Phenome-wide Association Study Research Articles

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients. AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome. AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation. British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

Large-Scale Pharmacogenomics Analysis of Patients With Cancer Within the 100,000 Genomes Project Combining Whole-Genome Sequencing and Medical Records to Inform Clinical Practice.

As part of the 100,000 Genomes Project, we set out to assess the potential viability and clinical impact of reporting genetic variants associated with drug-induced toxicity for patients with cancer recruited for whole-genome sequencing (WGS) as part of a genomic medicine service. Germline WGS from 76,805 participants was analyzed for pharmacogenetic (PGx) variants in four genes (DPYD, NUDT15, TPMT, UGT1A1) associated with toxicity induced by five drugs used in cancer treatment (capecitabine, fluorouracil, mercaptopurine, thioguanine, irinotecan). Linking genomic data with prescribing and hospital incidence records, a phenome-wide association study (PheWAS) was performed to identify whether phenotypes indicative of adverse drug reactions (ADRs) were enriched in drug-exposed individuals with the relevant PGx variants. In a subset of 7,081 patients with cancer, DPYD variants were reported back to clinicians and outcomes were collected. We identified clinically relevant PGx variants across the four genes in 62.7% of participants in our cohort. Extending this to annual prescription numbers in England for the drugs affected by these PGx variants, approximately 14,540 patients per year could potentially benefit from a reduced dose or alternative drug to reduce the risk of ADRs. Validating PGx associations in a real-world data set, we found a significant association between PGx variants in DPYD and toxicity-related phenotypes in patients treated with capecitabine or fluorouracil. Reported DPYD variants were deemed informative for clinical decision making in a majority of cases. Reporting PGx variants from germline WGS relevant to patients with cancer alongside primary findings related to their cancer can be clinically informative, informing prescribing to reduce the risk of ADRs. Extending the range of actionable variants to those found in patients of non-European ancestry is important and will extend the potential clinical impact.

Genetic and phenotypic architecture of biventricular trabeculation

Abstract Introduction Cardiac trabeculae form a network of muscular strands that line the inner surfaces of the heart, and their development involves molecular pathways that regulate structural complexity. Excessive or hypertrabeculation occurs as part of normal variation in apparently healthy individuals, as an adaptation to altered loading conditions, and is observed in patients with cardiomyopathies (CMs) and heart failure (HF). Purpose In adults, trabeculation is not a prognostic factor independent of the underlying myocardial disease but may play a role in the natural history of ventricular remodelling. We sought to (i) understand the genetic and non-genetic determinants of trabeculation, (ii) understand its role in the causality of disease, and (iii) evaluate trabeculation as a biomarker for diagnosis and stratification. Methods We report genes with a burden of rare variants and novel common variant associations across the allele frequency spectrum for biventricular trabecular morphology in 47,803 participants of the UK Biobank using fractal dimension analysis of cardiac imaging (Figure 1). We assessed environmental modifiers of trabecular morphology, correlation with cardiac traits, phenome-wide association studies, and the relationship with the progression of CMs and HF. Results We identified a burden of trabeculation-associated rare variants in genes that regulate myocardial contractility and cardiac development and GWAS identified novel loci near genes implicated in CMs, conduction traits, and signalling pathways that define the early development of trabeculation. For example, we identified 56 genes with a burden of rare variants and 68 novel loci from GWAS (e.g., CASQ2, CACNA1C, MYBPC3, MYH7, NKX2-5, NOTCH1, TBX20) for the left ventricle. The strongest imaging relationships with trabeculation were with measures of volume and strain. Modifiers of trabeculation included African ancestry, alcohol intake, and physical activity. Carriers of CM-associated pathogenic variants had increased trabeculation. Of participants with CM reported after imaging, 25% had hypertrabeculation on imaging and hypertrabeculation was a risk factor for a subsequent diagnosis of HF (HR=1.3), mitral valve disorders (HR=1.4), bundle branch block (HR=1.2). Comparisons of biventricular trabeculation showed similarities in trabeculation across the ventricles in CM. Reduced right ventricular trabeculation and end-diastolic volume associated with Type-2 diabetes. Conclusions These data provide new insights into the mechanisms that regulate structural complexity in the heart. Changes in trabeculation may occur early in the pathogenesis of heart disease, can be modified by genes regulating pathways outside the sarcomere, and causality depends on the underlying cardiomyopathic substrate. Trabeculation progresses with cardiovascular disease, is a useful marker of remodelling, and identifies novel genetic modifiers of ventricular complexity.Figure 1

Sex related cardiovascular risk is non-dichotomous: artificial intelligence enhanced electrocardiography reveals continuum of risk in females

Abstract Background Females are typically underserved in cardiovascular medicine and often considered lower risk of cardiovascular disease. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. Purpose We aimed to develop an artificial intelligence-enhanced ECG (AI-ECG)-derived continuous sex score that can capture the continuum of risk and sex phenotypes within each sex, with the particular goal of addressing the female disadvantage. Methods We trained a convolution neural network to identify sex using the 12-lead ECG. The derivation cohort was 1,163,401 ECGs from 189,538 subjects from a USA secondary care hospital cohort and validated inthe UK Biobank (UKB). The model outputs a continuous value from 0 to 1 for sex prediction. The AI-ECG sex discordance score is the difference between predicted sex and biological sex. Results AI-ECG accurately identified sex in both the USA cohort (AUROC 0.943 (0.942-0.943)) and UKB datasets (n = 42,386, AUROC 0.971 (0.969-0.972)). In explainability analyses we found QRS duration, T wave morphology, QT interval, heart rate and QRS voltage as the most important factors in contributing to AI-ECG sex identification. In females only but not in males, sex discordance score was associated with a covariate-adjusted increased risk of cardiovascular death in outpatients with normal ECGs from the USA cohort, (Females hazard ratio (HR) 1.64 (1.18-2.29) p = 0.003, Males HR 1.04 (0.66-1.63), p = 0.86). This pattern persisted in the UKB: Females HR 1.39 (1.08-1.78) p = 0.01, Males HR 0.97 (0.77-1.22), p = 0.80, Fig 1). In phenome-wide association studies, we found females with a higher sex score discordance were more likely to have heart failure or myocardial infarction. Covariate-adjusted cox models confirmed the association of sex score discordance with increased future heart failure (HR 1.21 (1.07-1.37) p = 0.002 and future myocardial infarction (HR 1.32 (1.11-1.56) p = 0.001). In age- and body size-adjusted analyses, females with higher sex discordance score had increased left ventricular mass, and chamber volumes as well as increased muscle mass, reduced body fat percentage (Fig 2). In a genome-wide association study of sex discordance score we identified variants adjacent to IGF1R and NDRG4 in females, which have been previously associated with LV mass and sex hormone levels (Fig 2). Conclusion We describe sex discordance score as a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. Sex discordance score may help uncover potential underlying mechanisms of cardiovascular disease among females, and future clinical studies are needed to determine its potential utility in identification of females at higher risk of cardiovascular events. Rather than exacerbating biases, AI-ECG has the potential to reduce female cardiovascular health inequalities.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

Abstract Background Obesity, a key risk factor for cardiometabolic diseases, is poorly evaluated by body mass index (BMI), which doesn't account for visceral fat and fat distribution. Given that obesity-related cardiac changes can manifest in surface ECG changes, we hypothesised that an AI-ECG model could predict BMI, and the discrepancy between AI-predicted and actual BMI (delta-BMI) could indicate cardiometabolic health. We also sought to understand the biological mechanisms contributing to the AI-ECG-derived delta-BMI. Methods The AI-ECG model was developed using a data split of 60/10/30% in a USA secondary care dataset of 512,950 ECGs and externally validated in the UK Biobank (N = 42,386), employing a residual neural network architecture. Model performance was evaluated using Mean Absolute Error (MAE), Pearson correlation coefficient (r), and coefficient of determination (R2). The study aimed at identifying incident cardiometabolic diseases, using Cox regression analyses adjusted for BMI, age, and sex. To assess the underlying biological associations of delta-BMI, phenome-wide, genome-wide, metabolome-wide, and proteome-wide association studies were performed. Results In the test set, the model achieved an MAE of 3.95 (3.93–3.97), r of 0.65 (0.65-0.66), and R2 of 0.43 (0.42-0.43). In the UKB, the model achieved an MAE of 2.94 (2.91–2.96), r of 0.62 (0.62–0.63) and R2 of 0.39 (0.38-0.40). The top tertile of delta-BMI showed an increased risk of future cardiometabolic disease (test set: HR 1.15, p<0.001; UKB: HR 1.58, p<0.001) and diabetes mellitus (test set: HR 1.25, p<0.001; UKB: HR 2.28, p<0.001) after adjusting for covariates including measured BMI. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits. These results highlight delta-BMI's potential as a non-invasive, ECG-based biomarker for cardiometabolic risk stratification. Conclusion Our study demonstrates the effectiveness of an AI-enhanced ECG model in accurately predicting BMI, validated across diverse populations. The introduction of delta-BMI as a predictor of cardiometabolic disease offers valuable additional prognostic insights beyond traditional BMI assessments. Our exploration of biological pathways, spanning phenotypic, genotypic, metabolomic, and proteomic associations, contributes to a comprehensive understanding of the association between delta-BMI and cardiometabolic disease. The clinical implications could extend to enhanced screening strategies, personalised risk assessment, and motivation for lifestyle interventions.

A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data.

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms. We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1). This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.

DGRPool, a web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

DGRPool, a web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits.

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

A phenome-wide association study of cross-disorder genetic liability in youth genetically similar to individuals from European reference populations

A phenome-wide association study of cross-disorder genetic liability in youth genetically similar to individuals from European reference populations

Validation of TYK2 and exploration of PRSS36 as drug targets for psoriasis using Mendelian randomization

Psoriasis is a chronic inflammatory skin disorder with multiple causes, including genetic and environmental factors. Despite advances in treatment, there remains a need to identify novel therapeutic targets. A Mendelian randomization (MR) analysis was conducted to identify therapeutic targets for psoriasis. Data on cis-expression quantitative trait loci were obtained from the eQTLGen Consortium (n = 31,684). Summary statistics for psoriasis (outcome) were sourced from the GWAS Catalog with a sample size of 484,598, including 5,427 cases and 479,171 controls. Colocalization analysis was used to assess whether psoriasis risk and gene expression were driven by shared single nucleotide polymorphisms. Drug prediction and molecular docking were utilized to validate the pharmacological value of the drug targets. The MR analysis found that 81 drug targets were significantly associated, and two (TYK2 and PRSS36) were supported by colocalization analysis (PP.H4 > 0.80). Phenome-wide association studies did not show any associations with other traits at the gene level. Biologically, these genes were closely related to immune function. Molecular docking revealed strong binding with drugs and proteins, as supported by available structural data. This study validated TYK2 as a drug target for psoriasis, in line with its existing clinical use, including the development of decucravacitinib. PRSS36 is a potential novel target requiring further investigation.

8235 Mutations in EMX2, a Homeodomain Transcription Factor, Cause Idiopathic Hypogonadotropic Hypogonadism

Abstract Disclosure: M. Stamou: None. M. Tompkin: None. H. Bow: None. H. Brand: None. L. Plummer: None. M. Talkowski: None. Y. Shen: None. D. Wu: None. R. Balasubramanian: None. S. Wray: None. S.B. Seminara: None. The genetic etiology of infertility remains largely unknown. To identify novel human infertility genes, we applied a de novo rare variant analysis in 142 parent-proband trios with idiopathic hypogonadotropic hypogonadism (IHH), a Mendelian disease caused by Gonadotropin Releasing Hormone (GnRH) deficiency. The discovery pipeline identified 43 rare de novo copy number variants (CNVs-deletions) spanning 263 genes and 213 rare de novo single nucleotide variants (SNVs) in 191 genes. Four genes were found to be affected by both rare de novo CNVs and SNVs among unrelated sporadic IHH probands. A rare variant association testing for protein truncating variants (PTVs) in the IHH cohort (N=1,394) compared to gnomAD controls (N=125,784) revealed that 3/4 genes demonstrated enrichment for PTVs in the IHH cohort compared to controls: the 2 known for IHH genes (ANOS1 and FGFR1) and one novel candidate gene, EMX2. IHH probands with de novo EMX2 variants demonstrated developmental delay and hearing loss. Common EMX2 variants were linked to delayed puberty/infertility, hearing loss and Parkinson’s disease in a phenome-wide association study of the Massachusetts General Brigham Biobank (N=65,253). Emx2 lineage tracing was conducted on Emx2Cre/+; RosatdT/+ and Emx2Cre/-; RosatdT/+ mouse embryos at embryonic day(E) 13.5. The tdT reporter was co-expressed in neuroendocrine GnRH (nGnRH) cells as they migrated from the olfactory pit to the nasal forebrain junction. WT and KO embryos from Emx2+/- matings were immunostained for GnRH across E12.5, E13.5, E14.5 and E16. The total number of nGnRH cells was greater in the Emx2 KO mice compared to WT littermates. However, by E16.5, fewer nGnRH cells were detected in the forebrain of Emx2 KO, compared to WT (WT= 496±28, KO=384±147, p=0.0125). Thus, failure of nGnRH neurons to enter the forebrain could contribute to the IHH pathogenesis. In conclusion, by utilizing a de novo variant analysis and a mouse model, EMX2 was uncovered as a novel gene for human infertility. Presentation: 6/1/2024

8235 Mutations in EMX2, a Homeodomain Transcription Factor, Cause Idiopathic Hypogonadotropic Hypogonadism

Abstract Disclosure: M. Stamou: None. M. Tompkin: None. H. Bow: None. H. Brand: None. L. Plummer: None. M. Talkowski: None. Y. Shen: None. D. Wu: None. R. Balasubramanian: None. S. Wray: None. S.B. Seminara: None. The genetic etiology of infertility remains largely unknown. To identify novel human infertility genes, we applied a de novo rare variant analysis in 142 parent-proband trios with idiopathic hypogonadotropic hypogonadism (IHH), a Mendelian disease caused by Gonadotropin Releasing Hormone (GnRH) deficiency. The discovery pipeline identified 43 rare de novo copy number variants (CNVs-deletions) spanning 263 genes and 213 rare de novo single nucleotide variants (SNVs) in 191 genes. Four genes were found to be affected by both rare de novo CNVs and SNVs among unrelated sporadic IHH probands. A rare variant association testing for protein truncating variants (PTVs) in the IHH cohort (N=1,394) compared to gnomAD controls (N=125,784) revealed that 3/4 genes demonstrated enrichment for PTVs in the IHH cohort compared to controls: the 2 known for IHH genes (ANOS1 and FGFR1) and one novel candidate gene, EMX2. IHH probands with de novo EMX2 variants demonstrated developmental delay and hearing loss. Common EMX2 variants were linked to delayed puberty/infertility, hearing loss and Parkinson’s disease in a phenome-wide association study of the Massachusetts General Brigham Biobank (N=65,253). Emx2 lineage tracing was conducted on Emx2Cre/+; RosatdT/+ and Emx2Cre/-; RosatdT/+ mouse embryos at embryonic day(E) 13.5. The tdT reporter was co-expressed in neuroendocrine GnRH (nGnRH) cells as they migrated from the olfactory pit to the nasal forebrain junction. WT and KO embryos from Emx2+/- matings were immunostained for GnRH across E12.5, E13.5, E14.5 and E16. The total number of nGnRH cells was greater in the Emx2 KO mice compared to WT littermates. However, by E16.5, fewer nGnRH cells were detected in the forebrain of Emx2 KO, compared to WT (WT= 496±28, KO=384±147, p=0.0125). Thus, failure of nGnRH neurons to enter the forebrain could contribute to the IHH pathogenesis. In conclusion, by utilizing a de novo variant analysis and a mouse model, EMX2 was uncovered as a novel gene for human infertility. Presentation: 6/1/2024

Phenome-wide association study in 25,639 pregnant Chinese women reveals loci associated with maternal comorbidities and child health

Phenome-wide association studies (PheWAS) have been less focused on maternal diseases and maternal-newborn comorbidities, especially in the Chinese population. To enhance our understanding of the genetic basis of these related diseases, we conducted a PheWAS on 25,639 pregnant women and 14,151 newborns in the Chinese Han population using ultra-low-coverage whole-genome sequence (ulcWGS). We identified 2,883 maternal trait-associated SNPs associated with 26 phenotypes, among which 99.5% were near established genome-wide association study (GWAS) loci. Further refinement delineated these SNPs to 442 unique trait-associated loci (TALs) predicated on linkage disequilibrium R2>0.8, revealing that 75.6% demonstrated pleiotropy and 50.9% were located in genes implicated in analogous phenotypes. Notably, we discovered 21 maternal SNPs associated with 35 neonatal phenotypes, including two SNPs associated with identical complications in both mothers and children. These findings underscore the importance of integrating ulcWGS data to enrich the discoveries derived from traditional PheWAS approaches.

A Phenome-Wide Association Study to Investigate the Role of Carbamylation in CKD: Findings from the Chronic Renal Insufficiency Cohort

A Phenome-Wide Association Study to Investigate the Role of Carbamylation in CKD: Findings from the Chronic Renal Insufficiency Cohort

A phenome-wide association study of polygenic scores for selected childhood cancer: Results from the UK Biobank

The aim of this study was to scan phenotypes in adulthood associated with polygenic risk scores (PRS) for childhood cancers with well-articulated genetic architectures: acute lymphoblastic leukemia (ALL), Ewing sarcoma, and neuroblastoma to examine genetic pleiotropy. Furthermore, we aimed to determine which single nucleotide polymorphisms (SNPs) could drive associations. Per-SNP summary statistics were extracted for PRS calculation. Participants with white British ancestry were exclusively included for analyses. SNPs were queried from the UK biobank genotype imputation data. Records from the cancer registry, death registry, and inpatient diagnoses were abstracted for phenome-wide scans. Firth logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) alongside corresponding p-values, adjusting for age at recruitment and sex. A total of 244,332 unrelated white British participants were included. We observed a significant association between ALL-PRS and ALL (OR: 1.20e+24, 95% CI: 9.08e+14-1.60e+33). In addition, we observed a significant association between high-risk neuroblastoma PRS and nonrheumatic aortic valve disorders (OR: 43.9, 95% CI: 7.42-260). There were no significant phenotype associations with Ewing sarcoma and neuroblastoma PRS. Regarding individual SNPs, rs17607816 increased the risk of ALL (OR: 6.40, 95% CI: 3.26-12.57). For high-risk neuroblastoma, rs80059929 elevated the risk of atrioventricular block (OR: 3.04, 95% CI: 1.85-4.99). Our findings suggest that individuals with genetic susceptibility to ALL may face a lifelong risk for developing ALL, along with a genetic pleiotropic association between high-risk neuroblastoma and circulatory diseases.

Proteogenomic analysis integrated with electronic health records data reveals disease-associated variants in Black Americans.

Most genome wide association studies (GWAS) of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry enriched protein quantitative loci (pQTL). We conducted a discovery GWAS of ~3,000 plasma proteins measured by the antibody based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS), and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs were further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome wide association study (PheWAS) across two large multi-ethnic electronic health record (EHR) systems in All of Us and BioMe. We identified 1002 pQTLs for 925 proteins. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including cis-pQTLs for Cathepsin L (CTSL) and Siglec-9 that were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, white blood cell count, and multiple sclerosis. Our findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal.

A phenome-wide association study of methylated GC-rich repeats identifies a GCC repeat expansion in AFF3 associated with intellectual disability.

GC-rich tandem repeat expansions (TREs) are often associated with DNA methylation, gene silencing and folate-sensitive fragile sites, and underlie several congenital and late-onset disorders. Through a combination of DNA-methylation profiling and tandem repeat genotyping, we identified 24 methylated TREs and investigated their effects on human traits using phenome-wide association studies in 168,641 individuals from the UK Biobank, identifying 156 significant TRE-trait associations involving 17 different TREs. Of these, a GCC expansion in the promoter of AFF3 was associated with a 2.4-fold reduced probability of completing secondary education, an effect size comparable to several recurrent pathogenic microdeletions. In a cohort of 6,371 probands with neurodevelopmental problems of suspected genetic etiology, we observed a significant enrichment of AFF3 expansions compared with controls. With a population prevalence that is at least fivefold higher than the TRE that causes fragile X syndrome, AFF3 expansions represent a major cause of neurodevelopmental delay.

Disentangling mechanisms behind the pleiotropic effects of proximal 16p11.2 BP4-5 CNVs.

Whereas 16p11.2 BP4-5 copy-number variants (CNVs) represent one of the most pleiotropic etiologies of genomic syndromes in both clinical and population cohorts, the mechanisms leading to such pleiotropy remain understudied. Identifying 73 deletion and 89 duplication carrier individuals among unrelated White British UK Biobank participants, we performed a phenome-wide association study (PheWAS) between the region's copy number and 117 complex traits and diseases, mimicking four dosage models. Forty-six phenotypes (39%) were affected by 16p11.2 BP4-5 CNVs, with the deletion-only, mirror, U-shape, and duplication-only models being the best fit for 30, 10, 4, and 2 phenotypes, respectively, aligning with the stronger deleteriousness of the deletion. Upon individually adjusting CNV effects for either body mass index (BMI), height, or educational attainment (EA), we found that sixteen testable deletion-driven associations-primarily with cardiovascular and metabolic traits-were BMI dependent, with EA playing a more subtle role and no association depending on height. Bidirectional Mendelian randomization supported that 13 out of these 16 associations were secondary consequences of the CNV's impact on BMI. For the 23 traits that remained significantly associated upon individual adjustment for mediators, matched-control analyses found that 10 phenotypes, including musculoskeletal traits, liver enzymes, fluid intelligence, platelet count, and pneumonia and acute kidney injury risk, remained associated under strict Bonferroni correction, with 10 additional nominally significant associations. These results paint a complex picture of 16p11.2 BP4-5's pleiotropic pattern that involves direct effects on multiple physiological systems and indirect co-morbidities consequential to the CNV's impact on BMI and EA, acting through trait-specific dosage mechanisms.

Phenome-Wide Association Study of Latent Autoimmune Diabetes from a Southern Mexican Population Implicates rs7305229 with Plasmatic Anti-Glutamic Acid Decarboxylase Autoantibody (GADA) Levels.

Latent autoimmune diabetes in adults (LADA) is characterized by the presence of glutamate decarboxylase autoantibodies (GADA). LADA has intermediate features between type 1 diabetes and type 2 diabetes. In addition, genetic risk factors for both types of diabetes are present in LADA. Nonetheless, evidence about the genetics of LADA in non-European populations is scarce. This study aims to perform a genome-wide association study with a phenome-wide association study of LADA in a southeastern Mexican population. We included 59 patients diagnosed with LADA from a previous study and 3121 individuals without diabetes from the MxGDAR/ENCODAT database. We utilized the GENESIS package in R to perform the genome-wide association study (GWAS) of LADA and PLINK for the phenome-wide association study (PheWAS) of LADA features. Nine polymorphisms reach the nominal association level (1 × 10-5) in the GWAS. The PheWAS showed that rs7305229 is genome-wide and associated with serum GADA levels in our sample (p = 1.84 × 10-8). rs7305229 is located downstream of the FAIM2 gene; previous reports associate FAIM2 variants with childhood obesity, body mass index, body adiposity measures, lymphocyte CD8+ activity, and anti-thyroid peroxidase antibodies. Our findings reveal that rs7305229 affects the GADA levels in patients with LADA from southeastern Mexico. More studies are needed to determine if this risk genotype exists in other populations with LADA.

Evaluation of circulating plasma proteins in prostate cancer using mendelian randomization

BackgroundThe proteome is an important resource for exploring potential diagnostic and therapeutic targets for cancer. This study aimed to investigate the causal associations between plasma proteins and prostate cancer (PCa), and to explore the downstream phenotypes that plasma proteins may influence and potential upstream intervening factors.MethodsProteome-wide Mendelian randomization was used to investigate the causal effects of plasma proteins on PCa. Colocalization analysis examined the common causal variants between plasma proteins and PCa. Summary-statistics-based Mendelian Randomization (SMR) analyses identified associations between the expression of protein-coding genes and PCa. Phenome-wide association study was performed to explore the effect of target proteins on downstream phenotypes. Finally, a systematic Mendelian randomization analysis between lifestyle factors and plasma proteins was performed to assess upstream intervening factors for plasma proteins.ResultsThe findings revealed a positive genetic association between the predicted plasma levels of nine proteins and an elevated risk of PCa, while four proteins exhibited an inverse association with PCa risk. SMR analyses revealed ZG16B, PEX14 in blood and ZG16B, NAPG in prostate tissue were potential drug targets for PCa. The genetic association of PEX14 with PCa was further supported by colocalization analysis. Further Phenome-wide association study showed possible side effects of ZG16B, PEX14 and NAPG as drug targets. 10 plasma proteins (RBP7, TPST1, NFASC, LAYN, HDGF, SERPIMA5, DLL4, EFNA3, LIMA1, and CCL27) could be modulated by lifestyle-related factors.ConclusionThis study explores the genetic associations between plasma proteins and PCa, provides evidence that plasma proteins serve as potential drug targets and enhances the understanding of the molecular etiology, prevention and treatment of PCa.