The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI)
Contributors: Walker, Lindsay, Chang, Lin-Ching, Nayak, Amritha, Irfanoglu, Okan, Botteron, Kelly, McCracken, James, McKinstry, Robert, Rivkin, Michael, Wang, Dah-Jyuu, Rumsey, Judith
... Abstract The NIH MRI Study of normal brain development sought to characterize typical brain development in a population of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity of the population of the United States. The study began in 1999 with data collection commencing in 2001 and concluding in 2007. The study was designed with the final goal of providing a controlled-access database; open to qualified researchers and clinicians,which could serve as a powerful tool for elucidating typical brain development and identifying deviations associated with brain-based disorders and diseases, and as a resource for developing computational methods and image processing tools. This paper focuses on the DTI component of the NIH MRI study of normal brain development. In this work, we describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data included in the database, along with database access requirements. For more details, visit http://www. pediatricmri.nih.gov. This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3 mm) DTI datasets from274 unique subjects, and 193 high resolution (2.5mm) DTI datasets from152 unique subjects. Subjects range in age from10 days (from date of birth) through 22 years. Additionally, a set of age-specific DTI templates are included. This forms one component of the larger NIHMRI study of normal brain development which also includes T1-, T2-, proton density-weighted, and proton magnetic resonance spectroscopy (MRS) imaging data, and demographic, clinical and behavioral data.
Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain
Contributors: Sadeghi, Neda, Nayak, Amritha, Walker, Lindsay, Irfanoglu, M, Albert, Paul, Pierpaoli, Carlo, Brain Development Cooperative Group,
... Abstract Metrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have been used in many studies of postnatal brain development. A common finding of previous studies is that these tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent interindividual biological differences as well as experimental noise. Moreover, when comparing different studies, additional variability can be introduced by different acquisition protocols. In this study we examined scans of 61 individuals (aged 4–22 years) from the NIH MRI study of normal brain development. Two scans were collected with different protocols (low and high resolution). Our goal was to separate the contributions of biological variability and experimental noise to the overall measured variance, as well as to assess potential systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of interest. We found that biological variability for both FA and MD varies widely across brain regions; biological variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the splenium. These regions with high inter-individual biological variability are the most likely candidates for assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects, the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared with the higher resolution protocol. However, the majority of the regions did not show any age–protocol interaction, indicating similar trajectories were obtained irrespective of the protocol used.
Contributors: Daniel, H. Geshwind, Virpi, M. Leppa, Stephanie, N. Kravitz, Christa, Lese Martin, Joris, Andrieux, Cedric, Le Caignec, Dominique, Martin-Coignard, Christina, DyBuncio, Stephan, J. Sanders, Jennifer, K. Lowe
... Abstract NOTE: NOT ALL DATA HAS BEEN UPLOADED FOR THIS STUDY. Rare mutations, including copy-number variants (CNVs), contribute significantly to autism spectrum disorder (ASD) risk. Although their importance has been established in families with only one affected child (simplex families), the contribution of both de novo and inherited CNVs to ASD in families with multiple affected individuals (multiplex families) is less well understood. We analyzed 1,532 families from the Autism Genetic Resource Exchange (AGRE) to assess the impact of de novo and rare CNVs on ASD risk in multiplex families. We observed a higher burden of large, rare CNVs, including inherited events, in individuals with ASD than in their unaffected siblings (odds ratio [OR] = 1.7), but the rate of de novo events was significantly lower than in simplex families. In previously characterized ASD risk loci, we identified 49 CNVs, comprising 24 inherited events, 19 de novo events, and 6 events of unknown inheritance, a significant enrichment in affected versus control individuals (OR = 3.3). In 21 of the 30 families (71%) in whom at least one affected sibling harbored an established ASD major risk CNV, including five families harboring inherited CNVs, the CNV was not shared by all affected siblings, indicating that other risk factors are contributing. We also identified a rare risk locus for ASD and language delay at chromosomal region 2q24 (implicating NR4A2) and another lower-penetrance locus involving inherited deletions and duplications of WWOX. The genetic architecture in multiplex families differs from that in simplex families and is complex, warranting more complete genetic characterization of larger multiplex ASD cohorts.
Contributors: Cai, James J, Guan, Jinting, Yang, Ence, Yang, Jizhou, Zeng, Yong, Ji, Guoli
... Abstract Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method-aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.
Contributors: Zhang, J, Walsh, MF, Wu, G, Edmonson, MN, Gruber, TA, Easton, J, Hedges, D, Ma, X, Zhou, X, Yergeau, DA
... Abstract Background The prevalence and spectrum of predisposing mutations among children and adolescents with cancer are largely unknown. Knowledge of such mutations may improve the understanding of tumorigenesis, direct patient care, and enable genetic counseling of patients and families. Methods In 1120 patients younger than 20 years of age, we sequenced the whole genomes (in 595 patients), whole exomes (in 456), or both (in 69). We analyzed the DNA sequences of 565 genes, including 60 that have been associated with autosomal dominant cancer-predisposition syndromes, for the presence of germline mutations. The pathogenicity of the mutations was determined by a panel of medical experts with the use of cancer-specific and locus-specific genetic databases, the medical literature, computational predictions, and second hits identified in the tumor genome. The same approach was used to analyze data from 966 persons who did not have known cancer in the 1000 Genomes Project, and a similar approach was used to analyze data from an autism study (from 515 persons with autism and 208 persons without autism). Results Mutations that were deemed to be pathogenic or probably pathogenic were identified in 95 patients with cancer (8.5%), as compared with 1.1% of the persons in the 1000 Genomes Project and 0.6% of the participants in the autism study. The most commonly mutated genes in the affected patients were TP53 (in 50 patients), APC (in 6), BRCA2 (in 6), NF1 (in 4), PMS2 (in 4), RB1 (in 3), and RUNX1 (in 3). A total of 18 additional patients had protein-truncating mutations in tumor-suppressor genes. Of the 58 patients with a predisposing mutation and available information on family history, 23 (40%) had a family history of cancer. Conclusions Germline mutations in cancer-predisposing genes were identified in 8.5% of the children and adolescents with cancer. Family history did not predict the presence of an underlying predisposition syndrome in most patients.
Contributors: Eichler, Evan, Tianyun, W, Hui, G, Bo, X, Holly, A.F. S, Huidan, W, Bradley, P. C, Tychele, N. T, Yanling, L, Wenjing, Z
... Abstract Recurrent de novo (DN) and likely gene-disruptive (LGD) mutations are important risk factors for autism spectrum disorders (ASD) but have been primarily investigated in cohorts of European ancestry. We sequenced 189 risk genes in 1,543 ASD probands (1,045 from trios) with Chinese ancestry. We report an 11-fold increase in the odds of DN LGD mutations compared to expectation under an exome-wide mutational rate model based on chimpanzee–human divergence. This enrichment for DN LGD mutations remains even after removing known syndromic ASD and intellectual disability genes from our panel (p = 1.17x10-5; odds ratio = 4.1). In aggregate, ~4% of ASD patients carry a DN mutation in one of just 29 autism risk genes. The most prevalent gene for recurrent DN mutations was SCN2A (1.1% of patients) followed by CHD8, DSCAM, MECP2, POGZ, WDFY3 and ASH1L. We identify novel DN LGD recurrences (GIGYF2, MYT1L, CUL3, DOCK8 and ZNF292) and DN mutations in genes previously implicated in ASD (ARHGAP32, NCOR1, PHIP, STXBP1, CDKL5 and SHANK1). Patient follow-up confirms phenotypic features associated with the genetic subtypes and highlights how large global cohorts might be leveraged to identify individually rare mutations in genes that together prove pathogenic significance.
Contributors: Schultz, Stephen, Gould, G
... Abstract Subject level data for associated publication
Contributors: Trakadis, YannisFulginiti, Vanessa, Dionne Laporte, Alexandre, Mcgrath, Sean
... Abstract Autism spectrum disorder (ASD) is a common highly heritable disorder with multifactorial influences.
Contributors: Sebat, Jonathan, Corsello, Christina, Iakoucheva, Lilia, Courchesne, Eric
... Abstract Genetic studies of autism spectrum disorder (ASD) have established that de novo duplications and deletions contribute to risk. However, ascertainment of structural variants (SVs) has been restricted by the coarse resolution of current approaches. By applying a custom pipeline for SV discovery, genotyping, and de novo assembly to genome sequencing of 235 subjects (71 affected individuals, 26 healthy siblings, and their parents), we compiled an atlas of 29,719 SV loci (5,213/genome), comprising 11 different classes. We found a high diversity of de novo mutations, the majority of which were undetectable by previous methods. In addition, we observed complex mutation clusters where combinations of de novo SVs, nucleotide substitutions, and indels occurred as a single event. We estimate a high rate of structural mutation in humans (20%) and propose that genetic risk for ASD is attributable to an elevated frequency of gene-disrupting de novo SVs, but not an elevated rate of genome rearrangement.
Targeted sequencing identifies 91 neurodevelopmental disorder risk genes with autism and developmental disability biases
Contributors: Eichler, Evan, Stessman, H, Xiong, B, Coe, B, Wang, T, Hoekzema, K, Fenckova, M, Kvarnung, M, Gerdts, J, Trinh, S
... Abstract Gene-disruptive mutations contribute to the biology of neurodevelopmental disorders (NDDs), but most pathogenic genes are not known. We sequenced 208 candidate genes from >11,730 patients and >2,867 controls. We report 91 genes with an excess of de novo mutations or private disruptive mutations in 5.7% of patients, including 38 novel NDD genes. Drosophila functional assays of a subset bolster their involvement in NDDs. We identify 25 genes that show a bias for autism versus intellectual disability and highlight a network associated with high-functioning autism (FSIQ>100). Clinical follow-up for NAA15, KMT5B, and ASH1L reveals novel syndromic and non-syndromic forms of disease. [Note: Upload of individual sample-level raw data files is ongoing and expected to be completed by 02/28/2017.]