Phenotypic Subtyping and Re-Analysis of Existing Methylation Data from Autistic Probands in Simplex Families Reveal ASD Subtype-Associated Differentially Methylated Genes and Biological Functions

Autism spectrum disorder (ASD) describes a group of neurodevelopmental disorders with core deficits in social communication and manifestation of restricted, repetitive, and stereotyped behaviors. Despite the core symptomatology, ASD is extremely heterogeneous with respect to the severity of symptoms and behaviors. This heterogeneity presents an inherent challenge to all large-scale genome-wide omics analyses. In the present study, we address this heterogeneity by stratifying ASD probands from simplex families according to the severity of behavioral scores on the Autism Diagnostic Interview-Revised diagnostic instrument, followed by re-analysis of existing DNA methylation data from individuals in three ASD subphenotypes in comparison to that of their respective unaffected siblings. We demonstrate that subphenotyping of cases enables the identification of over 1.6 times the number of statistically significant differentially methylated regions (DMR) and DMR-associated genes (DAGs) between cases and controls, compared to that identified when all cases are combined. Our analyses also reveal ASD-related neurological functions and comorbidities that are enriched among DAGs in each phenotypic subgroup but not in the combined case group. Moreover, relational gene networks constructed with the DAGs reveal signaling pathways associated with specific functions and comorbidities. In addition, a network comprised of DAGs shared among all ASD subgroups and the combined case group is enriched in genes involved in inflammatory responses, suggesting that neuroinflammation may be a common theme underlying core features of ASD. These findings demonstrate the value of phenotype definition in methylomic analyses of ASD and may aid in the development of subtype-directed diagnostics and therapeutics.

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Genome-wide screens in accelerated human stem cell-derived neural progenitor cells identify Zika virus host factors and drivers of proliferation

10.1101/476440 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael F. Wells ◽

Max R. Salick ◽

Federica Piccioni ◽

Ellen J. Hill ◽

Jana M. Mitchell ◽

...

Keyword(s):

Stem Cell ◽

Progenitor Cells ◽

Zika Virus ◽

Large Scale ◽

Neural Progenitor Cells ◽

Neural Progenitor ◽

Autism Spectrum ◽

Host Factors ◽

Protein Markers ◽

Genome Wide

SUMMARYNeural progenitor cells (NPCs) are essential to brain development and their dysfunction is linked to several disorders, including autism, Zika Virus Congenital Syndrome, and cancer. Understanding of these conditions has been improved by advancements with stem cell-derived NPC models. However, current differentiation methods require many days or weeks to generate NPCs and show variability in efficacy among cell lines. Here, we describe humanStem cell-derivedNGN2-acceleratedProgenitor cells (SNaPs), which are produced in only 48 hours. SNaPs express canonical forebrain NPC protein markers, are proliferative, multipotent, and like other human NPCs, are susceptible to Zika-mediated death. We further demonstrate SNaPs are valuable for large-scale investigations of genetic and environmental influencers of neurodevelopment by deploying them for genome-wide CRISPR-Cas9 screens. Our studies expand knowledge of NPCs by identifying known and novel Zika host factors, as well as new regulators of NPC proliferation validated by re-identification of the autism spectrum genePTEN.

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Use of the epigenetic toolbox to contextualize common variants associated with schizophrenia risk

Dialogues in Clinical Neuroscience ◽

10.31887/dcns.2019.21.4/sakbarian ◽

2019 ◽

Vol 21 (4) ◽

pp. 407-416 ◽

Cited By ~ 1

Keyword(s):

Gene Networks ◽

Large Scale ◽

Clinical Diagnostics ◽

Common Variants ◽

Diagnostic Measures ◽

Regulatory Variants ◽

Risk Variants ◽

Genome Wide ◽

Genomic Regions ◽

Generation Sequencing

Schizophrenia is a debilitating psychiatric disorder with a complex genetic architecture and limited understanding of its neuropathology, reflected by the lack of diagnostic measures and effective pharmacological treatments. Geneticists have recently identified more than 145 risk loci comprising hundreds of common variants of small effect sizes, most of which lie in noncoding genomic regions. This review will discuss how the epigenetic toolbox can be applied to contextualize genetic findings in schizophrenia. Progress in next-generation sequencing, along with increasing methodological complexity, has led to the compilation of genome-wide maps of DNA methylation, histone modifications, DNA expression, and more. Integration of chromatin conformation datasets is one of the latest efforts in deciphering schizophrenia risk, allowing the identification of genes in contact with regulatory variants across 100s of kilobases. Large-scale multiomics studies will facilitate the prioritization of putative causal risk variants and gene networks that contribute to schizophrenia etiology, informing clinical diagnostics and treatment downstream.

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Impact of sustained TGFβ receptor inhibition on chromatin accessibility and gene expression in cultured human endometrial MSC

10.1101/2020.05.01.073346 ◽

2020 ◽

Author(s):

Raffaella Lucciola ◽

Pavle Vrljicak ◽

Caitlin Filby ◽

Saeedeh Darzi ◽

Shanti Gurung ◽

...

Keyword(s):

Gene Expression ◽

Retinoic Acid ◽

Gene Networks ◽

Large Scale ◽

Transforming Growth Factor ◽

Retinoic Acid Receptor ◽

Human Endometrium ◽

Acid Receptor ◽

Genome Wide ◽

The Impact

AbstractEndometrial mesenchymal stem cells (eMSC) drive the extraordinary regenerative capacity of the human endometrium. Clinical application of eMSC for therapeutic purposes is hampered by spontaneous differentiation and cellular senescence upon large-scale expansion in vitro. A83-01, a selective transforming growth factor-β receptor (TGFβ-R) inhibitor, promotes pharmacological expansion of eMSC in culture by blocking differentiation and senescence, but the underlying mechanisms are incompletely understood. In this study, we combined RNA-seq and ATAC-seq to study the impact of sustained TGFβ-R inhibition on gene expression and chromatin architecture of eMSC. Treatment of primary eMSC with A83-01 for 5 weeks resulted in differential expression of 1,463 genes. Gene ontology analysis showed enrichment of genes implicated in cell growth whereas extracellular matrix genes and genes involved in cell fate commitment were downregulated. ATAC-seq analysis demonstrated that sustained TGFβ-R inhibition results in opening and closure of 3,555 and 2,412 chromatin loci, respectively. Motif analysis revealed marked enrichment of retinoic acid receptor (RAR) binding sites, which was paralleled by the induction of RARB, encoding retinoic acid receptor beta (RARβ). Selective RARβ inhibition attenuated proliferation and clonogenicity of A83-01 treated eMSC. Taken together, our study provides new insights into the gene networks and genome-wide chromatin changes that underpin maintenance of an undifferentiated phenotype of eMSC in prolonged culture.Significance statementCycling human endometrium is a rich source of adult stem/progenitor cells that could be exploited for clinical purposes. Small molecules, such as A83-01, that modulate cell identity may open new avenues to maintain the functional properties of eMSC upon expansion in culture. By integrating complementary genome-wide profiling techniques, we mapped the dynamic changes in chromatin landscape and gene expression in response to prolonged A83-01 treatment of eMSC. Our findings provide new insights into the mechanisms of action of TGFβ-R inhibition that may lead to the development of more targeted pharmacological approaches for MSC expansion.

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Genome-wide identification of directed gene networks using large-scale population genomics data

Nature Communications ◽

10.1038/s41467-018-05452-6 ◽

2018 ◽

Vol 9 (1) ◽

Cited By ~ 9

Author(s):

René Luijk ◽

◽

Koen F. Dekkers ◽

Maarten van Iterson ◽

Wibowo Arindrarto ◽

...

Keyword(s):

Gene Networks ◽

Large Scale ◽

Population Genomics ◽

Genome Wide ◽

Scale Population

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Splicing Programs and Cancer

Journal of Nucleic Acids ◽

10.1155/2012/269570 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 32

Author(s):

Sophie Germann ◽

Lise Gratadou ◽

Martin Dutertre ◽

Didier Auboeuf

Keyword(s):

Alternative Splicing ◽

Gene Networks ◽

Large Scale ◽

Splicing Factors ◽

Novel Technologies ◽

Normal Tissues ◽

Genome Wide ◽

Splicing Variants ◽

Cancer Initiation ◽

Genetic Mechanisms

Numerous studies report splicing alterations in a multitude of cancers by using gene-by-gene analysis. However, understanding of the role of alternative splicing in cancer is now reaching a new level, thanks to the use of novel technologies allowing the analysis of splicing at a large-scale level. Genome-wide analyses of alternative splicing indicate that splicing alterations can affect the products of gene networks involved in key cellular programs. In addition, many splicing variants identified as being misregulated in cancer are expressed in normal tissues. These observations suggest that splicing programs contribute to specific cellular programs that are altered during cancer initiation and progression. Supporting this model, recent studies have identified splicing factors controlling cancer-associated splicing programs. The characterization of splicing programs and their regulation by splicing factors will allow a better understanding of the genetic mechanisms involved in cancer initiation and progression and the development of new therapeutic targets.

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Shared genetics of asthma and mental health disorders: a large-scale genome-wide cross-trait analysis

European Respiratory Journal ◽

10.1183/13993003.01507-2019 ◽

2019 ◽

Vol 54 (6) ◽

pp. 1901507 ◽

Cited By ~ 12

Author(s):

Zhaozhong Zhu ◽

Xi Zhu ◽

Cong-Lin Liu ◽

Huwenbo Shi ◽

Sipeng Shen ◽

...

Keyword(s):

Mental Health ◽

Large Scale ◽

Mental Health Disorders ◽

Autism Spectrum ◽

Mendelian Randomisation ◽

Wide Cross ◽

Trait Analysis ◽

Genome Wide ◽

Health Disorders ◽

Regulated Gene Expression

Epidemiological studies demonstrate an association between asthma and mental health disorders, although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and the causal link between asthma and mental health disorders.We conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between asthma from the UK Biobank and eight mental health disorders from the Psychiatric Genomics Consortium: attention deficit hyperactivity disorder (ADHD), anxiety disorder (ANX), autism spectrum disorder, bipolar disorder, eating disorder, major depressive disorder (MDD), post-traumatic stress disorder and schizophrenia (sample size 9537–394 283).In the single-trait genome-wide association analysis, we replicated 130 previously reported loci and discovered 31 novel independent loci that are associated with asthma. We identified that ADHD, ANX and MDD have a strong genetic correlation with asthma at the genome-wide level. Cross-trait meta-analysis identified seven loci jointly associated with asthma and ADHD, one locus with asthma and ANX, and 10 loci with asthma and MDD. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to the exocrine/endocrine, digestive, respiratory and haemic/immune systems. Mendelian randomisation analyses suggested that ADHD and MDD (including 6.7% sample overlap with asthma) might increase the risk of asthma.This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between asthma and three mental health disorders (ADHD, ANX and MDD). Such shared genetics implicate potential new biological functions that are in common among them.

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Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective

Chronic Stress ◽

10.1177/2470547020924844 ◽

2020 ◽

Vol 4 ◽

pp. 247054702092484 ◽

Cited By ~ 1

Author(s):

Frank R. Wendt ◽

Gita A. Pathak ◽

Daniel S. Tylee ◽

Aranyak Goswami ◽

Renato Polimanti

Keyword(s):

Psychiatric Disorders ◽

Large Scale ◽

Association Studies ◽

Genetic Data ◽

Autism Spectrum ◽

Genome Wide Association Studies ◽

Compulsive Disorder ◽

Genome Wide ◽

A Genome ◽

Chronic Tic Disorder

Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.

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Implicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data

Human Molecular Genetics ◽

10.1093/hmg/ddaa192 ◽

2020 ◽

Vol 29 (R1) ◽

pp. R33-R41

Author(s):

Hillary R Dueñas ◽

Carina Seah ◽

Jessica S Johnson ◽

Laura M Huckins

Keyword(s):

Medical Records ◽

Large Scale ◽

Association Studies ◽

Careful Consideration ◽

Phenotypic Characterization ◽

Genome Wide Association Studies ◽

Phenotypic Analysis ◽

Algorithmic Approach ◽

Phenotype Definition ◽

Genome Wide

Abstract The ‘discovery’ stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur ‘outside’ the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.

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Connecting myelin-related and synaptic dysfunction in schizophrenia with SNP-rich gene expression hubs

10.1101/080044 ◽

2016 ◽

Author(s):

Hedi Hegyi

Keyword(s):

Gene Expression ◽

Gene Networks ◽

Human Life ◽

Pearson Correlation ◽

Data Sets ◽

Genome Wide ◽

Differentially Methylated Genes ◽

Genome Wide Data ◽

Schizophrenic Patients ◽

Genomic Regions

AbstractThe recent availability of several genome-wide data sets such as genome-wide mapping of SNP-rich regions and differentially methylated genes in schizophrenic individuals and gene expression data in all brain compartments across the span of human life prompted us to integrate these datasets to gain a better insight into the underlying gene networks driving this enigmatic disease.We summed up the differentially methylated “expression neighbors” (i.e. genes with positively or negatively correlating expression values) of genes that fall into one of 108 distinct schizophrenia-associated genetic loci with high number of SNPs in schizophrenic patients derived from a large cohort of pooled sequencing experiments. Surprisingly, the number of expression neighbors (with a Pearson correlation of R>=0.8 or R<=−0.7) of the genes falling into the 108 genomic regions were about 35 times higher for the positively correlating genes and 32 times higher for the negatively correlating ones than for the rest of the ~16000 genes outside these loci. While the genes in the 108 loci have relatively little known impact in schizophrenia, using this approach we identified many more known schizophrenia-related important genes with a high degree of connectedness to other genes and high scores of differentially methylated probes for their expression neighbors (such as MBP, MOBP, GRIA1, COMT, SYNGR1, MAP2 and DGCR6), validating our approach.The analysis revealed that the most positively correlating as well as the most negatively correlating genes affect synapse-related genes the most, offering an explanation and a unified view into the root cause of schizophrenia.

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Genetic effects on planum temporale asymmetry and their limited relevance to neurodevelopmental disorders, intelligence or educational attainment

10.1101/622381 ◽

2019 ◽

Cited By ~ 2

Author(s):

Amaia Carrion-Castillo ◽

Antonietta Pepe ◽

Xiang-Zhen Kong ◽

Simon E Fisher ◽

Bernard Mazoyer ◽

...

Keyword(s):

Educational Attainment ◽

Neurodevelopmental Disorders ◽

Large Scale ◽

Genetic Correlations ◽

Autism Spectrum ◽

Genome Wide Association ◽

Planum Temporale ◽

Genome Wide ◽

Significant Heritability ◽

Left Right Asymmetry

AbstractPrevious studies have suggested that altered asymmetry of the planum temporale (PT) is associated with neurodevelopmental disorders, including dyslexia, schizophrenia, and autism. Shared genetic factors have been suggested to link PT asymmetry to these disorders. In a dataset of unrelated subjects from the general population (UK Biobank, N= 18,057), we found that PT volume asymmetry had a significant heritability of roughly 14%. In genome-wide association analysis, two loci were significantly associated with PT asymmetry, including a coding polymorphism within the gene ITIH5 that is predicted to affect the protein’s function and to be deleterious (rs41298373, P=2.01×10-15), and a locus that affects the expression of the genes BOK and DTYMK (rs7420166, P=7.54×10-10). DTYMK showed left-right asymmetry of mRNA expression in post mortem PT tissue. Cortex-wide mapping of these SNP effects revealed influences on asymmetry that went somewhat beyond the PT. Using publicly available genome-wide association statistics from large-scale studies, we saw no significant genetic correlations of PT asymmetry with autism spectrum disorder, attention deficit hyperactivity disorder, schizophrenia, educational attainment or intelligence. Of the top two individual loci associated with PT asymmetry, rs41298373 showed a tentative association with intelligence (unadjusted P=0.025), while the locus at BOK/DTYMK showed tentative association with educational attainment (unadjusted Ps<0.05). These findings provide novel insights into the genetic contributions to human brain asymmetry, but do not support a substantial polygenic association of PT asymmetry with cognitive variation and mental disorders, as far as can be discerned with current sample sizes.

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