Next-generation genome sequencing of families afflicted by mental disorders is identifying thousands of mutations with variable effect on disease risk. Two main problems that genetic community is facing before it could move forward with translating their findings into disease mechanisms are: (1) how to distinguish the pathogenic disease-causative mutations from the neutral ones? (2) what biological function(s) does each pathogenic mutation disrupt to cause a disease? These are fundamental questions that need urgent attention. The large number of identified mutations and functional heterogeneity of the affected genes do not permit developing a generalized experimental high-throughput method for addressing these problems. Although modern technologies (such as CRISPR) provide hope for the future in this direction, there is still a long way before we are able to apply it to thousandsof mutations. Thus, predictive computational approaches that aid in genes and mutations prioritization and functional characterization are needed. Here, we propose to develop such methods and apply them to coding and non-coding variants identified in families with Autism Spectrum Disorders (ASD). Our ASD-focused model of genetic variant impact will integrate heterogeneous genetic data with brain-specific functional data sources, such as gene expression and brain splice isoform interaction networks that are uniquely tailored towards brain processes. The unique feature of our approach is that it starts with prediction of biological function of a protein encoded by a gene carrying mutation(s), proceeds with gene and variant prioritization and functional impact assignment, and ends with a risk model for early ASD diagnosis. We will accomplish these goals through the following specific aims: (1) Predicting Biological Function of Genes and Autism-Specific Candidate Gene Prioritization;(2) Predicting Functional Impact of Coding and Non-coding Variants Conferring High Risk for ASD;(3) Experimental Validation of Predictions by Characterizing the Effect of Mutations on Protein Interactions, Transcription and Translation;(4) Developing Algorithms for Estimating ASD Risk from the Exome or Genome Sequence. This is as a novel approach in ASD research that combines expertise from diverse areas of molecular psychiatry, molecular biology and computational biology. The broad range of expertise by the investigators and their collaborators ensures a principled and comprehensive approach to the problem.

Public Health Relevance

Our proposed study will gain insights into molecular mechanisms of ASD by improving our understanding of genes and mutations identified in sequencing studies of ASD families. We will determine the pathogenic effect of protein-coding and non-coding mutations in ASD. Our study will provide actionable targets for future in-depth functional investigation and therapeutic intervention.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
1R01MH105524-01
Application #
8800216
Study Section
Special Emphasis Panel (ZRG1-IMST-D (55))
Program Officer
Senthil, Geetha
Project Start
2014-09-25
Project End
2017-07-31
Budget Start
2014-09-25
Budget End
2015-07-31
Support Year
1
Fiscal Year
2014
Total Cost
$533,354
Indirect Cost
$117,246
Name
University of California San Diego
Department
Psychiatry
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
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Gandal, Michael J; Zhang, Pan; Hadjimichael, Evi et al. (2018) Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362:
Oetting, William S; BĂ©roud, Christophe; Brenner, Steven E et al. (2017) Non-Coding Variation: The 2016 Annual Scientific Meeting of the Human Genome Variation Society. Hum Mutat 38:460-463
Pagel, Kymberleigh A; Pejaver, Vikas; Lin, Guan Ning et al. (2017) When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants. Bioinformatics 33:i389-i398
Daneshjou, Roxana; Wang, Yanran; Bromberg, Yana et al. (2017) Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges. Hum Mutat 38:1182-1192
Cai, Binghuang; Li, Biao; Kiga, Nikki et al. (2017) Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges. Hum Mutat 38:1266-1276
Lin, Guan Ning; Corominas, Roser; Nam, Hyun-Jun et al. (2017) Comprehensive Analyses of Tissue-Specific Networks with Implications to Psychiatric Diseases. Methods Mol Biol 1613:371-402
Pejaver, Vikas; Mooney, Sean D; Radivojac, Predrag (2017) Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges. Hum Mutat 38:1092-1108
Lugo-Martinez, Jose; Pejaver, Vikas; Pagel, Kymberleigh A et al. (2016) The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease. PLoS Comput Biol 12:e1005091
Brandler, William M; Antaki, Danny; Gujral, Madhusudan et al. (2016) Frequency and Complexity of De Novo Structural Mutation in Autism. Am J Hum Genet 98:667-79

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