Functional Regulatory Variants Research Articles

Investigating the Shared Genetic Architecture Between Psychiatric Disorders and Executive Function

BackgroundEvidence for widespread comorbidity of executive dysfunctions with psychiatric disorders suggests common mechanisms underlying their pathophysiology. However, the shared genetic architectures between psychiatric disorders and executive function (EF) remain poorly understood. MethodsLeveraging large genome-wide association study datasets of European ancestry on bipolar disorder (N = 353,899), major depressive disorder (N = 674,452), and schizophrenia (N = 130,644) from the Psychiatric Genomics Consortium and iPSYCH and a common factor of EF (N = 427,037) from UK Biobank, we systematically investigated the shared genomic architectures between psychiatric disorders and EF with a set of statistical genetic, functional genomic, and gene-level analyses. ResultsOur study demonstrated substantial genetic overlaps and significant genetic correlations between psychiatric disorders and EF. EF showed an estimated 95.9%, 98.1%, and 99.2% of phenotype-influencing variants, as well as 50, 23, and 130 genomic loci shared with bipolar disorder, major depressive disorder, and schizophrenia, respectively. Single nucleotide polymorphism heritability enrichment suggests that the genetic architecture of psychiatric disorders and EF involves the brain’s frontal cortex and prefrontal glutamatergic neurons 1 and 2. Functional genomic analysis of shared variants identified 12 functional regulatory variants that regulate gene expression by affecting the binding affinities of 5 transcription factors. In addition, functional characterization analyses of shared genes revealed potential common biological mechanisms related to synaptic processes and fetal brain development. ConclusionsOur findings provide evidence for extensive shared genetic architectures between psychiatric disorders and EF and have valuable implications for future mechanistic investigations and drug development efforts.

Identification of Noncoding Functional Regulatory Variants of STIM1 Gene in Idiopathic Pulmonary Arterial Hypertension.

STIM1 (stromal interaction molecule 1) regulates store-operated calcium entry and is involved in pulmonary artery vasoconstriction and pulmonary artery smooth muscle cell proliferation, leading to pulmonary arterial hypertension (PAH). Bioinformatics analysis and a 2-stage matched case-control study were conducted to screen for noncoding variants that may potentially affect STIM1 transcriptional regulation in 242 patients with idiopathic PAH and 414 healthy controls. Luciferase reporter assay, real-time quantitative polymerase chain reaction, western blot, 5-ethynyl-2'-deoxyuridine (EdU) assay, and intracellular Ca2+ measurement were performed to study the mechanistic roles of those STIM1 noncoding variants in PAH. Five noncoding variants (rs3794050, rs7934581, rs3750996, rs1561876, and rs3750994) were identified and genotyped using Sanger sequencing. Rs3794050, rs7934581, and rs1561876 were associated with idiopathic PAH (recessive model, all P<0.05). Bioinformatics analysis showed that these 3 noncoding variants possibly affect the enhancer function of STIM1 or the microRNA (miRNA) binding to STIM1. Functional validation performed in HEK293 and pulmonary artery smooth muscle cells demonstrated that the noncoding variant rs1561876-G (STIM1 mutant) had significantly stronger transcriptional activity than the wild-type counterpart, rs1561876-A, by affecting the transcriptional regulatory function of both hsa-miRNA-3140-5p and hsa-miRNA-4766-5p. rs1561876-G enhanced intracellular Ca2+ signaling in human pulmonary artery smooth muscle cells secondary to calcium-sensing receptor activation and promoted proliferation of pulmonary artery smooth muscle cells under both normoxia and hypoxia conditions, suggesting a possible contribution to PAH development. The potential clinical implications of the 3 noncoding variants of STIM1, rs3794050, rs7934581, and rs1561876, are 2-fold, as they may help predict the risk and prognosis of idiopathic PAH and guide investigations on novel therapeutic pathway(s).

Massively parallel characterization of regulatory elements in the developing human cortex.

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and diseases. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 open chromatin regions, including thousands of sequences with cell type-specific accessibility and variants associated with brain gene regulation. In primary cells, we identified 46,802 active enhancer sequences and 164 variants that alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

SscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.

Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants' data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-of-the-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead single-nucleotide polymorphisms and the proxy variants in autoimmune GWAS data.

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.

Screening for functional regulatory variants in open chromatin using GenIE-ATAC.

Understanding the effects of genetic variation in gene regulatory elements is crucial to interpreting genome function. This is particularly pertinent for the hundreds of thousands of disease-associated variants identified by GWAS, which frequently sit within gene regulatory elements but whose functional effects are often unknown. Current methods are limited in their scalability and ability to assay regulatory variants in their endogenous context, independently of other tightly linked variants. Here, we present a new medium-throughput screening system: genome engineering based interrogation of enhancers assay for transposase accessible chromatin (GenIE-ATAC), that measures the effect of individual variants on chromatin accessibility in their endogenous genomic and chromatin context. We employ this assay to screen for the effects of regulatory variants in human induced pluripotent stem cells, validating a subset of causal variants, and extend our software package (rgenie) to analyse these new data. We demonstrate that this methodology can be used to understand the impact of defined deletions and point mutations within transcription factor binding sites. We thus establish GenIE-ATAC as a method to screen for the effect of gene regulatory element variation, allowing identification and prioritisation of causal variants from GWAS for functional follow-up and understanding the mechanisms of regulatory element function.

Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an “integrative prior” for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.

Functional regulatory variants implicate distinct transcriptional networks in dementia.

Predicting the function of noncoding variation is a major challenge in modern genetics. In this study, we used massively parallel reporter assays to screen 5706 variants identified from genome-wide association studies for both Alzheimer's disease (AD) and progressive supranuclear palsy (PSP), identifying 320 functional regulatory variants (frVars) across 27 loci, including the complex 17q21.31 region. We identified and validated multiple risk loci using CRISPR interference or excision, including complement 4 (C4A) and APOC1 in AD and PLEKHM1 and KANSL1 in PSP. Functional variants disrupt transcription factor binding sites converging on enhancers with cell type-specific activity in PSP and AD, implicating a neuronal SP1-driven regulatory network in PSP pathogenesis. These analyses suggest that noncoding genetic risk is driven by common genetic variants through their aggregate activity on specific transcriptional programs.

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Screening for functional regulatory variants in open chromatin using GenIE-ATAC

Screening for functional regulatory variants in open chromatin using GenIE-ATAC

Screening for functional regulatory variants in open chromatin using GenIE-ATAC

Screening for functional regulatory variants in open chromatin using GenIE-ATAC

Regulatory Variant rs2535629 in ITIH3 Intron Confers Schizophrenia Risk By Regulating CTCF Binding and SFMBT1 Expression.

Genome‐wide association studies have identified 3p21.1 as a robust risk locus for schizophrenia. However, the underlying molecular mechanisms remain elusive. Here a functional regulatory variant (rs2535629) is identified that disrupts CTCF binding at 3p21.1. It is confirmed that rs2535629 is also significantly associated with schizophrenia in Chinese population and the regulatory effect of rs2535629 is validated. Expression quantitative trait loci analysis indicates that rs2535629 is associated with the expression of three distal genes (GLT8D1, SFMBT1, and NEK4) in the human brain, and CRISPR‐Cas9‐mediated genome editing confirmed the regulatory effect of rs2535629 on GLT8D1, SFMBT1, and NEK4. Interestingly, differential expression analysis of GLT8D1, SFMBT1, and NEK4 suggested that rs2535629 may confer schizophrenia risk by regulating SFMBT1 expression. It is further demonstrated that Sfmbt1 regulates neurodevelopment and dendritic spine density, two key pathological characteristics of schizophrenia. Transcriptome analysis also support the potential role of Sfmbt1 in schizophrenia pathogenesis. The study identifies rs2535629 as a plausibly causal regulatory variant at the 3p21.1 risk locus and demonstrates the regulatory mechanism and biological effect of this functional variant, indicating that this functional variant confers schizophrenia risk by altering CTCF binding and regulating expression of SFMBT1, a distal gene which plays important roles in neurodevelopment and synaptic morphogenesis.

A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation.

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

Sequence-based correction of barcode bias in massively parallel reporter assays.

Massively parallel reporter assays (MPRAs) are a high-throughput method for evaluating in vitro activities of thousands of candidate cis-regulatory elements (CREs). In these assays, candidate sequences are cloned upstream or downstream from a reporter gene tagged by unique DNA sequences. However, tag sequences may themselves affect reporter gene expression and lead to major potential biases in the measured cis-regulatory activity. Here, we present a sequence-based method for correcting tag-sequence-specific effects and show that our method can significantly reduce this source of variation and improve the identification of functional regulatory variants by MPRAs. We also show that our model captures sequence features associated with post-transcriptional regulation of mRNA. Thus, this new method helps not only to improve detection of regulatory signals in MPRA experiments but also to design better MPRA protocols.

Genetics and Genomics of SOST: Functional Analysis of Variants and Genomic Regulation in Osteoblasts.

SOST encodes the sclerostin protein, which acts as a key extracellular inhibitor of the canonical Wnt pathway in bone, playing a crucial role in skeletal development and bone homeostasis. The objective of this work was to assess the functionality of two variants previously identified (the rare variant rs570754792 and the missense variant p.Val10Ile) and to investigate the physical interactors of the SOST proximal promoter region in bone cells. Through a promoter luciferase reporter assay we show that the minor allele of rs570754792, a variant located in the extended TATA box motif, displays a significant decrease in promoter activity. Likewise, through western blot studies of extracellular and intracellular sclerostin, we observe a reduced expression of the p.Val10Ile mutant protein. Finally, using a circular chromosome conformation capture assay (4C-seq) in 3 bone cell types (MSC, hFOB, Saos-2), we have detected physical interactions between the SOST proximal promoter and the ECR5 enhancer, several additional enhancers located between EVT4 and MEOX1 and a distant region containing exon 18 of DHX8. In conclusion, SOST presents functional regulatory and missense variants that affect its expression and displays physical contacts with far reaching genomic sequences, which may play a role in its regulation within bone cells.

Development of a massively parallel reporter assay to identify functional regulatory variants in the PICALM Alzheimer disease associated locus

AbstractBackgroundMost significant genome‐wide association markers for Alzheimer disease (AD) are in noncoding portions of the genome making assigning a corresponding gene involved in risk challenging. Coding variants identified in some these GWAS loci thus far do not explain the proportion of risk attributable to the associated markers, indicating a contribution of noncoding variants to AD. We set out to develop a high throughput method investigating the functional role of noncoding variants associated with AD using the PICALM locus on chromosome 14:85,600,000‐85,860,000, which has been associated with AD across multiple ancestries, as a proof of concept region.MethodWe used the gnomAD database of over 15,000 whole genomes to identify all variants located in the PICALM locus across a wide variety of ancestral populations (non‐Hispanic whites, Hispanics, African Americans, and Asians). We developed a pipeline for epigenomic annotation from available databases and the subsequent design of oligos carrying wild‐type and variant alleles for each variant. For unbiased analyses of functional potential to regulate enhancer activity of each of the alleles we established a large vector library for massively parallel reporter assays (MPRA) in AD relevant cell lines.ResultWe identified &gt;23,000 variants for analysis in the 260 kb PICALM locus. More than 4,000 of those had prior evidence for location in transcription factor binding sites, putative promotors/enhancers and eQTLs. 180 bp oligos centered around the variant alleles were synthesized by TWIST Biosciences. 20 bp random barcodes were added to each of the oligos through PCR amplification with a barcoded primer library. Sequencing of the resulting vector library indicated highly efficient incorporation with 95% unique barcodes present in the library. Reporter gene GFP and a minimal promoter were subsequently cloned in between the oligo and the barcode to establish the final MPRA library for transformation in cell lines.ConclusionHere we established a computational framework for in‐silico epigenomic annotation and oligo design for subsequent high‐throughput analyses and developed an efficient protocol to create a final MPRA library with highly unique barcodes at fragment incorporation. Cell line transformations and transcriptome analyses of the PICALM library to identify regulatory variants are ongoing.

Screening for functional transcriptional and splicing regulatory variants with GenIE.

Genome-wide association studies (GWAS) have identified numerous genetic loci underlying human diseases, but a fundamental challenge remains to accurately identify the underlying causal genes and variants. Here, we describe an arrayed CRISPR screening method, Genome engineering-based Interrogation of Enhancers (GenIE), which assesses the effects of defined alleles on transcription or splicing when introduced in their endogenous genomic locations. We use this sensitive assay to validate the activity of transcriptional enhancers and splice regulatory elements in human induced pluripotent stem cells (hiPSCs), and develop a software package (rgenie) to analyse the data. We screen the 99% credible set of Alzheimer's disease (AD) GWAS variants identified at the clusterin (CLU) locus to identify a subset of likely causal variants, and employ GenIE to understand the impact of specific mutations on splicing efficiency. We thus establish GenIE as an efficient tool to rapidly screen for the role of transcribed variants on gene expression.

Systems Genetics in Human Endothelial Cells Identifies Non-coding Variants Modifying Enhancers, Expression, and Complex Disease Traits

The identification of causal variants and mechanisms underlying complex disease traits in humans is important for the progress of human disease genetics; this requires finding strategies to detect functional regulatory variants in disease-relevant cell types. To achieve this, we collected genetic and transcriptomic data from the aortic endothelial cells of up to 157 donors and four epigenomic phenotypes in up to 44 human donors representing individuals of both sexes and three major ancestries. We found thousands of expression quantitative trait loci (eQTLs) at all ranges of effect sizes not detected by the Gene-Tissue Expression Project (GTEx) in human tissues, showing that novel biological relationships unique to endothelial cells (ECs) are enriched in this dataset. Epigenetic profiling enabled discovery of over 3,000 regulatory elements whose activity is modulated by genetic variants that most frequently mutated ETS, AP-1, and NF-kB binding motifs, implicating these motifs as governors of EC regulation. Using CRISPR interference (CRISPRi), allele-specific reporter assays, and chromatin conformation capture, we validated candidate enhancer variants located up to 750 kb from their target genes, VEGFC, FGD6, and KIF26B. Regulatory SNPs identified were enriched in coronary artery disease (CAD) loci, and this result has specific implications for PECAM-1, FES, and AXL. We also found significant roles for EC regulatory variants in modifying the traits pulse pressure, blood protein levels, and monocyte count. Lastly, we present two unlinked SNPs in the promoter of MFAP2 that exhibit pleiotropic effects on human disease traits. Together, this supports the possibility that genetic predisposition for complex disease is manifested through the endothelium.

A functional regulatory variant of MYH3 influences muscle fiber-type composition and intramuscular fat content in pigs.

Muscle development and lipid accumulation in muscle critically affect meat quality of livestock. However, the genetic factors underlying myofiber-type specification and intramuscular fat (IMF) accumulation remain to be elucidated. Using two independent intercrosses between Western commercial breeds and Korean native pigs (KNPs) and a joint linkage-linkage disequilibrium analysis, we identified a 488.1-kb region on porcine chromosome 12 that affects both reddish meat color (a*) and IMF. In this critical region, only the MYH3 gene, encoding myosin heavy chain 3, was found to be preferentially overexpressed in the skeletal muscle of KNPs. Subsequently, MYH3-transgenic mice demonstrated that this gene controls both myofiber-type specification and adipogenesis in skeletal muscle. We discovered a structural variant in the promotor/regulatory region of MYH3 for which Q allele carriers exhibited significantly higher values of a* and IMF than q allele carriers. Furthermore, chromatin immunoprecipitation and cotransfection assays showed that the structural variant in the 5′-flanking region of MYH3 abrogated the binding of the myogenic regulatory factors (MYF5, MYOD, MYOG, and MRF4). The allele distribution of MYH3 among pig populations worldwide indicated that the MYH3 Q allele is of Asian origin and likely predates domestication. In conclusion, we identified a functional regulatory sequence variant in porcine MYH3 that provides novel insights into the genetic basis of the regulation of myofiber type ratios and associated changes in IMF in pigs. The MYH3 variant can play an important role in improving pork quality in current breeding programs.

RegBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.