Biology subjects

Hultman, C.

Publications and source records attributed to Hultman, C..

4 recordsLinked to original sources

bioRxiv2017

Common risk variants identified in autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.

genetics↗

bioRxiv2017

Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum

There is a limited understanding about the impact of rare protein truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization and reduced age. Gene sets implicated from GWAS did not show a significant protein truncating variants-burden beyond what captured by established Mendelian genes. In conclusion, we provide the most thorough investigation to date of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.\n\nMain abbreviations

genetics↗

bioRxiv2017

Bayesian Integrated Analysis Of Multiple Types Of Rare Variants To Infer Risk Genes For Schizophrenia And Other Neurodevelopmental Disorders

BackgroundIntegrating rare variation from trio family and case/control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DD), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through study of rare variation, only two risk genes have been identified.\n\nMethodsWe used hierarchical Bayesian modeling of rare variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence (WES) data for schizophrenia (1,077 trios, 6,699 cases and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).\n\nResultsFor SCZ, we estimate 1,551 risk genes, more risk genes and weaker effects than for NDDs. We provide power analyses to predict the number of risk gene discoveries as more data become available, demonstrating greater value of case-control over trio samples. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs ({rho} > 0.55), but low between SCZ and the NDDs ({rho} < 0.3). In depth analysis of 288 NDD genes shows highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichments, single-cell RNA-seq (scRNAseq) cell types and multi-region developmental brain RNA-seq.\n\nConclusionsWe have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs. We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

genomics↗

bioRxiv2017

Evolutionary and functional data power search for obsessive-compulsive disorder genes

Obsessive-compulsive disorder (OCD) is a severe psychiatric disorder linked to abnormalities in the cortico-striatal circuit and in glutamate signaling. We sequenced coding and regulatory elements for 608 genes implicated in OCD from humans and two animal models (mouse and dog). Using a new method, PolyStrat, which prioritizes variants disrupting evolutionarily conserved, functional regions, we found four strongly associated genes when comparing 592 cases to 560 controls. These results were validated in a second, larger cohort. NRXN1 and HTR2A are enriched for coding variants altering postsynaptic protein-binding domains, while CTTNBP2 (synapse maintenance) and REEP3 (vesicle trafficking) are enriched for regulatory variants. The rare coding variant burden in NRXN1 achieves genomewide significance (p=6.37x10-11) when we include public data for 33,370 controls. Of 17 regulatory variants identified in CTTNBP2 and REEP3, we show that at least six alter transcription factor-DNA binding in human neuroblastoma cells. Our findings suggest synaptic adhesion as a key function in compulsive behaviors across three species, and demonstrate how combining targeted sequencing with functional annotations can identify potentially causative variants in both coding and noncoding regions, even when genomic data is limited.

genomics↗