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A Systematic Variant Annotation Approach for Ranking Genes Associated With Autism Spectrum Disorders

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A Systematic Variant Annotation Approach for Ranking Genes Associated With Autism Spectrum Disorders

Eric Larsen et al. Mol Autism.

Abstract

Background: 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. A major challenge involves assessing the collective evidence in an unbiased, systematic manner for their functional relevance.

Methods: Here, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence for each ASD-associated variant cataloged in AutDB (also known as SFARI Gene). We retrieved data from 889 publications to generate a dataset of 2187 rare and 711 common variants distributed across 461 genes implicated in ASD. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized parameters yielding a single score for each gene.

Results: There was a wide variation in scores; SHANK3, CHD8, and ADNP had distinctly higher scores than all other genes in the dataset. Our gene scores were significantly correlated with other recently published rankings of ASD genes (RSpearman = 0.40-0.63; p< 0.0001), providing support for our scoring algorithm.

Conclusions: This new resource, which is freely available, for the first time aggregates on one-platform variants identified from various study types (simplex, multiplex, multigenerational, and consanguineous families), from both common and rare variants, and also incorporates their putative functional consequences to arrive at a genetically and biologically driven ranking scheme. This work represents a major step in moving from simply cataloging autism variants to using data-driven approaches to gain insight into their significance.

Keywords: Autistic disorder; Autosomal recessive; Common variants; Genetic variation; Rare variants.

Figures

Fig. 1
Fig. 1
Global overview of the data in this study. a A pie chart showing the number of publications reporting only common (red), rare (blue), or both common and rare (green) genetic variants associated with autism spectrum disorder (ASD) in our database. b A pie chart showing the number of genes including only common (red), rare (blue), or both common and rare (green) genetic variants associated with ASD in our database. c A distribution of the common (red) and rare (blue) genetic variation in our database within four mutation categories: nonsynonymous, synonymous, non-coding, and copy number variations (CNVs)
Fig. 2
Fig. 2
Distributions of rare variant scores (RVS) and common variant scores (CVS). The distributions of the natural logarithm of genes’ RVS and genes’ CVS are depicted as histograms (a, b, respectively), and as a function of variant counts (c, d for rare and common variants, respectively). Both RVS and CVS are significantly correlated with the number of variants per gene (r = 0.84 and r = 0.70, respectively; p < 0.0001). e, f The obvious association between the number of variants and the number of publication per gene (r = 0.70 and r = 0.69, respectively; p < 0.0001)
Fig. 3
Fig. 3
Gene scores distribution. a A histogram of the natural logarithm of the total gene score (LnTGS) of the 461 genes associated with autism spectrum disorder in this study. Three genes (ADNP, CHD8, and SHANK3) have distinctly larger scores than all other genes in the dataset. b A scatterplot of the expected LnTGS according to a linear regression model based on the number of variants per gene vs. the observed LnTGS. 95 and 99 % confidence intervals (CI) of the predicted scores are depicted in dotted and dashed lines, respectively
Fig. 4
Fig. 4
A comparison to other ASD risk genes datasets. a Venn diagram of the 653 ASD risk genes included in AutDB, SFARI gene scoring module [22], Iossifov et al. [23], and Sanders et al. [11]. b A distribution of TGS across the different categories of genes in the SFARI gene scoring module (1 = “high confidence”; 2 = “strong candidate”; 3 = “suggestive evidence”; 4 = “minimal evidence”; 5 = “hypothesized”; 6 = “not supported”). c A scatterplot comparing the TGS of the genes in AutDB to their corresponding posterior probabilities in Iossifov et al. [23]. Genes with posterior probabilities ≥0.8 are highlighted in red. d A distribution of TGS across the different categories of genes reported in Table 4 of Sanders et al. [11]. Categories indicate the probability pf genes to be ASD risk genes (1 = FDR ≤ 0.01; 2 = 0.01 < FDR ≤ 0.05; 3 = 0.05 < FDR ≤ 0.1; 4 = 0.1 < FDR

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