Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies.

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet most of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n = 158), Europeans (EUR, n = 408), and East Asians (EAS, n = 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs linked to 1,276 genes and 198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2, VPS37B, DENR, FTCDNL1, and NT5DC2) and three potential regulatory variants in known risk genes (CNNM2, MTRFR, and MPHOSPH9) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of risk genes in SCZ.

Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.

Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the predicted expression for trait associations. Accurate gene expression prediction in stage 1 is crucial, as it directly impacts the power to identify associations in stage 2. Currently, the first stage of such studies is primarily conducted using linear models like elastic net regression, which fail to capture the nonlinear relationships inherent in biological systems. Deep learning methods have the potential to model such nonlinear effects, but have yet to demonstrably outperform linear methods at this task. To address this gap, we propose a new deep learning architecture to predict gene expression from genotypic variation across individuals. Our method utilizes a learnable input scaling layer in conjunction with a convolutional encoder to capture nonlinear effects and higher-order interactions without compromising on interpretability. We further augment this approach to allow for parameter sharing across multiple networks, enabling us to utilize prior information for individual variants in the form of functional annotations. Evaluations on real-world genomic data show that our method consistently outperforms elastic net regression across a large set of heritable genes. Furthermore, our model statistically significantly improved predictive performance by leveraging functional annotations, whereas elastic net regression failed to show equivalent gains when using the same information, suggesting that our method can capture nonlinear functional information beyond the capability of linear models.

Most Pleiotropic Effects of Gene Knockouts Are Evolutionarily Transient in Yeasts.

Pleiotropy, the phenomenon in which a single gene influences multiple traits, is a fundamental concept in genetics. However, the evolutionary mechanisms underlying pleiotropy require further investigation. In this study, we conducted parallel gene knockouts targeting 100 transcription factors in 2 strains of Saccharomyces cerevisiae. We systematically examined and quantified the pleiotropic effects of these knockouts on gene expression levels for each transcription factor. Our results showed that the knockout of a single gene generally affected the expression levels of multiple genes in both strains, indicating various degrees of pleiotropic effects. Strikingly, the pleiotropic effects of the knockouts change rapidly between strains in different genetic backgrounds, and ∼85% of them were nonconserved. Further analysis revealed that the conserved effects tended to be functionally associated with the deleted transcription factors, while the nonconserved effects appeared to be more ad hoc responses. In addition, we measured 184 yeast cell morphological traits in these knockouts and found consistent patterns. In order to investigate the evolutionary processes underlying pleiotropy, we examined the pleiotropic effects of standing genetic variations in a population consisting of ∼1,000 hybrid progenies of the 2 strains. We observed that newly evolved expression quantitative trait loci impacted the expression of a greater number of genes than did old expression quantitative trait loci, suggesting that natural selection is gradually eliminating maladaptive or slightly deleterious pleiotropic responses. Overall, our results show that, although being prevalent for new mutations, the majority of pleiotropic effects observed are evolutionarily transient, which explains how evolution proceeds despite complicated pleiotropic effects.

Network-wide risk convergence in gene co-expression identifies reproducible genetic hubs of schizophrenia risk.

The omnigenic model posits that genetic risk for traits with complex heritability involves cumulative effects of peripheral genes on mechanistic "core genes," suggesting that in a network of genes, those closer to clusters including core genes should have higher GWAS signals. In gene co-expression networks, we confirmed that GWAS signals accumulate in genes more connected to risk-enriched gene clusters, highlighting across-network risk convergence. This was strongest in adult psychiatric disorders, especially schizophrenia (SCZ), spanning 70% of network genes, suggestive of super-polygenic architecture. In snRNA-seq cell type networks, SCZ risk convergence was strongest in L2/L3 excitatory neurons. We prioritized genes most connected to SCZ-GWAS genes, which showed robust association to a CRISPRa measure of PGC3 regulation and were consistently identified across several brain regions. Several genes, including dopamine-associated ones, were prioritized specifically in the striatum. This strategy thus retrieves current drug targets and can be used to prioritize other potential drug targets.

TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean.

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

Genetic susceptibility of urolithiasis: comprehensive results from genome-wide analysis.

BACKGROUND: The pathogenesis of urolithiasis is multi-factorial and genetic factors have been shown to play a significant role in the development of urolithiasis. We tried to apply genome-wide Mendelian randomization (MR) analysis and figure out reliable gene susceptibility of urolithiasis from the largest samples to date in two independent genome-wide association studies (GWAS) database of European ancestry. METHODS: We extracted summary statistics of expression quantitative trait locus (eQTL) from eQTLGen consortium. Urolithiasis phenotype information was obtained from both FinnGen Biobank and UK Biobank. Multiple two-sample MR analysis with a Bonferroni-corrected P threshold (P < 2.5e-06) was conducted. The primary endpoint was the causal effect calculated by random-effect inverse variance weighted (IVW) method. Sensitivity analysis, volcano plots, scatter plots, and regional plots were also performed and visualized. RESULTS: After multiple MR tests between 19942 eQTLs and urolithiasis phenotype from both cohorts, 30 common eQTLs with consistent effect size direction were found to be causally associated with urolithiasis risk. Finally only one gene (LMAN2) was simultaneously identified among all top significant eQTLs from both FinnGen Biobank (beta = 0.6758, se = 0.0327, P = 6.775e-95) and UK Biobank (beta = 0.0044, se = 0.0009, P = 2.417e-06). We also found that LMAN2 was with the largest beta effect size on urolithiasis phenotype from the two cohorts. CONCLUSION: We for the first time implemented genome-wide MR analysis to investigate the genetic susceptibility of urolithiasis in general population of European ancestry. Our results provided novel insights into common genetic variants of urinary stone disease, which was of great help to subsequent researches.

Cis-eQTLs in seven duck tissues identify novel candidate genes for growth and carcass traits.

BACKGROUND: Expression quantitative trait loci (eQTL) studies aim to understand the influence of genetic variants on gene expression. The colocalization of eQTL mapping and GWAS strategy could help identify essential candidate genes and causal DNA variants vital to complex traits in human and many farm animals. However, eQTL mapping has not been conducted in ducks. It is desirable to know whether eQTLs within GWAS signals contributed to duck economic traits. RESULTS: In this study, we conducted an eQTL analysis using publicly available RNA sequencing data from 820 samples, focusing on liver, muscle, blood, adipose, ovary, spleen, and lung tissues. We identified 113,374 cis-eQTLs for 12,266 genes, a substantial fraction 39.1% of which were discovered in at least two tissues. The cis-eQTLs of blood were less conserved across tissues, while cis-eQTLs from any tissue exhibit a strong sharing pattern to liver tissue. Colocalization between cis-eQTLs and genome-wide association studies (GWAS) of 50 traits uncovered new associations between gene expression and potential loci influencing growth and carcass traits. SRSF4, GSS, and IGF2BP1 in liver, NDUFC2 in muscle, ELF3 in adipose, and RUNDC1 in blood could serve as the candidate genes for duck growth and carcass traits. CONCLUSIONS: Our findings highlight substantial differences in genetic regulation of gene expression across duck primary tissues, shedding light on potential mechanisms through which candidate genes may impact growth and carcass traits. Furthermore, this availability of eQTL data offers a valuable resource for deciphering further genetic association signals that may arise from ongoing extensive endeavors aimed at enhancing duck production traits.

Exploration of the causality of frailty index on psoriasis: A Mendelian randomization study.

BACKGROUND: Frailty is associated with a variety of diseases, but the relationship between frailty and psoriasis remains unclear. METHODS: First, we conducted a two-sample Mendelian randomization based on genome-wide association studies (GWAS) to investigate genetic causality between frailty index and common diseases in dermatology. Inverse variance weighted was used to estimate causality. Second, expression quantitative trait locus (eQTLs) analysis was conducted to identify the genes affected by Single nucleotide polymorphisms (SNPs). Third, we performed function and pathway enrichment, transcriptome-wide association studies (TWAS) analysis based on eQTLs. RESULTS: It was shown that the rise of frailty index could increase the risk of psoriasis (IVW, beta = 0.916, OR = 2.500, 95%CI:1.418-4.408, p = 0.002) through Mendelian randomization (MR), and there was no heterogeneity and pleiotropy. There was no causality between the frailty index and other common diseases in dermatology. We found 31 eQTLs based on strongly correlated SNPs in the causality. TWAS analysis found that the expressions of four genes were closely related to psoriasis, including HLA-DQA1, HLA-DQA2, HLA-DRB1 and HLA-DQB1. CONCLUSION: It suggested that the frailty index had a significant positive causality on the risk of psoriasis, which was well documented by combined genomic, transcriptome, and proteome analyses.

PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs.

Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.

Pleiotropy with sex-specific traits reveals genetic aspects of sex differences in Parkinson's disease.

Parkinson's disease is an age-related neurodegenerative disorder with a higher incidence in males than females. The causes for this sex difference are unknown. Genome-wide association studies (GWAS) have identified 90 Parkinson's disease risk loci, but the genetic studies have not found sex-specific differences in allele frequency on autosomal chromosomes or sex chromosomes. Genetic variants, however, could exert sex-specific effects on gene function and regulation of gene expression. To identify genetic loci that might have sex-specific effects, we studied pleiotropy between Parkinson's disease and sex-specific traits. Summary statistics from GWASs were acquired from large-scale consortia for Parkinson's disease (n cases = 13 708; n controls = 95 282), age at menarche (n = 368 888 females) and age at menopause (n = 69 360 females). We applied the conditional/conjunctional false discovery rate (FDR) method to identify shared loci between Parkinson's disease and these sex-specific traits. Next, we investigated sex-specific gene expression differences in the superior frontal cortex of both neuropathologically healthy individuals and Parkinson's disease patients (n cases = 61; n controls = 23). To provide biological insights to the genetic pleiotropy, we performed sex-specific expression quantitative trait locus (eQTL) analysis and sex-specific age-related differential expression analysis for genes mapped to Parkinson's disease risk loci. Through conditional/conjunctional FDR analysis we found 11 loci shared between Parkinson's disease and the sex-specific traits age at menarche and age at menopause. Gene-set and pathway analysis of the genes mapped to these loci highlighted the importance of the immune response in determining an increased disease incidence in the male population. Moreover, we highlighted a total of nine genes whose expression or age-related expression in the human brain is influenced by genetic variants in a sex-specific manner. With these analyses we demonstrated that the lack of clear sex-specific differences in allele frequencies for Parkinson's disease loci does not exclude a genetic contribution to differences in disease incidence. Moreover, further studies are needed to elucidate the role that the candidate genes identified here could have in determining a higher incidence of Parkinson's disease in the male population.

Sex bias in celiac disease: XWAS and monocyte eQTLs in women identify TMEM187 as a functional candidate gene.

BACKGROUND: Celiac disease (CeD) is an immune-mediated disorder that develops in genetically predisposed individuals upon gluten consumption. HLA risk alleles explain 40% of the genetic component of CeD, so there have been continuing efforts to uncover non-HLA loci that can explain the remaining heritability. As in most autoimmune disorders, the prevalence of CeD is significantly higher in women. Here, we investigated the possible involvement of the X chromosome on the sex bias of CeD. METHODS: We performed a X chromosome-wide association study (XWAS) and a gene-based association study in women from the CeD Immunochip (7062 cases, 5446 controls). We also constructed a database of X chromosome cis-expression quantitative trait loci (eQTLs) in monocytes from unstimulated (n = 226) and lipopolysaccharide (LPS)-stimulated (n = 130) female donors and performed a Summary-data-based MR (SMR) analysis to integrate XWAS and eQTL information. We interrogated the expression of the potentially causal gene (TMEM187) in peripheral blood mononuclear cells (PBMCs) from celiac patients at onset, on a gluten-free diet, potential celiac patients and non-celiac controls. RESULTS: The XWAS and gene-based analyses identified 13 SNPs and 25 genes, respectively, 22 of which had not been previously associated with CeD. The X chromosome cis-eQTL analysis found 18 genes with at least one cis-eQTL in naïve female monocytes and 8 genes in LPS-stimulated female monocytes, 2 of which were common to both situations and 6 were unique to LPS stimulation. SMR identified a potentially causal association of TMEM187 expression in naïve monocytes with CeD in women, regulated by CeD-associated, eQTL-SNPs rs7350355 and rs5945386. The CeD-risk alleles were correlated with lower TMEM187 expression. These results were replicated using eQTLs from LPS-stimulated monocytes. We observed higher levels of TMEM187 expression in PBMCs from female CeD patients at onset compared to female non-celiac controls, but not in male CeD individuals. CONCLUSION: Using X chromosome genotypes and gene expression data from female monocytes, SMR has identified TMEM187 as a potentially causal candidate in CeD. Further studies are needed to understand the implication of the X chromosome in the higher prevalence of CeD in women.

Celiac disease (CeD) is an immune-related condition triggered by gluten consumption in genetically susceptible individuals. Women present higher prevalence of CeD than men, but the biological explanation of such difference has not been elucidated. In this study, we investigated whether specific genetic variations on the X chromosome were associated with CeD in each sex. Surprisingly, we found 13 genetic variants and 25 genes significantly linked to CeD in women, but not in men. Additionally, we identified genetic variants on the X chromosome associated with gene expression of monocytes, a type of immune cells that is activated in CeD after gluten intake. Integrating these data with our previous findings, we found that lower expression of a gene termed TMEM187 might be associated with a potential increase in CeD risk in women. Finally, validation experiments confirmed higher TMEM187 levels in blood cells from female CeD patients compared to non-celiac women, while no such difference was seen in males. In summary, our study suggests that the X-chromosome gene TMEM187 may play a key role in CeD development, providing insights into the higher prevalence of CeD in females.

Evaluation of noninvasive biospecimens for transcriptome studies.

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

rs56405341 Variant Associates with Expression of C4orf33 and C4orf33 Was Downregulated in Alzheimer's Disease and Progressive Supranuclear Palsy.

The first primary age-related tauopathy (PART) genome-wide association study confirmed significant associations of Alzheimer's disease (AD) and progressive supranuclear palsy (PSP) genetic variants with PART, and highlighted a novel genetic variant rs56405341. Here, we perform a comprehensive analysis of rs56405341. We found that rs56405341 was significantly associated with C4orf33 mRNA expression, but not JADE1 mRNA expression in multiple brain tissues. C4orf33 was mainly expressed in cerebellar hemisphere and cerebellum, and JADE1 was mainly expressed in thyroid, and coronary artery. Meanwhile, we found significantly downregulated C4orf33 expression both AD and PSP compared with normal controls, respectively.

Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population.

BACKGROUND: Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. RESULTS: We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. CONCLUSIONS: By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.

Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data.

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen.

Causality assessment of circulating Vitamin D level on venous thromboembolism: A Mendelian randomization study.

BACKGROUND AND AIMS: The associations of vitamin D level with venous thromboembolism (VTE) reported in observational studies, whereas these causal associations were uncertain in European population. Therefore, we used Mendelian randomization (MR) method to explore the causal associations between 25-hydroxyvitamin D (25(OH)D) concentrations and the risk of VTE and its subtypes [including deep vein thrombosis (DVT) and pulmonary embolism (PE)]. METHODS AND RESULTS: We used three kinds of genetic instruments to proxy the exposure of 25(OH)D, including genetic variants significantly associated with 25(OH)D, expression quantitative trait loci of 25(OH)D target genes, and genetic variants within or nearby 25(OH)D target genes. MR analyses did not provide any evidence for the associations of 25(OH)D levels with VTE and its subtypes (p > 0.05). The summary-data-based MR (SMR) analyses indicated that elevated expression of VDR was associated with decreased risk of VTE (OR = 0.81; 95% CI, 0.65-0.998; p = 0.047) and PE (OR = 0.67; 95% CI, 0.50-0.91; p = 0.011), and expression of AMDHD1 was associated with PE (OR = 0.93; 95% CI, 0.88-0.99; p = 0.027). MR analysis provided a significant causal effect of 25(OH)D level mediated by gene AMDHD1 on PE risk (OR = 0.09; 95% CI, 0.01-0.60; p = 0.012). CONCLUSION: Our MR analysis did not support causal association of 25(OH)D level with the risk of VTE and its subtypes. In addition, the expression of VDR and AMDHD1 involved in vitamin D metabolism showed a strong association with VTE or PE and might represent targets for these conditions.

Expression quantitative trait loci mapping identified PtrXB38 as a key hub gene in adventitious root development in Populus.

Plant establishment requires the formation and development of an extensive root system with architecture modulated by complex genetic networks. Here, we report the identification of the PtrXB38 gene as an expression quantitative trait loci (eQTL) hotspot, mapped using 390 leaf and 444 xylem Populus trichocarpa transcriptomes. Among predicted targets of this trans-eQTL were genes involved in plant hormone responses and root development. Overexpression of PtrXB38 in Populus led to significant increases in callusing and formation of both stem-born roots and base-born adventitious roots. Omics studies revealed that genes and proteins controlling auxin transport and signaling were involved in PtrXB38-mediated adventitious root formation. Protein-protein interaction assays indicated that PtrXB38 interacts with components of endosomal sorting complexes required for transport machinery, implying that PtrXB38-regulated root development may be mediated by regulating endocytosis pathway. Taken together, this work identified a crucial root development regulator and sheds light on the discovery of other plant developmental regulators through combining eQTL mapping and omics approaches.

VEGF-A related SNPs: a cardiovascular context.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Currently, cardiovascular disease risk algorithms play a role in primary prevention. However, this is complicated by a lack of powerfully predictive biomarkers that could be observed in individuals before the onset of overt symptoms. A key potential biomarker for heart disease is the vascular endothelial growth factor (VEGF-A), a molecule that plays a pivotal role in blood vessel formation. This molecule has a complex biological role in the cardiovascular system due to the processes it influences, and its production is impacted by various CVD risk factors. Research in different populations has shown single nucleotide polymorphisms (SNPs) may affect circulating VEGF-A plasma levels, with some variants associated with the development of CVDs, as well as CVD risk factors. This minireview aims to give an overview of the VEGF family, and of the SNPs reported to influence VEGF-A levels, cardiovascular disease, and other risk factors used in CVD risk assessments.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × âˆ¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Functional link between sarcoidosis-associated gene variants and quantitative levels of bronchoalveolar lavage fluid cell types.

Background: Sarcoidosis is an inflammatory disease that affects multiple organs. Cell analysis from bronchoalveolar lavage fluid (BALF) is a valuable tool in the diagnostic workup and differential diagnosis of sarcoidosis. Besides the expansion of lymphocyte expression-specific receptor segments (Vα2.3 and Vß22) in some patients with certain HLA types, the relation between sarcoidosis susceptibility and BAL cell populations' quantitative levels is not well-understood. Methods: Quantitative levels defined by cell concentrations of BAL cells and CD4+/CD8+ ratio were evaluated together with genetic variants associated with sarcoidosis in 692 patients with extensive clinical data. Genetic variants associated with clinical phenotypes, Löfgren's syndrome (LS) and non-Löfgren's syndrome (non-LS), were examined separately. An association test via linear regression using an additive model adjusted for sex, age, and correlated cell type was applied. To infer the biological function of genetic associations, enrichment analysis of expression quantitative trait (eQTLs) across publicly available eQTL databases was conducted. Results: Multiple genetic variants associated with sarcoidosis were significantly associated with quantitative levels of BAL cells. Specifically, LS genetic variants, mainly from the HLA locus, were associated with quantitative levels of BAL macrophages, lymphocytes, CD3+ cells, CD4+ cells, CD8+ cells, CD4+/CD8+ ratio, neutrophils, basophils, and eosinophils. Non-LS genetic variants were associated with quantitative levels of BAL macrophages, CD8+ cells, basophils, and eosinophils. eQTL enrichment revealed an influence of sarcoidosis-associated SNPs and regulation of gene expression in the lung, blood, and immune cells. Conclusion: Genetic variants associated with sarcoidosis are likely to modulate quantitative levels of BAL cell types and may regulate gene expression in immune cell populations. Thus, the role of sarcoidosis-associated gene-variants may be to influence cellular phenotypes underlying the disease immunopathology.