A Brain Region-Specific Predictive Gene Map for Autism Derived by Profiling a Reference Gene Set (original) (raw)
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npj genomic medicine, 2024
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic component in which rare variants contribute significantly to risk. We performed whole genome and/or exome sequencing (WGS and WES) and SNP-array analysis to identify both rare sequence and copy number variants (SNVs and CNVs) in 435 individuals from 116 ASD families. We identified 37 rare potentially damaging de novo SNVs (pdSNVs) in the cases (n = 144). Interestingly, two of them (one stop-gain and one missense variant) occurred in the same gene, BRSK2. Moreover, the identification of 8 severe de novo pdSNVs in genes not previously implicated in ASD (AGPAT3, IRX5, MGAT5B, RAB8B, RAP1A, RASAL2, SLC9A1, YME1L1) highlighted promising candidates. Potentially damaging CNVs (pdCNVs) provided support to the involvement of inherited variants in PHF3, NEGR1, TIAM1 and HOMER1 in neurodevelopmental disorders (NDD), although mostly acting as susceptibility factors with incomplete penetrance. Interpretation of identified pdSNVs/pdCNVs according to the ACMG guidelines led to a molecular diagnosis in 19/144 cases, although this figure represents a lower limit and is expected to increase thanks to further clarification of the role of likely pathogenic variants in ASD/NDD candidate genes not yet established. In conclusion, our study highlights promising ASD candidate genes and contributes to characterize the allelic diversity, mode of inheritance and phenotypic impact of de novo and inherited risk variants in ASD/NDD genes.
Nature neuroscience, 2016
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-num...
Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder
2016
Autism spectrum disorder (ASD) is a range of major neurodevelopmental disabilities with a strong genetic basis. Yet, owing to extensive genetic heterogeneity, multiple modes of inheritance and limited study sizes, sequencing and quantitative genetics approaches have had limited success in characterizing the complex genetics of ASD. Currently, only a small fraction of potentially causal genes -about 65 genes out of an estimated several hundred- are known based on strong genetic evidence. Hence, there is a critical need for complementary approaches to further characterize the genetic basis of ASD, enabling development of better screening and therapeutics. Here, we use a machine-learning approach based on a human brain-specific functional gene interaction network to present a genome-wide prediction of autism-associated genes, including hundreds of candidate genes for which there is minimal or no prior genetic evidence. Our approach is validated in an independent case-control sequencing...
DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics
2014
Background: De novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes. Methods: To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motorsomatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk. Results: Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model. Conclusions: Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.
Human Molecular Genetics, 2012
Autism spectrum disorders (ASDs) are highly heritable, yet relatively few associated genetic loci have been replicated. Copy number variations (CNVs) have been implicated in autism; however, the majority of loci contribute to <1% of the disease population. Therefore, independent studies are important to refine associated CNV regions and discover novel susceptibility genes. In this study, a genome-wide SNP array was utilized for CNV detection by two distinct algorithms in a European ancestry case-control data set. We identify a significantly higher burden in the number and size of deletions, and disrupting more genes in ASD cases. Moreover, 18 deletions larger than 1 Mb were detected exclusively in cases, implicating novel regions at 2q22.1, 3p26.3, 4q12 and 14q23. Casespecific CNVs provided further evidence for pathways previously implicated in ASDs, revealing new candidate genes within the GABAergic signaling and neural development pathways. These include DBI, an allosteric binder of GABA receptors, GABARAPL1, the GABA receptor-associated protein, and SLC6A11, a postsynaptic GABA transporter. We also identified CNVs in COBL, deletions of which cause defects in neuronal cytoskeleton morphogenesis in model vertebrates, and DNER, a neuron-specific Notch ligand required for cerebellar development. Moreover, we found evidence of genetic overlap between ASDs and other neurodevelopmental and neuropsychiatric diseases. These genes include glutamate receptors (GRID1, GRIK2 and GRIK4), synaptic regulators (NRXN3, SLC6A8 and SYN3), transcription factor (ZNF804A) and RNA-binding protein FMR1. Taken together, these CNVs may be a few of the missing pieces of ASD heritability and lead to discovering novel etiological mechanisms.
A Genomewide Screen of 345 Families for Autism-Susceptibility Loci
The American Journal of Human Genetics, 2003
We previously reported a genomewide scan to identify autism-susceptibility loci in 110 multiplex families, showing suggestive evidence (P ! .01) for linkage to autism-spectrum disorders (ASD) on chromosomes 5, 8, 16, 19, and X and showing nominal evidence (P ! .05) on several additional chromosomes (2, 3, 4, 10, 11, 12, 15, 18, and 20). In this follow-up analysis we have increased the sample size threefold, while holding the study design constant, so that we now report 345 multiplex families, each with at least two siblings affected with autism or ASD phenotype. Along with 235 new multiplex families, 73 new microsatellite markers were also added in 10 regions, thereby increasing the marker density at these strategic locations from 10 cM to ∼2 cM and bringing the total number of markers to 408 over the entire genome. Multipoint maximum LOD scores (MLS) obtained from affected-sib-pair analysis of all 345 families yielded suggestive evidence for linkage on chromosomes 17, 5, 11, 4, and 8 (listed in order by MLS) (P ! .01). The most significant findings were an MLS of 2.83 (P p .00029) on chromosome 17q, near the serotonin transporter (5-hydroxytryptamine transporter [5-HTT]), and an MLS of 2.54 (P p .00059) on 5p. The present follow-up genome scan, which used a consistent research design across studies and examined the largest ASD sample collection reported to date, gave either equivalent or marginally increased evidence for linkage at several chromosomal regions implicated in our previous scan but eliminated evidence for linkage at other regions.
Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes
BMC genomics, 2017
Numerous studies have highlighted the elevated degree of comorbidity associated with autism spectrum disorder (ASD). These comorbid conditions may add further impairments to individuals with autism and are substantially more prevalent compared to neurotypical populations. These high rates of comorbidity are not surprising taking into account the overlap of symptoms that ASD shares with other pathologies. From a research perspective, this suggests common molecular mechanisms involved in these conditions. Therefore, identifying crucial genes in the overlap between ASD and these comorbid disorders may help unravel the common biological processes involved and, ultimately, shed some light in the understanding of autism etiology. In this work, we used a two-fold systems biology approach specially focused on biological processes and gene networks to conduct a comparative analysis of autism with 31 frequently comorbid disorders in order to define a multi-disorder subcomponent of ASD and pre...
A systematic variant annotation approach for ranking genes associated with autism spectrum disorders
Molecular Autism, 2016
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 (R Spearman = 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.
VariCarta: a comprehensive database of harmonized genomic variants found in ASD sequencing studies
2019
BackgroundRecent years has seen a boom in the application of the next-generation sequencing technology to the study of human diseases, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation.DescriptionHere we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling and consistently cataloguing literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmon...