CHARGE Consortium Research Articles

Using deep learning to detect atherosclerotic plaques on carotid ultrasound images in the UK Biobank

Abstract Background Atherosclerosis is the main underlying cause of cardiovascular disease (CVD). Existing CVD risk assessment tools do not consider the burden of subclinical atherosclerosis. The presence of carotid plaques on carotid ultrasound is a well-known marker of subclinical atherosclerosis. The accumulation of population-scale data on the presence of atherosclerotic plaques, along with deep phenotyping, can allow not only to address the effectiveness of carotid ultrasound in routine clinical practice, but to shed light on the biology of atherosclerosis development. Purpose To develop an effective deep learning model for plaque detection in carotid ultrasound images in the UK Biobank. Methods We used 680 carotid ultrasound images with manually annotated plaques to train a deep learning model employing the YOLOv8 architecture. Different augmentation techniques were used to increase the generalizability of the model. The developed model was applied to automatically detect plaques in raw ultrasound images from 19,507 UK Biobank participants. Logistic and Cox regression were used to explore the associations of plaque presence and number as predicted by the model with conventional CVD risk factors and the risk of future CVD events over follow-up. To explore the genetic architecture of subclinical atherosclerosis, we conducted a genome-wide association study (GWAS) on plaque presence, followed by meta-analysis with data from the CHARGE Consortium. Results Our plaque detection model achieved high classification metrics of accuracy, sensitivity, and specificity (89.3%, 89.5%, and 89.2%, respectively) and detected atherosclerotic plaques in 44% of UK Biobank participants. As expected, plaques were more common among men than women and their prevalence increased linearly with age. Both plaque presence and number of plaques were correlated with conventional CVD risk factors including diabetes, hypertension, and hyperlipidemia, and showed strong associations with future risk of incident CVD events (Hazard Ratio for plaque presence: 1.48 [95%CI: 1.21-1.82], for 2 plaques or more: 1.65, [95% CI: 1.28-2.13]). Incorporating plaque-derived phenotypes minimally altered the C-index of the time-to-event model. GWAS meta-analysis of carotid plaque presence revealed 5 previously known loci, as well as a significant locus including the LPA gene that had not previously been associated with carotid plaque. Conclusion We have developed and implemented an efficient plaque detection model to data from the UK Biobank, which holds significant promise for studying atherosclerosis at a population-wide scale through integration with multiomics data and electronic health records.

The relationship between coffee-related factors and cortical and hippocampal structure: a triangulation of evidence approach and Mendelian randomization research.

Existing studies have reported sustained changes in the cortical structure of rats due to coffee-related factors, which are speculated to occur in the human body. However, there is a lack of research on this topic. Additionally, previous observational studies have found the impact of diseases on cortical structure and the potential therapeutic effects of coffee on these diseases. Our aim was to study the causal effects of coffee-related factors on the human brain using SNPs (single nucleotide polymorphisms). We will connect these discovered causal effects to the impact of diseases on the brain. Through triangulating evidence, we will reveal the potential active areas of coffee in preventing diseases. We utilized GWAS data from multiple cohorts and their databases, selecting instrumental variables for genetic prediction of coffee intake and plasma levels of caffeine and its direct metabolites. We applied these instrumental variables to individual data on cortical thickness and surface area, as well as hippocampal volume, from the ENIGMA and CHARGE consortium for Mendelian randomization analysis (MR). Triangular evidence was obtained by integrating existing evidence through a specified retrieval strategy, calculating the overlap between coffee's effects on brain regions and disease-related brain regions to identify potential regions of action. The MR analysis yielded 93 positive results for 9 exposures, among which theobromine, a metabolite in the caffeine pathway, was found to be associated with increased hippocampal volume. For cortical structure, theobromine in the caffeine pathway was associated with a decrease in total surface area, while theobromine and caffeine in the pathway were associated with an increase in total thickness. The overlap rate of triangular evidence showed no difference in both overall and subgroup analyses, indicating a high overlap between the effects of coffee on brain regions and disease. From predicted outcomes from causal effects, coffee intake-related factors may have lasting effects on cortical structure. Additionally, theobromine and theophylline have the greatest impact on certain brain gyri, rather than caffeine. Triangulation evidence indicates that disease and coffee intake-related factors act on the same cortical regions, suggesting the presence of potential shared or antagonistic pathways.

Plasma fatty acids and attention deficit hyperactivity disorder: a Mendelian randomization investigation.

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder of childhood, and pathogenesis is not fully understood. Observational studies suggest an association between fatty acids abnormalities and ADHD, but there are contradictions and differences between these findings. To address this uncertainty, we employed a two-sample bidirectional Mendelian Randomization (MR) analysis to investigate the causal relationship between fatty acids and ADHD. We conducted a two-sample Mendelian Randomization (MR) study, selecting single nucleotide polymorphisms (SNPs) highly correlated with fatty acid levels from the CHARGE Consortium as our instruments. The outcome data were sourced from the Psychiatric Genomics Consortium (PGC) dataset on ADHD, comprising 225,534 individuals, with 162,384 cases and 65,693 controls. Inverse variance weighting, MR-Egger, and weighted median methods were employed to estimate the causal relationship between fatty acids and ADHD. Cochran's Q-test was used to quantify heterogeneity of instrumental variables. Sensitivity analyses included MR-Egger intercept tests, leave-one-out analyses, and funnel plots. The MR analysis revealed no significant associations between genetically predicted levels of various saturated, monounsaturated, and polyunsaturated fatty acids (including omega-3 and omega-6) and ADHD risk in the CHARGE and PGC cohorts. Notably, an initial association with Dihomo-gamma-linolenic acid (DGLA) (OR = 1.009, p = 0.032 by IVW) did not persist after correction for multiple testing (adjusted p-value = 0.286). Sensitivity analysis supported our findings, indicating robustness. Moreover, there was a lack of evidence supporting a causal link from ADHD to fatty acids. While our study on the basis of genetic data does not provide evidence to support the causal role of fatty acids in ADHD, it does not preclude their potential involvement in reducing the risk of ADHD. Further research is needed to explore this possibility.

Abstract P311: Metabolomic Associations With Blood Pressure and Hypertension in Multi-Ethnic Populations

Introduction: Hypertension (HTN) is a leading cause of cardiovascular disease and premature death. A few circulating metabolites have been associated with blood pressure or HTN; however, findings from multi-ethnic populations are limited. Methods: Discovery analysis included eight population-based cohorts from the Trans-Omics for Precision Medicine (TOPMed) consortium, including adults of White, Black and Hispanic/Latino ancestries. Replication analyses were performed in six independent studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and TOPMed. Fixed-effect inverse variance weighted meta-analysis was applied, on study- and ethnic-specific linear and logistic regression results, to estimate circulating metabolites association with systolic (SBP), diastolic blood pressure (DBP), and HTN cross-sectionally, adjusting for age, sex, body mass index, smoking status, lipids, kidney function, and diabetes prevalence. Global metabolomic analysis and pathway enrichment were performed to identify potentially altered metabolic pathways, and two-sample Mendelian randomization (MR) was performed to test for causal relationships between replicated metabolites and SBP, DBP, and HTN. Results: Out of 1,055 meta-analyzed metabolites, 336 were significantly associated (FDR&lt0.05) with all three BP traits among 17,767 participants (7,097 White, 5,406 Black, and 5,264 Hispanic/Latino). Xanthopterin had the strongest positive association (SBP: β=3.16, DBP: β=1.78, HTN: OR=1.34), and NH4_C18:2 CE had the strongest inverse association (SBP: β=-2.31, DBP: β=-1.49, HTN: OR=0.75), per SD increase of the metabolite level. The directions of associations were generally consistent across sex and racial/ethnic groups. Pathway enrichment analysis identified two enriched pathways (FDR&lt0.05): alanine, aspartate and glutamate metabolism and citrate cycle. Of 222 metabolites available for replication, 114 metabolites associated with all three traits (FDR&lt0.05) among 20,567 participants. MR analysis identified 18 potentially causal associations between metabolites and BP traits, including palmitoylcarnitine - a carnitine derivative that showed consistent positive causal associations across SBP, DBP and HTN (FDR&lt0.05).

Serum fetuin-a and risk of thoracic aortic aneurysms: a two-sample mendelian randomization study.

Recent studies have revealed a significant decrease in serum fetuin-A levels in atherosclerotic aneurysms, indicating that fetuin-A may play a protective role in the progression of arterial calcification. However, the specific mechanism behind this phenomenon remains unclear. We aimed to examine the association between fetuin-A levels in thoracic aortic aneurysms (TAAs) and risk of TAAs and to evaluate whether this association was causal. A total of 26 SNPs were selected as instrumental variables for fetuin-A in 9,055 participants of European ancestry from the CHARGE consortium, and their effects on thoracic aortic aneurysm and decreased descending thoracic aortic diameter were separately estimated in 353,049 and 39,688 individuals from FinnGen consortium. We used two-sample Mendelian randomization (MR) analysis to examine the causal association. At the same time, we employed various methods, including random-effects inverse variance weighting, weighted median, MR Egger regression, and MR PRESSO, to ensure the robustness of causal effects. We assessed heterogeneity using Cochran's Q value and examined horizontal pleiotropy through MR Egger regression and retention analysis. Fetuin-A level was associated with a significantly decreasing risk of thoracic aortic aneurysm (odds ratio (OR) 0.64, 95% CI 0.47 - 0.87, P = 0.0044). Genetically predicted fetuin-A was also correlated with the decreased descending thoracic aortic diameter (β = -0.086, standard error (SE) 0.036, P = 0.017). Serum fetuin-A level was negatively associated with risk of TTAs and correlated with the decreased descending thoracic aortic diameter. Mendelian randomization provides support for the potential causal relationship between fetuin-A and thoracic aortic aneurysm.

Protective effects of mitochondrial genome abundance on Alzheimer’s Disease: a Mendelian randomization study

AbstractBackgroundMitochondrial DNA copy number (mtDNAcn) is a surrogate measure of mitochondrial function that is associated with higher cognitive performance and reduced risk of AD neuropathology. Here, we use two‐sample Mendelian randomization (MR) to estimate the causal association of blood‐based mtDNAcn on AD/dementia while controlling for latent confounders, sample overlap, and testing bidirectional effects.MethodGWAS Summary statistics of mtDNAcn were procured from three separate studies using various bioinformatic methods to estimate mtDNAcn; microarray, whole exome sequencing (WES), and whole genome sequencing (WGS) datasets using the UK Biobank (UKBB) and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Longchamps et al., 2021. uses quantitative PCR (CHARGE) and WES, WGS (UKBB). Hagg et al., 2021 use microarray approximation, as well as WGS and qPCR. Chong et al., 2022 employ the novel AutoMitoC in addition to WES data (UKBB). Kunkle et al., 2019 employed late‐onset clinical AD cases/controls (n = 94,437), and Bellenguez et al., 2022 (n = 487,511) used AD/dementia from clinical and proxy cases/controls. MR estimates the causal association between mtDNAcn and AD and AD/dementia using IVW analysis, and MR Egger, Weighted Mode, and Weighted Median as sensitivity analysis. Latent Heritable Confounder‐MR (LHC‐MR) was used to investigate bidirectional effects in the presence of sample overlap between the mtDNAcn datasets and AD/Dementia.ResultThere were no significant causal associations of mtDNAcn on AD or AD/dementia using univariate MR analysis. However, using LHC‐MR, higher genetically predicted mtDNAcn was causally associated with AD/dementia using all three mtDNAcn datasets (OR [95%CI]: Chong = 0.84 [0.78, 0.90], p < 0.001; Longchamps = 0.89 [0.82, 0.96], p = 0.003; Hagg = 0.84 [0.78, 0.91], p < 0.001). In the reverse direction, genetic liability for AD/dementia was causally associated with higher mtDNAcn levels (β [SE]: 0.10 [0.05], p = 0.017).ConclusionHigher blood‐based mtDNAcn was causally associated with reduced risk of AD, with limited evidence to suggest a bidirectional effect. Discrepancies between univariate MR results and LHC‐MR may be explained by the increased power and the inclusion of a latent confounder.

Assessment of racial/ethnic representation in Alzheimer’s disease genetics datasets

AbstractBackgroundBiased representation of populations in genetic research leads to disparities in genetics knowledge and utility. Here, we compare the racial/ethnic demographics of all participants in nationally funded Alzheimer’s disease (AD) genetics cohorts to demographics of US AD mortality.MethodsWe used death certificate data from the CDC Wide‐ranging Online Data for Epidemiologic Research (WONDER) Database to calculate age‐adjusted AD mortality of adults aged 65 years and older for 2016‐2020. We compared the racial/ethnic distributions in mortality to the racial/ethnic distribution of AD genetics participants across 16 studies in the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) that met the following filters: Disease = `AD`; Molecular Data = `Genotype`; and Type = `GWAS` as well as five studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Demographic data was obtained directly from data or via literature review. In addition to the GWAS data, we also compared racial demographic data from sequencing studies released as part of the Alzheimer’s Disease Sequencing Project (ADSP) Umbrella (dssNIAGADS NG00067).ResultsTable 1 describes the racial/ethnic distribution of the age‐adjusted AD mortality rate of the US population from 2016‐2020. Non‐Hispanic Whites had the highest rate (per 100,000 person‐years): 254.2 (95% CI 253.5‐254.9), and Asian or Pacific Islander (AS/PI) had the lowest rate: 125.6 (123.6‐127.71). The racial/ethnic distribution of the 13,909 AD cases across 21 genotype datasets available in NIAGADS or CHARGE are presented in Table 1. Most (87.6%) of AD cases are non‐Hispanic White, with the least (0.25%) being AS/PI. Preliminary analysis of ADSP data show increased proportions of Hispanic and Black participants (Table 1).ConclusionThe racial/ethnic distribution of AD genetics participants is representative of proportion of deaths, but after adjusting for age and population distribution, AS/PI have 50% lower mortality rate compared to non‐Hispanic Whites while comprising less than 5% of participants in genetics cohorts. While progress is being made to diversify sequencing data, AS/PI and American Indian or Alaska Natives remain underrepresented in AD genetics research, which may lead to inequitable benefits from future research and pharmaceutical treatments.

NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS): 2023 Update

AbstractBackgroundNIAGADS is a national genomics data repository that facilitates access of genotypic and sequencing data to qualified investigators for the study of the genetics of Alzheimer’s disease (AD) and related neurological diseases. Collaborations with large consortia and centers such as the Alzheimer’s Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Alzheimer’s Disease Sequencing Project (ADSP), and the Genome Center for Alzheimer’s Disease (GCAD) allow NIAGADS to lead the effort in managing large AD datasets that can be easily accessed and fully utilized by the research community.MethodNIAGADS is supported by National Institute on Aging (NIA) under a cooperative agreement. All data derived from NIA funded AD genetics studies are expected to be deposited in NIAGADS or another NIA approved site. NIAGADS manages a Data Sharing Service (DSS) that facilitates the deposition and sharing of genomic data and association results with approved users in the neurodegenerative research community. In addition, researchers are able to freely use the NIAGADS Alzheimer’s Genomics Database (www.niagads.org/genomics/) to search annotation resources that link published AD studies to AD‐relevant sequence features and genome‐wide annotations.ResultAs of January 2023, NIAGADS houses 93 datasets comprised of >149,000 samples including GWAS, sequencing, gene expression, annotations, deep phenotypes, and summary statistics. Qualified investigators can retrieve ADSP sequencing data with ease and flexibility through the NIAGADS DSS. To date, the ADSP and other contributing studies have completed whole exome sequencing (WES) of 20,503 samples and whole genome sequencing (WGS) of 36,361 samples. Raw WES and WGS files, quality controlled VCF files, and phenotype data files are available via qualified access. The next round of sequencing currently underway will generate around 30,000 additional genomes to be released in late 2023.ConclusionNIAGADS is a rich resource for AD researchers, with the goal of facilitating advances in Alzheimer’s genetics research. By housing datasets from many projects and institutions, NIAGADS enables AD researchers to meet their research goals more efficiently. Datasets, guidelines, and features are available on our website at https://www.niagads.org.

A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies.

Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight. We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D. The T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes. In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.

Genome-wide association studies and fine-mapping identify genomic loci for n-3 and n-6 polyunsaturated fatty acids in Hispanic American and African American cohorts

Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) play critical roles in human health. Prior genome-wide association studies (GWAS) of n-3 and n-6 PUFAs in European Americans from the CHARGE Consortium have documented strong genetic signals in/near the FADS locus on chromosome 11. We performed a GWAS of four n-3 and four n-6 PUFAs in Hispanic American (n = 1454) and African American (n = 2278) participants from three CHARGE cohorts. Applying a genome-wide significance threshold of P < 5 × 10−8, we confirmed association of the FADS signal and found evidence of two additional signals (in DAGLA and BEST1) within 200 kb of the originally reported FADS signal. Outside of the FADS region, we identified novel signals for arachidonic acid (AA) in Hispanic Americans located in/near genes including TMX2, SLC29A2, ANKRD13D and POLD4, and spanning a > 9 Mb region on chromosome 11 (57.5 Mb ~ 67.1 Mb). Among these novel signals, we found associations unique to Hispanic Americans, including rs28364240, a POLD4 missense variant for AA that is common in CHARGE Hispanic Americans but absent in other race/ancestry groups. Our study sheds light on the genetics of PUFAs and the value of investigating complex trait genetics across diverse ancestry populations.

Association between Left Atrial Late Gadolinium Enhancement and Atrial Fibrillation: The Multi-Ethnic Study of Atherosclerosis (MESA).

To determine the prevalence and correlates of left atrial (LA) late gadolinium enhancement (LGE) at cardiac MRI and its association with atrial fibrillation (AF) in a population-based sample from the Multi-Ethnic Study of Atherosclerosis (MESA). In this secondary post hoc analysis of the MESA cohort (ClinicalTrials.gov no. NCT00005487), participants without AF underwent LGE cardiac MRI at the fifth examination (2010-2012). LA LGE burden was quantified using the image intensity ratio technique on biplane long-axis two-dimensional (2D) LGE images without fat saturation. Survival analysis was performed with log-rank testing and Cox regression. Of 1697 participants (mean age, 67 years ± 9 [SD]; 872 men), 1035 (61%) had LA LGE, and 75 (4.4%) developed AF during follow-up (median, 3.95 years). At univariable analysis, LA LGE was associated with age (β = .010 [95% CI: .005, .015], P < .001), diastolic blood pressure (β = .005 [95% CI: .001, .009], P = .02), HbA1c level (β = .06 [95% CI: .02, .11], P = .009), heart failure (β = .60 [95% CI: .11, 1.08], P = .02), LA volume (β = .008 [95% CI: .004, .012], P < .001), and LA function (emptying fraction, LA global longitudinal strain, LA early diastolic peak longitudinal strain rate, and LA late diastolic peak strain rate; all P < .05). After adjusting for the variables in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) AF score, LA LGE independently helped predict incident AF (hazard ratio = 1.46 [95% CI: 1.13, 1.88], P = .003). The highest tertile (LGE > 2%) was twice as likely to develop AF. Although limited by the 2D LGE technique employed, LA LGE was associated with adverse atrial remodeling and helped predict AF in a multiethnic population-based sample.Clinical trial registration no. NCT00005487Keywords: MR Imaging, Cardiac, Epidemiology Supplemental material is available for this article. © RSNA, 2023.

Downregulation of interleukin 6 signaling might reduce the risk of periodontitis: a drug target Mendelian randomization study.

Interleukin 6 (IL-6) is considered to play a role in the dysbiotic host response in the development of periodontitis. While the inhibition of the IL-6 receptor using monoclonal antibodies is a well-established therapy for some diseases, so far, its potential benefit in patients with periodontitis has not been examined. We tested the association of genetically proxied downregulation of IL-6 signaling with periodontitis to explore whether downregulation of IL-6 signaling could represent a viable treatment target for periodontitis. As proxies for IL-6 signaling downregulation, we selected 52 genetic variants in close vicinity of the gene encoding IL-6 receptor that were associated with lower circulating C-reactive protein (CRP) levels in a genome-wide association study (GWAS) of 575 531 participants of European ancestry from the UK Biobank and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Associations with periodontitis were tested with inverse-variance weighted Mendelian randomization in a study of 17 353 cases and 28 210 controls of European descent in the Gene-Lifestyle Interactions in Dental Endpoints (GLIDE) consortium. In addition, the effect of CRP reduction independent of the IL-6 pathway was assessed. Genetically proxied downregulation of IL-6 signaling was associated with lower odds of periodontitis (odds ratio (OR) = 0.81 per 1-unit decrement in log-CRP levels; 95% confidence interval (CI): [0.66;0.99]; P = 0.0497). Genetically proxied reduction of CRP independent of the IL-6 pathway had a similar effect (OR = 0.81; 95% CI: [0.68; 0.98]; P = 0.0296). In conclusion, genetically proxied downregulation of IL-6 signaling was associated with lower odds of periodontitis and CRP might be a causal target for the effect of IL-6 on the risk of periodontitis.

Shared molecular genetic factors influence subcortical brain morphometry and Parkinson’s disease risk

Parkinson’s disease (PD) is a late-onset and genetically complex neurodegenerative disorder. Here we sought to identify genes and molecular pathways underlying the associations between PD and the volume of ten brain structures measured through magnetic resonance imaging (MRI) scans. We leveraged genome-wide genetic data from several cohorts, including the International Parkinson’s Disease Genomics Consortium (IPDG), the UK Biobank, the Adolescent Brain Cognitive Development (ABCD) study, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Enhancing Neuroimaging Genetics through Meta-Analyses (ENIGMA), and 23andMe. We observed significant positive genetic correlations between PD and intracranial and subcortical brain volumes. Genome-wide association studies (GWAS) - pairwise analyses identified 210 genomic segments with shared aetiology between PD and at least one of these brain structures. Pathway enrichment results highlight potential links with chronic inflammation, the hypothalamic-pituitary-adrenal pathway, mitophagy, disrupted vesicle-trafficking, calcium-dependent, and autophagic pathways. Investigations for putative causal genetic effects suggest that a larger putamen volume could influence PD risk, independently of the potential causal genetic effects of intracranial volume (ICV) on PD. Our findings suggest that genetic variants influencing larger intracranial and subcortical brain volumes, possibly during earlier stages of life, influence the risk of developing PD later in life.

Epidemiologic and Genetic Associations of Endometriosis With Depression, Anxiety, and Eating Disorders

ABSTRACT Endometriosis is a chronic gynecologic disease that causes pelvic pain and is often seen in association with depression and anxiety. The association with these mental health conditions was previously linked to chronic pain, although some evidence suggests this is not the sole factor. A genetic correlation has shown a potential causal effect of depression on endometriosis; however, further analyses are needed to determine whether pleiotropy observed is due to cause-effect relationships or to shared biological pathways. This genetic association study (GWAS) aimed to conduct phenotypic and genetic association analyses on the comorbidity of endometriosis with depression, anxiety, and eating disorders. Individual-level data were obtained from the UK Biobank (UKB) and combined with genome-wide association statistics from data sets including the Psychiatric Genomics Consortium, the Million Veteran Program, the FinnGen study (Finland), and the CHARGE consortium. Individuals with endometriosis were identified by the International Classification of Diseases Tenth Revision codes as well as self-reported diagnoses from UKB surveys, and individuals with depression, anxiety, and eating disorders were identified using the International Classification of Diseases Tenth Revision codes. Logistic regression models were used to evaluate the association, and bidirectional 1-sample Mendelian randomization was used to test causal associations between endometriosis and anxiety, depression, and eating disorders. The GWAS studies were conducted for each of the mental health disorders being studied, and the Scalable Genetic Correlation Estimator (SCORE) method was used to compute single-nucleotide variant (SNV)–based heritability and genetic correlation from individual genotype and phenotype data. The linkage disequilibrium score regression method was used to calculate SNV-based heritability and genetic correlation using GWAS data. A total of 8276 participants with endometriosis (mean age, 53.1 years) and 194,000 controls (mean age, 56.7 years) were included in this analysis. Regression models accounting for demographic and obstetric covariates, as well as psychiatric comorbidities, found endometriosis to be associated with increased odds of depression (odds ratio [OR], 3.61; 95% confidence interval [CI], 3.32–3.92), eating disorders (OR, 2.94; 95% CI, 1.96–4.41), and anxiety (OR, 2.61; 95% CI, 2.30–2.97). The GWAS studies showed endometriosis to be correlated with depression (genetic correlation [rg] = 0.36, P = 1.5 × 10−9), anxiety (rg = 0.33, P = 1.17 × 10−5), and eating disorders (r = 0.61, P = 0.03). The SCORE analysis of UKB phenotypic and genetic data calculated female-specific SNV-based heritability (SNV-h2) for endometriosis (mean [SE] SNV-h2, 0.086 [0.015]), depression (SNV-h2, 0.019 [0.003]), anxiety (SNV-h2, 0.012 [0.002]), and eating disorders (SNV-h2, 0.004 [0.002]). When applying linkage disequilibrium score regression to UKB genome-wide association statistics, endometriosis showed a consistent genetic correlation with anxiety (rg = 0.36, P = 3 × 10−4) and depression (rg = 0.34, P = 1 × 10−5). Mendelian randomization found significant associations for depression (OR, 1.09; 95% CI, 1.08–1.11) and anxiety (OR, 1.39; 95% CI, 1.13–1.65) with endometriosis, but no association with eating disorders (OR, 0.73; 95% CI, 0.04–1.43) was observed. Only the pleiotropic variant DGKB rs12666606 between endometriosis and depression survived genome-wide multiple testing correction. The results of this GWAS show that eating disorders, depression, and anxiety are all associated with endometriosis and that genetic correlations were in line with phenotypic associations.

Effect of C-reactive protein on the risk of Heart failure: a mendelian randomization study

BackgroundTraditional observational studies have shown positive associations between c-reactive protein (CRP) and heart failure (HF) risk. However, this association has not been fully elucidated. Therefore, Mendelian randomization was used to examine CRP’s possible etiological roles with HF.MethodsWe implemented a two-sample Mendelian randomization framework to examine the causality of the association between CRP and HF based on summary statistics by large-scale genome-wide association studies (GWAS) datasets of European ancestry through inverse-variance weighted, weighted median, MREgger regression, and MR-PRESSO methods. The summary statistics dataset on the association of genetic variants with CRP was used from the published GWAS of European descent in UK Biobank participants (N = 427,367) and the CHARGE consortium (N = 575,531). The GWAS dataset used to identify genetic variants underlying HF from the HERMES consortium includes 977,323 participants (47,309 cases and 930,014 controls). The odds ratio (OR) with 95% confidence intervals (CIs) was employed to examine this association.ResultsThe results of our IVW indicated that CRP was strongly associated with HF (OR = 4.18, 95% CI = 3.40–5.13, p < 0.001). The Cochran heterogeneity test showed significant heterogeneity among SNPs of CRP (Q = 317.55, p < 0.001; I2 = 37.6%), and no considerable pleiotropy was detected for the association of CRP with HF [intercept = 0.003; p = 0.234]. This finding remained consistent using different Mendelian randomization methods and sensitivity analyses.ConclusionOur MR study did identify convincing evidence to support CRP associated with HF risk. Human genetic data suggest that CRP is a causative factor in HF. Hence, CRP assessment may offer additional prognostic information as an adjuvant to overall risk assessment in HF patients. These findings prompt significant questions about the function of inflammation in the progression of HF. More research into the role of inflammation in HF is needed to guide trials of anti-inflammation management.

Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis.

Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk. The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF. Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals' 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)-AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI≥30 kg/m2), as well as the relation between self-reported physical activity and predicted 5-year AF risk. We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: n=563, 61%) who had a median daily step count of 7227 (IQR 5699-8970). Most participants (n=823, 89.2%) had a <2.5% CHARGE-AF risk. Every 1000 steps were associated with a 0.08% lower CHARGE-AF risk (P<.001). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk. Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation.

Effects of genetically determined mineral status on life expectancy: a Mendelian randomization study.

Effects of genetically determined mineral status on life expectancy: a Mendelian randomization study.

Epidemiologic and Genetic Associations of Endometriosis With Depression, Anxiety, and Eating Disorders

Endometriosis is a common chronic gynecologic pathology with a large negative impact on women's health. Beyond severe physical symptoms, endometriosis is also associated with several psychiatric comorbidities, including depression and anxiety. To investigate whether pleiotropy contributes to the association of endometriosis with depression, anxiety, and eating disorders. This genetic association study was performed between September 13, 2021, and June 24, 2022, in 202 276 unrelated female participants. Genotypic and phenotypic information from the UK Biobank was combined with genome-wide association statistics available from the Psychiatric Genomics Consortium (11 countries), the Million Veteran Program (US), the FinnGen study (Finland), and the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium (5 countries). The main outcomes were the phenotypic and genetic associations of endometriosis with anxiety, depression, and eating disorders. A total of 8276 women with endometriosis (mean [SD] age, 53.1 [7.9] years) and 194 000 female controls (mean [SD] age, 56.7 [7.9] years) were included in the study. In a multivariate regression analysis accounting for age, body mass index, socioeconomic status, chronic pain-related phenotypes, irritable bowel syndrome, and psychiatric comorbidities, endometriosis was associated with increased odds of depression (odds ratio [OR], 3.61; 95% CI, 3.32-3.92), eating disorders (OR, 2.94; 95% CI, 1.96-4.41), and anxiety (OR, 2.61; 95% CI, 2.30-2.97). These associations were supported by consistent genetic correlations (rg) (depression rg, 0.36, P = 1.5 × 10-9; anxiety rg, 0.33, P = 1.17 × 10-5; and eating disorders rg, 0.61, P = .02). With the application of a 1-sample mendelian randomization, the genetic liabilities to depression and anxiety were associated with increased odds of endometriosis (depression: OR, 1.09; 95% CI, 1.08-1.11; anxiety: OR, 1.39; 95% CI, 1.13-1.65). A genome-wide analysis of pleiotropic associations shared between endometriosis and psychiatric disorders identified 1 locus, DGKB rs12666606, with evidence of pleiotropy between endometriosis and depression after multiple testing correction (z = -9.46 for endometriosis, z = 8.10 for depression, P = 5.56 × 10-8; false discovery rate q = 4.95 × 10-4). These findings highlight that endometriosis is associated with women's mental health through pleiotropic mechanisms. To our knowledge, this is the first large-scale study to provide genetic and phenotypic evidence of the processes underlying the psychiatric comorbidities of endometriosis.

Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption.

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.

NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS): 2022 Update

AbstractBackgroundNIAGADS is a national genomics data repository that facilitates access of genotypic and sequencing data to qualified investigators for the study of the genetics of Alzheimer’s disease (AD) and related neurological diseases. Collaborations with large consortia and centers such as the Alzheimer’s Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Alzheimer’s Disease Sequencing Project (ADSP), and the Genome Center for Alzheimer’s Disease (GCAD) allow NIAGADS to lead the effort in managing large AD datasets that can be easily accessed and fully utilized by the research community.MethodNIAGADS is supported by National Institute on Aging (NIA) under a cooperative agreement. All data derived from NIA funded AD genetics studies are expected to be deposited in NIAGADS or another NIA approved site. NIAGADS manages a Data Sharing Service (DSS) that facilitates the deposition and sharing of genomic data and association results with approved users in the neurodegenerative research community. In addition, researchers are able to freely use the NIAGADS Alzheimer’s Genomics Database (www.niagads.org/genomics/) to search annotation resources that link published AD studies to AD‐relevant sequence features and genome‐wide annotations.ResultAs of January 2022, NIAGADS houses 82 datasets comprised of &gt;93,000 samples including GWAS, sequencing, gene expression, annotations, deep phenotypes, and summary statistics. Qualified investigators can retrieve ADSP sequencing data with ease and flexibility through the NIAGADS DSS. As of January 2022, the ADSP and other contributing studies have completed whole exome sequencing (WES) of 20,503 samples and whole‐genome sequencing (WGS) of 16,905 samples. Raw WES and WGS files, quality controlled VCF files, and phenotype data files are available via qualified access. The next round of sequencing currently underway will generate around 18,000 additional genomes to be released at the middle of 2022.ConclusionNIAGADS is a rich resource for AD researchers, with the goal of facilitating advances in Alzheimer’s genetics research. By housing datasets from many projects and institutions, NIAGADS enables AD researchers to meet their research goals more efficiently. Datasets, guidelines, and new features are available on our website at https://www.niagads.org.