Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes.

Heterogeneity in early language development in autism spectrum disorder (ASD) is clinically important and may reflect neurobiologically distinct subtypes. Here, we identified a large-scale association between multiple coordinated blood leukocyte gene coexpression modules and the multivariate functional neuroimaging (fMRI) response to speech. Gene coexpression modules associated with the multivariate fMRI response to speech were different for all pairwise comparisons between typically developing toddlers and toddlers with ASD and poor versus good early language outcome. Associated coexpression modules were enriched in genes that are broadly expressed in the brain and many other tissues. These coexpression modules were also enriched in ASD-associated, prenatal, human-specific, and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known. Associations between neuroimaging measures and gene expression levels in blood leukocytes may offer a unique in vivo window into identifying brain-relevant molecular mechanisms in ASD

Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism.

Differences in cortical morphology-in particular, cortical volume, thickness and surface area-have been reported in individuals with autism. However, it is unclear what aspects of genetic and transcriptomic variation are associated with these differences. Here we investigate the genetic correlates of global cortical thickness differences (ΔCT) in children with autism. We used Partial Least Squares Regression (PLSR) on structural MRI data from 548 children (166 with autism, 295 neurotypical children and 87 children with ADHD) and cortical gene expression data from the Allen Institute for Brain Science to identify genetic correlates of ΔCT in autism. We identify that these genes are enriched for synaptic transmission pathways and explain significant variation in ΔCT. These genes are also significantly enriched for genes dysregulated in the autism post-mortem cortex (Odd Ratio (OR) = 1.11, Pcorrected  10-14), driven entirely by downregulated genes (OR = 1.87, Pcorrected  10-15). We validated the enrichment for downregulated genes in two independent data sets: Validation 1 (OR = 1.44, Pcorrected = 0.004) and Validation 2 (OR = 1.30; Pcorrected = 0.001). We conclude that transcriptionally downregulated genes implicated in autism are robustly associated with global changes in cortical thickness variability in children with autism

Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders

Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways

A Potential Contributory Role for Ciliary Dysfunction in the 16p11.2 600 kb BP4-BP5 Pathology

The 16p11.2 600 kb copy-number variants (CNVs) are associated with mirror phenotypes on BMI, head circumference, and brain volume and represent frequent genetic lesions in autism spectrum disorders (ASDs) and schizophrenia. Here we interrogated the transcriptome of individuals carrying reciprocal 16p11.2 CNVs. Transcript perturbations correlated with clinical endophenotypes and were enriched for genes associated with ASDs, abnormalities of head size, and ciliopathies. Ciliary gene expression was also perturbed in orthologous mouse models, raising the possibility that ciliary dysfunction contributes to 16p11.2 pathologies. In support of this hypothesis, we found structural ciliary defects in the CA1 hippocampal region of 16p11.2 duplication mice. Moreover, by using an established zebrafish model, we show genetic interaction between KCTD13, a key driver of the mirrored neuroanatomical phenotypes of the 16p11.2 CNV, and ciliopathy-associated genes. Overexpression of BBS7 rescues head size and neuroanatomical defects of kctd13 morphants, whereas suppression or overexpression of CEP290 rescues phenotypes induced by KCTD13 under-or overexpression, respectively. Our data suggest that dysregulation of ciliopathy genes contributes to the clinical phenotypes of these CNVs

Gene family information facilitates variant interpretation and identification of disease-associated genes in neurodevelopmental disorders

Background Classifying pathogenicity of missense variants represents a major challenge in clinical practice during the diagnoses of rare and genetic heterogeneous neurodevelopmental disorders (NDDs). While orthologous gene conservation is commonly employed in variant annotation, approximately 80% of known disease-associated genes belong to gene families. The use of gene family information for disease gene discovery and variant interpretation has not yet been investigated on a genome-wide scale. We empirically evaluate whether paralog-conserved or non-conserved sites in human gene families are important in NDDs. Methods Gene family information was collected from Ensembl. Paralog-conserved sites were defined based on paralog sequence alignments; 10,068 NDD patients and 2078 controls were statistically evaluated for de novo variant burden in gene families. Results We demonstrate that disease-associated missense variants are enriched at paralog-conserved sites across all disease groups and inheritance models tested. We developed a gene family de novo enrichment framework that identified 43 exome-wide enriched gene families including 98 de novo variant carrying genes in NDD patients of which 28 represent novel candidate genes for NDD which are brain expressed and under evolutionary constraint. Conclusion This study represents the first method to incorporate gene family information into a statistical framework to interpret variant data for NDDs and to discover new NDD-associated genes

Transcriptome of iPSC-derived neuronal cells reveals a module of co-expressed genes consistently associated with autism spectrum disorder

Evaluation of expression profile in autism spectrum disorder (ASD) patients is an important approach to understand possible similar functional consequences that may underlie disease pathophysiology regardless of its genetic heterogeneity. Induced pluripotent stem cell (iPSC)-derived neuronal models have been useful to explore this question, but larger cohorts and different ASD endophenotypes still need to be investigated. Moreover, whether changes seen in this in vitro model reflect previous findings in ASD postmortem brains and how consistent they are across the studies remain underexplored questions. We examined the transcriptome of iPSC-derived neuronal cells from a normocephalic ASD cohort composed mostly of high-functioning individuals and from non-ASD individuals. ASD patients presented expression dysregulation of a module of co-expressed genes involved in protein synthesis in neuronal progenitor cells (NPC), and a module of genes related to synapse/neurotransmission and a module related to translation in neurons. Proteomic analysis in NPC revealed potential molecular links between the modules dysregulated in NPC and in neurons. Remarkably, the comparison of our results to a series of transcriptome studies revealed that the module related to synapse has been consistently found as upregulated in iPSC-derived neurons-which has an expression profile more closely related to fetal brain-while downregulated in postmortem brain tissue, indicating a reliable association of this network to the disease and suggesting that its dysregulation might occur in different directions across development in ASD individuals. Therefore, the expression pattern of this network might be used as biomarker for ASD and should be experimentally explored as a therapeutic target

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council (MRC), UKRI, Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (J.K.B., 223164/Z/21/Z) a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. WGS was performed by Illumina at Illumina Laboratory Services and was overseen by Genomics England. We would like to thank all at Genomics England who have contributed to the sequencing, clinical and genomic data analysis. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref. MC_PC_20029). A.D.B. would like to acknowledge funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z) and the Edinburgh Clinical Academic Track (ECAT) programme. We thank the research participants and employees of 23andMe for making this work possible. Genomics England and the 100,000 Genomes Project were funded by the National Institute for Health Research, the Wellcome Trust, the MRC, Cancer Research UK, the DHSC and NHS England. We are grateful for the support from S. Hill and the team in NHS England and the 13 Genomic Medicine Centres that delivered the 100,000 Genomes Project, which provided most of the control genome sequences for this study. We thank the participants in the 100,000 Genomes Project, who made this study possible, and the Genomics England Participant Panel for their strategic advice, involvement and engagement. We acknowledge NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre, who provided life-course longitudinal clinical data on the participants. This work forms part of the portfolio of research of the National Institute for Health Research Barts Biomedical Research Centre. Mark Caulfield is an NIHR Senior Investigator. This study owes a great deal to the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. Additional replication was conducted using the UK Biobank Resource (project 26041). The Penn Medicine BioBank is funded by a gift from the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health under CTSA award number UL1TR001878; and the Perelman School of Medicine at the University of Pennsylvania. We thank the AncestryDNA customers who voluntarily contributed information in the COVID-19 survey. HRS (dbGaP accession: phs000428.v1.p1): HRS was supported by the National Institute on Aging (NIA U01AG009740). The genotyping was funded separately by the National Institute on Aging (RC2 AG036495, RC4 AG039029). Genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation of the data were performed by the Genetics Coordinating Center at the University of Washington. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by the NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 22 August 2021 (GTEx Analysis Release v.8 (dbGaP Accession phs000424.v8.p2). We thank the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available at https://www.covid19hg.org/acknowledgements. The views expressed are those of the authors and not necessarily those of the DHSC, NHS, Department for International Development (DID), NIHR, MRC, Wellcome Trust or Public Health England