Integrative genomics identifies a convergent molecular subtype that links epigenomic with transcriptomic differences in autism

Ramaswami, Gokul; Won, Hyejung; Gandal, Michael J.; Haney, Jillian R.; Wang, Jerry C.; Wong, Chloe; Sun, Wenjie; Prabhakar, Shyam; Mill, Jonathan; Geschwind, Daniel H.

doi:10.1101/718932

Cited by 3 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 ) related to synaptic connectivity, neurotransmitter, neuron projection and vesicles, and chromatin remodeling pathways (Ayhan & Konopka, 2019 ; Gordon et al, 2019 ; Voineagu et al, 2011 ). Recently, an integrated genomic study also identified from autism brain tissue a component of upregulated immune processes associated with hypomethylation (Ramaswami et al, 2020 ). These reported pathways are in strong accordance with numerous independent autism studies that integrated genetic data with brain transcriptomes (Courchesne, Gazestani, & Lewis, 2020 ; Uddin et al, 2014 ; Yuen et al, 2017 ).…”

Section: Geneticsmentioning

confidence: 99%

Genetic contributions to autism spectrum disorder

et al. 2021

View full text Add to dashboard Cite

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous (de novo). More than 100 risk genes have been implicated by rare, often de novo, potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse, each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

show abstract

Section: Geneticsmentioning

confidence: 99%

Genetic contributions to autism spectrum disorder

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In parallel to data generation in fetal brain, similar efforts have been carried out for adult human brain [22,[99][100][101][102]. Not much has been published comparing these two major stages of human brain development, so we performed some additional analyses for this review to highlight the major differences between epigenomes at these two stages.…”

Section: Developmental Enhancers In the Cnsmentioning

confidence: 99%

Characterization of Gene Regulatory Elements in Human Fetal Cortical Development: Enhancing Our Understanding of Neurodevelopmental Disorders and Evolution

Guo¹,

Wu²,

Geschwind³

2023

Dev Neurosci

Self Cite

View full text Add to dashboard Cite

The neocortex is the region that most distinguishes human brain from other mammals and primates [Annu Rev Genet. 2021 Nov;55(1):555–81]. Studying the development of human cortex is important in understanding the evolutionary changes occurring in humans relative to other primates, as well as in elucidating mechanisms underlying neurodevelopmental disorders. Cortical development is a highly regulated process, spatially and temporally coordinated by expression of essential transcriptional factors in response to signaling pathways [Neuron. 2019 Sep;103(6):980–1004]. Enhancers are the most well-understood <i>cis</i>-acting, non-protein-coding regulatory elements that regulate gene expression [Nat Rev Genet. 2014 Apr;15(4):272–86]. Importantly, given the conservation of both DNA sequence and molecular function of the majority of proteins across mammals [Genome Res. 2003 Dec;13(12):2507–18], enhancers [Science. 2015 Mar;347(6226):1155–9], which are far more divergent at the sequence level, likely account for the phenotypes that distinguish the human brain by changing the regulation of gene expression. In this review, we will revisit the conceptual framework of gene regulation during human brain development, as well as the evolution of technologies to study transcriptional regulation, with recent advances in genome biology that open a window allowing us to systematically characterize <i>cis</i>-regulatory elements in developing human brain [Hum Mol Genet. 2022 Oct;31(R1):R84–96]. We provide an update on work to characterize the suite of all enhancers in the developing human brain and the implications for understanding neuropsychiatric disorders. Finally, we discuss emerging therapeutic ideas that utilize our emerging knowledge of enhancer function.

show abstract

“…When merging the global similarity matrices from all omics datasets, SNF conducts averaging of similarity matrices with iterative updating. It has demonstrated high efficiency in identifying clinical subtypes of cancers and other disorders such as autism (Cavalli et al, 2017;Ramaswami et al, 2020).…”

Section: Snf (Similarity Network Fusion)mentioning

confidence: 99%

Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Heo

Hwa

Lee

et al. 2021

Molecules and Cells

View full text Add to dashboard Cite

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multiview clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

show abstract

Integrative genomics identifies a convergent molecular subtype that links epigenomic with transcriptomic differences in autism

Cited by 3 publications

References 56 publications

Genetic contributions to autism spectrum disorder

Genetic contributions to autism spectrum disorder

Characterization of Gene Regulatory Elements in Human Fetal Cortical Development: Enhancing Our Understanding of Neurodevelopmental Disorders and Evolution

Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Contact Info

Product

Resources

About