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.
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.
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|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|
|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|
|Wijsman, Ellen M (2012) The role of large pedigrees in an era of high-throughput sequencing. Hum Genet 131:1555-63|
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