Genetic insights into resting heart rate and its role in cardiovascular disease

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.

ARTICLE HIGHLIGHTS

We aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.

Abstract MP19: An eGFR Polygenic Score Predicts Chronic Kidney Disease in African Americans

Chronic kidney disease (CKD) is a risk factor for cardiovascular disease and early death. Genetic factors contribute to CKD, and recently, polygenic scores (PGS) have been developed to quantify risk for complex diseases, such as CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PGS development overall; moreover, European-ancestry derived PGSs demonstrate diminished predictive performance in African ancestry populations. This study aimed to develop a PGS for CKD using genotype and phenotype data from African American (AA) participants of observational cohort studies. We obtained score weights from a meta-analysis of genome-wide association studies (GWAS) for estimated glomerular filtration rate (eGFR) in the Million Veteran Program (MVP) and Reasons for Geographical and Racial Differences in Stroke (REGARDS) Study (total n~66,000). We then optimized the PGS in a cohort of AAs from the Hypertension Genetic Epidemiology Network (HyperGEN) Study (n~1,900) using the PRS-CS software and evaluated the predictive performance of the PGS at multiple global shrinkage parameters. We further adjusted the PGS for APOL1 risk status. In HyperGEN, the eGFR-based PGS was significantly associated with the odds of prevalent CKD—defined as baseline eGFR <60 mL/min/1.73m 2 — in logistic regression models adjusted for age, sex, and population structure. Further, accounting for APOL1 risk status—a putative variant for CKD unique to those of sub-Saharan African descent—improved the score’s accuracy, with the APOL1 -adjusted PGS explaining 1.9% (1.1% without APOL1 ) of the variance in CKD and an area under the curve (AUC) of 58.9% [95% CI: 53.0%-64.9%] (without APOL1 , 58.2% [95% CI: 52.3%-64.1%]). Sensitivity analyses and validation in external cohorts, as well as comparisons to previously published PGS, are ongoing. In this study, we developed a PGS that was significantly associated with CKD with improved predictive accuracy in AAs over previously published PGS.

Abstract P548: Epigenome-Wide Association Meta-Analysis of Obesity in African Americans

Background: Despite considerable advances in obesity genetics, much remains to be learned regarding the pathway from genes to obesity development. Prior studies have shown that that 40-70% of the variation in body mass index (BMI), a measure of obesity, is due to heritable factors, yet BMI-identified genetic variants only account for a limited portion of the heritability. Research suggests epigenetic variation, which reflects both genetic and environmental influence, may account for some of the missing heritability. However, such studies have largely been conducted in primarily European ancestry populations, despite the disproportionate burden of obesity in African Americans (AAs).

Methods: To identify CpG sites associated with BMI in AAs, we conducted an epigenome-wide association study (EWAS) using participants from the Jackson Heart Study (JHS, n=1604) and a subset of AA participants from the Multi-Ethnic Study of Atherosclerosis (MESA, n=179) using the Illumina HumanMethylationEPIC array (~850K CpG sites). Analyses were stratified by study and sex and meta-analyzed using an inverse-variance weighted approach. EWAS was performed using a linear regression model with methylation M-values (n=573,388 CpGs) as the outcome and continuous BMI as the predictor. Covariates in the linear models included age, centered-mean-age-squared, smoking status (never/past/current), 10 genetic ancestry principal components, and Houseman-estimated blood cell proportions.

Results: The meta-analysis resulted in 204 CpGs that reached epigenome-wide significance (p < 8.72 x 10 -8 ) after multiple-testing correction, and these include both previously identified and novel BMI-associated sites. 48 of the 204 have been previously reported in EWAS of obesity phenotypes, 11 of which were in studies of AAs. 156 of the methylation sites significantly associated with BMI in this study are novel, and potentially AA-specific. Differentially methylated region (DMR) analysis showed significant association of these CpGs in the promoters of genes previously associated with obesity traits, such as SOCS3, ABCG1, and TGFB1 , although TGFB1 had not previously been associated in AAs. In addition, the DMR analysis revealed novel obesity-associated CpGs in genes, including JAK3, which has previously been shown to have differential gene expression in association with BMI in MESA.

Conclusion: We identified novel, potentially AA-specific, methylation sites associated with BMI in the largest African-ancestry EWAS conducted thus far, supporting that continued analysis in diverse populations is crucial for increased understanding of obesity.

Abstract 66: Clonal Hematopoiesis of Indeterminate Potential and Incident Type 2 Diabetes Risk

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in elevated risks for coronary heart disease (CHD) and death, but its association with incident type 2 diabetes (T2D) is unknown.

Hypothesis: We hypothesized that CHIP is associated with elevated risk of incident T2D.

Methods: CHIP was derived from whole genome sequencing of blood DNA in NHLBI Trans-omics for Precision Medicine (TOPMed) cohorts. We analyzed 17,637 participants without prior T2D, cardiovascular disease, or cancer at blood draw, with prospective follow-up for incident T2D. We evaluated baseline prevalence of CHIP vs. no CHIP with incident T2D risk using Cox regression. We also investigated CHIP variants previously related to CHD: DNMT3A , TET2 , ASXL1 , JAK2 , and TP53 . We estimated multivariable-adjusted hazard ratios and 95% confidence intervals (HR [CI]) adjusted for age, sex, body mass index, smoking, alcohol, and education. We combined cohort estimates via fixed effects meta-analysis.

Results: On average, participants were age 63.4 years (SD=11.5) and 76% female. Prevalence of CHIP was 6.0% (1,055) at baseline. There were 2,467 incident T2D cases over mean=9.8 years follow-up. Compared to those without a mutation, having CHIP was associated with a 23% higher T2D risk, both overall (combined HR=1.23; 95% CI=1.04, 1.45), and among those with CHD-related CHIP mutations (87% of total CHIP): HR=1.23 (1.03, 1.46). Although those with CHIP mutations of TET2 (HR=1.48; 1.05, 2.08) and ASXL1 (HR=1.76; 1.03, 2.99) had larger elevations in T2D risk, and DNMT3A was suggestive of increased T2D risk (HR=1.15; 0.93, 1.43), statistical power was limited for JAK2 and TP53 mutation analyses.

Conclusions: CHIP was associated with higher incidence of T2D. CHIP mutations located on loci previously implicated in aging and CHD were also related to T2D, suggesting shared pathology of atherosclerosis and T2D.

NetSHy: network summarization via a hybrid approach leveraging topological properties

MOTIVATION

Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information.

RESULTS

In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation.

AVAILABILITY AND IMPLEMENTATION

R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Effective Subject Representation based on Multi-omics Disease Networks using Graph Embedding

The study of complex behavior of biological systems has become increasingly dependent on evolutionary network modeling. In particular, multi-omics networks capture interactions between biomolecules such as proteins and metabolites, providing a basis for predicting relationships between such biomolecules and various phenotypic traits of complex diseases. In this paper, we introduce an integrative framework that given a multi-omics network representing a cohort of subjects, learns expressive representations for network nodes, and combines the learned nodes representations with the biological profiles of individual subjects for enriched representation of the subjects. With extensive empirical evaluation using real-world multi-omics networks, we show that our proposed framework significantly outperforms existing and baseline methods in terms of subject representation accuracy, particularly when the multi-omics network representing the cohort is sparse and structured and therefore, more informative.

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data

RATIONALE

Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear.

OBJECTIVES

We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program.

METHODS

Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants.

MEASUREMENTS AND MAIN RESULTS

In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability.

CONCLUSIONS

Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.

Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke

BACKGROUND AND OBJECTIVES

Current genome-wide association studies of ischemic stroke have focused primarily on late-onset disease. As a complement to these studies, we sought to identify the contribution of common genetic variants to risk of early-onset ischemic stroke.

METHODS

We performed a meta-analysis of genome-wide association studies of early-onset stroke (EOS), ages 18-59 years, using individual-level data or summary statistics in 16,730 cases and 599,237 nonstroke controls obtained across 48 different studies. We further compared effect sizes at associated loci between EOS and late-onset stroke (LOS) and compared polygenic risk scores (PRS) for venous thromboembolism (VTE) between EOS and LOS.

RESULTS

We observed genome-wide significant associations of EOS with 2 variants in ABO, a known stroke locus. These variants tag blood subgroups O1 and A1, and the effect sizes of both variants were significantly larger in EOS compared with LOS. The odds ratio (OR) for rs529565, tagging O1, was 0.88 (95% confidence interval [CI]: 0.85-0.91) in EOS vs 0.96 (95% CI: 0.92-1.00) in LOS, and the OR for rs635634, tagging A1, was 1.16 (1.11-1.21) for EOS vs 1.05 (0.99-1.11) in LOS; p-values for interaction = 0.001 and 0.005, respectively. Using PRSs, we observed that greater genetic risk for VTE, another prothrombotic condition, was more strongly associated with EOS compared with LOS (p = 0.008).

DISCUSSION

The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with EOS than with LOS, supporting a stronger role of prothrombotic factors in EOS.

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

SMOC2 gene interacts with APOL1 in the development of end-stage kidney disease: A genome-wide association study

Background

Some but not all African-Americans (AA) who carry APOL1 nephropathy risk variants (APOL1) develop kidney failure (end-stage kidney disease, ESKD). To identify genetic modifiers, we assessed gene-gene interactions in a large prospective cohort of the REasons for Geographic and Racial Differences in Stroke (REGARDS) study.

Methods

Genotypes from 8,074 AA participants were obtained from Illumina Infinium Multi-Ethnic AMR/AFR Extended BeadChip. We compared 388 incident ESKD cases with 7,686 non-ESKD controls, using a two-locus interaction approach. Logistic regression was used to examine the effect of APOL1 risk status (using recessive and additive models), single nucleotide polymorphism (SNP), and APOL1^*SNP interaction on incident ESKD, adjusting for age, sex, and ancestry. APOL1 ^*SNP interactions that met the threshold of 1.0 × 10^-5 were replicated in the Genetics of Hypertension Associated Treatment (GenHAT) study (626 ESKD cases and 6,165 controls). In a sensitivity analysis, models were additionally adjusted for diabetes status. We conducted additional replication in the BioVU study.

Results

Two APOL1 risk alleles prevalence (recessive model) was similar in the REGARDS and GenHAT studies. Only one APOL1-SNP interaction, for rs7067944 on chromosome 10, ~10 KB from the PCAT5 gene met the genome-wide statistical threshold (P _interaction = 3.4 × 10^-8), but this interaction was not replicated in the GenHAT study. Among other relevant top findings (with P _interaction < 1.0 × 10^-5), a variant (rs2181251) near SMOC2 on chromosome six interacted with APOL1 risk status (additive) on ESKD outcomes (REGARDS study, P _interaction =5.3 × 10^-6) but the association was not replicated (GenHAT study, P _interaction = 0.07, BioVU study, P _interaction = 0.53). The association with the locus near SMOC2 persisted further in stratified analyses. Among those who inherited ≥1 alternate allele of rs2181251, APOL1 was associated with an increased risk of incident ESKD (OR [95%CI] = 2.27[1.53, 3.37]) but APOL1 was not associated with ESKD in the absence of the alternate allele (OR [95%CI] = 1.34[0.96, 1.85]) in the REGARDS study. The associations were consistent after adjusting for diabetes.

Conclusion

In a large genome-wide association study of AAs, a locus SMOC2 exhibited a significant interaction with the APOL1 locus. SMOC2 contributes to the progression of fibrosis after kidney injury and the interaction with APOL1 variants may contribute to an explanation for why only some APOLI high-risk individuals develop ESKD.

Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

Lung function impairment and risk of incident heart failure: the NHLBI Pooled Cohorts Study

AIMS

The aim is to evaluate associations of lung function impairment with risk of incident heart failure (HF).

METHODS AND RESULTS

Data were pooled across eight US population-based cohorts that enrolled participants from 1987 to 2004. Participants with self-reported baseline cardiovascular disease were excluded. Spirometry was used to define obstructive [forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) <0.70] or restrictive (FEV1/FVC ≥0.70, FVC <80%) lung physiology. The incident HF was defined as hospitalization or death caused by HF. In a sub-set, HF events were sub-classified as HF with reduced ejection fraction (HFrEF; EF <50%) or preserved EF (HFpEF; EF ≥50%). The Fine-Gray proportional sub-distribution hazards models were adjusted for sociodemographic factors, smoking, and cardiovascular risk factors. In models of incident HF sub-types, HFrEF, HFpEF, and non-HF mortality were treated as competing risks. Among 31 677 adults, there were 3344 incident HF events over a median follow-up of 21.0 years. Of 2066 classifiable HF events, 1030 were classified as HFrEF and 1036 as HFpEF. Obstructive [adjusted hazard ratio (HR) 1.17, 95% confidence interval (CI) 1.07-1.27] and restrictive physiology (adjusted HR 1.43, 95% CI 1.27-1.62) were associated with incident HF. Obstructive and restrictive ventilatory defects were associated with HFpEF but not HFrEF. The magnitude of the association between restrictive physiology and HFpEF was similar to associations with hypertension, diabetes, and smoking.

CONCLUSION

Lung function impairment was associated with increased risk of incident HF, and particularly incident HFpEF, independent of and to a similar extent as major known cardiovascular risk factors.

InSight Pressure Data Recalibration, and Its Application to the Study of Long-Term Pressure Changes on Mars

Observations of the South Polar Residual Cap suggest a possible erosion of the cap, leading to an increase of the global mass of the atmosphere. We test this assumption by making the first comparison between Viking 1 and InSight surface pressure data, which were recorded 40 years apart. Such a comparison also allows us to determine changes in the dynamics of the seasonal ice caps between these two periods. To do so, we first had to recalibrate the InSight pressure data because of their unexpected sensitivity to the sensor temperature. Then, we had to design a procedure to compare distant pressure measurements. We propose two surface pressure interpolation methods at the local and global scale to do the comparison. The comparison of Viking and InSight seasonal surface pressure variations does not show changes larger than ±8 Pa in the CO₂ cycle. Such conclusions are supported by an analysis of Mars Science Laboratory (MSL) pressure data. Further comparisons with images of the south seasonal cap taken by the Viking 2 orbiter and MARCI camera do not display significant changes in the dynamics of this cap over a 40 year period. Only a possible larger extension of the North Cap after the global storm of MY 34 is observed, but the physical mechanisms behind this anomaly are not well determined. Finally, the first comparison of MSL and InSight pressure data suggests a pressure deficit at Gale crater during southern summer, possibly resulting from a large presence of dust suspended within the crater.

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

APOL1 Risk Variants Associated with Serum Albumin in a Population-Based Cohort Study

INTRODUCTION

The association of apolipoprotein L1 (APOL1) nephropathy risk variants (APOL1), unique to African-ancestry (African-American [AA]) populations, with systemic inflammation, a contributor to chronic kidney disease (CKD) and end-stage kidney disease (ESKD) is ill-defined. This study aimed to describe the role of inflammatory markers in the relationship between APOL1 and incident kidney outcomes using a prospective cohort study.

METHODS

APOL1 high-risk status under a recessive genetic model was studied in 10,605 AA adults aged ≥45 years from the Reasons for Geographic and Racial Differences in Stroke study. The primary variables of interest were inflammatory markers: C-reactive protein (mg/dL), white blood cell count (cells/mm3), and serum albumin (sALB) (mg/dL). High inflammation status was defined if at least one of these inflammatory markers exceeded clinical threshold. The association between APOL1 and biomarkers were assessed using regression models adjusting for age, sex, ancestry, hypertension, lipid medications, albumin-to-creatinine ratio, and estimated glomerular filtration rate (eGFR). Models were stratified by diabetes status. We identified incident ESKD using USRDS linkage, and we defined incident CKD as an eGFR <60 mL/min/1.73 m2 and ≥25% decline in the eGFR and normal baseline eGFR and tested for mediation of APOL1 and outcomes by biomarkers using the causal inference approach.

RESULTS

Among 7,151 participants with data available on all inflammation markers, 4,479 participants had ≥1 marker meeting the clinical threshold. APOL1 high-risk status was associated with lower adjusted odds of reduced sALB {odds ratio (OR) (95% confidence interval [CI]): 0.59 [0.36, 0.96])}, and this association was significant in people with diabetes (OR [95% CI]: 0.40 [0.18, 0.89]) but not in those without diabetes. There was no association of APOL1 high-risk status with other markers or high inflammation status. APOL1 was independently associated with ESKD (OR [95% CI] = 1.78 [1.28, 2.48]) and CKD (OR [95% CI] = 1.38 [1.00, 1.91]). On mediation analysis, the direct effect between APOL1 and ESKD strengthened after accounting for sALB, but the estimated mediated effect was not statistically significant (OR [95% CI]: 0.98 [0.92, 1.05], p = 0.58).

CONCLUSION

APOL1 high-risk variants were associated with sALB. However, sALB did not statistically mediate the association between APOL1 and incident ESKD.

Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation

Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 × 10-15) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 × 10-8) and mtDNA replication (p = 1.2 × 10-7). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 × 10-4).

Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol

OBJECTIVE

LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown.

RESEARCH DESIGN AND METHODS

We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses.

RESULTS

A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (β = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04).

CONCLUSIONS

These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

Associations between DNA methylation and BMI vary by metabolic health status: a potential link to disparate cardiovascular outcomes

Background

Body mass index (BMI), a well-known risk factor for poor cardiovascular outcomes, is associated with differential DNA methylation (DNAm). Similarly, metabolic health has also been associated with changes in DNAm. It is unclear how overall metabolic health outside of BMI may modify the relationship between BMI and methylation profiles, and what consequences this may have on downstream cardiovascular disease. The purpose of this study was to identify cytosine-phosphate-guanine (CpG) sites at which the association between BMI and DNAm could be modified by overall metabolic health.

Results

The discovery study population was derived from three Women’s Health Initiative (WHI) ancillary studies (n = 3977) and two Atherosclerosis Risk in Communities (ARIC) ancillary studies (n = 3520). Findings were validated in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort (n = 1200). Generalized linear models regressed methylation β values on the interaction between BMI and metabolic health Z score (BMI × MHZ) adjusted for BMI, MHZ, cell composition, chip number and location, study characteristics, top three ancestry principal components, smoking, age, ethnicity (WHI), and sex (ARIC). Among the 429,566 sites examined, differential associations between BMI × MHZ and DNAm were identified at 22 CpG sites (FDR q < 0.05), with one site replicated in MESA (cg18989722, in the TRAPPC9 gene). Three of the 22 sites were associated with incident coronary heart disease (CHD) in WHI. For each 0.01 unit increase in DNAm β value, the risk of incident CHD increased by 9% in one site and decreased by 6–10% in two sites over 25 years.

Conclusions

Differential associations between DNAm and BMI by MHZ were identified at 22 sites, one of which was validated (cg18989722) and three of which were predictive of incident CHD. These sites are located in several genes related to NF-kappa-B signaling, suggesting a potential role for inflammation between DNA methylation and BMI-associated metabolic health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01194-3.

Genome-wide Association Study of Lipid Traits in Youth With Type 2 Diabetes

CONTEXT

Dyslipidemia is highly prevalent in youth with type 2 diabetes (T2D), yet the pathogenic components of dyslipidemia in youth with T2D are poorly understood.

OBJECTIVE

To evaluate the genetic determinants of lipid traits in youth with T2D through a genome-wide association study.

DESIGN PARTICIPANTS AND MAIN OUTCOME MEASURES

We genotyped 206 928 variants and imputed 17 642 824 variants in 1076 youth (mean age 15.0 ± 2.48 years) with T2D from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) and SEARCH for Diabetes in Youth (SEARCH) studies as part of the Progress in Diabetes Genetics in Youth (ProDiGY) consortium. We performed association testing for triglyceride and low-density lipoprotein cholesterol and high-density lipoprotein cholesterol (HDL-c) concentrations adjusted for the genetic relationship matrix within each substudy followed by meta-analyses for each trait.

RESULTS

We identified a novel association between a deletion on chromosome 3 (3:67817380_AT/A_Deletion:RP11-81N13.1) and triglyceride levels at genome-wide level of significance (P = 2.3 × 10-8) with each risk allele increasing triglycerides by 20%. We also identified a genome-wide significant signal at rs247617 (P = 5.1 × 10-9) between HERFUD1 and CETP associated with HDL-c, with carriers of 1 copy of the risk allele having twice higher HDL-c.

CONCLUSIONS

Our genetic analyses of lipid traits in youth with T2D have identified 1 novel and 1 previously known locus. Additional studies are needed to further characterize the genetic architecture of dyslipidemia in youth with T2D.

Peripheral Blood Cytopenia and Risk of Cardiovascular Disease and Mortality

Background

Individual blood cell count abnormalities have been associated with cardiovascular disease and increased mortality. In this study, we defined a “cytopenia phenotype,” reflecting bone marrow hypoproliferation, to determine if peripheral blood cytopenia is associated with increased cardiovascular disease and mortality risk.

Methods and Results

Study participants were derived from a biracial observational cohort study, REGARDS (Reasons for Geographic and Racial Differences in Stroke), that enrolled 30 239 Black and White participants aged ≥45 years between 2003 and 2007. Median follow up was ≈9 years. The current study included 19 864 participants from REGARDS study (37.9% men, 40% Black participants) who have complete blood count available at study enrollment. We defined a cytopenia phenotype based on age‐, sex‐, and race‐adjusted lowest fifth percentile of blood counts. Multivariable Cox proportional hazards models estimated the hazard ratios (HR) and 95% CI of cytopenia for mortality and incident cardiovascular disease in adjusted models. Mean age of the study participants was 64 years (SD:9.7). The prevalence of cytopenia was 1.9% (n=378). Cytopenia was associated with increased risk of all‐cause mortality (HR, 1.73; 95% CI, 1.34–2.22) and cardiovascular disease mortality (HR, 1.56; 95% CI, 1.11–2.29). Cytopenia was associated with stroke risk in Black but not White participants (HR, 1.96 versus 0.86; P‐interaction for race=0.08) and was not associated with coronary heart disease risk.

Conclusions

We defined a cytopenia phenotype with clinical implications for mortality and stroke risk in a large biracial and geographically diverse population. Whether generated through somatic mutations or decreased organ function, cytopenia was associated with mortality risk and was a race‐specific risk factor for stroke.

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

Citizen science reveals unexpected solute patterns in semiarid river networks

Human modification of water and nutrient flows has resulted in widespread degradation of aquatic ecosystems. The resulting global water crisis causes millions of deaths and trillions of USD in economic damages annually. Semiarid regions have been disproportionately affected because of high relative water demand and pollution. Many proven water management strategies are not fully implemented, partially because of a lack of public engagement with freshwater ecosystems. In this context, we organized a large citizen science initiative to quantify nutrient status and cultivate connection in the semiarid watershed of Utah Lake (USA). Working with community members, we collected samples from ~200 locations throughout the 7,640 km2 watershed on a single day in the spring, summer, and fall of 2018. We calculated ecohydrological metrics for nutrients, major ions, and carbon. For most solutes, concentration and leverage (influence on flux) were highest in lowland reaches draining directly to the lake, coincident with urban and agricultural sources. Solute sources were relatively persistent through time for most parameters despite substantial hydrological variation. Carbon, nitrogen, and phosphorus species showed critical source area behavior, with 10-17% of the sites accounting for most of the flux. Unlike temperate watersheds, where spatial variability often decreases with watershed size, longitudinal variability showed an hourglass shape: high variability among headwaters, low variability in mid-order reaches, and high variability in tailwaters. This unexpected pattern was attributable to the distribution of human activity and hydrological complexity associated with return flows, losing river reaches, and diversions in the tailwaters. We conclude that participatory science has great potential to reveal ecohydrological patterns and rehabilitate individual and community relationships with local ecosystems. In this way, such projects represent an opportunity to both understand and improve water quality in diverse socioecological contexts.

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.

DNAm-based signatures of accelerated aging and mortality in blood are associated with low renal function

BACKGROUND

The difference between an individual's chronological and DNA methylation predicted age (DNAmAge), termed DNAmAge acceleration (DNAmAA), can capture life-long environmental exposures and age-related physiological changes reflected in methylation status. Several studies have linked DNAmAA to morbidity and mortality, yet its relationship with kidney function has not been assessed. We evaluated the associations between seven DNAm aging and lifespan predictors (as well as GrimAge components) and five kidney traits (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [uACR], serum urate, microalbuminuria and chronic kidney disease [CKD]) in up to 9688 European, African American and Hispanic/Latino individuals from seven population-based studies.

RESULTS

We identified 23 significant associations in our large trans-ethnic meta-analysis (p < 1.43E-03 and consistent direction of effect across studies). Age acceleration measured by the Extrinsic and PhenoAge estimators, as well as Zhang's 10-CpG epigenetic mortality risk score (MRS), were associated with all parameters of poor kidney health (lower eGFR, prevalent CKD, higher uACR, microalbuminuria and higher serum urate). Six of these associations were independently observed in European and African American populations. MRS in particular was consistently associated with eGFR (β =  - 0.12, 95% CI = [- 0.16, - 0.08] change in log-transformed eGFR per unit increase in MRS, p = 4.39E-08), prevalent CKD (odds ratio (OR) = 1.78 [1.47, 2.16], p = 2.71E-09) and higher serum urate levels (β = 0.12 [0.07, 0.16], p = 2.08E-06). The "first-generation" clocks (Hannum, Horvath) and GrimAge showed different patterns of association with the kidney traits. Three of the DNAm-estimated components of GrimAge, namely adrenomedullin, plasminogen-activation inhibition 1 and pack years, were positively associated with higher uACR, serum urate and microalbuminuria.

CONCLUSION

DNAmAge acceleration and DNAm mortality predictors estimated in whole blood were associated with multiple kidney traits, including eGFR and CKD, in this multi-ethnic study. Epigenetic biomarkers which reflect the systemic effects of age-related mechanisms such as immunosenescence, inflammaging and oxidative stress may have important mechanistic or prognostic roles in kidney disease. Our study highlights new findings linking kidney disease to biological aging, and opportunities warranting future investigation into DNA methylation biomarkers for prognostic or risk stratification in kidney disease.

Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci

BACKGROUND

DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach.

METHODS

The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses.

RESULTS

We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development.

CONCLUSIONS

We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context.

Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10-72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10-4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10-5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

The Role of MicroRNA Expression for Proliferation and Apoptosis of Tumor Cells: Impact of Hypoxia-Related Acidosis

The metabolic microenvironment in tumors is characterized by hypoxia and acidosis. Extracellular pH sometimes decreases to even below 6.0. Previous experiments showed that tissue pH has an impact on tumor cell proliferation and apoptosis. However, the mechanism of how cell cycle progression is affected by decreased pH is not fully understood yet. One possible mechanism includes changes in the expression of miRNAs. The aim of this study was to analyze the impact of pH-regulated miRNAs (miR-183 and miR-215) on proliferation, apoptosis, and necrosis of tumor cells. Therefore, AT1 prostate and Walker-256 mammary carcinoma cells were transfected with the miRNAs or with the respective antagomirs and incubated at pH 7.4 and 6.6 for 24 h. AT1 cells underwent a G0/G1 cell cycle arrest under acidic conditions and showed a marked reduction of the number of actively DNA-synthesizing cells. In Walker-256 cells, acidosis induced a reduction of apoptosis and additionally a significant increase in necrotic cell death. Transfection of tumor cells with miR-183 or miR-215, which were significantly downregulated under acidic conditions, had no impact on cell death of AT1 or Walker-256 cells. Overexpression of miR-183, which is also downregulated by acidosis, intensified G0/G1 cell cycle arrest in AT1 cells. Previous studies revealed that hypoxia-related tumor acidosis affects the expression of different small noncoding RNAs. However, not all of these acidosis-regulated miRNAs seem to have an impact on proliferation, apoptosis, and necrosis of tumor cells. While miR-215 had no influence, miR-183 seems to be an interesting candidate that could amplify the impact of extracellular acidosis on malignant behavior of tumor cells.

Functional Impact of Acidosis-Regulated MicroRNAs on the Migration and Adhesion of Tumor Cells

Tumor tissue shows special features in metabolism in contrast to healthy tissue. Besides a distinctive oxygen deficiency, tumors often show a reduced extracellular pH (acidosis) resulting from an intensified glycolysis not only under hypoxic but also under normoxic conditions (Warburg effect). As shown in previous studies, cell migration is increased in AT1 prostate carcinoma cells after incubation at pH 6.6, and this leads to an increased number of lung metastases in vivo. However, the signaling pathway causing these functional changes is still unknown. Possible mediators could be acidosis-regulated microRNAs (miR-7, miR-183, miR-203, miR-215). The aim of the study was therefore to analyze whether a change in the expression of these microRNAs has an impact on the tumor cell migration and adhesion. Studies were performed with AT1 rat prostate cancer cells which were incubated for 24 h at pH 7.4 or 6.6. Keeping AT1 tumor cells at low pH increased the migratory capacity by about 100%. But also the decrease of miR-203 and miR-215 expression (at normal pH) led to an increase in migration velocity by 50%. In contrast, cell adhesion was increased by about 75% at low pH. However, an increase in miR-215 expression at pH 6.6 reduced the adhesion by trend. These results clearly indicated that the extracellular pH has an impact on migration and adhesion of tumor cells. In this mechanism, pH-regulated microRNAs could play a role since changes in the expression of these microRNAs (especially miR-203) are also able to modulate the migratory behavior.

Inherited causes of clonal haematopoiesis in 97,691 whole genomes

Age is the dominant risk factor for most chronic human diseases; yet the mechanisms by which aging confers this risk are largely unknown.¹ Recently, the age-related acquisition of somatic mutations in regenerating hematopoietic stem cell populations leading to clonal expansion was associated with both hematologic cancer ^2–4 and coronary heart disease⁵, a phenomenon termed ‘Clonal Hematopoiesis of Indeterminate Potential’ (CHIP).⁶ Simultaneous germline and somatic whole genome sequence analysis now provides the opportunity to identify root causes of CHIP. Here, we analyze high-coverage whole genome sequences from 97,691 participants of diverse ancestries in the NHLBI TOPMed program and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid, and inflammatory traits specific to different CHIP genes. Association of a genome-wide set of germline genetic variants identified three genetic loci associated with CHIP status, including one locus at TET2 that was African ancestry specific. In silico-informed in vitro evaluation of the TET2 germline locus identified a causal variant that disrupts a TET2 distal enhancer resulting in increased hematopoietic stem cell self-renewal. Overall, we observe that germline genetic variation shapes hematopoietic stem cell function leading to CHIP through mechanisms that are both specific to clonal hematopoiesis and shared mechanisms leading to somatic mutations across tissues.

Capsid Engineering Overcomes Barriers Toward Adeno-Associated Virus Vector-Mediated Transduction of Endothelial Cells

Endothelial cells (EC) are targets in gene therapy and regenerative medicine, but they are inefficiently transduced with adeno-associated virus (AAV) vectors of various serotypes. To identify barriers hampering efficient transduction and to develop an optimized AAV variant for EC transduction, we screened an AAV serotype 2-based peptide display library on primary human macrovascular EC. Using a new high-throughput selection and monitoring protocol, we identified a capsid variant, AAV-V_EC, which outperformed the parental serotype as well as first-generation targeting vectors in EC transduction. AAV vector uptake was improved, resulting in significantly higher transgene expression levels from single-stranded vector genomes detectable within a few hours post-transduction. Notably, AAV-V_EC transduced not only proliferating EC but also quiescent EC, although higher particle-per-cell ratios had to be applied. Also, induced pluripotent stem cell-derived endothelial progenitor cells, a novel tool in regenerative medicine and gene therapy, were highly susceptible toward AAV-V_EC transduction. Thus, overcoming barriers by capsid engineering significantly expands the AAV tool kit for a wide range of applications targeting EC.

Der Stand der allergologischen Weiterbildung von Kinderärzten

Hintergrund

Ein deutlicher Rückgang abgelegter Prüfungen im Fach Allergologie und gleichzeitig eine Zunahme allergologischer Erkrankungen haben die Nachwuchsgruppe der Gesellschaft für Pädiatrische Allergologie und Umweltmedizin (GPAU) dazu bewogen, die Qualität und die Bedingungen der allergologischen Ausbildung in der Pädiatrie zu untersuchen. Zeitgleich wurde beim Deutschen Ärztetag ein neues Format der Zusatzweiterbildung Allergologie (Musterweiterbildungsordnung [MWBO]) verabschiedet.

Ziel der Arbeit

Ziel dieser Umfrage war es, die Rahmenbedingungen der allergologischen Ausbildung von Kinderärzten in Deutschland genauer zu untersuchen.

Material und Methoden

Es wurde eine Umfrage mithilfe eines Fragebogens sowohl bei den Weiterbildungsermächtigten (insgesamt 169) als auch bei deren Weiterbildungsassistent*innen durchgeführt.

Ergebnisse und Diskussion

Von insgesamt 56 Weiterbildungsermächtigten und 32 Weiterbildungsassistent*innen  wurde  der  Fragebogen ausgewertet. Es zeigt sich in der Auswertung, dass der Prozentsatz an Frauen bei den Weiterbildungsermächtigten deutlich geringer ist im Vergleich zu den Weiterbildungsassistent*innen (20 vs. 66 %), jedoch sind mittlerweile mehr als zwei Drittel der Medizinstudierenden weiblich. In der Umfrage stellt sich heraus, dass die allergologische Weiterbildung bei fast 20 % der Weiterbildungsassistent*innen weniger als 10 % der Gesamtarbeitszeit ausmacht und oft außerhalb der Regelarbeitszeit stattfindet. Es sind in Zukunft sowohl flexiblere Arbeitszeitmodelle als auch kombinierte Ausbildungsmodelle zwischen Klinik und Praxis und feste Strukturen für eine fundierte Ausbildung im klinischen Alltag notwendig, um der wachsenden Anzahl allergologischer Fragestellungen v. a. bei Kindern flächendeckend gerecht werden zu können. In der neuen MWBO darf nicht nur der Nachweis von Untersuchungstechniken eine Rolle spielen, sondern sie muss auch die Betreuung von komplexen allergologischen Fällen abbilden, die gerade in der Pädiatrie vorzufinden sind, z. B. im Rahmen von Hospitationen.

Genome-wide meta-analysis of variant-by-diuretic interactions as modulators of lipid traits in persons of European and African ancestry

Hypertension (HTN) is a significant risk factor for cardiovascular morbidity and mortality. Metabolic abnormalities, including adverse cholesterol and triglycerides (TG) profiles, are frequent comorbid findings with HTN and contribute to cardiovascular disease. Diuretics, which are used to treat HTN and heart failure, have been associated with worsening of fasting lipid concentrations. Genome-wide meta-analyses with 39,710 European-ancestry (EA) individuals and 9,925 African-ancestry (AA) individuals were performed to identify genetic variants that modify the effect of loop or thiazide diuretic use on blood lipid concentrations. Both longitudinal and cross-sectional data were used to compute cohort-specific interaction results, which were then combined through meta-analysis in each ancestry. These ancestry-specific results were further combined through trans-ancestry meta-analysis. Analysis of EA data identified two genome-wide significant (p < 5×10⁻⁸) loci with single nucleotide variant (SNV)-loop diuretic interaction on TG concentrations (including COL11A1). Analysis of AA data identified one genome-wide significant locus adjacent to BMP2 with SNV-loop diuretic interaction on TG concentrations. Trans-ancestry analysis strengthened evidence of association for SNV-loop diuretic interaction at two loci (KIAA1217 and BAALC). There were few significant SNV-thiazide diuretic interaction associations on TG concentrations and for either diuretic on cholesterol concentrations. Several promising loci were identified that may implicate biologic pathways that contribute to adverse metabolic side effects from diuretic therapy.

Genome-Wide Association Study of Apparent Treatment-Resistant Hypertension in the CHARGE Consortium: The CHARGE Pharmacogenetics Working Group

BACKGROUND

Only a handful of genetic discovery efforts in apparent treatment-resistant hypertension (aTRH) have been described.

METHODS

We conducted a case-control genome-wide association study of aTRH among persons treated for hypertension, using data from 10 cohorts of European ancestry (EA) and 5 cohorts of African ancestry (AA). Cases were treated with 3 different antihypertensive medication classes and had blood pressure (BP) above goal (systolic BP ≥ 140 mm Hg and/or diastolic BP ≥ 90 mm Hg) or 4 or more medication classes regardless of BP control (nEA = 931, nAA = 228). Both a normotensive control group and a treatment-responsive control group were considered in separate analyses. Normotensive controls were untreated (nEA = 14,210, nAA = 2,480) and had systolic BP/diastolic BP < 140/90 mm Hg. Treatment-responsive controls (nEA = 5,266, nAA = 1,817) had BP at goal (<140/90 mm Hg), while treated with one antihypertensive medication class. Individual cohorts used logistic regression with adjustment for age, sex, study site, and principal components for ancestry to examine the association of single-nucleotide polymorphisms with case-control status. Inverse variance-weighted fixed-effects meta-analyses were carried out using METAL.

RESULTS

The known hypertension locus, CASZ1, was a top finding among EAs (P = 1.1 × 10-8) and in the race-combined analysis (P = 1.5 × 10-9) using the normotensive control group (rs12046278, odds ratio = 0.71 (95% confidence interval: 0.6-0.8)). Single-nucleotide polymorphisms in this locus were robustly replicated in the Million Veterans Program (MVP) study in consideration of a treatment-responsive control group. There were no statistically significant findings for the discovery analyses including treatment-responsive controls.

CONCLUSION

This genomic discovery effort for aTRH identified CASZ1 as an aTRH risk locus.

Loss of resources and demoralization in the chronically ill

OBJECTIVE

The study examined whether the association between the severity of physical symptoms and demoralization is mediated by loss of resources in individuals with chronic conditions including conventional diagnoses, functional somatic syndromes, and medically unexplained symptoms.

METHOD

This cross-sectional study evaluated N = 194 patients (mean age = 46, 83.5% female) who reported at least 3 months of persistent physical symptoms using the following self-report instruments: PHQ-15 (modified), Loss of Resources Inventory, Psychosocial Questionnaire - Demoralization Subscale, and PHQ-8. The mediation hypothesis was tested by multiple regression analyses controlling for age, race, employment status, income, educational attainment, and depression.

RESULTS

Participants experienced M = 9.3 out of 16 possible health-related losses (SD = 4.4). Average to severe demoralization scores were indicated by 59.1% of individuals, of which only 17.1% experienced high demoralization. Loss of resources fully mediated the effect of symptom severity on demoralization, explaining 56% of the variance of demoralization and inhibiting the initially significant effect of symptom severity on demoralization to nonsignificant levels [from b = 0.67, 95% CI (0.26, 1.07) to b = 0.03, 95% CI (-0.27, 0.32)].

CONCLUSION

Early recognition of the loss of resources phenomena and interventions to reduce its progression through the introduction of resource gains may diminish, or even prevent, the installation of demoralization in individuals with chronic symptoms.

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

A fully adjusted two-stage procedure for rank-normalization in genetic association studies

When testing genotype-phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. Because genotypes are expected to have small effects (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice, no further adjustment is done in the second stage. Here, we show that this widely used approach can lead to tests with undesirable statistical properties, due to both combination of a mis-specified mean-variance relationship and remaining covariate associations between the rank-normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome-wide and whole-genome sequencing association studies. We further propose and evaluate an alternative fully adjusted two-stage approach that adjusts for covariates both when residuals are obtained and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.

Harmonization of Respiratory Data From 9 US Population-Based Cohorts: The NHLBI Pooled Cohorts Study

Chronic lower respiratory diseases (CLRDs) are the fourth leading cause of death in the United States. To support investigations into CLRD risk determinants and new approaches to primary prevention, we aimed to harmonize and pool respiratory data from US general population-based cohorts. Data were obtained from prospective cohorts that performed prebronchodilator spirometry and were harmonized following 2005 ATS/ERS standards. In cohorts conducting follow-up for noncardiovascular events, CLRD events were defined as hospitalizations/deaths adjudicated as CLRD-related or assigned relevant administrative codes. Coding and variable names were applied uniformly. The pooled sample included 65,251 adults in 9 cohorts followed-up for CLRD-related mortality over 653,380 person-years during 1983-2016. Average baseline age was 52 years; 56% were female; 49% were never-smokers; and racial/ethnic composition was 44% white, 22% black, 28% Hispanic/Latino, and 5% American Indian. Over 96% had complete data on smoking, clinical CLRD diagnoses, and dyspnea. After excluding invalid spirometry examinations (13%), there were 105,696 valid examinations (median, 2 per participant). Of 29,351 participants followed for CLRD hospitalizations, median follow-up was 14 years; only 5% were lost to follow-up at 10 years. The NHLBI Pooled Cohorts Study provides a harmonization standard applied to a large, US population-based sample that may be used to advance epidemiologic research on CLRD.

Device-measured physical activity data for classification of patients with ventricular arrhythmia events: A pilot investigation

Low levels of physical activity are associated with increased mortality risk, especially in cardiac patients, but most studies are based on self-report. Cardiac implantable electronic devices (CIEDs) offer an opportunity to collect data for longer periods of time. However, there is limited agreement on the best approaches for quantification of activity measures due to the time series nature of the data. We examined physical activity time series data from 235 subjects with CIEDs and at least 365 days of uninterrupted measures. Summary statistics for raw daily physical activity (minutes/day), including statistical moments (e.g., mean, standard deviation, skewness, kurtosis), time series regression coefficients, frequency domain components, and forecasted predicted values, were calculated for each individual, and used to predict occurrence of ventricular tachycardia (VT) events as recorded by the device. In unsupervised analyses using principal component analysis, we found that while certain features tended to cluster near each other, most provided a reasonable spread across activity space without a large degree of redundancy. In supervised analyses, we found several features that were associated with the outcome (P < 0.05) in univariable and multivariable approaches, but few were consistent across models. Using a machine-learning approach in which the data was split into training and testing sets, and models ranging in complexity from simple univariable logistic regression to ensemble decision trees were fit, there was no improvement in classification of risk over naïve methods for any approach. Although standard approaches identified summary features of physical activity data that were correlated with risk of VT, machine-learning approaches found that none of these features provided an improvement in classification. Future studies are needed to explore and validate methods for feature extraction and machine learning in classification of VT risk based on device-measured activity.

KCNJ11 variants and their effect on the association between serum potassium and diabetes risk in the Atherosclerosis Risk in Communities (ARIC) Study and Jackson Heart Study (JHS) cohorts

BACKGROUND

In the Atherosclerosis Risk in Communities (ARIC) Study and Jackson Heart Study (JHS) cohorts, serum potassium (K) is an independent predictor of diabetes risk, particularly among African-American participants. Experimental studies show that serum K levels affects insulin secretion. The KCNJ11 gene encodes for a K channel that regulates insulin secretion and whose function is affected by serum K levels. Variants in KCNJ11 are associated with increased diabetes risk. We hypothesized that there could be a gene-by-environment interaction between KCNJ11 variation and serum K on diabetes risk.

METHODS

Evaluating a combined cohort of ARIC and JHS participants, we sought to determine if KCNJ11 variants are risk factors for diabetes; and if KCNJ11 variants modify the association between serum K and diabetes risk. Among participants without diabetes at baseline, we performed multivariable logistic regression to determine the effect of serum K, KCNJ11 variants, and their interactions on the odds of incident diabetes mellitus over 8-9 years in the entire cohort and by race.

RESULTS

Of 11,812 participants, 3220 (27%) participants developed diabetes. 48% and 47% had 1 or 2 diabetes risk alleles of rs5215 and rs5219, respectively. Caucasians had higher proportions of these risk alleles compared to African Americans (60% vs 17% for rs5215 and 60% vs 13% for rs5219, p<0.01). Serum K was a significant independent predictor of incident diabetes. Neither rs5215 nor rs5219 was associated with incident diabetes. In multivariable models, we found no statistically significant interactions between race and either rs5215 or rs5219 (P-values 0.493 and 0.496, respectively); nor between serum K and either rs5215 or rs5219 on odds of incident diabetes (P-values 0.534 and 0.687, respectively).

CONCLUSION

In this cohort, rs5215 and rs5219 of KCNJ11 were not significant predictors of incident diabetes nor effect modifiers of the association between serum K and incident diabetes.

Multiethnic meta-analysis identifies ancestry-specific and cross-ancestry loci for pulmonary function

Nearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European (N = 60,552), African (N = 8429), Asian (N = 9959), and Hispanic/Latino (N = 11,775) ethnicities. We identify over 50 additional loci at genome-wide significance in ancestry-specific or multiethnic meta-analyses. Using recent fine-mapping methods incorporating functional annotation, gene expression, and differences in linkage disequilibrium between ethnicities, we further shed light on potential causal variants and genes at known and newly identified loci. Several of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12. Our study highlights the utility of multiethnic and integrative genomics approaches to extend existing knowledge of the genetics of lung function and clinical relevance of implicated loci.

Factors increasing the risk for a severe reaction in anaphylaxis: An analysis of data from The European Anaphylaxis Registry

BACKGROUND

Preventive measures to decrease the frequency and intensity of anaphylactic events are essential to provide optimal care for allergic patients. Aggravating factors may trigger or increase the severity of anaphylaxis and therefore need to be recognized and avoided.

OBJECTIVE

To identify and prioritize factors associated with an increased risk of developing severe anaphylaxis.

METHODS

Data from the Anaphylaxis Registry (122 centers in 11 European countries) were used in logistic regression models considering existing severity grading systems, elicitors, and symptoms to identify the relative risk of factors on the severity of anaphylaxis.

RESULTS

We identified higher age and concomitant mastocytosis (OR: 3.1, CI: 2.6-3.7) as the most important predictors for an increased risk of severe anaphylaxis. Vigorous physical exercise (OR: 1.5, CI: 1.3-1.7), male sex (OR: 1.2, CI: 1.1-1.3), and psychological burden (OR: 1.4, CI: 1.2-1.6) were more often associated with severe reactions. Additionally, intake of beta-blockers (OR: 1.9, CI: 1.5-2.2) and ACE-I (OR: 1.28, CI: 1.05, 1.51) in temporal proximity to allergen exposition was identified as an important factor in logistic regression analysis.

CONCLUSION

Our data suggest it may be possible to identify patients who require intensified preventive measures due to their relatively higher risk for severe anaphylaxis by considering endogenous and exogenous factors.

Type 2 Diabetes Genetic Risk Scores Are Associated With Increased Type 2 Diabetes Risk Among African Americans by Cardiometabolic Status

The relationship between genetic risk variants associated with glucose homeostasis and type 2 diabetes risk has yet to be fully explored in African American populations. We pooled data from 4 prospective studies including 4622 African Americans to assess whether β-cell dysfunction (BCD) and/or insulin resistance (IR) genetic variants were associated with increased type 2 diabetes risk. The BCD genetic risk score (GRS) and combined BCD/IR GRS were significantly associated with increased type 2 diabetes risk. In cardiometabolic-stratified models, the BCD and IR GRS were associated with increased type 2 diabetes risk among 5 cardiometabolic strata: 3 clinically healthy strata and 2 clinically unhealthy strata. Genetic risk scores related to BCD and IR were associated with increased risk of type 2 diabetes in African Americans. Notably, the GRSs were significant predictors of type 2 diabetes among individuals in clinically normal ranges of cardiometabolic traits.