Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium

A regulatory variant rs9379874 in T1D risk region 6p22.2 affects BTN3A1 expression regulating T cell function

OBJECTIVE

Genome-wide association studies (GWAS) have identified that 6p22.2 region is associated with type 1 diabetes (T1D) risk in the Chinese Han population. This study aims to reveal associations between this risk region and T1D subgroups and related clinical features, and further identify causal variant(s) and target gene(s) in this region.

METHODS

2608 T1D and 4814 healthy controls were recruited from East, Central, and South China. Baseline data and genotyping for rs4320356 were collected. The most likely causal variant and gene were identified by bioinformatics analysis, dual-luciferase reporter assays, expression quantitative trait loci (eQTL), and functional annotation of the non-coding region within the 6p22.2 region.

RESULTS

The leading variant rs4320356 in the 6p22.2 region was associated with T1D risk in the Chinese and Europeans. However, this variant was not significantly associated with islet function or autoimmunity. In silico analysis suggested rs9379874 was the most potential causal variant for T1D risk among thymus, spleen, and T cells, overlapping with the enhancer-related histone mark in multiple T cell subsets. Dual luciferase reporter assay and eQTL showed that the T allele of rs9379874 increased BTN3A1 expression by binding to FOXA1. Public single-cell RNA sequencing analysis indicated that BTN3A1 was related to T-cell activation, ATP metabolism, and cytokine metabolism pathways, which might contribute to T1D development.

CONCLUSION

This study indicates that a functional variant rs9379874 regulates BTN3A1 expression, expanding the genomic landscape of T1D risk and offering a potential target for developing novel therapies.

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Missense variants in FRS3 affect body mass index in populations of diverse ancestries

Obesity is associated with adverse effects on health and quality of life. Improved understanding of its underlying pathophysiology is essential for developing counteractive measures. To search for sequence variants with large effects on BMI, we perform a multi-ancestry meta-analysis of 13 genome-wide association studies on BMI, including data derived from 1,534,555 individuals of European ancestry, 339,657 of Asian ancestry, and 130,968 of African ancestry. We identify an intergenic 262,760 base pair deletion at the MC4R locus that associates with 4.11 kg/m2 higher BMI per allele, likely through downregulation of MC4R. Moreover, a rare FRS3 missense variant, p.Glu115Lys, only found in individuals from Finland, associates with 1.09 kg/m2 lower BMI per allele. We also detect three other low-frequency FRS3 missense variants that associate with BMI with smaller effects and are enriched in different ancestries. We characterize FRS3 as a BMI-associated gene, encoding an adaptor protein known to act downstream of BDNF and TrkB, which regulate appetite, food intake, and energy expenditure through unknown signaling pathways. The work presented here contributes to the biological foundation of obesity by providing a convincing downstream component of the BDNF-TrkB pathway, which could potentially be targeted for obesity treatment.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

The complex web of obesity: from genetics to precision medicine

INTRODUCTION

Obesity is a growing public health concern affecting both children and adults. Since it involves both genetic and environmental components, the management of obesity requires both, an understanding of the underlying genetics and changes in lifestyle. The knowledge of obesity genetics will enable the possibility of precision medicine in anti-obesity medications.

AREAS COVERED

Here, we explore health complications and the prevalence of obesity. We discuss disruptions in energy balance as a symptom of obesity, examining evolutionary theories, its multi-factorial origins, and heritability. Additionally, we discuss monogenic and polygenic obesity, the converging biological pathways, potential pharmacogenomics applications, and existing anti-obesity medications - specifically focussing on the leptin-melanocortin and incretin pathways. Comparisons between childhood and adult obesity genetics are made, along with insights into structural variants, epigenetic changes, and environmental influences on epigenetic signatures.

EXPERT OPINION

With recent advancements in anti-obesity drugs, genetic studies pinpoint new targets and allow for repurposing existing drugs. This creates opportunities for genotype-informed treatment options. Also, lifestyle interventions can help in the prevention and treatment of obesity by altering the epigenetic signatures. The comparison of genetic architecture in adults and children revealed a significant overlap. However, more robust studies with diverse ethnic representation is required in childhood obesity.

Genome-wide association study based on clustering by obesity-related variables uncovers a genetic architecture of obesity in the Japanese and the UK populations

Whether all obesity-related variants contribute to the onset of obesity or one or a few variants cause obesity in genetically heterogeneous populations remains obscure. Here, we investigated the genetic architecture of obesity by clustering the Japanese and British populations with obesity using obesity-related factors. In Step-1, we conducted a genome-wide association study (GWAS) with body mass index (BMI) as the outcome for eligible participants. In Step-2, we assigned participants with obesity (BMI ≥25 kg/m2) to five clusters based on obesity-related factors. Subsequently, participants from each cluster and those with a BMI <25 kg/m2 were combined. A GWAS was conducted for each cluster. Several previously identified obesity-related genes were verified in Step-1. Of the genes detected in Step-1, unique obesity-related genes were detected separately for each cluster in Step-2. Our novel findings suggest that a smaller sample size with increased homogeneity may provide insights into the genetic architecture of obesity.

Anthropometric traits and risk of multiple myeloma: differences by race, sex and diagnostic clinical features

BACKGROUND

Obesity is an established modifiable risk factor for multiple myeloma (MM). However, associations of obesity and MM risk in Black populations, for whom obesity and MM are more common, is less clear.

METHODS

Using participants enrolled in the Integrative Molecular And Genetic Epidemiology study, we evaluated the association of anthropometric traits with MM risk overall, stratified by race and sex. Among cases, we assessed the association of BMI with the presence of myeloma-defining events.

RESULTS

We observed an 18% increase in MM risk for every 5 kg/m2 increase in usual adult BMI. Participants with severe obesity (BMI ≥ 40 kg/m2) had the highest risk compared to those with a normal usual adult BMI (18.5-24.9 kg/m2; OR = 1.87, 95% CI 1.25-2.80), particularly among Black men (OR = 3.94, 95% CI 0.90-17.36). Furthermore, MM cases with overweight/obesity (BMI ≥ 25 kg/m2) were more likely to present at diagnosis with low renal function (OR = 1.62, 95% CI 1.09-2.40), deletion 13q (OR = 1.73, 95% CI 1.08-2.76) and lytic lesions or compression fractures (OR = 2.39, 95% CI 0.82-7.01) and less likely to present with severe diffuse osteopenia (OR = 0.51, 95% CI 0.31-0.81).

CONCLUSIONS

Findings underscore the importance of obesity as a modifiable risk factor for MM, particularly in high-risk populations, and for the clinical presentation of disease.

Latin American Trans-ancestry INitiative for OCD genomics (LATINO): Study protocol

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder. Worldwide, its prevalence is ~2% and its etiology is mostly unknown. Identifying biological factors contributing to OCD will elucidate underlying mechanisms and might contribute to improved treatment outcomes. Genomic studies of OCD are beginning to reveal long-sought risk loci, but >95% of the cases currently in analysis are of homogenous European ancestry. If not addressed, this Eurocentric bias will result in OCD genomic findings being more accurate for individuals of European ancestry than other ancestries, thereby contributing to health disparities in potential future applications of genomics. In this study protocol paper, we describe the Latin American Trans-ancestry INitiative for OCD genomics (LATINO, https://www.latinostudy.org). LATINO is a new network of investigators from across Latin America, the United States, and Canada who have begun to collect DNA and clinical data from 5000 richly phenotyped OCD cases of Latin American ancestry in a culturally sensitive and ethical manner. In this project, we will utilize trans-ancestry genomic analyses to accelerate the identification of OCD risk loci, fine-map putative causal variants, and improve the performance of polygenic risk scores in diverse populations. We will also capitalize on rich clinical data to examine the genetics of treatment response, biologically plausible OCD subtypes, and symptom dimensions. Additionally, LATINO will help elucidate the diversity of the clinical presentations of OCD across cultures through various trainings developed and offered in collaboration with Latin American investigators. We believe this study will advance the important goal of global mental health discovery and equity.

Physical activity alters the effect of genetic determinants of adiposity on hypertension among individuals of European ancestry in the UKB

Hypertension is a leading risk factor for cardiovascular disease and is modulated by genetic variants. This study aimed to assess the effect of obesity genetic liability and physical activity on hypertension among European and African ancestry individuals within the UK Biobank (UKB). Participants were 230 115 individuals of European ancestry and 3239 individuals of African ancestry from UKB. Genetic liability for obesity were estimated using previously published data including genetic variants and effect sizes for body mass index (BMI), waist-hip ratio (WHR) and waist circumference (WC) using Plink software. The outcome was defined as stage 2 hypertension (systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥90 mmHg, or the use of anti-hypertensive medications). The association between obesity genetic liability and the outcome was assessed across categories of self-reported physical activity using logistic regression. Among European ancestry participants, there was up to a 1.2 greater odds of hypertension in individuals with high genetic liability and low physical activity compared to individuals with low genetic liability and high physical activity (p < 0.001). In individuals engaging in low levels of physical activity compared with moderate/high physical activity, the effect of BMI genetic liability on hypertension was greater (p interaction = 0.04). There was no evidence of an association between obesity genetic liability and hypertension in individuals of African ancestry in the whole sample or within separate physical activity groups (p > 0.05). This study suggests that higher physical activity levels are associated with lower odds of stage 2 hypertension among European ancestry individuals who carry high genetic liability for obesity. This cannot be inferred for individuals of African ancestry, possibly due to the low African ancestry sample size within the UKB.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Genetics and Epigenetics in Obesity: What Do We Know so Far?

PURPOSE OF REVIEW

Enormous progress has been made in understanding the genetic architecture of obesity and the correlation of epigenetic marks with obesity and related traits. This review highlights current research and its challenges in genetics and epigenetics of obesity.

RECENT FINDINGS

Recent progress in genetics of polygenic traits, particularly represented by genome-wide association studies, led to the discovery of hundreds of genetic variants associated with obesity, which allows constructing polygenic risk scores (PGS). In addition, epigenome-wide association studies helped identifying novel targets and methylation sites being important in the pathophysiology of obesity and which are essential for the generation of methylation risk scores (MRS). Despite their great potential for predicting the individual risk for obesity, the use of PGS and MRS remains challenging. Future research will likely discover more loci being involved in obesity, which will contribute to better understanding of the complex etiology of human obesity. The ultimate goal from a clinical perspective will be generating highly robust and accurate prediction scores allowing clinicians to predict obesity as well as individual responses to body weight loss-specific life-style interventions.

Landscape genomics analysis provides insights into future climate change-driven risk in rhesus macaque

Climate change significantly affects the suitability of wildlife habitats. Thus, understanding how animals adapt ecologically and genetically to climate change is important for targeted species protection. Rhesus macaques (Macaca mulatta) are widely distributed and multi-climatically adapted primates. This study explored how rhesus macaques adapt to climate change by integrating ecological and genetic methods and applying species distribution models (SDMs) and a gradient forest (GF) model. The findings suggested that temperature seasonality primarily affects habitat suitability and indicated that climate change will have a dramatic impact on macaque populations in the future. We also applied genotype-environment association (GEA) analyses and selection signature analyses to identify genes associated with climate change and provide possible explanations for the adaptation of rhesus macaques to climatic environments. The population genomics analyses suggested that the Taihang population has the highest genomic vulnerability with inbreeding and low heterozygosity. Combined with the higher ecological vulnerability, additional conservation strategies are required for this population under higher risk of climate change. Our work measured the impact of climate change and enabled the identification of populations that exhibit high vulnerability to severe climate change. Such information is useful for selecting populations of rhesus macaques as subject of long-term monitoring or evolutionary rescue under future climate change.

Precision medicine of obesity as an integral part of type 2 diabetes management - past, present, and future

Obesity is a complex and heterogeneous condition that leads to various metabolic complications, including type 2 diabetes. Unfortunately, for some, treatment options to date for obesity are insufficient, with many people not reaching sustained weight loss or having improvements in metabolic health. In this Review, we discuss advances in the genetics of obesity from the past decade-with emphasis on developments from the past 5 years-with a focus on metabolic consequences, and their potential implications for precision management of the disease. We also provide an overview of the potential role of genetics in guiding weight loss strategies. Finally, we propose a vision for the future of precision obesity management that includes developing an obesity-centred multidisease management algorithm that targets both obesity and its comorbidities. However, further collaborative efforts and research are necessary to fully realise its potential and improve metabolic health outcomes.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Genetics and epigenetics in the obesity phenotyping scenario

Obesity is a common complex trait that elevates the risk for various diseases, including type 2 diabetes and cardiovascular disease. A combination of environmental and genetic factors influences the pathogenesis of obesity. Advances in genomic technologies have driven the identification of multiple genetic loci associated with this disease, ranging from studying severe onset cases to investigating common multifactorial polygenic forms. Additionally, findings from epigenetic analyses of modifications to the genome that do not involve changes to the underlying DNA sequence have emerged as key signatures in the development of obesity. Such modifications can mediate the effects of environmental factors, including diet and lifestyle, on gene expression and clinical presentation. This review outlines what is known about the genetic and epigenetic contributors to obesity susceptibility, along with the albeit limited therapeutic options currently available. Furthermore, we delineate the potential mechanisms of actions through which epigenetic changes can mediate environmental influences and the related opportunities they present for future interventions in the management of obesity.

Genes, environment, and African ancestry in cardiometabolic disorders

The past two decades have been characterized by a substantial global increase in cardiometabolic diseases, but the prevalence and incidence of these diseases and related traits differ across populations. African ancestry populations are among the most affected yet least included in research. Populations of African descent manifest significant genetic and environmental diversity and this under-representation is a missed opportunity for discovery and could exacerbate existing health disparities and curtail equitable implementation of precision medicine. Here, we discuss cardiometabolic diseases and traits in the context of African descent populations, including both genetic and environmental contributors and emphasizing novel discoveries. We also review new initiatives to include more individuals of African descent in genomics to address current gaps in the field.

Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts

Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations.

WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Beyond borders: A commentary on the benefit of promoting immigrant populations in genome-wide association studies

Immigrants are an important part of many high-income nations, in that they contribute to the sociocultural tapestry, economic well-being, and demographic diversity of their receiving countries and communities. Yet, genomic studies to date have generally focused on non-immigrant, European-ancestry populations. Although this approach has proven fruitful in discovering and validating genomic loci, within the context of racially/ethnically diverse countries like the United States-wherein half of immigrants hail from Latin America and another quarter from Asia-this approach is insufficient. There is a persistent diversity gap in genomic research in terms of both current samples and genome-wide association studies, meaning that the field's understanding of genetic architecture and gene-environmental interactions is being hampered. In this commentary, I provide motivating examples of recent research developments related to the following: (1) how the increased ancestral diversity, such as seen among Latin American immigrants, improves power to discover and document genomic loci, (2) informs how environmental factors, such as immigration-related exposures, interact with genotypes to influence phenotypes, and (3) how inclusion can be promoted through community-engaged research programs and policies. I conclude that greater inclusion of immigrants in genomic research can move the field forward toward novel discoveries and interventions to address racial/ethnic health disparities.

meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans

Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans.

Comparing Pruning and Thresholding with Continuous Shrinkage Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study®

Using individuals' genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.

GWAS-Identified Variants for Obesity Do Not Influence the Risk of Developing Multiple Myeloma: A Population-Based Study and Meta-Analysis

Multiple myeloma (MM) is an incurable disease characterized by the presence of malignant plasma cells in the bone marrow that secrete specific monoclonal immunoglobulins into the blood. Obesity has been associated with the risk of developing solid and hematological cancers, but its role as a risk factor for MM needs to be further explored. Here, we evaluated whether 32 genome-wide association study (GWAS)-identified variants for obesity were associated with the risk of MM in 4189 German subjects from the German Multiple Myeloma Group (GMMG) cohort (2121 MM cases and 2068 controls) and 1293 Spanish subjects (206 MM cases and 1087 controls). Results were then validated through meta-analysis with data from the UKBiobank (554 MM cases and 402,714 controls) and FinnGen cohorts (914 MM cases and 248,695 controls). Finally, we evaluated the correlation of these single nucleotide polymorphisms (SNPs) with cQTL data, serum inflammatory proteins, steroid hormones, and absolute numbers of blood-derived cell populations (n = 520). The meta-analysis of the four European cohorts showed no effect of obesity-related variants on the risk of developing MM. We only found a very modest association of the POC5_rs2112347G and ADCY3_rs11676272G alleles with MM risk that did not remain significant after correction for multiple testing (per-allele OR = 1.08, p = 0.0083 and per-allele OR = 1.06, p = 0.046). No correlation between these SNPs and functional data was found, which confirms that obesity-related variants do not influence MM risk.

Latin American Trans-ancestry INitiative for OCD genomics (LATINO): Study Protocol

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder. Worldwide, its prevalence is ~2% and its etiology is mostly unknown. Identifying biological factors contributing to OCD will elucidate underlying mechanisms and might contribute to improved treatment outcomes. Genomic studies of OCD are beginning to reveal long-sought risk loci, but >95% of the cases currently in analysis are of homogenous European ancestry. If not addressed, this Eurocentric bias will result in OCD genomic findings being more accurate for individuals of European ancestry than other ancestries, thereby contributing to health disparities in potential future applications of genomics. In this study protocol paper, we describe the Latin American Trans-ancestry INitiative for OCD genomics (LATINO, www.latinostudy.org). LATINO is a new network of investigators from across Latin America, the United States, and Canada who have begun to collect DNA and clinical data from 5,000 richly-phenotyped OCD cases of Latin American ancestry in a culturally sensitive and ethical manner. In this project, we will utilize trans-ancestry genomic analyses to accelerate the identification of OCD risk loci, fine-map putative causal variants, and improve the performance of polygenic risk scores in diverse populations. We will also capitalize on rich clinical data to examine the genetics of treatment response, biologically plausible OCD subtypes, and symptom dimensions. Additionally, LATINO will help elucidate the diversity of the clinical presentations of OCD across cultures through various trainings developed and offered in collaboration with Latin American investigators. We believe this study will advance the important goal of global mental health discovery and equity.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Leveraging Multi-Ancestry Polygenic Risk Scores for Body Mass Index to Predict Antiretroviral Therapy-Induced Weight Gain

Widespread availability of antiretroviral therapies (ART) for HIV-1 have generated considerable interest in understanding the pharmacogenomics of ART. In some individuals, ART has been associated with excessive weight gain, which disproportionately affects women of African ancestry. The underlying biology of ART-associated weight gain is poorly understood, but some genetic markers which modify weight gain risk have been suggested, with more genetic factors likely remaining undiscovered. To overcome limitations in available sample sizes for genome-wide association studies (GWAS) in people with HIV, we explored whether a multi-ancestry polygenic risk score (PRS) derived from large, publicly available non-HIV GWAS for body mass index (BMI) can achieve high cross-ancestry performance for predicting baseline BMI in diverse, prospective ART clinical trials datasets, and whether that PRSBMI is also associated with change in BMI over 48 weeks on ART. We show that PRSBMI explained ∼5-7% of variability in baseline (pre-ART) BMI, with high performance in both European and African genetic ancestry groups, but that PRSBMI was not associated with change in BMI on ART. This study argues against a shared genetic predisposition for baseline (pre-ART) BMI and ART-associated weight gain.

Genomics and phenomics of body mass index reveals a complex disease network

Elevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI as well as extensive connections across communities. Mendelian randomization analysis confirms numerous phenotypes across a breadth of organ systems, including conditions of the circulatory (heart failure, ischemic heart disease, atrial fibrillation), genitourinary (chronic renal failure), respiratory (respiratory failure, asthma), musculoskeletal and dermatologic systems that are deeply interconnected within and across the disease communities. This work shows that the complex genetic architecture of BMI associates with a broad range of major health conditions, supporting the need for comprehensive approaches to prevent and treat obesity.

Genome-wide association study reveals the genetic basis of brace root angle and diameter in maize

Brace roots are the main organ to support the above-ground part of maize plant. It involves in plant growth and development by water absorption and lodging resistance. The bracing root angle (BRA) and diameter (BRD) are important components of brace root traits. Illuminating the genetic basis of BRA and BRD will contribute the improvement for mechanized harvest and increasing production. A GWAS of BRA and BRD was conducted using an associated panel composed of 508 inbred lines of maize. The broad-sense heritability of BRA and BRD was estimated to be respectively 71% ± 0.19 and 52% ± 0.14. The phenotypic variation of BRA and BRD in the non-stiff stalk subgroup (NSS) and the stiff stalk subgroup (SS) subgroups are significantly higher than that in the tropical/subtropical subgroup (TST) subgroups. In addition, BRA and BRD are significantly positive with plant height (PH), ear length (EL), and kernel number per row (KNPR). GWAS revealed 27 candidate genes within the threshold of p < 1.84 × 10⁻⁶ by both MLM and BLINK models. Among them, three genes, GRMZM2G174736, GRMZM2G445169 and GRMZM2G479243 were involved in cell wall function, and GRMZM2G038073 encoded the NAC transcription factor family proteins. These results provide theoretical support for clarifying the genetic basis of brace roots traits.

Genetic risk score enhances the risk prediction of severe obesity in adult survivors of childhood cancer

Adult survivors of childhood cancer have high rates of obesity, which, in combination with the cardiotoxic effects of specific cancer therapies, places them at high risk for cardiovascular morbidity. Here we show the contribution of genetic risk scores (GRSs) to increase prediction of those survivors of childhood cancer who are at risk for severe obesity (body mass index ≥40 kg m-2) as an adult. Among 2,548 individuals of European ancestry from the St. Jude Lifetime Cohort Study who were 5-year survivors of childhood cancer, the GRS was found to be associated with 53-fold-higher odds of severe obesity. Addition of GRSs to risk prediction models based on cancer treatment exposures and lifestyle factors significantly improved model prediction (area under the curve increased from 0.68 to 0.75, resulting in the identification of 4.3-times more high-risk survivors), which was independently validated in 6,064 individuals from the Childhood Cancer Survivor Study. Genetic predictors improve identification of patients who could benefit from heightened surveillance and interventions to mitigate the risk of severe obesity and associated cardio-metabolic complications.

Susceptibility Loci for Type 2 Diabetes in the Ethnically Endogamous Indian Sindhi Population: A Pooled Blood Genome-Wide Association Study

Type 2 diabetes (T2D) is a complex metabolic derangement that has a strong genetic basis. There is substantial population-specificity in the association of genetic variants with T2D. The Indian urban Sindhi population is at a high risk of T2D. The genetic basis of T2D in this population is unknown. We interrogated 28 pooled whole blood genomes of 1402 participants from the Diabetes In Sindhi Families In Nagpur (DISFIN) study using Illumina's Global Screening Array. From a total of 608,550 biallelic variants, 140 were significantly associated with T2D after adjusting for comorbidities, batch effects, pooling error, kinship status and pooling variation in a random effects multivariable logistic regression framework. Of the 102 well-characterized genes that these variants mapped onto, 70 genes have been previously reported to be associated with T2D to varying degrees with known functional relevance. Excluding open reading frames, intergenic non-coding elements and pseudogenes, our study identified 22 novel candidate genes in the Sindhi population studied. Our study thus points to the potential, interesting candidate genes associated with T2D in an ethnically endogamous population. These candidate genes need to be fully investigated in future studies.

Do adverse childhood experiences and genetic obesity risk interact in relation to body mass index in young adulthood? Findings from the National Longitudinal Study of Adolescent to Adult Health

BACKGROUND

Few studies have focused on the role of adverse childhood experiences (ACEs) in relation to genetic susceptibility to obesity.

OBJECTIVE

We aimed to examine the interaction between the presence of ACEs (i.e., physical, psychological and sexual abuse) before the age of 18 and BMI polygenic score.

METHODS

Data came from the National Longitudinal Study of Adolescent to Adult Health (Add Health) Wave IV (2007/2008) where saliva samples were collected for DNA genotyping and information on BMI and ACEs were obtained from 5854 European American (EA), 2073 African American (AA) and 1448 Hispanic American (HA) participants aged 24 to 32 years old. Polygenic scores were calculated as the sum of the number of risk alleles of BMI-related SNPs which were weighted by effect size. A race/ethnicity-stratified mixed-effects linear regression model was used to test for differential association between BMI polygenic score and BMI by the presence of ACEs.

RESULTS

We did not find any evidence of significant interaction between ACEs and polygenic score in relation to BMI among EA (p = 0.289), AA (p = 0.618) or HA (p = 0.870). In main effects models, polygenic score was positively associated with BMI in all race/ethnic groups, yet the presence of ACEs was associated with increased BMI only among EA.

CONCLUSION

We did not find any evidence that ACEs exacerbate genetic predisposition to increased BMI in early adulthood.

Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits

Genetic associations for lipid traits have identified hundreds of variants with clear differences across European, Asian and African studies. Based on a sub-Saharan-African GWAS for lipid traits in the population cross-sectional AWI-Gen cohort (N = 10,603) we report a novel LDL-C association in the GATB region (P-value=1.56 × 10-8). Meta-analysis with four other African cohorts (N = 23,718) provides supporting evidence for the LDL-C association with the GATB/FHIP1A region and identifies a novel triglyceride association signal close to the FHIT gene (P-value =2.66 × 10-8). Our data enable fine-mapping of several well-known lipid-trait loci including LDLR, PMFBP1 and LPA. The transferability of signals detected in two large global studies (GLGC and PAGE) consistently improves with an increase in the size of the African replication cohort. Polygenic risk score analysis shows increased predictive accuracy for LDL-C levels with the narrowing of genetic distance between the discovery dataset and our cohort. Novel discovery is enhanced with the inclusion of African data.

METRO: Multi-ancestry transcriptome-wide association studies for powerful gene-trait association detection

Integrative analysis of genome-wide association studies (GWASs) and gene expression studies in the form of a transcriptome-wide association study (TWAS) has the potential to better elucidate the molecular mechanisms underlying disease etiology. Here we present a method, METRO, that can leverage gene expression data collected from multiple genetic ancestries to enhance TWASs. METRO incorporates expression prediction models constructed in different genetic ancestries through a likelihood-based inference framework, producing calibrated p values with substantially improved TWAS power. We illustrate the benefits of METRO in both simulations and applications to seven complex traits and diseases obtained from four GWASs. These GWASs include two of primarily European ancestry (n = 188,577 and 339,226) and two of primarily African ancestry (n = 42,752 and 23,827). In the real data applications, we leverage gene expression data measured on 1,032 African Americans and 801 European Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study to identify a substantially larger number of gene-trait associations as compared to existing TWAS approaches. The benefits of METRO are most prominent in applications to GWASs of African ancestry where the sample size is much smaller than GWASs of European ancestry and where a more powerful TWAS method is crucial. Among the identified associations are high-density lipoprotein-associated genes including PLTP and PPARG that are critical for maintaining lipid homeostasis and the type II diabetes-associated gene MAPT that supports microtubule-associated protein tau as a key component underlying impaired insulin secretion.

Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study

AIMS/HYPOTHESIS

Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study.

METHODS

We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci.

RESULTS

Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis.

CONCLUSIONS/INTERPRETATION

Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.

DATA AVAILABILITY

Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

The genetics of obesity: from discovery to biology

The prevalence of obesity has tripled over the past four decades, imposing an enormous burden on people's health. Polygenic (or common) obesity and rare, severe, early-onset monogenic obesity are often polarized as distinct diseases. However, gene discovery studies for both forms of obesity show that they have shared genetic and biological underpinnings, pointing to a key role for the brain in the control of body weight. Genome-wide association studies (GWAS) with increasing sample sizes and advances in sequencing technology are the main drivers behind a recent flurry of new discoveries. However, it is the post-GWAS, cross-disciplinary collaborations, which combine new omics technologies and analytical approaches, that have started to facilitate translation of genetic loci into meaningful biology and new avenues for treatment.

A Genetic Map of the Modern Urban Society of Amsterdam

Genetic differences between individuals underlie susceptibility to many diseases. Genome-wide association studies (GWAS) have discovered many susceptibility genes but were often limited to cohorts of predominantly European ancestry. Genetic diversity between individuals due to different ancestries and evolutionary histories shows that this approach has limitations. In order to gain a better understanding of the associated genetic variation, we need a more global genomics approach including a greater diversity. Here, we introduce the Healthy Life in an Urban Setting (HELIUS) cohort. The HELIUS cohort consists of participants living in Amsterdam, with a level of diversity that reflects the Dutch colonial and recent migration past. The current study includes 10,283 participants with genetic data available from seven groups of inhabitants, namely, Dutch, African Surinamese, South-Asian Surinamese, Turkish, Moroccan, Ghanaian, and Javanese Surinamese. First, we describe the genetic variation and admixture within the HELIUS cohort. Second, we show the challenges during imputation when having a genetically diverse cohort. Third, we conduct a body mass index (BMI) and height GWAS where we investigate the effects of a joint analysis of the entire cohort and a meta-analysis approach for the different subgroups. Finally, we construct polygenic scores for BMI and height and compare their predictive power across the different ethnic groups. Overall, we give a comprehensive overview of a genetically diverse cohort from Amsterdam. Our study emphasizes the importance of a less biased and more realistic representation of urban populations for mapping genetic associations with complex traits and disease risk for all.

APOL1 renal risk variants are associated with obesity and body composition in African ancestry adults: An observational genotype-phenotype association study

ABSTRACT

While increased obesity prevalence among persons of African ancestry (AAs) compared to persons of European ancestry (EAs) is linked to social, environmental and behavioral factors, there are no gene variants that are common and significantly associated with obesity in AA populations. We sought to explore the association between ancestry specific renal risk variants in the apolipoprotein L1 (APOL1) gene with obesity related traits in AAs.We conducted a genotype-phenotype association study from 3 electronic medical record linked cohorts (BioMe Biobank, BioVU, nuGENE); randomized controlled trials (genetic testing to understand and address renal disease disparities) and prospective cohort study (Jackson Heart Study). We analyzed association of APOL1 renal risk variants with cross-sectional measures of obesity (average body mass index (BMI), and proportion of overweight and obesity) and with measures of body composition (in Jackson Heart Study).We had data on 11,930 self-reported AA adults. Across cohorts, mean age was from 42 to 49 years and percentage female from 58% to 75.3%. Individuals who have 2 APOL1 risk alleles (14% of AAs) have 30% higher obesity odds compared to others (recessive model adjusted odds ratio 1.30; 95% confidence interval 1.16-1.41; P = 2.75 × 10-6). An additive model better fit the association, in which each allele (47% of AAs) increases obesity odds by 1.13-fold (adjusted odds ratio 1.13; 95% confidence interval 1.07-1.19; P = 3.07 × 10-6) and increases BMI by 0.36 kg/m2 (∼1 kg, for 1.7 m height; P = 2 × 10-4). APOL1 alleles are not associated with refined body composition traits overall but are significantly associated with fat free mass index in women [0.30 kg/m2 increment per allele; P = .03].Thus, renal risk variants in the APOL1 gene, found in nearly half of AAs, are associated with BMI and obesity in an additive manner. These variants could, either on their own or interacting with environmental factors, explain a proportion of ethnic disparities in obesity.

False positive findings during genome-wide association studies with imputation: Influence of allele frequency and imputation accuracy

Genotype imputation is widely used in genetic studies to boost the power of GWAS, to combine multiple studies for meta-analysis and to perform fine mapping. With advances of imputation tools and large reference panels, genotype imputation has become mature and accurate. However, the uncertain nature of imputed genotypes can cause bias in the downstream analysis. Many studies have compared the performance of popular imputation approaches, but few investigated bias characteristics of downstream association analyses. Herein, we showed that the imputation accuracy is diminished if the real genotypes contain minor alleles. Although these genotypes are less common, which is particularly true for loci with low minor allele frequency, a large discordance between imputed and observed genotypes significantly inflated the association results, especially in data with a large portion of uncertain SNPs. The significant discordance of p-values happened as the p-value approached 0 or the imputation quality was poor. Although elimination of poorly imputed SNPs can remove false positive (FP) SNPs, it sacrificed, sometimes, more than 80% true positive (TP) SNPs. For top ranked SNPs, removing variants with moderate imputation quality cannot reduce the proportion of FP SNPs, and increasing sample size in reference panels did not greatly benefit the results as well. Additionally, samples with a balanced ratio between cases and controls can dramatically improve the number of TP SNPs observed in the imputation based GWAS. These results raise concerns about results from analysis of association studies when rare variants are studied, particularly when case-control studies are unbalanced.

Genetic underpinnings of regional adiposity distribution in African Americans: Assessments from the Jackson Heart Study

BACKGROUND

African ancestry individuals with comparable overall anthropometric measures to Europeans have lower abdominal adiposity. To explore the genetic underpinning of different adiposity patterns, we investigated whether genetic risk scores for well-studied adiposity phenotypes like body mass index (BMI) and waist circumference (WC) also predict other, less commonly measured adiposity measures in 2420 African American individuals from the Jackson Heart Study.

METHODS

Polygenic risk scores (PRS) were calculated using GWAS-significant variants extracted from published studies mostly representing European ancestry populations for BMI, waist-hip ratio (WHR) adjusted for BMI (WHRBMIadj), waist circumference adjusted for BMI (WCBMIadj), and body fat percentage (BF%). Associations between each PRS and adiposity measures including BF%, subcutaneous adiposity tissue (SAT), visceral adiposity tissue (VAT) and VAT:SAT ratio (VSR) were examined using multivariable linear regression, with or without BMI adjustment.

RESULTS

In non-BMI adjusted models, all phenotype-PRS were found to be positive predictors of BF%, SAT and VAT. WHR-PRS was a positive predictor of VSR, but BF% and BMI-PRS were negative predictors of VSR. After adjusting for BMI, WHR-PRS remained a positive predictor of BF%, VAT and VSR but not SAT. WC-PRS was a positive predictor of SAT and VAT; BF%-PRS was a positive predictor of BF% and SAT only.

CONCLUSION

These analyses suggest that genetically driven increases in BF% strongly associate with subcutaneous rather than visceral adiposity and BF% is strongly associated with BMI but not central adiposity-associated genetic variants. How common genetic variants may contribute to observed differences in adiposity patterns between African and European ancestry individuals requires further study.

Genome-wide association study of body fat distribution traits in Hispanics/Latinos from the HCHS/SOL

Central obesity is a leading health concern with a great burden carried by ethnic minority populations, especially Hispanics/Latinos. Genetic factors contribute to the obesity burden overall and to inter-population differences. We aimed to identify the loci associated with central adiposity measured as waist-to-hip ratio (WHR), waist circumference (WC) and hip circumference (HIP) adjusted for body mass index (adjBMI) by using the Hispanic Community Health Study/Study of Latinos (HCHS/SOL); determine if differences in associations differ by background group within HCHS/SOL and determine whether previously reported associations generalize to HCHS/SOL. Our analyses included 7472 women and 5200 men of mainland (Mexican, Central and South American) and Caribbean (Puerto Rican, Cuban and Dominican) background residing in the USA. We performed genome-wide association analyses stratified and combined across sexes using linear mixed-model regression. We identified 16 variants for waist-to-hip ratio adjusted for body mass index (WHRadjBMI), 22 for waist circumference adjusted for body mass index (WCadjBMI) and 28 for hip circumference adjusted for body mass index (HIPadjBMI), which reached suggestive significance (P < 1 × 10-6). Many loci exhibited differences in strength of associations by ethnic background and sex. We brought a total of 66 variants forward for validation in cohorts (N = 34 161) with participants of Hispanic/Latino, African and European descent. We confirmed four novel loci (P < 0.05 and consistent direction of effect, and P < 5 × 10-8 after meta-analysis), including two for WHRadjBMI (rs13301996, rs79478137); one for WCadjBMI (rs3168072) and one for HIPadjBMI (rs28692724). Also, we generalized previously reported associations to HCHS/SOL, (8 for WHRadjBMI, 10 for WCadjBMI and 12 for HIPadjBMI). Our study highlights the importance of large-scale genomic studies in ancestrally diverse Hispanic/Latino populations for identifying and characterizing central obesity susceptibility that may be ancestry-specific.

Contribution of lncRNA CASC8, CASC11, and PVT1 Genetic Variants to the Susceptibility of Coronary Heart Disease

The purpose of this study was to explore the relationship between lncRNA CASC8, CASC11, and plasmacytoma variant translocation 1 (PVT1). genetic variants and coronary heart disease (CHD) susceptibility among a Chinese Han population. Five single nucleotide polymorphisms were genotyped by Agena MassARRAY platform among 464 CHD patients and 510 healthy controls. Binary logistic regression models by calculating odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the association between selected single nucleotide polymorphisms and CHD risk. Multifactor dimensionality reduction analysis was performed to analyze gene-gene interaction. PVT1 rs4410871 (OR = 0.77, P = 0.040) was associated with a reduced risk of CHD occurrence in the Chinese population. CASC11 rs9642880 (OR = 1.49, P = 0.021) was a risk factor for increased CHD susceptibility in subjects over 60 years old, and PVT1 rs4410871 was a protective factor for CHD susceptibility in males (OR = 0.67, P = 0.015) and smokers (OR = 0.62, P = 0.047). Complications (hypertension or diabetes) of CHD influenced the association between CASC8, CASC11, and PVT1 genetic polymorphisms and CHD predisposition. Moreover, CASC8, CASC11, and PVT1 polymorphisms were related to the number of pathological branches and Gensini score in CHD patients. The study displayed the contribution of CASC8, CASC11, and PVT1 genetic polymorphisms to CHD predisposition, and these variants could serve as potential biomarkers of CHD susceptibility. These findings contribute to enhancing the understanding of the role of lncRNA polymorphisms in CHD risk.

Distinct Associations of BMI and Fatty Acids With DNA Methylation in Fasting and Postprandial States in Men

We have previously shown that blood global DNA methylation (DNAm) differs between postprandial state (PS) and fasting state (FS) and is associated with BMI and polyunsaturated fatty acid (PUFA) (negatively and positively, respectively) in 12 metabolically healthy adult Mexican men (AMM cohort) equally distributed among conventional BMI classes. Here, we detailed those associations at CpG dinucleotide level by exploiting the Infinium methylation EPIC array (Illumina). We sought differentially methylated CpG (dmCpG) that were (1) associated with BMI (BMI-dmCpG) and/or fatty acids (FA) (FA-dmCpG) in FS or PS and (2) different across FS and PS within a BMI class. BMI-dmCpG and FA-dmCpG were more numerous in FS compared to PS and largely prandial state-specific. For saturated and monounsaturated FA, dmCpG overlap was higher across than within the respective saturation group. Several BMI- and FA-dmCpG mapped to genes involved in metabolic disease and in some cases matched published experimental data sets. Notably, SETDB1 and MTHFS promoter dmCpG could explain the previously observed associations between global DNAm, PUFA content, and BMI in FS. Surprisingly, overlap between BMI-dmCpG and FA-dmCpG was limited and the respective dmCpG were differentially distributed across functional genomic elements. BMI-dmCpG showed the highest overlap with dmCpG of the saturated FA palmitate, monounsaturated C20:1 and PUFA C20:2. Of these, selected promoter BMI-dmCpG showed opposite associations with palmitate compared to C20:1 and C20:2. As for the comparison between FS and PS within BMI classes, dmCpG were strikingly more abundant and variably methylated in overweight relative to normoweight or obese subjects (∼70–139-fold, respectively). Overweight-associated dmCpG-hosting genes were significantly enriched in targets for E47, SREBP1, and RREB1 transcription factors, which are known players in obesity and lipid homeostasis, but none overlapped with BMI-dmCpG. We show for the first time that the association of BMI and FA with methylation of disease-related genes is distinct in FS and PS and that limited overlap exists between BMI- and FA-dmCpG within and across prandial states. Our study also identifies a transcriptional regulation circuitry in overweight that might contribute to adaptation to that condition or to transition to obesity. Further work is necessary to define the pathophysiological implications of these findings.

African genetic diversity and adaptation inform a precision medicine agenda

The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.

Genetics of Obesity: What We Have Learned Over Decades of Research

There is a genetic component to human obesity that accounts for 40% to 50% of the variability in body weight status but that is lower among normal weight individuals (about 30%) and substantially higher in the subpopulation of individuals with obesity and severe obesity (about 60%-80%). The appreciation that heritability varies across classes of BMI represents an important advance. After controlling for BMI, ectopic fat and fat distribution traits are characterized by heritability levels ranging from 30% to 55%. Defects in at least 15 genes are the cause of monogenic obesity cases, resulting mostly from deficiencies in the leptin-melanocortin signaling pathway. Approximately two-thirds of the BMI heritability can be imputed to common DNA variants, whereas low-frequency and rare variants explain the remaining fraction. Diminishing allele effect size is observed as the number of obesity-associated variants expands, with most BMI-increasing or -decreasing alleles contributing only a few grams or less to body weight. Obesity-promoting alleles exert minimal effects in normal weight individuals but have larger effects in individuals with a proneness to obesity, suggesting a higher penetrance; however, it is not known whether these larger effect sizes precede obesity or are caused by an obese state. The obesity genetic risk is conditioned by thousands of DNA variants that make genetically based obesity prevention and treatment a major challenge.

Association of MC4R (rs17782313) gene polymorphism with obesity measures in Western India

BACKGROUND AND AIMS

The association of melanocortin receptor 4 (MC4R) gene with adiposity measures is widely studied in European populations. Only six studies have investigated the role of MC4R gene with adiposity measures among Indian populations. We have evaluated the role of MC4R (rs17782313) gene polymorphism in influencing adiposity measures in India among children and adults.

MATERIALS AND METHODS

The present population based cross sectional study was conducted among 303 individuals (208 children and 95 adults) of age group 10-30 years, belonging to Rajasthan. Somatometric measurements (standing height, weight, and waist and hip girths) and blood samples were taken after obtaining written informed consent. Genotyping of MC4R rs17782313 single nucleotide polymorphism was done using restriction fragment length polymorphism method for polymerase chain reaction amplified fragments. We examined association between rs17782313 and different adiposity measures (height, weight, BMI, WHR, and waist and hip girths) using linear regression models.

RESULTS

The MC4R variant (rs17782313) predicted increased body weight (0.15 kg, S.E ± 0.076, P = 0.043) among children. In combined population, the rs17782313 variant was moderately associated with body weight (0.13 kg, S.E ± 0.070, P = 0.057). This variant was not found to be associated with any other adiposity measure.

CONCLUSION

Further studies are needed to evaluate the association of MC4R variants through sequencing and functional genomics with different adiposity measures in Indian populations for understanding the genetic underpinnings of adiposity in India.