Debate and argument. Concordance for the syndrome of autism

Disease gene characterization through large-scale co-expression analysis

BACKGROUND

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).

RESULTS

Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.

DISCUSSION

UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis.

Autism from developmental and neuropsychological perspectives

In this review of the research literature on autism, we argue that the application of developmental and neuropsychological perspectives has contributed importantly to the understanding of the core deficits in autism and their underlying neural bases. The three classes of theories postulated to explain the developmental and neuropsychological deficits in autism are considered in terms of the specificity, uniqueness, and universality of these impairments in autism. Because we believe that a primary reason for our lack of understanding of the developmental trajectory in autism stems from our inability to diagnose the syndrome in the first three years of life, research approaches to early identification are discussed, as are longitudinal studies aimed at identifying later-life outcomes and their predictors. In contrast to the progress made in defining the core deficits and arriving at criteria for diagnosis, less progress has been made in identifying the causes of autism and in creating and testing interventions aimed at ameliorating the impairments of autism, possibly because these activities have been less tied to the developmental and neuropsychological models that have enlightened the investigation of core deficits.

Stratification based on language-related endophenotypes in autism: attempt to replicate reported linkage

The identification of autism susceptibility genes has been hampered by phenotypic heterogeneity of autism, among other factors. However, the use of endophenotypes has shown preliminary success in reducing heterogeneity and identifying potential autism-related susceptibility regions. To further explore the utility of using language-related endophenotypes, we performed linkage analysis on multiplex autism families stratified according to delayed expressive speech and also assessed the extent to which parental phenotype information would aid in identifying regions of linkage. A whole genome scan using a multipoint non-parametric linkage approach was performed in 133 families, stratifying the sample by phrase speech delay and word delay (WD). None of the regions reached suggested genome-wide or replication significance thresholds. However, several loci on chromosomes 1, 2, 4, 6, 7, 8, 9, 10, 12, 15, and 19 yielded nominally higher linkage signals in the delayed groups. The results did not support reported linkage findings for loci on chromosomes 7 or 13 that were a result of stratification based on the language delay endophenotype. In addition, inclusion of information on parental history of language delay did not appreciably affect the linkage results. The nominal increase in NPL scores across several regions using language delay endophenotypes for stratification suggests that this strategy may be useful in attenuating heterogeneity. However, the inconsistencies in regions identified across studies highlight the importance of increasing sample sizes to provide adequate power to test replications in independent samples.

The genetics of autism

Autism spectrum disorders (ASD) are among the most heritable of all neuropsychiatric disorders. Discovery of autism susceptibility genes has been the focus of intense research efforts over the last 10 years, and current estimates suggest that 10 to 20 different interacting genes are involved. Evidence from twin and family studies demonstrates increased risk in family members not only for autistic disorder, but also for a milder constellation of similar symptoms referred to as the broader phenotype. In addition, several genetic syndromes and chromosomal anomalies have been associated with ASD. Large family studies using linkage-analysis techniques have demonstrated several chromosomal regions thought to harbor genes related to the disorder. Finally, specific candidate genes based on function and location have been explored; these studies are reviewed here.