone-lysine N-methyltransferase Likelihood Functions Poisson Distribution Protein-serine-threonine Kinases Retrospective Studies Research Support, N.i.h., Extramural Discipline Human Genetics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics Genetics (clinical) |
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