cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15154/1163545
title: De Novo Gene Disruptions in Children on the Autistic Spectrum

description: Public Abstract:

Exome sequencing of 343 families, each with a single child on the autism spectrum and at least one unaffected sibling, reveal de novo small indels and point substitutions, which come mostly from the paternal line in an age-dependent manner. We do not see significantly greater numbers of de novo missense mutations in affected versus unaffected children, but gene-disrupting mutations (nonsense, splice site, and frame shifts) are twice as frequent, 59 to 28. Based on this differential and the number of recurrent and total targets of gene disruption found in our and similar studies, we estimate between 350 and 400 autism susceptibility genes. Many of the disrupted genes in these studies are associated with the fragile X protein, FMRP, reinforcing links between autism and synaptic plasticity. We find FMRP-associated genes are under greater purifying selection than the remainder of genes and suggest they are especially dosage-sensitive targets of cognitive disorders.
DOI: 10.15154/1163547
title: Multiplex Targeted Sequencing Identifies Recurrently Mutated Genes in Autism Spectrum Disorders

description: Abstract:

Exome sequencing studies of autism spectrum disorders (ASDs) have identified many de novo mutations but few recurrently disrupted genes. We therefore developed a modified molecular inversion probe method enabling ultra-low-cost candidate gene resequencing in very large cohorts. To demonstrate the power of this approach, we captured and sequenced 44 candidate genes in 2446 ASD probands. We discovered 27 de novo events in 16 genes, 59% of which are predicted to truncate proteins or disrupt splicing. We estimate that recurrent disruptive mutations in six genes-CHD8, DYRK1A, GRIN2B, TBR1, PTEN, and TBL1XR1¿may contribute to 1% of sporadic ASDs. Our data support associations between specific genes and reciprocal subphenotypes (CHD8-macrocephaly and DYRK1A-microcephaly) and replicate the importance of a B-catenin-chromatin-remodeling network to ASD etiology.
DOI: 10.15154/1163548
title: Whole-genome sequencing in autism identifies hot spots for de novo germline mutation.

description: De novo mutation plays an important role in autism spectrum disorders (ASDs). Notably, pathogenic copy number variants (CNVs) are characterized by high mutation rates. We hypothesize that hypermutability is a property of ASD genes and may also include nucleotide-substitution hot spots. We investigated global patterns of germline mutation by whole-genome sequencing of monozygotic twins concordant for ASD and their parents. Mutation rates varied widely throughout the genome (by 100-fold) and could be explained by intrinsic characteristics of DNA sequence and chromatin structure. Dense clusters of mutations within individual genomes were attributable to compound mutation or gene conversion. Hypermutability was a characteristic of genes involved in ASD and other diseases. In addition, genes impacted by mutations in this study were associated with ASD in independent exome-sequencing data sets. Our findings suggest that regional hypermutation is a significant factor shaping patterns of genetic variation and disease risk in humans.
DOI: 10.15154/1163550
title: Detection of structural variants and indels within exome data.

description: We report an algorithm to detect structural variation and indels from 1 base pair (bp) to 1 Mbp within exome sequence data sets. Splitread uses one end-anchored placements to cluster the mappings of subsequences of unanchored ends to identify the size, content and location of variants with high specificity and sensitivity. The algorithm discovers indels, structural variants, de novo events and copy number-polymorphic processed pseudogenes missed by other methods.
DOI: 10.15154/1163551
title: A genome-wide scan for common alleles affecting risk for autism.

description: Publication authors did not provide a list of Subject IDs used in this publication, cohorts in this study consist of all subjects with SNP genotyping data deposited with NDAR under data-set 8133. 

Publication Abstract:
Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P &lt; 5 × 10(-8). When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P &lt; 5 × 10(-8) threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C.
DOI: 10.15154/1163553
title: De novo mutations revealed by whole exome sequencing are strongly associated with autism

description: Multiple studies have confirmed the contribution of rare de novo copy number variations (CNVs) to the risk for Autism Spectrum Disorders (ASD). While de novo single nucleotide variants (SNVs) have been identified in affected individuals, their contribution to risk has yet to be clarified. Specifically, the frequency and distribution of these mutations has not been well characterized in matched unaffected controls, data that are vital to the interpretation of de novo coding mutations observed in probands. Here we show, via whole-exome sequencing of 928 individuals, including 200 phenotypically discordant sibling pairs, that highly disruptive (nonsense and splice-site) de novo mutations in brain-expressed genes are associated with ASD and carry large effects (OR=5.65; CI: 1.44-22.2; p=0.01 asymptotic test). Based on mutation rates in unaffected individuals, we demonstrate that multiple independent de novo SNVs in the same gene among unrelated probands reliably identifies risk alleles, providing a clear path forward for gene discovery. Among a total of 279 identified de novo coding mutations, there is a single instance in probands, and none in siblings, in which two independent nonsense variants disrupt the same gene, SCN2A (Sodium Channel, Voltage-Gated, Type II, Alpha Subunit), a result that is highly unlikely by chance (p=0.005).
DOI: 10.15154/1163554
title: Transcriptional consequences of 16p11.2 deletion and duplication in mouse cortex and multiplex autism families

description: Publication Abstract:

Reciprocal copy number variation (CNV) of a 593 kb region of 16p11.2 is a common genetic cause of autism spectrum disorder (ASD), yet it is not completely penetrant and can manifest in a wide array of phenotypes.  To explore its molecular consequences, we performed RNA-sequencing of cerebral cortex from mouse models with CNV of the syntenic 7qF3 region, and lymphoblast lines from 34 members of seven multiplex ASD families harboring the 16p11.2 CNV.  Expression of all genes in the CNV region correlated well with their DNA copy number, with no evidence of dosage compensation.  We observed effects on gene expression outside the CNV region, including apparent positional effects in cis and in trans at genomic segments with evidence of physical interaction in Hi-C chromosome conformation data. One of the most significant positional effects was telomeric to the 16p11.2 CNV and includes the previously described 'distal' 16p11.2 microdeletion. Overall, 16p11.2 CNV was associated with altered expression of genes and networks that converge on multiple hypotheses of ASD pathogenesis, including synaptic function (e.g., NRXN1, NRXN3), chromatin modification (e.g., CHD8, EHMT1, MECP2), transcriptional regulation (e.g.,TCF4, SATB2), and intellectual disability (e.g., FMR1, CEP290). However, there were differences between tissues and species, with the strongest effects being consistently within the CNV region itself. Our analyses suggest that through a combination of indirect regulatory effects and direct effects on nuclear architecture, alteration of 16p11.2 genes disrupts expression networks that involve other genes and pathways known to contribute to ASD, suggesting an overlap in mechanisms of pathogenesis.
DOI: 10.15154/1165646
title: Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using C-PAC pipeline and ANTs

description: 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.
DOI: 10.15154/1165648
title: Genotypes of Swedish subjects used in "Most genetic risk for autism resides with common variation" study

description: A key component of genetic architecture is the allelic spectrum influencing trait variability. For autism spectrum disorder (herein termed autism), the nature of the allelic spectrum is uncertain. Individual risk-associated genes have been identified from rare variation, especially de novo mutations. From this evidence, one might conclude that rare variation dominates the allelic spectrum in autism, yet recent studies show that common variation, individually of small effect, has substantial impact en masse. At issue is how much of an impact relative to rare variation this common variation has. Using a unique epidemiological sample from Sweden, new methods that distinguish total narrow-sense heritability from that due to common variation and synthesis of results from other studies, we reach several conclusions about autism's genetic architecture: its narrow-sense heritability is ~52.4%, with most due to common variation, and rare de novo mutations contribute substantially to individual liability, yet their contribution to variance in liability, 2.6%, is modest compared to that for heritable variation.
DOI: 10.15154/1165651
title: Epigenetics Marks as Peripheral Biomarkers of Autism

description: 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 27578 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.
DOI: 10.15154/1165652
title: Autism Genome Project

description: The Autism Genome Project (AGP) Consortium represents more than 50 centers in North America and Europe. In an ongoing effort, the international AGP Consortium is collecting ASD families for ongoing genetic studies. The first phase of this initiative involved examining genetic linkage and chromosomal rearrangements in 1,168 families having at least two ASD individuals (PMID: 17322880). In this second phase of the project, we collected more families and genotyped them to examine for Copy Number Variation (CNVs) and SNPs affecting risk for ASD
DOI: 10.15154/1169189
title: Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using LONI Workflows

description: LONI used de-identified data from NDAR's cloud and a LONI image processing pipeline to perform a secondary structural MRI study. The workflow used in this study pulls data from and provided by NDAR to an instance on a LONI cloud, aligns data to a standard orientation using FSLreorient2stsd, and undergoes further image processing to eventually identify, extract, and analyze cortical and sub-cortical structures in different MRI brain volumes. Two methods were used for this image processing: the first uses Freesurfer Recon_All to extract brain matter, align the data to an atlas, and identify, extract and analyze regions of interest; the second uses FSL to extract brain matter (BET), align the data to an atlas, and extract ROIs (FIRST). The second method also uses Freesurfer (mri_segstats) to perform statistical analysis of these ROIs. Lastly, the pipeline pushes the data processed and extracted by Freesurfer and FSL back to NDAR's cloud.
DOI: 10.15154/1169193
title: Complete Realignment of Whole Exome Sequencing data from 2415 families in SSC Collection

description: Whole Exome Sequencing has been completed for ~ 2500 families from the Simons Simplex Collection. Sequencing was performed at three individual sequencing centers with original data submitted to NDAR Collections 1878, 1895, and 1936; subsets of these data have been analyzed by various methods and published. This study represents an effort to realign sequencing data from all three collection sin a uniform manner using the latest toolchains and algorithms available, which can be used as a resource for the entire ASD Community. Original sequence data has been realigned to a single reference genome (1000 Genomes / GRCh37) using BWA, Picardtools, Samtools, and some custom python scripts. QC summary data were generated as part of the realignment process using the aforementioned tools in addition to QPLOT and some custom scripts. Complete methods, including source code for pipeline and custom scripts can be found at: https://github.com/nkrumm/asd-jre-public. The data package for this study represents the genomics_subject02, genomics_sample03, and omics_qa01 data structures which include realigned BAM files and QC files (i.e., QPLOT output and BAM header files). Variant calling an annotation for these data are provided in NDAR Studies 348 (https://ndar.nih.gov/study.html?id=348) and 349 (https://ndar.nih.gov/study.html?id=349).
DOI: 10.15154/1169195
title: Variant Recalling (GATK) from Whole Exome Sequencing data for 2415 families in SSC Collection

description: Whole Exome Sequencing has been completed for ~ 2500 families from the Simons Simplex Collection.  Sequencing was performed at three individual sequencing centers with original data submitted to NDAR Collections 1878, 1895, and 1936; subsets of these data have been analyzed by various methods and published.  This study represents an effort to call and annotate SNPs and Indels on data from all three collections in a uniform manner using the latest toolchains and algorithms available.

Variant calls from this study were generated using GATK, Famseq, and some custom scripts; annotation was provided by SnpEff, dbNSFP, and vcftools.  Note that variants were called in batches with ~ 20 families per batch.  Complete methods, including source code for pipeline and custom scripts can be found at: https://github.com/nkrumm/asd-jre-public

The data package for this study represents the genomics_subject02, genomics_sample03 structures which include annotated and un-annotated VCF files for each family.  Another NDAR Study (349) is available with VCF files generated using FreeBayes (https://ndar.nih.gov/study.html?id=349), and the complete set of BAM files used for variant calling are available in NDAR Study 334 (https://ndar.nih.gov/study.html?id=334)
DOI: 10.15154/1169197
title: Variant Recalling (FreeBayes) from Whole Exome Sequencing data for 2415 families in SSC Collection

description: Whole Exome Sequencing has been completed for ~ 2500 families from the Simons Simplex Collection.  Sequencing was performed at three individual sequencing centers with original data submitted to NDAR Collections 1878, 1895, and 1936; subsets of these data have been analyzed by various methods and published.  This study represents an effort to call and annotate SNPs and Indels on data from all three collections in a uniform manner using the latest toolchains and algorithms available.

Variant calls from this study were generated using FreeBayes, Famseq, and some custom scripts; annotation was provided by SnpEff, dbNSFP, and vcftools.  Note that variants were called in batches with ~ 20 families per batch.  Complete methods, including source code for pipeline and custom scripts can be found at: https://github.com/nkrumm/asd-jre-public

The data package for this study includes the genomics_sample02, genomics_sample03 structures with annotated and un-annotated VCF files for each family.  Another NDAR Study (348) is available with VCF files generated using GATK (https://ndar.nih.gov/study.html?id=348), and the complete set of BAM files used for variant calling are available in NDAR Study 334 (https://ndar.nih.gov/study.html?id=334)
DOI: 10.15154/1169318
title: Copy Number Variants from SSC Collection ~ 2500 families by two Methods (XHMM and Conifer)

description: XHMM was run on a set of realigned BAM files from the SSC collection (see NDAR Study 334 for BAM files)  using the attached scripts.  These scripts calculate depth of coverage using GATK, pull the GATK output from an instance on NDAR's cloud, merge the output of GATK into a single matrix, process the read depth matrix (filter, center), normalize the matrix using principal component analysis (PCA), process the normalized read depth matrix (filter, z-score), run a hidden markov model (HMM) on this matrix to identify CNVs in the normalized data, and generate family level vcfs from the xhmm data. XHMM  produces as output coverage summary tables produced by GATK (sample_interval_statistics, sample_interval_summary, sample_summary, sample_statistics), principal component data files, a genotyped CNV output VCF file, and some example plots and graphics. For this study, the GATK output is available.

Additional information about XHMM is available here: http://atgu.mgh.harvard.edu/xhmm/tutorial.shtml
DOI: 10.15154/1170165
title: fMRI for affective psychological studies

description: Taking advantage a newly developed dyadic fMRI system, this project studied two brains interactions via eye contact. This is the first direct observation of the dyadic affective states mediated by reciprocal visual stimulation. The dual system activation due to in vivo reciprocity, empathy system and metallization system, are clear evidences,  while such systems often are not fully activated by pictorial stimuli. This method offers a direct and quantitative approach to estimate deficiency of Autism patients in close social relations.
DOI: 10.15154/1170418
title: Predicting Health Utilities for Children With Autism Spectrum Disorders

description: Comparative effectiveness of interventions for children with autism spectrum disorders (ASDs) that incorporates costs is lacking due to the scarcity of information on health utility scores or preference-weighted outcomes typically used for calculating quality-adjusted life years (QALYs). This study created algorithms for mapping clinical and behavioral measures for children with ASDs to health utility scores. The algorithms could be useful for estimating the value of different interventions and treatments used in the care of children with ASDs. Participants were recruited from two Autism Treatment Network sites. Health utility data based on the Health Utilities Index Mark 3 (HUI3) for the child were obtained from the primary caregiver (proxy-reported) through a survey (N = 224). During the initial clinic visit, proxy-reported measures of the Child Behavior Checklist, Vineland II Adaptive Behavior Scales, and the Pediatric Quality of Life Inventory 4.0 (start measures) were obtained and then merged with the survey data. Nine mapping algorithms were developed using the HUI3 scores as dependent variables in ordinary least squares regressions along with the start measures, the Autism Diagnostic Observation Schedule, to measure severity, child age, and cognitive ability as independent predictors. In-sample cross-validation was conducted to evaluate predictive accuracy. Multiple imputation techniques were used for missing data. The average age for children with ASDs in this study was 8.4 (standard deviation = 3.5) years. Almost half of the children (47%) had cognitive impairment (IQ &lt; 70). Total scores for all of the outcome measures were significantly associated with the HUI3 score. The algorithms can be applied to clinical studies containing start measures of children with ASDs to predict QALYs gained from interventions.
DOI: 10.15154/1171641
title: Loss of delta-catenin function in severe autism

description: Autism is a multifactorial neurodevelopmental disorder affecting more males than females; consequently, under a multifactorial genetic hypothesis, females are affected only when they cross a higher biological threshold. We hypothesize that deleterious variants at conserved residues are enriched in severely affected patients arising from female-enriched multiplex families with severe disease, enhancing the detection of key autism genes in modest numbers of cases. Here we show the use of this strategy by identifying missense and dosage sequence variants in the gene encoding the adhesive junction-associated delta-catenin protein (CTNND2) in female-enriched multiplex families and demonstrating their loss-of-function effect by functional analyses in zebrafish embryos and cultured hippocampal neurons from wild-type and Ctnnd2 null mouse embryos. Finally, through gene expression and network analyses, we highlight a critical role for CTNND2 in neuronal development and an intimate connection to chromatin biology. Our data contribute to the understanding of the genetic architecture of autism and suggest that genetic analyses of phenotypic extremes, such as female-enriched multiplex families, are of innate value in multifactorial disorders.