Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum.

Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, that affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (p < 5×10-8). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes (MYBPC3, BAG3), as well as regulators of lipoprotein (LPL) and glucose metabolism (GIPR, GLP1R), and are enriched in cardiac, muscle, nerve, and vascular tissues, as well as myocyte and adipocyte cell types. Gene burden studies across three biobanks (PMBB, UKB, AOU) including 27,208 individuals with HF and 349,126 without uncover exome-wide significant (p < 3.15×10-6) associations for HF and rare predicted loss-of-function (pLoF) variants in TTN, MYBPC3, FLNC, and BAG3. Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, and is lower than heritability attributable to common variants (4.3%, 95% CI 3.9-4.7%) which is more diffusely spread throughout the genome. Finally, we demonstrate that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. These findings suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.

Kinetic Determination of Acetylsalicylic Acid Using a CdTe/AgInS2 Photoluminescence Probe and Different Chemometric Models.

The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS2 quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I × J × K (where I represents the samples' number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs).

Opinion Dynamics Systems on Barabási-Albert Networks: Biswas-Chatterjee-Sen Model.

A discrete version of opinion dynamics systems, based on the Biswas-Chatterjee-Sen (BChS) model, has been studied on Barabási-Albert networks (BANs). In this model, depending on a pre-defined noise parameter, the mutual affinities can assign either positive or negative values. By employing extensive computer simulations with Monte Carlo algorithms, allied with finite-size scaling hypothesis, second-order phase transitions have been observed. The corresponding critical noise and the usual ratios of the critical exponents have been computed, in the thermodynamic limit, as a function of the average connectivity. The effective dimension of the system, defined through a hyper-scaling relation, is close to one, and it turns out to be connectivity-independent. The results also indicate that the discrete BChS model has a similar behavior on directed Barabási-Albert networks (DBANs), as well as on Erdös-Rènyi random graphs (ERRGs) and directed ERRGs random graphs (DERRGs). However, unlike the model on ERRGs and DERRGs, which has the same critical behavior for the average connectivity going to infinity, the model on BANs is in a different universality class to its DBANs counterpart in the whole range of the studied connectivities.

Genetics of atrial fibrillation.

PURPOSE OF REVIEW: Atrial fibrillation is the most common cardiac arrhythmia worldwide. There is considerable interest in better understanding the molecular genetics and biology of atrial fibrillation to inform the development of new therapies and improve clinical management. This review summarizes recent advances in our understanding of the genetic basis of atrial fibrillation and new efforts to utilize genetics to inform clinical management. RECENT FINDINGS: Genome-wide association studies in diverse populations have increased the number of genetic loci associated with atrial fibrillation and its specific subtypes. Large-scale biobanks with deep phenotyping have provided invaluable data to study the impact of both common and rare variants on atrial fibrillation, susceptibility, and prognosis. Polygenic risk scores help improve individual atrial fibrillation risk stratification and prognostication. SUMMARY: Our understanding of atrial fibrillation genetics is rapidly improving with larger and more diverse genome-wide association studies. Translating genetic discoveries into molecular pathways and new therapeutic targets remains a bottleneck in the development of new therapies for atrial fibrillation. Genetic risk scores have shown early promise in improving atrial fibrillation risk stratification; however, their broader utility for the general population remains unclear.

The poly(C)-binding protein Pcbp2 is essential for CD4+ T cell activation and proliferation.

The RNA-binding protein Pcbp2 is widely expressed in the innate and adaptive immune systems and is essential for mouse development. To determine whether Pcbp2 is required for CD4+ T cell development and function, we derived mice with conditional Pcbp2 deletion in CD4+ T cells and assessed their overall phenotype and proliferative responses to activating stimuli. We found that Pcbp2 is essential for T conventional cell (Tconv) proliferation, working through regulation of co-stimulatory signaling. Pcbp2 deficiency in the CD4+ lineage did not impact Treg abundance in vivo or function in vitro. In addition, our data demonstrate a clear association between Pcbp2 control of Runx1 exon 6 splicing in CD4+ T cells and a specific role for Pcbp2 in the maintenance of peripheral CD4+ lymphocyte population size. Last, we show that Pcbp2 function is required for optimal in vivo Tconv cell activation in a T cell adoptive transfer colitis model system.

Chemometric-assisted surface-enhanced Raman spectroscopy for metformin determination using gold nanoparticles as substrate.

A fast, simple, and reliable method for determination of metformin was developed by coupling surface-enhanced Raman spectroscopy (SERS) with chemometric methods. This relayed on the utilization of a portable Raman spectrometer and of citrate stabilized gold nanoparticles (AuNPs) as substrate, to carry out the measurement of SERS scattering signals, thus assuring improved sensitivity. The obtained datasets were analysed using principal component analysis (PCA) and partial least squares (PLS) regression. Upon optimization of the PLS model, in terms of latent variables, spectral region and pre-processing techniques, RMSECV and R2CV values of 0.42 mg/L and 0.94, respectively, were obtained. The optimized PLS regression model was further validated with the projection of commercial pharmaceutical samples, providing good results in terms of R2P (0.97), RE (4.54 %) and analytical sensitivity (2.13 mg/L).

Moral values, social ideologies and threat-based cognition: Implications for intergroup relations.

Moral foundations theory (MFT) has provided an account of the moral values that underscore different cultural and political ideologies, and these moral values of harm, fairness, loyalty, authority, and purity can help to explain differences in political and cultural ideologies; however, the extent to which moral foundations relate to strong social ideologies, intergroup processes and threat perceptions is still underdeveloped. To explore this relationship, we conducted two studies. In Study 1 (N = 157), we considered how the moral foundations predicted strong social ideologies such as authoritarianism (RWA) and social dominance orientation (SDO) as well as attitudes toward immigrants. Here, we demonstrated that more endorsement of individualizing moral foundations (average of harm and fairness) was related to less negative intergroup attitudes, which was mediated by SDO, and that more endorsement of binding moral foundations (the average of loyalty, authority, and purity) was related to more negative attitudes, which was mediated by RWA. Crucially, further analyses also suggested the importance of threat perceptions as an underlying explanatory variable. Study 2 (N = 388) replicated these findings and extended them by measuring attitudes toward a different group reflecting an ethnic minority in the United States, and by testing the ordering of variables while also replicating and confirming the threat effects. These studies have important implications for using MFT to understand strong ideologies, intergroup relations, and threat perceptions.

Cellulose-based hydrogel on quantum dots with molecularly imprinted polymers for the detection of CA19-9 protein cancer biomarker.

Molecularly imprinted polymers MIPs were successfully assembled around quantum dots (QDs), for the detection of the protein biomarker CA19-9 associated to pancreatic cancer (PC). These imprinted materials MIP@QDs were incorporated within the cellulose hydrogel with retention of its conformational structure inside the binding cavities. The concept is to use MIPs which function as the biorecognition elements, conjugated to cadmium telluride QDs as the sensing system. The excitation wavelength was set to 477 nm and the fluorescence signal was measured at its maximum intensity, with an emission range between 530 and 780 nm. The fluorescence quenching of the imprinted cellulose hydrogels occurred with increasing concentrations of CA19-9, showing linearity in the range 2.76 × 10 -2 - 5.23 × 10 2 U/ml, in a 1000-fold diluted human serum. Replicates of the imprinted hydrogel show a linear response below the cut-off values for pancreatic cancer diagnosis (< 23 U/ml), a limit of detection of 1.58 × 10 -3 U/ml and an imprinting factor (IF) of 1.76. In addition to the fact that the imprinted cellulose hydrogel displays good stability and selectivity towards CA19-9 when compared with the non-imprinted controls, the conjugation of MIPs to QDs increases the sensitivity of the system for an optical detection method towards ranges within clinical significance. This fact shows potential for the imprinted hydrogel to be applied as a sensitive, low-cost format for point-of-care tests (PoCTs).

Photoluminescent and visual determination of ibandronic acid using a carbon dots/AgInS2 quantum dots ratiometric sensing platform.

A sensing platform combining carbon dots (CDs, with blue emission) and thiomalic acid (TMA)-capped AgInS2 quantum dots (QDs, with orange emission) was developed aiming the photoluminescence (PL) ratiometric determination of ibandronic acid (IBAN), a bisphosphonate pharmaceutical. The ternary AgInS2 QDs were used for IBAN probing, undergoing a concentration-related PL quenching in its presence, whilst the PL of CDs remained practically unaffected due to its chemical inertness towards the antiresorptive drug, provided an intrinsic self-reference fluorophore. In addition, a visual sensing approach was also proposed, employing for the first time ternary QDs. This relied on RGB images acquired by means of a digital camera and seek the development of a rapid IBAN screening test. The developed sensing platforms were employed for IBAN determination in samples with pharmaceutical interest providing good results, in accordance to the reported IBAN levels, and obtaining recovery values between 98 and 103%.

Chemometric-assisted kinetic determination of oxytetracycline using AgInS2 quantum dots as PL sensing platforms.

In this work a kinetic fluorometric methodology relying on the time-based monitoring of the photoluminescence quenching of AgInS2 ternary quantum dots induced by oxytetracycline, was developed. The kinetic approach allowed not only to reduce the LOD and improve sensitivity and selectivity but also to collect second-order data that was explored for the quantification of the target analyte in the presence of uncalibrated interfering species. Upon processing the acquired second-order kinetic PL data by unfolded partial least-squares (U-PLS), oxytetracycline was quantified in commercially available pharmaceutical formulations. The obtained results, namely an R2P higher than 0.99 and RE lower than 8%, proved the suitability and accuracy of the developed approach.

Moving Morality Beyond the In-Group: Liberals and Conservatives Show Differences on Group-Framed Moral Foundations and These Differences Mediate the Relationships to Perceived Bias and Threat.

Moral foundations research suggests that liberals care about moral values related to individual rights such as harm and fairness, while conservatives care about those foundations in addition to caring more about group rights such as loyalty, authority, and purity. However, the question remains about how conservatives and liberals differ in relation to group-level moral principles. We used two versions of the moral foundations questionnaire with the target group being either abstract or specific ingroups or outgroups. Across three studies, we observed that liberals showed more endorsement of Individualizing foundations (Harm and Fairness foundations) with an outgroup target, while conservatives showed more endorsement of Binding foundations (Loyalty, Authority, and Purity foundations) with an ingroup target. This general pattern was found when the framed, target-group was abstract (i.e., 'ingroups' and 'outgroups' in Study 1) and when target groups were specified about a general British-ingroup and an immigrant-outgroup (Studies 2 and 3). In Studies 2 and 3, both Individualizing-Ingroup Preference and Binding-Ingroup Preference scores predicted more Attitude Bias and more Negative Attitude Bias toward immigrants (Studies 2 and 3), more Implicit Bias (Study 3), and more Perceived Threat from immigrants (Studies 2 and 3). We also demonstrated that increasing liberalism was associated with less Attitude Bias and less Negative Bias toward immigrants (Studies 2 and 3), less Implicit Bias (Study 3), and less Perceived Threat from immigrants (Studies 2 and 3). Outgroup-individualizing foundations and Ingroup-Binding foundations showed different patterns of mediation of these effects.

Comparison of near infrared spectroscopy and Raman spectroscopy for the identification and quantification through MCR-ALS and PLS of peanut oil adulterants.

Peanut oil is considered one of the best frying oils, and, consequently there is an increasing worldwide demand. This has led to adulteration practices with unhealthy, synthetic or less expensive oils which raises concerns related with public health safety. Therefore, there is a high need for rapid, versatile, low-cost and reliable analytical methods, such as vibrational spectroscopic techniques, capable of identifying and quantifying the respective adulteration. The objective of this work focused on the application of two different vibrational spectroscopic techniques (NIR and Raman spectroscopy) for the qualitative and quantitative analysis of two adulterants in pure peanut oil, namely corn oil and vegetable oil. For the quantitative analysis two chemometric methods, namely PLS and MCR-ALS, were compared while for the qualitative analysis only MCR-ALS was tested. The analysis of peanut oil adulteration was performed by adding each adulterant individually and also by blending the peanut oil with both adulterants simultaneously. A total of 69 samples were analyzed, which was comprised by two sets of 20 samples each containing just one adulterant and another set of 29 samples containing both adulterants. Several pre-processing techniques were tested. The qualitative analysis performed by MCR-ALS allowed the identification of all the adulterants using both NIR and Raman spectra, with correlation coefficients higher than 0.99. For the quantification, none of the chemometric methods as well as the vibrational spectroscopic techniques tested showed significant better results. Nonetheless, the determination coefficients and the relative percentage errors for the validation samples for most of the developed models were higher than 0.98 and lower than 15%, respectively. Concluding, MCR-ALS was capable of correctly extracting the spectral profiles of all the adulterants in very complex mixtures (as the pure spectra of the adulterants and peanut oil are very similar) and both MCR-ALS and PLS were able to quantify the adulteration with low RE. To the best of our knowledge, it was the first time that MCR-ALS was used for the qualitative analysis of peanut oil adulteration (with all adulterants added simultaneously) and MCR-ALS and PLS were compared for the quantification of peanut oil adulteration using both NIR and Raman spectroscopy.

Disrupting upstream translation in mRNAs is associated with human disease.

Ribosome-profiling has uncovered pervasive translation in non-canonical open reading frames, however the biological significance of this phenomenon remains unclear. Using genetic variation from 71,702 human genomes, we assess patterns of selection in translated upstream open reading frames (uORFs) in 5'UTRs. We show that uORF variants introducing new stop codons, or strengthening existing stop codons, are under strong negative selection comparable to protein-coding missense variants. Using these variants, we map and validate gene-disease associations in two independent biobanks containing exome sequencing from 10,900 and 32,268 individuals, respectively, and elucidate their impact on protein expression in human cells. Our results suggest translation disrupting mechanisms relating uORF variation to reduced protein expression, and demonstrate that translation at uORFs is genetically constrained in 50% of human genes.

Imprinted Fluorescent Cellulose Membranes for the On-Site Detection of Myoglobin in Biological Media.

In this work, the conjugation of molecularly imprinted polymers (MIPs) to quantum dots (QDs) was successfully applied in the assembly of an imprinted cellulose membrane [hydroxy ethyl cellulose (HEC)/MIP@QDs] for the specific recognition of the cardiac biomarker myoglobin (Myo) as a sensitive, user-friendly, and portable system with the potential for point-of-care (POC) applications. The concept is to use the MIPs as biorecognition elements, previously prepared on the surface of semiconductor cadmium telluride QDs as detection particles. The fluorescent quenching of the membrane occurred with increasing concentrations of Myo, showing linearity in the interval range of 7.39-291.3 pg/mL in a1000-fold diluted human serum. The best membrane showed a linear response below the cutoff values for myocardial infarction (23 ng/mL), a limit of detection of 3.08 pg/mL, and an imprinting factor of 1.65. The incorporation of the biorecognition element MIPs on the cellulose substrate brings an approach toward a portable and user-friendly device in a sustainable manner. Overall, the imprinted membranes display good stability and selectivity toward Myo when compared with the nonimprinted membranes (HEC/NIP@QDs) and have the potential to be applied as a sensitive system for Myo detection in the presence of other proteins. Moreover, the conjugation of MIPs to QDs increases the sensitivity of the system for an optical label-free detection method, reaching concentration levels with clinical significance.

Determination of atenolol based on the reversion of the fluorescence resonance energy transfer between AgInS2 quantum dots and Au nanoparticles.

The present work focused on the development of a fluorescence resonance energy transfer (FRET)-based sensing platform for the monitoring of atenolol in pharmaceutical formulations. The implemented approach involved the assembly of d-penicillamine-capped AgInS2/ZnS quantum dots (QDs), as energy donors, and gold nanoparticles (AuNPs) as acceptors and the establishment of electrostatic interaction between both capping ligands at the nanoparticle surface, which induced the inhibition of the ternary QD photoluminescence (PL). The presence of a ZnS shell around the ternary QD core and the use of cysteamine (CA) as the AuNP capping ligand, instead of the typical citrate, allowed a more efficient FRET process to occur. The ability of Cd-free ternary QDs to be used as a sensing element in FRET-based assays was demonstrated, emphasizing the advantages relative to the common Cd-based QDs, when seeking the implementation of more environmentally friendly and less toxic analytical methodologies. The influence of several ß-blocker drugs on the FRET donor-acceptor assemblies was thoroughly assessed. Atenolol and nadolol caused the aggregation of CA-AuNPs via hydrogen bonding interactions which reduced the spectral overlap between the donor and acceptor, impairing the FRET process and consequently the emission of the QDs was restored. Under the optimized conditions, the obtained results exhibited a linear relationship between the QD PL recovery signal and atenolol concentration of up to 11.22 mg L-1 with a detection limit of 1.05 mg L-1. This FRET sensing platform was successfully applied in the determination of atenolol in pharmaceutical formulations with recovery values ranging from 97.4 to 104.3%.

On the Mechanism of Self-Assembly by a Hydrogel-Forming Peptide.

Self-assembling peptide-based hydrogels are a class of tunable soft materials that have been shown to be highly useful for a number of biomedical applications. The dynamic formation of the supramolecular fibrils that compose these materials has heretofore remained poorly characterized. A better understanding of this process would provide important insights into the behavior of these systems and could aid in the rational design of new peptide hydrogels. Here, we report the determination of the microscopic steps that underpin the self-assembly of a hydrogel-forming peptide, SgI37-49. Using theoretical models of linear polymerization to analyze the kinetic self-assembly data, we show that SgI37-49 fibril formation is driven by fibril-catalyzed secondary nucleation and that all the microscopic processes involved in SgI37-49 self-assembly display an enzyme-like saturation behavior. Moreover, this analysis allows us to quantify the rates of the underlying processes at different peptide concentrations and to calculate the time evolution of these reaction rates over the time course of self-assembly. We demonstrate here a new mechanistic approach for the study of self-assembling hydrogel-forming peptides, which is complementary to commonly used materials science characterization techniques.

Portable and benchtop Raman spectrometers coupled to cluster analysis to identify quinine sulfate polymorphs in solid dosage forms and antimalarial drug quantification in solution by AuNPs-SERS with MCR-ALS.

This paper proposes for the first time: (a) a qualitative analytical method based on portable and benchtop backscattering Raman spectrometers coupled to hierarchical cluster analysis (HCA) and multivariate curve resolution - alternating least-squares (MCR-ALS) to identify two polymorphs of antimalarial quinine sulfate in commercial pharmaceutical tablets in their intact forms and (b) a quantitative analytical method based on gold nanoparticles (AuNPs) as active substrates for surface-enhanced Raman scattering (SERS) in combination with MCR-ALS to quantify quinine sulfate in commercial pharmaceutical tablets in solution. The pure concentration and spectral profiles recovered by MCR-ALS proved that both formulations present different polymorphs. These results were also confirmed by two clusters observed in the HCA model, according to their similarities within and among the samples that provided useful information about the homogeneity of different pharmaceutical manufacturing processes. AuNPs-SERS coupled to MCR-ALS was able to quantify quinine sulfate in the calibration range from 150.00 to 200.00 ng mL-1 even with the strong overlapping spectral profile of the background SERS signal, proving that it is a powerful ultrahigh sensitivity analytical method. This reduced linearity was validated throughout a large calibration range from 25.00 to 175.00 µg mL-1 used in a reference analytical method based on high performance liquid chromatography with a diode array detector (HPLC-DAD) coupled to MCR-ALS for analytical validation purposes, even in the presence of a coeluted compound. The analytical methods developed herein are fast, because second-order chromatographic data and first-order SERS spectroscopic data were obtained in less than 6 and 2 min, respectively. Concentrations of quinine sulfate were estimated with low root mean square error of prediction (RMSEP) values and a low relative error of prediction (REP%) in the range 1.8-4.5%.

Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence: A short communication.

The present short communication reports a promising analytical method for authentication of milk based on first-order near-infrared (NIR) spectroscopic data coupled to data driven soft independent modeling of class analogy (DD-SIMCA). This one-class classifier was able to correctly classify all samples of genuine milk powder as members of the target class from samples of milk powder adulterated with melamine and sucrose in a concentration range of 0.8-2% (w/w) and 1-3% (w/w), respectively. Multivariate curve resolution - alternating least-squares (MCR-ALS) was applied as a complementary chemometric model to DD-SIMCA aimed at retrieving pure profiles, allowing to identify the chemical composition of samples properly attributed in the target class or not, providing further investigation from forensic point of view. In order to extend the prime focus of the present report, which was aimed at developing an appropriate chemometric model for authentication purposes, the quantification analysis was also performed. This was done by successful bilinear data decomposition of NIR spectra into pure profiles for the contributing components contained in the system studied (milk and adulterants), allowing to quantify analytes with strong overlapping profiles, even in the presence of an uncalibrated interferent, as demonstrated in this short communication using MCR-ALS under various constraints in order to decrease the rotational ambiguity.

Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II).

This work focused on the combination of CdTe and AgInS2 quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS2 combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R2P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7-9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.

Integrative analysis reveals RNA G-quadruplexes in UTRs are selectively constrained and enriched for functional associations.

G-quadruplex (G4) sequences are abundant in untranslated regions (UTRs) of human messenger RNAs, but their functional importance remains unclear. By integrating multiple sources of genetic and genomic data, we show that putative G-quadruplex forming sequences (pG4) in 5' and 3' UTRs are selectively constrained, and enriched for cis-eQTLs and RNA-binding protein (RBP) interactions. Using over 15,000 whole-genome sequences, we find that negative selection acting on central guanines of UTR pG4s is comparable to that of missense variation in protein-coding sequences. At multiple GWAS-implicated SNPs within pG4 UTR sequences, we find robust allelic imbalance in gene expression across diverse tissue contexts in GTEx, suggesting that variants affecting G-quadruplex formation within UTRs may also contribute to phenotypic variation. Our results establish UTR G4s as important cis-regulatory elements and point to a link between disruption of UTR pG4 and disease.