Genome-wide Association Studies Variants Research Articles

CRISPR-Cas13d screens identify KILR, a breast cancer risk-associated lncRNA that regulates DNA replication and repair

BackgroundLong noncoding RNAs (lncRNAs) have surpassed the number of protein-coding genes, yet the majority have no known function. We previously discovered 844 lncRNAs that were genetically linked to breast cancer through genome-wide association studies (GWAS). Here, we show that a subset of these lncRNAs alter breast cancer risk by modulating cell proliferation, and provide evidence that a reduced expression on one lncRNA increases breast cancer risk through aberrant DNA replication and repair.MethodsWe performed pooled CRISPR-Cas13d-based knockdown screens in breast cells to identify which of the 844 breast cancer-associated lncRNAs alter cell proliferation. We selected one of the lncRNAs that increased cell proliferation, KILR, for follow-up functional studies. KILR pull-down followed by mass spectrometry was used to identify binding proteins. Knockdown and overexpression studies were performed to assess the mechanism by which KILR regulates proliferation.ResultsWe show that KILR functions as a tumor suppressor, safeguarding breast cells against uncontrolled proliferation. The half-life of KILR is significantly reduced by the risk haplotype, revealing an alternative mechanism by which variants alter cancer risk. Mechanistically, KILR sequesters RPA1, a subunit of the RPA complex required for DNA replication and repair. Reduced KILR expression promotes breast cancer cell proliferation by increasing the available pool of RPA1 and speed of DNA replication. Conversely, KILR overexpression promotes apoptosis in breast cancer cells, but not normal breast cells.ConclusionsOur results confirm lncRNAs as mediators of breast cancer risk, emphasize the need to annotate noncoding transcripts in relevant cell types when investigating GWAS variants and provide a scalable platform for mapping phenotypes associated with lncRNAs.

A powerful approach to identify replicable variants in genome-wide association studies

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

P124 Elucidating the role of risk variants in the BIM gene enhancer in T-Cell dysregulation and rheumatoid arthritis pathogenesis

Abstract Background/Aims The balance governing T-cell responses is pivotal for immune homeostasis. Disruptions to this balance may lead to aberrant behaviours, potentially resulting in autoimmunity. Rheumatoid Arthritis (RA), an autoimmune disorder primarily manifesting in the joints, is characterised by a defective immune response, resulting in chronic inflammation. Enhanced T-cell apoptosis resistance in this context can contribute to sustained activation and abnormal proliferation in a pro-inflammatory environment. Understanding these disease mechanisms could lead to improvements in clinical care. Genome-wide association studies (GWAS) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with RA, preferentially enriched in non-coding, T-cell-specific putative enhancers. Enhancers are DNA elements that can regulate the transcription of specific genes, often from many kilobases away. Our previous work identified BCL2L11, or BIM, as the probable causal gene within the RA locus rs13396472, despite the GWAS variants' intronic positioning relative to ACOXL. The RA risk variant disrupts a distant enhancer regulating the BIM gene, pivotal for apoptosis. Given the role of apoptosis in immune tolerance, disruption of this pathway driven by RA risk variants could pave the way for autoimmune responses. Our aim was to investigate the effect of this enhancer on T-cell proliferation and function in an in vivo model. Methods We mapped the human RA-locus to the mouse genome and utilised CRISPR-Cas9 to delete this putative RA enhancer in mice. Two sgRNAs, combined with Cas9, were microinjected into C57BL6/J zygote pronuclei, followed by embryo transfer to pseudo-pregnant mice. PCR and Sanger sequencing confirmed enhancer deletion in pups, establishing an RA-enhancer deletion colony. To gauge cell proportion differences, murine splenocytes or thymocytes were stained with specific fluorochrome markers and analysed via a flow cytometer. For the antigen-induced arthritis (AIA) model, wildtype and RA-enhancer deletion mice were subjected to arthritis induction via intra-articular injection of methylated bovine serum albumin, following prior immunisation, and monitored for arthritis signs. Results We observed an increased CD4+CD8+ double positive (DP) T-cell population in aged mice homozygous and heterozygous for the RA-enhancer deletion, but not in younger and wild-type controls. Knockout mice exhibited visibly enlarged spleens in response to the immunisation step prior to AIA induction. Further work will establish the mechanisms underlying this and disease progression after arthritis induction. These observations underscore a potential impact this enhancer might have on immune responses and survival upon immune system alteration. Conclusion Our research indicates that perturbing mouse non-coding sequences syntenic to human GWAS loci can provide a physiological model to elucidate novel mechanisms underlying RA, potentially suggesting therapeutic targets. Future research will explore the mechanism by which the enhancer deletion leads to an increase in DP T-cells and the altered phenotype, and the implications of such a change for T- and B-cell biology and RA. Disclosure S. Rossi: None. E. Richards: None. J. Ding: None. C. Shi: None. M. Menon: None. J. Gibbs: None. R. Grencis: None. A. Adamson: None. G. Orozco: None.

Parkinson's Disease Risk Variant rs9638616 is Non-Specifically Associated with Altered Brain Structure and Function.

A genome-wide association study (GWAS) variant associated with Parkinson's disease (PD) risk in Asians, rs9638616, was recently reported, and maps to WBSCR17/GALNT17, which is involved in synaptic transmission and neurite development. To test the association of the rs9638616 T allele with imaging-derived measures of brain microstructure and function. We analyzed 3-Tesla MRI and genotyping data from 116 early PD patients (aged 66.8±9.0 years; 39% female; disease duration 1.25±0.71 years) and 57 controls (aged 68.7±7.4 years; 54% female), of Chinese ethnicity. We performed voxelwise analyses for imaging-genetic association of rs9638616 T allele with white matter tract fractional anisotropy (FA), grey matter volume and resting-state network functional connectivity. The rs9638616 T allele was associated with widespread lower white matter FA (t = -1.75, p = 0.042) and lower functional connectivity of the supplementary motor area (SMA) (t = -5.05, p = 0.001), in both PD and control groups. Interaction analysis comparing the association of rs9638616 and FA between PD and controls was non-significant. These imaging-derived phenotypes mediated the association of rs9638616 to digit span (indirect effect: β= -0.21 [-0.42,-0.05], p = 0.031) and motor severity (indirect effect: β= 0.15 [0.04,0.26], p = 0.045). We have shown that a novel GWAS variant which is biologically linked to synaptic transmission is associated with white matter tract and functional connectivity dysfunction in the SMA, supported by changes in clinical motor scores. This provides pathophysiologic clues linking rs9638616 to PD risk and might contribute to future risk stratification models.

Colocalization analysis of 3' UTR alternative polyadenylation quantitative trait loci reveals novel mechanisms underlying associations with lung function.

While many disease-associated single nucleotide polymorphisms (SNPs) are expression quantitative trait loci (eQTLs), a large proportion of genome-wide association study (GWAS) variants are of unknown function. Alternative polyadenylation (APA) plays an important role in posttranscriptional regulation by allowing genes to shorten or extend 3' untranslated regions (UTRs). We hypothesized that genetic variants that affect APA in lung tissue may lend insight into the function of respiratory associated GWAS loci. We generated alternative polyadenylation (apa) QTLs using RNA sequencing and whole genome sequencing on 1241 subjects from the Lung Tissue Research Consortium (LTRC) as part of the NHLBI TOPMed project. We identified 56 179 APA sites corresponding to 13 582 unique genes after filtering out APA sites with low usage. We found that a total of 8831 APA sites were associated with at least one SNP with q-value < 0.05. The genomic distribution of lead APA SNPs indicated that the majority are intronic variants (33%), followed by downstream gene variants (26%), 3' UTR variants (17%), and upstream gene variants (within 1kb region upstream of transcriptional start site, 10%). APA sites in 193 genes colocalized with GWAS data for at least one phenotype. Genes containing the top APA sites associated with GWAS variants include membrane associated ring-CH-type finger 2 (MARCHF2), nectin cell adhesion molecule 2 (NECTIN2), and butyrophilin subfamily 3 member A2 (BTN3A2). Overall, these findings suggest that APA may be an important mechanism for genetic variants in lung function and chronic obstructive pulmonary disease (COPD).

Abstract 7331: Establishing a single-cell eQTL dataset of lung tissues from Asian never-smokers to identify cell-type specific lung cancer susceptibility genes

Abstract Lung cancer is one of the most prevalent and deadly forms of cancer worldwide. Whilst smoking is the main determinant, genetic factors also play a crucial role as genome-wide association studies (GWAS) have identified &amp;gt;50 loci associated with lung cancer risk. Yet, for most of these loci, it is still unknown how they contribute to lung cancer risk. Expression quantitative trait loci (eQTL) studies have been powerful in linking GWAS variants to potential target genes, providing genetic mechanisms underlying common diseases such as cancers. However, the current eQTL resources lack ancestral diversity and are primarily based on bulk tissues. Emerging single-cell eQTL (sc-eQTL) approaches can detect context-specific gene regulation but are mainly of blood samples or cultured cells and still representing European populations. This limits our abilities to test GWAS variants in cancer-relevant cell types as well as in diverse populations. To generate a resource to characterize lung cancer GWAS loci, we are building a lung sc-eQTL dataset of Asian population while addressing common challenges of tissue sc-eQTL. Namely, processing fresh tissue in a population scale is logistically challenging and costly, and epithelial cells (including cell types of lung cancer origin) are vulnerable to the dissociation and freezing/thawing process. To address these issues, we incorporated sample multiplexing and cell type balancing. We collected fresh tumor-distant normal lung tissues from 131 never-smoking Korean women and dissociated them before cryopreservation. We then performed single-cell RNA sequencing (scRNA-seq) using 10x Chromium Single Cell 3’ v3.1 chemistry with multiplexing of ~6 samples/batch. To enrich for epithelial cells, we utilized flow cytometry with surface markers of four major lung cell types (epithelial: EpCAM+/CD45-, immune: EpCAM-/CD45+, endothelial and stromal: EpCAM-/CD45-) before 10X library preparation. Concurrently, we performed DNA genotyping and imputation using matched blood samples. Following scRNA-seq (~36,000 reads/cell) we performed a genotype-based sample demultiplexing using Demuxlet. By integrating Demuxlet and Scrublet, we identified ~89% of the detected cells as singlets. After applying QC to filter empty droplets and low-quality cells, we obtained 428,619 cells or 3,272 cells/patient. Cell annotation guided by Azimuth label transfer using the Human Lung Cell Atlas, identified 28 cell types including 7 of epithelial origins. We will further perform eQTL analyses for individual cell types using pseudo-bulk and LIMIX (linear mixed model) or SAIGE-qtl (poisson regression) methods followed by aggregation across the cell types. By incorporating lung cancer GWAS data, we will identify cell-type specific susceptibility genes. Our dataset will provide a unique resource for lung cancer research. Citation Format: Thong Luong, Erping Long, Jinhu Yin, Bolun Li, Ju Hye Shin, Elelta Sisay, Alexander Kane, Alyxandra Golden, Yoon Soo Chang, Nicholas Banovich, Nathaniel Rothman, Jinyoung Byun, Qing Lan, Christopher Amos, Jianxin Shi, Jin Gu Lee, Eun Young Kim, Jiyeon Choi. Establishing a single-cell eQTL dataset of lung tissues from Asian never-smokers to identify cell-type specific lung cancer susceptibility genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7331.

Abstract 6142: Interactions of processed meat and red meat intake with pathway-based polygenic risk scores for colorectal cancer: A novel approach for PRSs construction

Abstract Introduction: Colorectal cancer (CRC) is the third most common cancer, and second cause of cancer death worldwide. Established CRC risk factors (E) include high red meat and processed meat intake. Genome-wide association studies (GWAS) have reported over 200 genetic variants associated with CRC risk. We used functional annotation data to identify subsets of GWAS variants within known pathways and constructed corresponding pathway Polygenic Risk Scores (pPRS). We evaluated pPRS by E interactions to determine whether genes within specific pathways interacted with meat intake to impact CRC risk. Methods: A pooled sample of 54,531 CRC controls and 48,260 cases of European ancestry from 27 studies were analyzed. Study-specific quartiles for red and processed meat intake were generated through in-person interviews and structured self-administered questionnaires. Variants were imported to AnnoQ for annotation (n=203) and analyzed for overrepresentation in PANTHER-reported pathways with Fisher’s exact test. Standard approaches were used to compute polygenic risk score weights relating the 203 variants to CRC. The subset of weights corresponding to each of five pathways were then used to compute the corresponding pPRS. Covariate-adjusted logistic regression models evaluated pPRSxE interactions with red meat and processed meat intake. Results: A total of 34 unique variants were overrepresented in five pathways: Apoptosis signaling, Alzheimer disease-presenilin, Wnt-signaling, Gonadotropin-releasing hormone receptor, and TGF-beta signaling. We found a statistically significant interaction between TGF-beta-pPRS and red meat intake (p = 0.0012). Stratified analyses reported a dose-response trend in the red meat and CRC risk association, with decreasing estimates of red meat and CRC risk association, comparing first to fourth quartiles of meat intake, with increasing quartiles of TGF-beta-pPRS: 13% (Q1), 12% (Q2), 7% (Q3), and 6% (Q4). This association remained significant after adjustment for all other variants (p = 0.0013), and all other pPRS with variants that did not overlap with the TGF-beta-pPRS (p = 0.0012). Independent GxE interaction models for individual variants that were included in the TGF-beta pathway showed significant interactions with red meat for rs2337113 (intron SMAD7 gene, Chr18), and rs2208603 (intergenic region BMP5, Chr6) (p = 0.013 &amp; 0.0108, respectively). We did not find significant pPRS x red meat interactions for the other four pathways or with any pPRS x processed meat. Conclusion: This pathway-based interaction analysis revealed a statistical interaction between SNPs in the TGF-beta pathway and red meat consumption that impacts CRC risk. These findings shed light into the possible mechanistic link between CRC risk and red meat consumption. Citation Format: Joel Sanchez Mendez, Mariana C. Stern, Yubo Fu, John Morrison, Juan P. Lewinger, Eric Kawaguchi, Bryan Queme, Huaiyu Mi, Flora Qu, Ulrike Peters, Li Hsu, Stephen B. Gruber, Li Li, Michelle Cotterchio, Loic Le Marchand, Andrew J. Pellat, Elizabeth A. Platz, W James Gauderman. Interactions of processed meat and red meat intake with pathway-based polygenic risk scores for colorectal cancer: A novel approach for PRSs construction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6142.

A common cis-regulatory variant impacts normal-range and disease-associated human facial shape through regulation of PKDCC during chondrogenesis.

Genome-wide association studies (GWAS) identified thousands of genetic variants linked to phenotypic traits and disease risk. However, mechanistic understanding of how GWAS variants influence complex morphological traits and can, in certain cases, simultaneously confer normal-range phenotypic variation and disease predisposition, is still largely lacking. Here, we focus on rs6740960, a single nucleotide polymorphism (SNP) at the 2p21 locus, which in GWAS studies has been associated both with normal-range variation in jaw shape and with an increased risk of non-syndromic orofacial clefting. Using in vitro derived embryonic cell types relevant for human facial morphogenesis, we show that this SNP resides in an enhancer that regulates chondrocytic expression of PKDCC - a gene encoding a tyrosine kinase involved in chondrogenesis and skeletal development. In agreement, we demonstrate that the rs6740960 SNP is sufficient to confer chondrocyte-specific differences in PKDCC expression. By deploying dense landmark morphometric analysis of skull elements in mice, we show that changes in Pkdcc dosage are associated with quantitative changes in the maxilla, mandible, and palatine bone shape that are concordant with the facial phenotypes and disease predisposition seen in humans. We further demonstrate that the frequency of the rs6740960 variant strongly deviated among different human populations, and that the activity of its cognate enhancer diverged in hominids. Our study provides a mechanistic explanation of how a common SNP can mediate normal-range and disease-associated morphological variation, with implications for the evolution of human facial features.

Convergence of coronary artery disease genes onto endothelial cell programs.

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association studies.

Fine-mapping in genome-wide association studies attempts to identify causal SNPs from a set of candidate SNPs in a local genomic region of interest and is commonly performed in one genetic ancestry at a time. Here, we present multi-ancestry sum of the single effects model (MESuSiE), a probabilistic multi-ancestry fine-mapping method, to improve the accuracy and resolution of fine-mapping by leveraging association information across ancestries. MESuSiE uses summary statistics as input, accounts for the diverse linkage disequilibrium pattern observed in different ancestries, explicitly models both shared and ancestry-specific causal SNPs, and relies on a variational inference algorithm for scalable computation. We evaluated the performance of MESuSiE through comprehensive simulations and multi-ancestry fine-mapping of four lipid traits with both European and African samples. In the real data, MESuSiE improves fine-mapping resolution by 19.0% to 72.0% compared to existing approaches, is an order of magnitude faster, and captures and categorizes shared and ancestry-specific causal signals with enhanced functional enrichment.

Comparing variants related to chronic diseases from genome-wide association study (GWAS) and the cancer genome atlas (TCGA)

BackgroundSeveral genome-wide association studies (GWAS) have been performed to identify variants related to chronic diseases. Somatic variants in cancer tissues are associated with cancer development and prognosis. Expression quantitative trait loci (eQTL) and methylation QTL (mQTL) analyses were performed on chronic disease-related variants in TCGA dataset.MethodsMuTect2 calling variants for 33 cancers from TCGA and 296 GWAS variants provided by LocusZoom were used. At least one mutation was found in TCGA 22 cancers and LocusZoom 23 studies. Differentially expressed genes (DEGs) and differentially methylated regions (DMRs) from the three cancers (TCGA-COAD, TCGA-STAD, and TCGA-UCEC). Variants were mapped to the world map using population locations of the 1000 Genomes Project (1GP) populations. Decision tree analysis was performed on the discovered features and survival analysis was performed according to the cluster.ResultsBased on the DEGs and DMRs with clinical data, the decision tree model classified seven and three nodes in TCGA-COAD and TCGA-STAD, respectively. A total of 11 variants were commonly detected from TCGA and LocusZoom, and eight variants were selected from the 1GP variants, and the distribution patterns were visualized on the world map.ConclusionsVariants related to tumors and chronic diseases were selected, and their geological regional 1GP-based proportions are presented. The variant distribution patterns could provide clues for regional clinical trial designs and personalized medicine.

Immune-response 3′UTR alternative polyadenylation quantitative trait loci contribute to variation in human complex traits and diseases

Genome-wide association studies (GWASs) have identified thousands of non-coding variants that are associated with human complex traits and diseases. The analysis of such GWAS variants in different contexts and physiological states is essential for deciphering the regulatory mechanisms underlying human disease. Alternative polyadenylation (APA) is a key post-transcriptional modification for most human genes that substantially impacts upon cell behavior. Here, we mapped 9,493 3′-untranslated region APA quantitative trait loci in 18 human immune baseline cell types and 8 stimulation conditions (immune 3′aQTLs). Through the comparison between baseline and stimulation data, we observed the high responsiveness of 3′aQTLs to immune stimulation (response 3′aQTLs). Co-localization and mendelian randomization analyses of immune 3′aQTLs identified 678 genes where 3′aQTL are associated with variation in complex traits, 27.3% of which were derived from response 3′aQTLs. Overall, these analyses reveal the role of immune 3′aQTLs in the determination of complex traits, providing new insights into the regulatory mechanisms underlying disease etiologies.

FTO is a major genetic link between breast cancer, obesity, and diabetes.

Breast cancer (BC), obesity, and type 2 diabetes mellitus (T2DM) are three complex diseases and health problems that are prevalent worldwide. The aim of this study was to investigate the common genetic associations between these diseases by referring back to the previous genome-wide association studies (GWAS). To this end, significant GWAS variants and common variants associated with BC, obesity, or diabetes were identified from the GWAS catalog. To perform candidate variants, the 1000-Genomes Project was used to find variants with linkage disequilibrium. Common variants between each category were identified (common candidate haplotypic variants). Finally, these variants and their associated genes were examined for SNP function analysis, gene expression, gene-gene correlation, and pathway analysis. The results identified 7 variants associated with both T2DM and BC, 8 variants associated with both obesity and BC, and 167 variants associating obesity with T2DM. 91 variants and 4 haplotypic blocks such as CTC were identified on the FTO gene associated with obesity, BC, and T2DM. The results of TCGA data showed that FTO in gene expression was correlated with 6 other genes in the DNA repair pathway in BC subjects. This study suggests that the FTO gene is one of the major genes shared by BC, T2DM, and obesity based on two DNA repair and inflammatory mechanisms. These results may provide a new perspective on the important role of the FTO gene and repair mechanism in the relationship between BC, obesity, and T2DM for future studies.

The value of genetic data from 665,460 individuals in managing iron deficiency anaemia and suitability to donate blood.

Although the genetic determinants of haemoglobin and ferritin have been widely studied, those of the clinically and globally relevant iron deficiency anaemia (IDA) and deferral due to hypohaemoglobinemia (Hb-deferral) are unclear. In this investigation, we aimed to quantify the value of genetic information in predicting IDA and Hb-deferral. We analysed genetic data from up to 665,460 participants of the FinnGen, Blood Service Biobank and UK Biobank, and used INTERVAL (N = 39,979) for validation. We performed genome-wide association studies (GWASs) of IDA and Hb-deferral and utilized publicly available genetic associations to compute polygenic scores for IDA, ferritin and Hb. We fitted models to estimate the effect sizes of these polygenic risk scores (PRSs) on IDA and Hb-deferral risk while accounting for the individual's age, sex, weight, height, smoking status and blood donation history. Significant variants in GWASs of IDA and Hb-deferral appear to be a small subset of variants associated with ferritin and Hb. Effect sizes of genetic predictors of IDA and Hb-deferral are similar to those of age and weight which are typically used in blood donor management. A total genetic score for Hb-deferral was estimated for each individual. The odds ratio estimate between first decile against that at ninth decile of total genetic score distribution ranged from 1.4 to 2.2. The value of genetic data in predicting IDA or suitability to donate blood appears to be on a practically useful level.

CisDynet: An integrated platform for modeling gene‐regulatory dynamics and networks

AbstractChromatin accessibility sequencing has been widely used for uncovering genetic regulatory mechanisms and inferring gene regulatory networks. However, effectively integrating large‐scale chromatin accessibility datasets has posed a significant challenge. This is due to the lack of a comprehensive end‐to‐end solution, as many existing tools primarily emphasize data preprocessing and overlook downstream analyses. To bridge this gap, we have introduced cisDynet, a holistic solution that combines streamlined data preprocessing using Snakemake and R functions with advanced downstream analysis capabilities. cisDynet excels in conventional data analyses, encompassing peak statistics, peak annotation, differential analysis, motif enrichment analysis, and more. Additionally, it allows to perform sophisticated data exploration, such as tissue‐specific peak identification, time course data modeling, integration of RNA‐seq data to establish peak‐to‐gene associations, constructing regulatory networks, and conducting enrichment analysis of genome‐wide association study (GWAS) variants. As a proof of concept, we applied cisDynet to reanalyze comprehensive ATAC‐seq datasets across various tissues from the Encyclopedia of DNA Elements (ENCODE) project. The analysis successfully delineated tissue‐specific open chromatin regions (OCRs), established connections between OCRs and target genes, and effectively linked these discoveries with 1861 GWAS variants. Furthermore, cisDynet was instrumental in dissecting the time course open chromatin data of mouse embryonic development, revealing the dynamic behavior of OCRs over developmental stages and identifying key transcription factors governing differentiation trajectories. In summary, cisDynet offers researchers a user‐friendly solution that minimizes the need for extensive coding, ensures the reproducibility of results, and greatly simplifies the exploration of epigenomic data.

OR35-03 Uncovering Causative Mutations in Polycystic Ovary Syndrome

Abstract Disclosure: J. Srivastava: None. I. Ovcharenko: None. PCOS is a complex disorder manifesting as reproductive and metabolic abnormalities in women. The undefined etiology stems from our incomplete understanding of genetic aberrations contributing to the diseased phenotype. This has resulted in speculations surrounding the cause-consequence relationship between phenotypic features of PCOS and limited our knowledge of comprehensive genetic signatures of PCOS. Pleiotropic effects are salient features of genetic regulation in mammalian genomes. The contribution of a set of regulatory elements (REs) to upregulation of a single gene, or the genetic control of multigene loci by a single RE, give rise to heterogenous phenotypes. Enhancers in particular dictate the spatio-temporal scale of gene expression by controlling transcription factor (TF) binding events. The complex traits of PCOS are suggestive of pleiotropy originating from genetic and epigenetic changes that arise from nucleotide variations in REs of the genome. Genome Wide Association Studies (GWAS) have identified ∼100 risk variants associated with PCOS and implicated the involvement of several genes such as FSHB/R, THADA, DENND1A, etc. However, mechanisms by which they contribute to pathophysiology are unknown. Additionally, twin studies have implicated the heritability of PCOS to be ∼80% and yet, the heritability from GWAS variants accounts for less than 10%. Experimental data may account for the ‘missing heritability problem’ specifically through rare variants that are missed in GWAS. To address this issue, we developed a Deep Learning model of gene regulation in PCOS identifying hundreds of active REs orchestrating the activity of PCOS genes. We also established a foundation for quantifying the impact of single-nucleotide noncoding mutations on TF binding and RE activity. By combining thousands of experimental assays associated with a large number of biological events such as tissue-specific epigenetic maps of the human genome, multi-tissue profiling of TF binding and gene expression using data from high throughput experiments we devised a strategy to accurately identify functionally map causative mutations in the GWAS loci of PCOS genes. MPRA experimental data validates a large fraction of our predicted causative variants thus suggesting that our studies have a potential of a leap forward accurate dissection of genomic and epigenetic mechanisms of PCOS. Consolidated results of this analysis and experimental validation of our predictions will lead to detailing the regulatory mechanisms of PCOS. Presentation Date: Sunday, June 18, 2023

A novel multi-ancestry proteome-wide Mendelian randomization study implicates extracellular proteins, tubular cells, and fibroblasts in estimated glomerular filtration rate regulation

Estimated glomerular filtration rate (eGFR) impacts the concentration of plasma biomarkers confounding biomarker association studies of eGFR with reverse causation. To identify biomarkers causally associated with eGFR, we performed a proteome-wide Mendelian randomization study. Genetic variants nearby biomarker coding genes were tested for association with plasma concentration of 1,161 biomarkers in a multi-ancestry sample of 12,066 participants from the Prospective Urban and Rural Epidemiological (PURE) study. Using two-sample Mendelian randomization, individual variants' effects on biomarker concentration were correlated with their effects on eGFR and kidney traits from published genome-wide association studies (GWAS). Genetically altered concentrations of 22 biomarkers were associated with eGFR above a Bonferroni-corrected significance threshold. Five biomarkers were previously identified by GWAS (UMOD, FGF5, LGALS7, NINJ1, COL18A1). Nine biomarkers were within 1 Mb of the lead GWAS variant but the gene for the biomarker was unidentified as the candidate for the GWAS signal (INHBC, TNFRSF11A, TCN2, PXN1, PRTN3, PSMD9, TFPI, ITGB6, CA3). Single-cell transcriptomic data indicated the 22 biomarkers are expressed in kidney tubules, collecting duct, fibroblasts, and immune cells. Pathway analysis showed significant enrichment of identified biomarkers in the extracellular kidney parenchyma. Thus, using genetic regulators of biomarker concentration via proteome-wide Mendelian randomization, we identified 22 biomarkers that appear to causally impact eGFR in either a beneficial or adverse manner. The current study provides rationale for novel therapeutic targets for eGFR and emphasized a role for extracellular proteins produced by tubular cells and fibroblasts for impacting eGFR.

Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants

Genome-wide association studies (GWASs) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium makes it challenging to discern causal variants. We performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia GWAS variants in primary human neural progenitors and identified 439 variants with allelic regulatory effects (MPRA-positive variants). Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit expressive quantitative trait loci signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. To predict the combinatorial effect of MPRA-positive variants on gene regulation, we propose an accessibility-by-contact model that combines MPRA-measured allelic activity with neuronal chromatin architecture.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants

AbstractRare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.