A protein interaction network of alternatively-spliced isoforms from brain links genetic risk factors for autism
Contributors: Iakoucheva, LM, Corominas, R, Yang, X, Lin, GN, Kang, S, Shen, Y, Ghamsari, L, Broly, M, Rodriguez, M, Tam, S
... Abstract Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.
Contributors: Kennedy, D, Haselgrove, C
... Abstract Using the National Database for Autism Research cloud platform, MRI data were analyzed using neuroimaging pipelines that included packages available as part of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Computational Environment to derive standardized measures of MR image quality. Structural QA was performed according to Haselgrove, et al (http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00052/abstract) to provide values for Signal to Noise Ratio that can be compared to other subjects. Diffusion QA was performed according to Liu, et al (http://www.ncbi.nlm.nih.gov/pubmed/24353379), which provides a number of pass/fail checks and advisory flags.
Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using C-PAC pipeline and ANTs
Contributors: Craddock, C, Clark, D
... Abstract An automated pipeline was developed to reference Neuroimages hosted by the National Database for Autism Research (NDAR) and derive volumes for distinct brain structures using Advanced Normalization Tools (ANTs) and the Configurable-Pipeline for the Analysis of Connectomes (C-PAC) platform. This pipeline utilized the ANTs cortical thickness methodology discuessed in "Large-Scale Evaluation of ANTs and Freesurfer Cortical Tchickness Measurements" [http://www.ncbi.nlm.nih.gov/pubmed/24879923] to extract a cortical thickness volume from T1-weighted anatomical MRI data gathered from the NDAR database. This volume was then registered to an stereotaxic-space anatomical template (OASIS-30 Atropos Template) which was acquired from the Mindboggle Project webpage [http://mindboggle.info/data.html]. After registration, the mean cortical thickness was calculated at 31 ROIs on each hemisphere of the cortex and using the Desikan-Killiany-Tourville (DKT-31) cortical labelling protocol [http://mindboggle.info/faq/labels.html] over the OASIS-30 template. As a result, each subject that was processed has a cortical thickness volume image and a text file with the mean thickness ROIs (in mm) stored in Amazon Web Services (AWS) Simple Storage Service (S3). Additionally, these results were tabulated in an AWS-hosted database (through NDAR) to enable simple, efficient querying and data access. All of the code used to perform this analysis is publicly available on Github [https://github.com/FCP-INDI/ndar-dev]. Additionally, as a computing platform, we developed an Amazon Machine Image (AMI) that comes fully equipped to run this pipeline on any dataset. Using AWS Elastic Cloud Computing (EC2), users can launch our publicly available AMI ("C-PAC with benchmark", AMI ID: "ami-fee34296", N. Virginia region) and run the ANTs cortical thickness pipeline. The AMI is fully compatible with Sun Grid Engine as well; this enables users to perform many pipeline runs in parallel over a cluster-computing framework.
Contributors: Wigler, M, Iossifov, , I., , O'Roak, , B.J., , Sanders, , S.J., , Ronemus, , M., , Krumm,
... Abstract Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females. PLEASE NOTE: Additional data on these subjects, unrelated to this publication exist in other NDAR Studies. These data include realigned BAM files, unfiltered SNV/InDel variant calls (made by GATK and FreeBayes), and CNVs. Please see this news item for more details: https://ndar.nih.gov/ndarpublicweb/aboutNDAR.html#news_item_201
Contributors: Kennedy, D, Haselgrove, C
... Abstract Using the National Database for Autism Research cloud platform, MRI data were analyzed using neuroimaging pipelines that included packages available as part of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Computational Environment to derive standardized measures of MR image quality. Time series QA was performed according to Friedman, et al. (http://www.ncbi.nlm.nih.gov/pubmed/16952468) providing values for Signal to Noise Ratio that can be compared to other subjects. Diffusion QA was performed according to Liu, et al. (http://www.ncbi.nlm.nih.gov/pubmed/24353379), which provides a number of pass/fail checks and advisory flags.
Contributors: Anjali, J, Burke, JP, Yang, W, Kelly, JP, Kaiser, M, Becker, L, Lawer, L, Newschaffer, CJ
... Abstract The purpose of this study was to validate autism spectrum disorder cases identified through claims-based case identification algorithms against a clinical review of medical charts. Charts were reviewed for 432 children who fell into one of the three following groups: (a) more than or equal to two claims with an autism spectrum disorder diagnosis code (n = 182), (b) one claim with an autism spectrum disorder diagnosis code (n = 190), and (c) those who had no claims for autism spectrum disorder but had claims for other developmental or neurological conditions (n = 60). The algorithm-based diagnoses were compared with documented autism spectrum disorders in the medical charts. The algorithm requiring more than or equal to two claims for autism spectrum disorder generated a positive predictive value of 87.4%, which suggests that such an algorithm is a valid means to identify true autism spectrum disorder cases in claims data.
Study of potential epigenetic component to the autism spectrum disorders (ASD) by interrogating DNA methylation (DNAm) using an established high-throughput assay that interrogates DNA methylation (DNAm) at over 450,000 CpG dinucleotid
Contributors: Warren, S
... Abstract We are studying a potential epigenetic component to the autism spectrum disorders (ASD) by interrogating DNA methylation (DNAm) using an established high-throughput assay that interrogates DNA methylation (DNAm) at over 450,000 highly informative CpG dinucleotides. To date, we have determined the DNA methylation (DNAm) profiles from whole blood-extracted DNA of over four hundred male sib-pairs discordant for ASD. A close statistical examination may ultimately identify ASD peripheral biomarkers and further characterize the role of DNAm in the pathogenesis of autism.
Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism.
Contributors: Willsey, AJ, Sanders, SJ, Li, M, Dong, S, Tebbenkamp, AT, Muhle, RA, Reilly, SK, Lin, L, Fertuzinhos, S, Miller, JA
... Abstract NDAR Data for this study consist of Whole Exome sequencing for the additional 56 families from SSC collection. Other Whole Exome sequencing data and results used in this study were originally published elsewhere. NDAR Studies 340, 320, and 317 describe the data published in Iossifov et al., 2012; Neale et al., 2012; O'Roak et al., 2012b, respectively, as cited in this publication. The RNA-Seq data from this publication are available from NCBI at the given BioProject accession. Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD "seed" genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology.
Whole-Exome Sequencing and Homozygosity Analysis Implicate Depolarization-Regulated Neuronal Genes in Autism
Contributors: Walsh, CA, Chahrour, MH, Yu, TW, Lim, ET, Ataman, B, Coulter, ME, Hill, RS, Stevens, CR, Schubert, CR, ARRA Autism Sequencing Collaboration,
... Abstract Although autism has a clear genetic component, the high genetic heterogeneity of the disorder has been a challenge for the identification of causative genes. We used homozygosity analysis to identify probands from nonconsanguineous families that showed evidence of distant shared ancestry, suggesting potentially recessive mutations. Whole-exome sequencing of 16 probands revealed validated homozygous, potentially pathogenic recessive mutations that segregated perfectly with disease in 4/16 families. The candidate genes (UBE3B, CLTCL1, NCKAP5L, ZNF18) encode proteins involved in proteolysis, GTPase-mediated signaling, cytoskeletal organization, and other pathways. Furthermore, neuronal depolarization regulated the transcription of these genes, suggesting potential activity-dependent roles in neurons. We present a multidimensional strategy for filtering whole-exome sequence data to find candidate recessive mutations in autism, which may have broader applicability to other complex, heterogeneous disorders
Contributors: Eichler, EE, Krumm, N, O'Roak, BJ, Karakoc, E, Mohajeri, K, Nelson, B, Vives, L, Jacquemont, S, Munson, J, Bernier, R
... Abstract Cohorts: 411 ASD Quads from Simons Simplex Collection 177 Quads from Sanders et al. (PubMed ID: 22495306) 166 Quads from I. Iossifov et al. (PubMed ID: 22542183) 71 Quads from O'Roak et al. (PubMed ID: 22495309) Publication Abstract: We searched for disruptive, genic rare copy-number variants (CNVs) among 411 families affected by sporadic autism spectrum disorder (ASD) from the Simons Simplex Collection by using available exome sequence data and CoNIFER (Copy Number Inference from Exome Reads). Compared to high-density SNP microarrays, our approach yielded ¿2× more smaller genic rare CNVs. We found that affected probands inherited more CNVs than did their siblings (453 versus 394, p = 0.004; odds ratio [OR] = 1.19) and that the probands' CNVs affected more genes (921 versus 726, p = 0.02; OR = 1.30). These smaller CNVs (median size 18 kb) were transmitted preferentially from the mother (136 maternal versus 100 paternal, p = 0.02), although this bias occurred irrespective of affected status. The excess burden of inherited CNVs among probands was driven primarily by sibling pairs with discordant social-behavior phenotypes (p 0.5). Finally, we found enrichment of brain-expressed genes unique to probands, especially in the SRS-discordant group (p = 0.0035). In a combined model, our inherited CNVs, de novo CNVs, and de novo single-nucleotide variants all independently contributed to the risk of autism (p < 0.05). Taken together, these results suggest that small transmitted rare CNVs play a role in the etiology of simplex autism. Importantly, the small size of these variants aids in the identification of specific genes as additional risk factors associated with ASD.