Quantitative genome scan and Ordered-Subsets Analysis of autism endophenotypes support language QTLs.

Autism is a neurodevelopmental syndrome with early childhood onset and deficits in three behavioral and cognitive dimensions: language, social skills and repetitive or restrictive behaviors. We hypothesized that using these endophenotypes would provide more power to detect linkage than the diagnosis of autism. Previously, we reported results for a nonparametric quantitative trait locus (QTL) genome scan in 152 families with autism, which revealed a linkage peak related to spoken language on 7q35. Here, we present the results of a nonparametric QTL scan of autism endophenotypes in 291 multiplex families, including the original 152. The strongest evidence for an 'age at first word' QTL was on chromosomes 3q at 147 cM (Z=3.10, P<0.001), and 17q at 93 cM (Z=2.84, P=0.002), both represent novel susceptibility loci for autism endophenotypes. There was also support for a previously identified autism peak on chromosome 17 at 43 cM (Z=2.22, P=0.013) with 'age at first phrase'. The 7q35 language peak was attenuated (Z=2.05, P=0.02) compared with the original finding. To explore the possibility of increased heterogeneity resulting from the addition of 135 families to the sample, we conducted an Ordered-Subsets Analysis on chromosome 7; these results suggest that the 132 autism families with the earliest average age at first word are responsible for the QTL on 7q35. This locus on 7q35 may harbor a gene contributing variability in spoken language that is not uniquely related to language delay in autism.

Bioinformatic analysis of autism positional candidate genes using biological databases and computational gene network prediction.

Common genetic disorders are believed to arise from the combined effects of multiple inherited genetic variants acting in concert with environmental factors, such that any given DNA sequence variant may have only a marginal effect on disease outcome. As a consequence, the correlation between disease status and any given DNA marker allele in a genomewide linkage study tends to be relatively weak and the implicated regions typically encompass hundreds of positional candidate genes. Therefore, new strategies are needed to parse relatively large sets of 'positional' candidate genes in search of actual disease-related gene variants. Here we use biological databases to identify 383 positional candidate genes predicted by genomewide genetic linkage analysis of a large set of families, each with two or more members diagnosed with autism, or autism spectrum disorder (ASD). Next, we seek to identify a subset of biologically meaningful, high priority candidates. The strategy is to select autism candidate genes based on prior genetic evidence from the allelic association literature to query the known transcripts within the 1-LOD (logarithm of the odds) support interval for each region. We use recently developed bioinformatic programs that automatically search the biological literature to predict pathways of interacting genes (PATHWAYASSIST and GENEWAYS). To identify gene regulatory networks, we search for coexpression between candidate genes and positional candidates. The studies are intended both to inform studies of autism, and to illustrate and explore the increasing potential of bioinformatic approaches as a compliment to linkage analysis.