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A System for Gene Ranking through Integrative Variant Annotation in Autism Spectrum Disorders
The search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency and function. These data are summarized in our Autism Database (AutDB; also known as SFARI Gene), an open-access database for genetic variation associated with ASD. However, a major challenge in the field of ASD biology involves assessing the collective genetic evidence in an unbiased, systematic manner.
Objectives: Here, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence from each ASD-associated variant in AutDB.
Methods:
A total of 928 annotated research articles were analyzed to generate a dataset of 2187 rare variants and 711 common variants distributed across 461 ASD-associated genes. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized scoring parameters that were summed up to yield a single score for each gene in the database.
Results: There were remarkable variations in gene scores resulting in a log-normal distribution of scores with a mean gene score of 16.65 ± 29.57. Interestingly, there were 12 genes with scores deviating more than two standard deviations (SDs) from the mean score of all genes, with very high scores for three genes (SHANK3, CHD8, and ADNP). Importantly, the gene scores generated by our approach were significantly correlated with that in the SFARI Gene scoring module (Spearman r = -0.63; P<0.0001) indicating a strong agreement between gene prioritization using our approach and the expert-mediated SFARI Gene scoring initiative. We further validated our scoring strategy using two recently published ASD risk gene lists and prioritized a new set of genes with cumulative evidence.
Conclusions:
Our scoring algorithm provides a framework for assessment of diverse types of genetic variants associated with ASD that are likely to be important for defining the genetic risk architecture in ASD.