Leveraging mouse chromatin data for heritability enrichment informs common disease architecture and reveals cortical layer contributions to schizophrenia

A Genomic Link From Heart Failure to Atrial Fibrillation Risk: FOG2 Modulates a TBX5/GATA4-Dependent Atrial Gene Regulatory Network

BACKGROUND

The relationship between heart failure (HF) and atrial fibrillation (AF) is clear, with up to half of patients with HF progressing to AF. The pathophysiological basis of AF in the context of HF is presumed to result from atrial remodeling. Upregulation of the transcription factor FOG2 (friend of GATA2; encoded by ZFPM2) is observed in human ventricles during HF and causes HF in mice.

METHODS

FOG2 expression was assessed in human atria. The effect of adult-specific FOG2 overexpression in the mouse heart was evaluated by whole animal electrophysiology, in vivo organ electrophysiology, cellular electrophysiology, calcium flux, mouse genetic interactions, gene expression, and genomic function, including a novel approach for defining functional transcription factor interactions based on overlapping effects on enhancer noncoding transcription.

RESULTS

FOG2 is significantly upregulated in the human atria during HF. Adult cardiomyocyte-specific FOG2 overexpression in mice caused primary spontaneous AF before the development of HF or atrial remodeling. FOG2 overexpression generated arrhythmia substrate and trigger in cardiomyocytes, including calcium cycling defects. We found that FOG2 repressed atrial gene expression promoted by TBX5. FOG2 bound a subset of GATA4 and TBX5 co-bound genomic locations, defining a shared atrial gene regulatory network. FOG2 repressed TBX5-dependent transcription from a subset of co-bound enhancers, including a conserved enhancer at the Atp2a2 locus. Atrial rhythm abnormalities in mice caused by Tbx5 haploinsufficiency were rescued by Zfpm2 haploinsufficiency.

CONCLUSIONS

Transcriptional changes in the atria observed in human HF directly antagonize the atrial rhythm gene regulatory network, providing a genomic link between HF and AF risk independent of atrial remodeling.

Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs

BACKGROUND

Enhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking.

RESULTS

Here, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model.

CONCLUSIONS

Our work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

BACKGROUND

The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits.

RESULTS

Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes.

CONCLUSIONS

Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

AdaLiftOver: high-resolution identification of orthologous regulatory elements with Adaptive liftOver

MOTIVATION

Elucidating functionally similar orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from genome-wide association studies (GWAS). Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues.

RESULTS

We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying functionally similar orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets across a wide range of model organisms and GWAS SNPs, yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs.

AVAILABILITY AND IMPLEMENTATION

The R package and the data for AdaLiftOver is available from https://github.com/keleslab/AdaLiftOver.

Comparative chromatin accessibility upon BDNF stimulation delineates neuronal regulatory elements

Neuronal stimulation induced by the brain-derived neurotrophic factor (BDNF) triggers gene expression, which is crucial for neuronal survival, differentiation, synaptic plasticity, memory formation, and neurocognitive health. However, its role in chromatin regulation is unclear. Here, using temporal profiling of chromatin accessibility and transcription in mouse primary cortical neurons upon either BDNF stimulation or depolarization (KCl), we identify features that define BDNF-specific chromatin-to-gene expression programs. Enhancer activation is an early event in the regulatory control of BDNF-treated neurons, where the bZIP motif-binding Fos protein pioneered chromatin opening and cooperated with co-regulatory transcription factors (Homeobox, EGRs, and CTCF) to induce transcription. Deleting cis-regulatory sequences affect BDNF-mediated Arc expression, a regulator of synaptic plasticity. BDNF-induced accessible regions are linked to preferential exon usage by neurodevelopmental disorder-related genes and the heritability of neuronal complex traits, which were validated in human iPSC-derived neurons. Thus, we provide a comprehensive view of BDNF-mediated genome regulatory features using comparative genomic approaches to dissect mammalian neuronal stimulation.

Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain

The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.

Modelling the genetic aetiology of complex disease: human-mouse conservation of noncoding features and disease-associated loci

Understanding the genetic aetiology of loci associated with a disease is crucial for developing preventative measures and effective treatments. Mouse models are used extensively to understand human pathobiology and mechanistic functions of disease-associated loci. However, the utility of mouse models is limited in part by evolutionary divergence in transcription regulation for pathways of interest. Here, we summarize the alignment of genomic (exonic and multi-cell regulatory) annotations alongside Mendelian and complex disease-associated variant sites between humans and mice. Our results highlight the importance of understanding evolutionary divergence in transcription regulation when interpreting functional studies using mice as models for human disease variants.

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA's superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/ .

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants

Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs 'in silico saturated mutagenesis' analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.

Cardiac cell type-specific gene regulatory programs and disease risk association

Misregulated gene expression in human hearts can result in cardiovascular diseases that are leading causes of mortality worldwide. However, the limited information on the genomic location of candidate cis-regulatory elements (cCREs) such as enhancers and promoters in distinct cardiac cell types has restricted the understanding of these diseases. Here, we defined >287,000 cCREs in the four chambers of the human heart at single-cell resolution, which revealed cCREs and candidate transcription factors associated with cardiac cell types in a region-dependent manner and during heart failure. We further found cardiovascular disease-associated genetic variants enriched within these cCREs including 38 candidate causal atrial fibrillation variants localized to cardiomyocyte cCREs. Additional functional studies revealed that two of these variants affect a cCRE controlling KCNH2/HERG expression and action potential repolarization. Overall, this atlas of human cardiac cCREs provides the foundation for illuminating cell type-specific gene regulation in human hearts during health and disease.