Autism is one of most heritable neurodevelopmental disorders with genetic basis well established through family and twin epidemiological studies. Genome-wide association studies have not found strong evidence for the contribution of common variants, and linkage studies indicate the presence of multiple loci, each of which contributes negligibly to the genetic variants on a population level. The goal of this proposal is to apply novel analytic approach to identify families from existing UW-ACE and NIH collections where inheritance is most parsimonious with single gene transmission, and to apply novel genomic technologies to identify the genes that are responsible. To identify families where the likelihood of dominant and recessive inheritance is increased we will apply identity-by-descent an extent-of-homozygosity analyses to the existing genotype data. Next, we will capitalize on newly available methods for a whole genome evaluation of protein coding regions for sequence and copy number variants that might be causal. Lastly, the candidate genes identified will be evaluated for association with autism in a large case control study and functional analyses of candidate genes with the highest evidence for causality will be initiated. To facilitate validation of our findings we will deposit genotype information in NIMH data-base. The understanding of genetics of autism will facilitate early interventions by enabling presymptomatic diagnosis, implicate additional biological pathways involved in autism and increase the number of targets for causative treatments.

Public Health Relevance

The goal of this proposal is to identify novel genes that are responsible for autism. To achieve that, we will apply novel analytic approach to identify families from existing UW-ACE and NIH collections where inheritance is most parsimonious with single gene transmission. We will analyze such families with newly available comparative genomic hybridization, target capture and massively parallel sequencing of all protein coding regions of human genome (exome). Our novel approach will likely identify novel autism genes and pave the way for gene identification in other genetic diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH092367-03
Application #
8397663
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Addington, Anjene M
Project Start
2011-02-03
Project End
2015-12-31
Budget Start
2013-01-01
Budget End
2013-12-31
Support Year
3
Fiscal Year
2013
Total Cost
$644,126
Indirect Cost
$231,225
Name
University of Washington
Department
Psychiatry
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Rubinstein, M; Patowary, A; Stanaway, I B et al. (2018) Association of rare missense variants in the second intracellular loop of NaV1.7 sodium channels with familial autism. Mol Psychiatry 23:231-239
Patowary, Ashok; Nesbitt, Ryan; Archer, Marilyn et al. (2017) Next Generation Sequencing Mitochondrial DNA Analysis in Autism Spectrum Disorder. Autism Res 10:1338-1343
Higdon, Roger; Earl, Rachel K; Stanberry, Larissa et al. (2015) The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders. OMICS 19:197-208
Nato Jr, Alejandro Q; Chapman, Nicola H; Sohi, Harkirat K et al. (2015) PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers. Bioinformatics 31:3790-8
Chapman, Nicola H; Nato Jr, Alejandro Q; Bernier, Raphael et al. (2015) Whole exome sequencing in extended families with autism spectrum disorder implicates four candidate genes. Hum Genet 134:1055-68
Saad, Mohamad; Wijsman, Ellen M (2014) Combining family- and population-based imputation data for association analysis of rare and common variants in large pedigrees. Genet Epidemiol 38:579-90
Cheung, Charles Y K; Thompson, Elizabeth A; Wijsman, Ellen M (2014) Detection of Mendelian consistent genotyping errors in pedigrees. Genet Epidemiol 38:291-9
Saad, Mohamad; Wijsman, Ellen M (2014) Power of family-based association designs to detect rare variants in large pedigrees using imputed genotypes. Genet Epidemiol 38:1-9
Blue, Elizabeth M; Sun, Lei; Tintle, Nathan L et al. (2014) Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond. Genet Epidemiol 38 Suppl 1:S21-8
Cheung, Charles Y K; Thompson, Elizabeth A; Wijsman, Ellen M (2013) GIGI: an approach to effective imputation of dense genotypes on large pedigrees. Am J Hum Genet 92:504-16

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