Non-coding Single Nucleotide Polymorphisms Research Articles

MicroRNA-Mediated Suppression of Glial Cell Line-Derived Neurotrophic Factor Expression Is Modulated by a Schizophrenia-Associated Non-Coding Polymorphism.

Plasma levels of glial cell line-derived neurotrophic factor (GDNF), a pivotal regulator of differentiation and survival of dopaminergic neurons, are reportedly decreased in schizophrenia. To explore the involvement of GDNF in the pathogenesis of the disease, a case-control association analysis was performed between five non-coding single nucleotide polymorphisms (SNP) across the GDNF gene and schizophrenia. Of them, the 'G' allele of the rs11111 SNP located in the 3' untranslated region (3'-UTR) of the gene was found to associate with schizophrenia. In silico analysis revealed that the rs11111 'G' allele might create binding sites for three microRNA (miRNA) species. To explore the significance of this polymorphism, transient co-transfection assays were performed in human embryonic kidney 293T (HEK293T) cells with a luciferase reporter construct harboring either the 'A' or 'G' allele of the 3'-UTR of GDNF in combination with the hsa-miR-1185-1-3p pre-miRNA. It was demonstrated that in the presence of the rs11111 'G' (but not the 'A') allele, hsa-miR-1185-2-3p repressed luciferase activity in a dose-dependent manner. Deletion of the miRNA binding site or its substitution with the complementary sequence abrogated the modulatory effect. Our results imply that the rs11111 'G' allele occurring more frequently in patients with schizophrenia might downregulate GDNF expression in a miRNA-dependent fashion.

A regulatory variant impacting TBX1 expression contributes to basicranial morphology in Homo sapiens

Changes in gene regulatory elements play critical roles in human phenotypic divergence. However, identifying the base-pair changes responsible for the distinctive morphology of Homo sapiens remains challenging. Here, we report a noncoding single-nucleotide polymorphism (SNP), rs41298798, as a potential causal variant contributing to the morphology of the skull base and vertebral structures found in Homo sapiens. Screening for differentially regulated genes between Homo sapiens and extinct relatives revealed 13 candidate genes associated with basicranial development, with TBX1, implicated in DiGeorge syndrome, playing a pivotal role. Epigenetic markers and in silico analyses prioritized rs41298798 within a TBX1 intron for functional validation. CRISPR editing revealed that the 41-base-pair region surrounding rs41298798 modulates gene expression at 22q11.21. The derived allele of rs41298798 acts as an allele-specific enhancer mediated by E2F1, resulting in increased TBX1 expression levels compared to the ancestral allele. Tbx1-knockout mice exhibited skull base and vertebral abnormalities similar to those seen in DiGeorge syndrome. Phenotypic differences associated with TBX1 deficiency are observed between Homo sapiens and Neanderthals (Homo neanderthalensis). In conclusion, the regulatory divergence of TBX1 contributes to the formation of skull base and vertebral structures found in Homo sapiens.

Non-coding autoimmune risk variant defines role for ICOS in T peripheral helper cell development

Fine-mapping and functional studies implicate rs117701653, a non-coding single nucleotide polymorphism in the CD28/CTLA4/ICOS locus, as a risk variant for rheumatoid arthritis and type 1 diabetes. Here, using DNA pulldown, mass spectrometry, genome editing and eQTL analysis, we establish that the disease-associated risk allele is functional, reducing affinity for the inhibitory chromosomal regulator SMCHD1 to enhance expression of inducible T-cell costimulator (ICOS) in memory CD4+ T cells from healthy donors. Higher ICOS expression is paralleled by an increase in circulating T peripheral helper (Tph) cells and, in rheumatoid arthritis patients, of blood and joint fluid Tph cells as well as circulating plasmablasts. Correspondingly, ICOS ligation and carriage of the rs117701653 risk allele accelerate T cell differentiation into CXCR5-PD-1high Tph cells producing IL-21 and CXCL13. Thus, mechanistic dissection of a functional non-coding variant in human autoimmunity discloses a previously undefined pathway through which ICOS regulates Tph development and abundance.

Mutagenic primer-based novel multiplex PCR-RFLP technique to genotype BECN1 SNPs rs10512488 and rs11552192.

Single Nucleotide Polymorphisms (SNPs) in candidate autophagy gene BECN1 could influence its functions thereby autophagy process. BECN1 noncoding SNPs were found to be significantly associated with neurodegenerative disease and type 2 diabetes mellitus. This study aimed to develop a simultaneous genotyping technique for two BECN1 SNPs (rs10512488 and rs11552192). A mutagenic primer-based approach was used to introduce a NdeI restriction site to genotype rs10512488 by Artificial-Restriction Fragment Length Polymorphism (A-RFLP) along with rs11552192 by Polymerase Chain Reaction (PCR)-RFLP. Multiplexing PCR and restriction digestion reactions were set up for simultaneous genotyping of both SNPs in 100 healthy individuals. Genotypic and allele frequencies were manually calculated, and the Hardy-Weinberg Equilibrium was assessed using the chi-square test. We successfully developed PCR and RFLP conditions for the amplification and restriction digestion of both SNPs within the same tube for genotyping. The results of genotyping by newly developed multiplexing PCR-RFLP technique were concordant with the genotypes obtained by Sanger sequencing of samples. Allelic frequencies of rs10512488 obtained were 0.15 (A) and 0.85 (G), whereas allelic frequencies of rs11552192 were 0.16 (T) and 0.84 (A). The newly developed technique is rapid, cost-effective and time-saving for large-scale applications compared to sequencing methods and would play an important role in low-income settings. For the first time, allelic frequencies of rs10512488 and rs11552192 were reported among the North Indian population.

Evaluating the regulatory function of non-coding autism-associated single nucleotide polymorphisms on gene expression in human brain tissue.

Common variants account for most of the estimated heritability associated with autism spectrum disorder (autism). Although several replicable single nucleotide polymorphisms (SNPs) for the condition have been detected using genome-wide association study (GWAS) methodologies, their pathophysiological relevance remains elusive. Examining this is complicated, however, as all detected loci are situated within non-coding regions of the genome. It is therefore likely that they possess roles of regulatory function as opposed to directly affecting gene coding sequences. To bridge the gap between SNP discovery and mechanistic insight, we applied a comprehensive bioinformatic pipeline to functionally annotate autism-associated polymorphisms and their non-coding linkage disequilibrium (i.e., non-randomly associated) partners. We identified 82 DNA variants of probable regulatory function that may contribute to autism pathogenesis. To validate these predictions, we measured the impact of 11 high-confidence candidates and their GWAS linkage disequilibrium partners on gene expression in human brain tissue from Autistic and non-Autistic donors. Although a small number of the surveyed variants exhibited measurable influence on gene expression as determined via quantitative polymerase chain reaction, these did not survive correction for multiple comparisons. Additionally, no significant genotype-by-diagnosis effects were observed for any of the SNP-gene associations. We contend that this may reflect an inability to effectively capture the modest, neurodevelopmental-specific impact of individual variants on biological dysregulation in available post-mortem tissue samples, as well as limitations in the existing autism GWAS data.

Comprehensive characterization of coding and non-coding single nucleotide polymorphisms of the Myoneurin (MYNN) gene using molecular dynamics simulation and docking approaches.

Genome-wide association studies (GWAS) identified a coding single nucleotide polymorphism, MYNN rs10936599, at chromosome 3q. MYNN gene encodes myoneurin protein, which has been associated with several cancer pathogenesis and disease development processes. However, there needed to be a more detailed characterization of this polymorphism's (and other coding and non-coding polymorphisms) structural, functional, and molecular impact. The current study addressed this gap and analyzed different properties of rs10936599 and non-coding SNPs of MYNN via a thorough computational method. The variant, rs10936599, was predicted functionally deleterious by nine functionality prediction approaches, like SIFT, PolyPhen-2, and REVEL, etc. Following that, structural modifications were estimated through the HOPE server and Mutation3D. Moreover, the mutation was found in a conserved and active residue, according to ConSurf and CPORT. Further, the secondary structures were predicted, followed by tertiary structures, and there was a significant deviation between the native and variant models. Similarly, molecular simulation also showed considerable differences in the dynamic pattern of the wildtype and mutant structures. Molecular docking revealed that the variant binds with better docking scores with ligand NOTCH2. In addition to that, non-coding SNPs located at the MYNN locus were retrieved from the ENSEMBL database. These were found to disrupt the transcription factor binding regulatory regions; nonetheless, only two affect miRNA target sites. Again, eight non-coding variants were detected in the testes with normalized expression, whereas HaploReg v4.1 unveiled annotations for non-coding variants. In summary, in silico comprehensive characterization of coding and non-coding single nucleotide polymorphisms of MYNN gene will assist researchers to work on MYNN gene and establish their association with certain types of cancers.

Computational Investigation of Regulatory Region SNPs of Autophagy Gene BECN1

The autophagy process plays a cytoprotective role and ensures the healthy survival of a cell. The role of autophagy has been implicated in various diseases, making it an essential candidate for therapeutic interventions. Beclin 1, a candidate autophagy protein, plays a critical role during autophagy initiation and maturation by interacting with various other autophagy proteins. Beclin1 has been reported to be involved in various human diseases. This study uses a computational approach to study the effect of non-coding region single nucleotide polymorphisms (SNPs) of gene encoding beclin1. RegulomeDB, SNP2TFBS, and PROMO ALLGEN were used to predict the effect of promoter region variants on transcription factor binding sites. SNPs located within 3'UTR were analyzed by miRdSNP, PolymiRTS Database 3.0, miRNASNP-V3, MicroSNIPER, and miRmap. Nine promoter region variants that alter the transcription factor binding sites and 4 variants in 3'UTR were identified that either create a new target site for miRNA or disrupt an existing one. The functional analysis of these identified SNPs could be done experimentally to unravel their relation with a particular disease and the genetic predisposition of human subjects for a disease.

Single cell multi‐omics implicates immune and vascular cells in Alzheimer’s etiology

AbstractBackgroundUnderstanding the genetic etiology of Alzheimer’s disease (AD) is critical to inform effective therapies but remains a challenge. Genetic heritability of late‐onset AD is ∼60‐80%, and genome‐wide association studies (GWAS) have uncovered dozens of noncoding single nucleotide polymorphisms (SNPs) that influence AD risk. Two challenges for the functional interpretation of AD genetic risk are (1) determining the cell types in which SNPs operate and (2) identifying the genes they dysregulate to drive AD pathogenesis. Single‐nucleus sequencing studies of human neurons, microglia, and other brain cell types have begun addressing these challenges. Yet, many AD risk SNPs remain unmapped. One possibility is that they are expressed in immunovascular brain cell types largely missed by current methods.MethodHere, we optimized Vessel Isolation and Nuclei Extraction for Sequencing (VINE‐seq) to capture multimodal gene expression and chromatin accessibility assays across all major brain cell types from 30 cognitively normal, MCI, and AD human prefrontal cortex samples.ResultWe discover early gene dysregulation in immune and vascular cell types along AD progression. We highlight the gene regulatory networks mediating this dysregulation and implicate processes associated with AD pathology and cognitive decline. Finally, we map noncoding AD risk variants to diverse immune and vascular cell types, thereby expanding our understanding of AD etiology.ConclusionDysfunction of diverse human brain immune and vascular cell types mediate the genetic risk for Alzheimer’s disease.

Abstract 16731: Post-GWAS Functional Analysis of CBLN2 Locus Reveals the Role of CUX1 in the Inflammatory Pathogenesis of Pulmonary Arterial Hypertension

Introduction: Pulmonary arterial hypertension (PAH) is a complex and deadly pulmonary vascular disease highly relevant to genetic susceptibility and acquired inflammatory triggers. Chr18q22.3 near cerebellin 2 precursor (CBLN2) gene is the first genetic locus identified by Genome-wide Association Study (GWAS) that is significantly associated with PAH risk in the European population. This locus includes closely linked non-coding intergenic single nucleotide polymorphisms (SNPs) downstream of CBLN2. Although CBLN2 is upregulated in PAH and causes pulmonary vascular dysfunction, it remains unknown how these non-coding SNPs remotely affect CBLN2 and in turn, alter the genetic susceptibility of PAH. Methods and Results: We combined Hi-C chromatin interaction analysis, electrophoretic mobility shift assay, and SNP genotype-PAH correlation analysis with the All of Us database (N=20,343). In doing so, we identified rs11151770(A/G) as a PAH-associated functional SNP (fSNP), among 19 haplotype SNPs in linkage disequilibrium with the associated CBLN2 locus. The genomic region containing rs11151770(A/G) exhibited long-range chromatin interaction with the CBLN2 promotor. Proteomic analysis revealed that nuclear transcription factor Cut-like Homeobox 1 (CUX1) binds specifically to the risk allele A of rs11151770 but not to the non-risk allele G, regulating the expression of CBLN2 as an activator in pulmonary arterial endothelial cells. CUX1-CBLN2 was upregulated by acquired inflammatory PAH triggers IL-1β and IL-6. SNP genotyping in PAH patients (N=159) and non-PAH controls (N=87) showed a 2.2-fold enrichment of the risk allele A only in patients with PAH associated with connective tissue disorders (N=77, p=0.036). These findings supported a pathogenic mechanism of an inflammation-sensitive pathway (CUX1-CBLN2) controlled by an fSNP (risk allele A) underlying the reported genetic association of the CBLN2 locus with PAH risk. Conclusion: Our post-GWAS functional genomics analysis defined a pathogenic CUX1-mediated, fSNP rs11151770(A/G)-controlled, and inflammation-responsive pathway that provides a mechanistic explanation for the GWAS-identified association between genetic variants in CBLN2 locus and PAH susceptibility.

Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans

BackgroundTranscription factors (TFs) exhibit heterogeneous DNA-binding specificities in individual cells and whole organisms under natural conditions, and de novo motif discovery usually provides multiple motifs, even from a single chromatin immunoprecipitation-sequencing (ChIP-seq) sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored.ResultsHere, we applied MOCCS2, our k-mer-based motif discovery method, to a collection of human TF ChIP-seq samples across diverse TFs and cell types, and systematically computed profiles of TF-binding specificity scores for all k-mers. After quality control, we compiled a set of TF-binding specificity score profiles for 2,976 high-quality ChIP-seq samples, comprising 473 TFs and 398 cell types. Using these high-quality samples, we confirmed that the k-mer-based TF-binding specificity profiles reflected TF- or TF-family dependent DNA-binding specificities. We then compared the binding specificity scores of ChIP-seq samples with the same TFs but with different cell type classes and found that half of the analyzed TFs exhibited differences in DNA-binding specificities across cell type classes. Additionally, we devised a method to detect differentially bound k-mers between two ChIP-seq samples and detected k-mers exhibiting statistically significant differences in binding specificity scores. Moreover, we demonstrated that differences in the binding specificity scores between k-mers on the reference and alternative alleles could be used to predict the effect of variants on TF binding, as validated by in vitro and in vivo assay datasets. Finally, we demonstrated that binding specificity score differences can be used to interpret disease-associated non-coding single-nucleotide polymorphisms (SNPs) as TF-affecting SNPs and provide candidates responsible for TFs and cell types.ConclusionsOur study provides a basis for investigating the regulation of gene expression in a TF-, TF family-, or cell-type-dependent manner. Furthermore, our differential analysis of binding-specificity scores highlights noncoding disease-associated variants in humans.

Causal role of a promoter polymorphism in natural variation of the Arabidopsis floral repressor gene FLC

Noncoding polymorphism frequently associates with phenotypic variation, but causation and mechanism are rarely established. Noncoding single-nucleotide polymorphisms (SNPs) characterize the major haplotypes of the Arabidopsis thaliana floral repressor gene FLOWERING LOCUS C (FLC). This noncoding polymorphism generates a range of FLC expression levels, determining the requirement for and the response to winter cold. The major adaptive determinant of these FLC haplotypes was shown to be the autumnal levels of FLC expression. Here, we investigate how noncoding SNPs influence FLC transcriptional output. We identify an upstream transcription start site (uTSS) cluster at FLC, whose usage is increased by an A variant at the promoter SNP-230. This variant is present in relatively few Arabidopsis accessions, with the majority containing G at this site. We demonstrate a causal role for the A variant at -230 in reduced FLC transcriptional output. The G variant upregulates FLC expression redundantly with the major transcriptional activator FRIGIDA (FRI). We demonstrate an additive interaction of SNP-230 with an intronic SNP+259, which also differentially influences uTSS usage. Combinatorial interactions between noncoding SNPs and transcriptional activators thus generate quantitative variation in FLC transcription that has facilitated the adaptation of Arabidopsis accessions to distinct climates.

Machine Learning Study of SNPs in Noncoding Regions to Predict Non-small Cell Lung Cancer Susceptibility

Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. Both environmental and genetic factors have been reported to impact the lung cancer susceptibility. We conducted a genome-wide association study (GWAS) of 287 NSCLC patients and 467 healthy controls in a Chinese population using the Illumina Genome-Wide Asian Screening Array Chip on 712,095 SNPs (single nucleotide polymorphisms). Using logistic regression modeling, GWAS identified 17 new noncoding region SNP loci associated with the NSCLC risk, and the top three (rs80040741, rs9568547, rs6010259) were under a stringent p-value (<3.02e-6). Notably, rs80040741 and rs6010259 were annotated from the intron regions of MUC3A and MLC1, respectively. Together with another five SNPs previously reported in Chinese NSCLC patients and another four covariates (e.g., smoking status, age, low dose CT screening, sex), a predictive model by machine learning methods can separate the NSCLC from healthy controls with an accuracy of 86%. This is the first time to apply machine learning method in predicting the NSCLC susceptibility using both genetic and clinical characteristics. Our findings will provide a promising method in NSCLC early diagnosis and improve our understanding of applying machine learning methods in precision medicine.

Abstract P386: Rat Remap Initiative At The Rat Genome Database (RGD): A Resource Of Integrated Transcriptomic And Epigenomic Data For Laboratory Rats

Complex traits including the genetic control of blood pressure have been associated with both coding and non-coding single nucleotide polymorphisms (SNPs) at more than a thousand loci across the human genome. Many of these haplotypes are found between and distant from genes, and their mechanisms leading to blood pressure variation are largely unknown. Attempts to compare and translate these non-coding mechanisms to animal model systems such as the laboratory rat are difficult, but even further complicated by the difficulty of accessing already existing large-scale genome-wide datasets including transcriptomic and epigenomic (e.g. chromatin accessibility, transcription factor binding, and DNA methylation) data that are currently distributed across a variety of public data repositories and supplementary datasets. These datasets are often annotated to previous and less complete reference genome assemblies, further hampering their broad use without time consuming reanalysis. Collectively, these challenges impede discovery and leveraging of these datasets across the broader research community toward understanding mechanisms controlling complex traits such as blood pressure. To address these challenges, the Rat ReMap Initiative seeks to establish a framework for a publicly accessible resource for rat genomic, transcriptomic and epigenomic data to housed within RGD, with analysis updated to the current reference genome assembly. Users will be able to explore and find these datasets, link to the original raw data, and access analyzed summary data files. Remapped datasets will be linked to existing genomic features, strain, and genetic variation annotations to link with disease, pathways, phenotypes, and models portals already annotated and maintained in the Rat Genome Database (RGD). Here we report our initial efforts to standardize metadata and remap a targeted set of publicly available rat transcriptome, chromatin accessibility and transcription factor binding datasets, some across multiple ages and both sexes, onto the newest rat reference assembly (mRatBN7.2), and to integrate them into an updated genome browser (JBrowse2) and into a comparative genome map browser (VCMap) at RGD.

Abstract 076: The Blood Pressure-associated Non-coding Single Nucleotide Polymorphism Rs28451064 Influences Gene Expression In Human Endothelial Cells

Most Blood Pressure (BP)-associated single nucleotide polymorphisms (SNPs) are in intronic or intergenic regions. For the noncoding SNP rs28451064, allele A is associated with elevated diastolic BP as well as increased risk of myocardial infarction and angina pectoris. rs28451064 is mapped to the nearby protein coding gene KCNE2 and is an expression quantitative trait locus for RCAN1 . We performed a series of studies to experimentally identify genes influenced by rs28451064 and to begin to understand the mechanism. We differentiated a human induced pluripotent stem cell (iPSC) line to endothelial cells (iECs). The iECs were purified by sorting with VE-cadherin. Chromatin conformation analysis using Micro-C identified several genes including MRPS6 and SLC5A3 as sharing a chromatin loop with rs28451064. We used CRISPR-Cas9 to generate isogenic iPSC lines with either homozygous low BP (G) or high BP allele (A) of rs28451064. Three clones of iPSCs with each genotype were established by screening several hundred clones. The edited iPSCs were differentiated into iECs for RNA-seq analysis, which showed that several genes that are genomically close to rs28451064 tended to be differentially expressed, including KCNE2 , SMIM34 , and RCAN1 . The edited iPSC lines were subjected to six rounds of differentiation to iECs for targeted real-time PCR analysis of genes near rs28451064, including the genes mentioned above. The results indicated significantly increased expression of SMIMI11 (1.93±0.25 fold) and SMIM34 (1.45±0.13 fold) (n=34) and decreased expression of RCAN1 (0.90 ± 0.03 fold) and SLC5A3 (0.90 ± 0.03 fold) (n=36) in iECs with the high BP allele compared to the low BP allele of rs28451064 (p&lt;0.05). SMIM11 and SMIM34 encode small integral membrane proteins with unknown functions. RCAN1 encodes regulator of calcineurin 1, and SLC5A3 encodes sodium/myo-inositol cotransporter 1. Interestingly, the closest genes, MRPS6 and KCNE2 , along with MRPL23 , a control gene on a different chromosome, were not significantly differentially expressed. In summary, we have shown definitively that rs28451064 influences the expression of several local genes in an allele-specific manner. The results provide new insights into the function of a BP-associated noncoding SNP.

Abstract P1017: Post-GWAS Functional Analysis Of CBLN2 Locus Reveals The Role Of CUX1 In The Inflammatory Pathogenesis Of Pulmonary Arterial Hypertension

Background: Pulmonary arterial hypertension (PAH) is a deadly pulmonary vascular disease highly relevant to genetic susceptibility and acquired pathogenic triggers, especially inflammation. Chr18q22.3 near cerebellin 2 (CBLN2) gene is the first genetic locus identified by Genome-wide Association Study (GWAS) that is significantly associated with PAH risk in population of European ancestry. This locus includes a group of closely linked non-coding intergenic single nucleotide polymorphisms (SNPs) downstream of CBLN2. Although CBLN2 is upregulated in PAH and causes pulmonary vascular dysfunction, it remains unknown how these non-coding SNPs remotely affect CBLN2 and in turn alter the genetic susceptibility of PAH. Methods and Results: We applied Hi-C analysis, combined with electrophoretic mobility shift assay, to identify rs11151770 as a functional SNP (fSNP) characterized with allele-imbalanced nuclear protein binding and chromatin interaction with CBLN2 promoter, among 19 haplotype SNPs in a linkage disequilibrium (LD r 2 &gt;0.8) in CBLN2 locus. As an intergenic non-coding SNP, rs11151770 is located 110 kbp downstream of CBLN2 promoter. Proteomic analysis revealed nuclear transcription factor CUX1 binds specifically to the risk allele A of rs11151770 but not to the non-risk allele C. Acting as the activator for the expression of target gene CBLN2, CUX1 knockdown prevented, but CUX1 overexpression induced, the pathophenotypes of human pulmonary arterial endothelial cells (PAECs) associated with PAH. CUX1-CBLN2 was upregulated by acquired inflammatory PAH trigger IL-1β and was increased in lung tissues extracted from PAH patients. SNP genotyping in PAH patients and non-PAH controls showed a 2.2-fold risk allele A enrichment only in patients with PAH associated with connective tissue disorders, supporting a pathogenic mechanism of an inflammation-sensitive pathway (CUX1-CBLN2) combined with a PAH susceptible genotype (risk allele A of rs11151770) for the manifestation of clinical PAH. Conclusion: Our post-GWAS functional genomics analysis revealed a CUX1-mediated inflammatory pathway and provided a mechanistic explanation for the GWAS-identified association between the genetic variants in CBLN2 locus and PAH pathogenesis.

High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes

Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.

Construction and evaluation of the functional polygenic risk score for gastric cancer in a prospective cohort of the European population.

A polygenic risk score (PRS) derived from 112 single-nucleotide polymorphisms (SNPs) for gastric cancer has been reported in Chinese populations (PRS-112). However, its performance in other populations is unknown. A functional PRS (fPRS) using functional SNPs (fSNPs) may improve the generalizability of the PRS across populations with distinct ethnicities. We performed functional annotations on SNPs in strong linkage disequilibrium (LD) with the 112 previously reported SNPs to identify fSNPs that affect protein-coding or transcriptional regulation. Subsequently, we constructed an fPRS based on the fSNPs by using the LDpred2-infinitesimal model and then analyzed the performance of the PRS-112 and fPRS in the risk prediction of gastric cancer in 457,521 European participants of the UK Biobank cohort. Finally, the performance of the fPRS in combination with lifestyle factors were evaluated in predicting the risk of gastric cancer. During 4,582,045 person-years of follow-up with a total of 623 incident gastric cancer cases, we found no significant association between the PRS-112 and gastric cancer risk in the European population (hazard ratio [HR] = 1.00 [95% confidence interval (CI) 0.93-1.09], P = 0.846). We identified 125 fSNPs, including seven deleterious protein-coding SNPs and 118 regulatory non-coding SNPs, and used them to constructed the fPRS-125. Our result showed that the fPRS-125 was significantly associated with gastric cancer risk (HR = 1.11 [95% CI, 1.03-1.20], P = 0.009). Compared to participants with a low fPRS-125 (bottom quintile), those with a high fPRS-125 (top quintile) had a higher risk of incident gastric cancer (HR = 1.43 [95% CI, 1.12-1.84], P = 0.005). Moreover, we observed that participants with both an unfavorable lifestyle and a high genetic risk had the highest risk of incident gastric cancer (HR = 4.99 [95% CI, 1.55-16.10], P = 0.007) compared to those with both a favorable lifestyle and a low genetic risk. These results indicate that the fPRS-125 derived from fSNPs may act as an indicator to measure the genetic risk of gastric cancer in the European population.

0036 ARC Genotype Modulates REM EEG Spectral Power Following Total Sleep Deprivation

Abstract Introduction EEG spectral power in the alpha range is indicative of a wake-like state, with alpha power typically high during wakefulness and reduced during sleep. We previously reported that a single nucleotide polymorphism (SNP) of the activity-regulated cytoskeleton associated protein (ARC) gene modulates non-REM theta and alpha spectral power. Here we sought to determine whether ARC genotype is also associated with phenotypic differences in EEG spectral power during REM sleep. Methods 43 healthy adults (27.6±4.8y; 23 females) participated in one of two in-laboratory studies. Each participant had a 10h baseline sleep opportunity (22:00–08:00), 38h TSD, and 10h recovery sleep opportunity (22:00–08:00). Sleep periods were recorded polysomnographically and visually scored according to AASM criteria. Genomic DNA was assayed for the ARC c.*742 + 58C&amp;gt;T non-coding SNP, rs35900184. Log-transformed EEG spectral power (C3-M3 derivation) for REM sleep over 0.2 Hz frequency bins in each of four frequency bands – delta (0.8–4.0 Hz), theta (4.2–8.0 Hz), alpha (8.2–12.0 Hz), and beta (12.2–16.0 Hz) – was analyzed by band using mixed-effects ANOVA with fixed effects for ARC genotype, night (baseline, recovery), frequency bin, and their interactions. Analyses included study and age as covariates and a random effect over subjects on the intercept. Results The genotype distribution in this sample was 28 C/C homozygotes, 10 C/T heterozygotes, and 5 T/T homozygotes. There was a significant genotype by night interaction in the alpha band (F2,1560=23.80, p&amp;lt; 0.001). Compared to baseline sleep, T/T homozygotes had 13.0% more alpha power during post-TSD recovery sleep, whereas C/C homozygotes had 1.2% less alpha power and C/T heterozygotes had 3.2% less alpha power. This differential TSD effect for the ARC genotypes was not seen in the other spectral bands. Conclusion Our results show that ARC genotype mediates the REM sleep EEG response to TSD, with T/T homozygotes displaying a pronounced increase in REM alpha power following sleep deprivation. The ARC SNP has been associated with schizophrenia, a psychiatric disorder with known sleep disturbances. As such, the ARC-mediated increase in REM EEG alpha power for T/T homozygotes might be a marker of a psychosis-like predisposition. Support (if any) ONR N00014-13-1-0302; NIH R21CA167691; USAMRDC W81XWH-18-1-0100; ARO W911NF2210223

Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences

MotivationGenome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal mechanism. Predicting molecular processes based on the DNA sequence represents a promising approach to understand the role of those non-coding SNPs. Over the past years, deep learning was successfully applied to regulatory sequence prediction using supervised learning. Supervised learning required DNA sequences associated with functional data for training, whose amount is strongly limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is exponentially increasing due to ongoing large sequencing projects, but without functional data in most cases.ResultsTo alleviate the limitations of supervised learning, we propose a paradigm shift with semi-supervised learning, which does not only exploit labeled sequences (e.g. human genome with ChIP-seq experiment), but also unlabeled sequences available in much larger amounts (e.g. from other species without ChIP-seq experiment, such as chimpanzee). Our approach is flexible and can be plugged into any neural architecture including shallow and deep networks, and shows strong predictive performance improvements compared to supervised learning in most cases (up to 70%).Availability and implementationhttps://forgemia.inra.fr/raphael.mourad/deepgnn.

Low-grade glioma risk SNP rs11706832 is associated with type I interferon response pathway genes in cell lines

Genome-wide association studies (GWAS) have contributed to our understanding of glioma susceptibility. To date, 25 risk loci for development of any of the glioma subtypes are known. However, GWAS studies reveal little about the molecular processes that lead to increased risk, especially for non-coding single nucleotide polymorphisms (SNP). A particular SNP in intron 2 of LRIG1, rs11706832, has been shown to increase the susceptibility for IDH1 mutated low-grade gliomas (LGG). Leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) is important in cancer development as it negatively regulates the epidermal growth factor receptor (EGFR); however, the mechanism responsible for this particular risk SNP and its potential effect on LRIG1 are not known. Using CRISPR-CAS9, we edited rs11706832 in HEK293T cells. Four HEK293T clones with the risk allele were compared to four clones with the non-risk allele for LRIG1 and SLC25A26 gene expression using RT-qPCR, for global gene expression using RNA-seq, and for metabolites using gas chromatography-mass spectrometry (GC–MS). The experiment did not reveal any significant effect of the SNP on the expression levels or splicing patterns of LRIG1 or SLC25A26. The global gene expression analysis revealed that the risk allele C was associated with upregulation of several mitochondrial genes. Gene enrichment analysis of 74 differentially expressed genes in the genome revealed a significant enrichment of type I interferon response genes, where many genes were downregulated for the risk allele C. Gene expression data of IDH1 mutated LGGs from the cancer genome atlas (TCGA) revealed a similar under expression of type I interferon genes associated with the risk allele. This study found the expression levels and splicing patterns of LRIG1 and SLC25A26 were not affected by the SNP in HEK293T cells. However, the risk allele was associated with a downregulation of genes involved in the innate immune response both in the HEK293T cells and in the LGG data from TCGA.