Search

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB(50k)) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB(50k), demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB(50k) analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

Background: Cigarette smoking is associated with increased head and neck cancer (HNC) risk. Tobacco-related carcinogens are known to cause bulky DNA adducts. Nucleotide excision repair (NER) genes encode enzymes that remove adducts and may be independently associated with HNC, as well as modifiers of the association between smoking and HNC.Methods: Using population-based case–control data from the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study (1,227 cases and 1,325 controls), race-stratified (White, African American), conventional, and hierarchical logistic regression models were used to estimate ORs with 95% intervals (I) for the independent and joint effects of cigarette smoking and 84 single-nucleotide polymorphisms (SNP) from 15 NER genes on HNC risk.Results: The odds of HNC were elevated among ever cigarette smokers and increased with smoking duration and frequency. Among Whites, rs4150403 on ERCC3 was associated with increased HNC odds (AA+AG vs. GG; OR, 1.28; 95% CI, 1.01–1.61). Among African Americans, rs4253132 on ERCC6 was associated with decreased HNC odds (CC+CT vs. TT; OR, 0.62; 95% CI, 0.45–0.86). Interactions between ever cigarette smoking and three SNPs (rs4253132 on ERCC6, rs2291120 on DDB2, and rs744154 on ERCC4) suggested possible departures from additivity among Whites.Conclusions: We did not find associations between some previously studied NER variants and HNC. We did identify new associations between two SNPs and HNC and three suggestive cigarette–SNP interactions to consider in future studies.Impact: We conducted one of the most comprehensive evaluations of NER variants, identifying a few SNPs from biologically plausible candidate genes associated with HNC and possibly interacting with cigarette smoking. Cancer Epidemiol Biomarkers Prev; 22(8); 1428–45. ©2013 AACR.

PDF file - 195K, Table 1S. Single Nucleotide Polymorphisms (SNPs) in Nucleotide Excision Repair (NER) Genes Inclusion and Exclusion Criteria, Carolina Head and Neck Cancer Epidemiology (CHANCE) Study.: Table 2S. Odds Ratios for Environmental Tobacco Smoke and Head and Neck Cancer in the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study.: Table 3S. Odds Ratios for Single Nucleotide Polymorphisms (SNPs) in ERCC3 and Head and Neck Cancer Tumor Sites Using Hierarchical Regression, the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study, Whites.: Table 4S. Odds Ratios for Single Nucleotide Polymorphisms (SNPs) in ERCC6 Genes and Laryngeal Cancer Using Hierarchical Regression, the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study, African Americans.: Table 5S. Odds Ratios for Single Nucleotide Polymorphisms (SNPs) in Nucleotide Excision Repair (NER) Genes and Head and Neck Cancer Using Conventional Logistic Regression, the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study, Whites.: Table 6S. Odds Ratios for Single Nucleotide Polymorphisms (SNPs) in Nucleotide Excision Repair (NER) Genes and Head and Neck Cancer Using Conventional Logistic Regression, the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study, African Americans.: Table 7S. Odds Ratios and Relative Excess Risk Due to Interaction Estimates for Joint Effects of Single Nucleotide Polymorphisms (SNPs) in Nucleotide Excision Repair (NER) Genes and Former/Current Cigarette Smoking on Head and Neck Cancer Risk Using Conventional Logistic Regression, the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study, Whites.: Table 8S. Odds Ratios and Relative Excess Risk Due to Interaction Estimates for Joint Effects of Single Nucleotide Polymorphisms (SNPs) in Nucleotide Excision Repair (NER) Genes and Ever Environmental Tobacco Smoke on Head and Neck Cancer Risk Using Conventional Logistic Regression, the Carolina Head and Neck Cancer Epidemiology (CHANCE) Study, Whites.

Introduction: The aromatic amino acids tyrosine and phenylalanine may increase type 2 diabetes risk, but few studies have examined whether these critical molecular precursors exert additional phenotypic effects and whether these effects are independent of insulin resistance. We evaluated associations between two aromatic amino acids - tyrosine and phenylalanine - with a broad range of phenotypic categories in the UK Biobank using polygenic risk score (PRS) instrumental variables that are robust to confounding and reverse causation and tested for mediation by glycated hemoglobin (HbA1c) as a measure of insulin resistance. Hypothesis: We hypothesized that tyrosine and phenylalanine would show broad phenotypic effects and that many of these effects would be mediated by HbA1c. Methods: We constructed PRS (Crosspred) using all nominally significant and common (minor allele frequency >5%) variants from genome-wide association studies (SAIGE) of unrelated European ancestry participants in the UK Biobank. We evaluated associations with 273 curated phenotypes spanning 20 categories, including endocrine, circulatory, and cancer related traits. We corrected for multiple comparisons using false discovery rate Results: A total of 108,554 participants had tyrosine or phenylalanine measured at baseline (mean age=57 years; 54% (58,880/108,554) female). PRS were predictive of tyrosine (R 2 =0.28) and phenylalanine (R 2 =0.26). Tyrosine and phenylalanine PRS showed significant associations with 121/273 (44%) and 124/273 (45%) of phenotypes, respectively, including chronic kidney disease, serum testosterone, lymphocyte count, and myocardial infarction (MI; n=3,349 cases, mean years of follow-up=11). For incident MI, every one standard deviation increase in the tyrosine PRS increased the odds of MI by 4.4% (total effect odds ratio (OR) = 1.044 (95% confidence interval (CI): 1.002, 1.089). The direct effect of the tyrosine PRS on incident MI was slightly decreased (OR= 1.040 (95% CI: 0.997, 1.084) when extending the statistical model to examine mediation by HbA1c, with an estimated indirect effect of 1.004 (95% CI: 1.003, 1.006) and an estimated percent mediated by HbA1c of 9.95% (95% CI: -0.04%, 19.90%). Findings were consistent for phenylalanine, where the percent of the total effect mediated by HbA1c was 8.94% (95% CI: -1.93, 19.80%). Conclusions: The effect of tyrosine and phenylalanine on incident MI may primarily operate through pathways other than glucose dysregulation.

Objective: There is known heterogeneity in the relationship between diet quality and cardiovascular disease by self-reported race and sex, which may underlie inequities in cardiovascular health. Methods: We used data from 2,832 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study (2005-06, aged 37-55, 57% women, 45% Black). Using fasted blood samples, we obtained ultra-high-performance liquid chromatography high resolution mass spectrometry untargeted metabolomics and derived an a priori diet quality score using a validated diet history questionnaire, based on classification of 46 food groups with potential beneficial (n=20), adverse (n=13), or neutral (n=13) implications for cardiovascular health. We tested effect modification of associations between metabolites and diet quality by race and sex, separately, using multivariable-adjusted linear regression for 7,522 metabolite peaks, accounting for multiple comparisons, adjusted for batch, field center, demographics, lifestyle behaviors, total energy intake, hypertension and diabetes status, medication use, and BMI. We also used race- and sex-stratified multivariate Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) analyses to examine how metabolites distinguish by diet quality 1 st and 4 th quartiles. We identified differing metabolite pathways by race and sex through biochemical pathway analysis (Mummichog version 1.0.10) from regression and OPLS-DA models. Results: In linear regression, race significantly modified metabolite-diet quality associations for 231 metabolite peaks; sex modified associations for 1 peak (FDR Conclusions: Metabolite pathways were statistically different by self-reported race, and to a lesser extent, by sex. Differences in metabolite-diet quality associations may reflect differences in contextual or social variables, and potentially, heterogeneity in metabolism.

Introduction: Associations of nonalcoholic fatty liver disease (NAFLD) with dementia are controversial. The full spectrum of NAFLD from simple steatosis to fibrosis has been less investigated. Hypothesis: The severity of NAFLD, especially with the stage of liver fibrosis, is associated with dementia. Methods: The associations between midlife (1990-92, N=9283, mean age 57, female 55%) and late-life (2011-13, N=5138, mean age 75, female 58%) NAFLD and all cause dementia were quantified by Cox regression. NAFLD was characterized using the fatty liver index and liver fibrosis assessment model (FIB-4). Dementia was adjudicated and ascertained through Dec. 31, 2019 from neuropsychological assessments, annual participant or informant contact, and medical record surveillance. We used Cox models with adjustment for demographics, APOE ε4, alcohol, and kidney function. Additional adjustments were made for metabolic factors to explore the independent contribution of NAFLD to dementia. Results: During a median follow-up of 24.5 years after midlife NAFLD assessment, 1854 participants developed dementia. During a median of 6.3 years in late-life, there were 893 dementia cases. A graded increase in dementia risk across the spectrum of NAFLD was observed at midlife (p-trend Figure ). In fact, NAFLD in late-life tended to be protective against dementia. After adjusting for the metabolic factors, the associations remained statistically significant in some categories. Conclusions: Remodeling of hepatic architecture and dysregulation in hepatocellular function in NAFLD at midlife plausibly induce peripheral promoters of neurodegeneration and may expose a large segment of the population to increased risk of dementia. The inverse association of NAFLD with dementia at late-life requires further investigation, possibly reflecting the depletion of susceptibles, the reversibility of NAFLD due to weight loss, and the lack of accuracy in identifying NAFLD using clinical prediction models at older age.

Background: While obesity is associated with hypertension, there is heterogeneity in risk by weight status and we know little about whether and which metabolic pathways play a role in exacerbating differential risk across weight status categories. Methods: Fasting blood was collected at the Yr. 20 follow-up exam in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. We conducted Ultra-High-Performance Liquid Chromatography high resolution mass spectrometry untargeted metabolomics in 1,769 individuals (ages 37-54, 58% female, 66% Black) classified as normotensive (no hypertension diagnosis or medication and SBP/DBP ≤ 130/80). We identified 7,522 metabolomic peaks and 8 principal components, which we included in a multivariable-adjusted proportional hazards model with an interaction term to assess whether the association between these metabolite measures and incident hypertension (HTN) varied by BMI. We then employed multivariate analysis using Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) models using 70% of the sample within each obesity category to predict incident HTN at ten years in normal weight, overweight and obese groups based on the high dimensional metabolomics data. The remaining 30% of individuals in each obesity category were then used to evaluate the predictive value of the OPLS-DA models. Finally, we used mummichog software and Fisher’s exact tests, to conduct pathway enrichment analysis using data from both the individual peak and the OPLS-DA models. Results: We identified 723 metabolites where the association with long term risk of HTN differed by BMI, although none were statistically significant after adjustment for multiple comparisons. However, we found strong evidence of effect modification for metabolites via principal components and OPLS-DA. For the 3 rd principal component, which explained 5% of the variation across all peaks, we found a significant interaction with BMI (p-value = 0.0043). In external test data sets, OPLS-DA models predicted incident HTN relatively well in normal weight individuals (AUC of 77%) but not well in overweight and obese individuals (AUC of 49% and 56% respectively). We identified several pathways with a differential HTN risk by BMI shown in the OPLS-DA and proportional hazards models. In the phenylamine pathway there was substantial differentiation between those with and without incident HTN risk for normal weight only and across the lysine degradation pathway only for individuals who were obese. Conclusions: Our findings identify variation in hypertension risk by BMI, with suggestive evidence for a role of metabolomic pathways and combinations of metabolites. Overall, metabolites predicted 10-year incident hypertension very well in individuals of normal weight (but not obese status), indicating heterogeneity in hypertension risk by weight status.

Obesity remains a global public health burden, with 4.7 million premature deaths globally attributed to obesity. Severe obesity (SevO: defined as body mass index (BMI)≥40) is a major risk factor for other comorbidities, including heart disease and Type 2 Diabetes, which disproportionately impact historically marginalized populations, including Hispanic/Latinos. Based on BRFSS data, 24.5% of US Hispanic/Latino adults are projected to have severe obesity by 2030. However, the etiology of the underlying metabolic dysfunction remains unknown. To address this gap, we identified metabolites associated with SevO in genotyped participants aged ≥20 with 25 < BMI ≥ 40 and metabolic data in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We investigated cross-sectional associations between Blom-transformed metabolites and baseline SevO, adjusting for age, study center, background group, smoking status, and principal components of ancestry, stratified by sex (SUGEN), and meta-analyzed (SAS 9.4). For the top 20 metabolites, we extracted publicly available HCHS/SOL metabolite GWAS (mGWAS) summary statistics to derive metaboQTLs, and summary statistics from a multi-population meta-analysis of SevO (N>70,000) and implemented forward MR analysis using the MendelianRandomization R package (v0.3.0), which provides various robust causal estimation methods for summary data. Anthropometry and data for 640 known Metabolon metabolites were available for 551 females (mean age: 43.3 years, 27% SevO) and 371 males (mean age 43.4 years, 15% SevO). We identified Bonferroni-corrected significant SevO associations (p Cytidine is a pyrimidine nucleoside consisting of D-ribose and cytosine, which is a precursor of cytidine triphosphate required in the one-carbon metabolism pathway to convert phosphatidylcholine (PC) to phosphatidylethanolamine (PE). PC biosynthesis is higher in adipose tissue macrophages in obese mice and humans. Indoleproprionate is a microbial metabolite of tryptophan produced by gut bacteria. Indoleproprionate levels have been shown to be associated with higher microbiome diversity and lower incidence of T2D. Our work points to future efforts to validate findings in other cohorts, including reverse MR to further elucidate the causal relationship between metabolites and severe obesity.

Groups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks using IBD mapping. Clustering algorithms play an important role in finding these groups accurately and at scale. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare the statistical power of clustering algorithms via simulating 2.3 million clusters across 850 experiments. We found Infomap and Markov Clustering (MCL) community detection methods to have high statistical power in most of the scenarios. They yield a 30% increase in power compared to the current state-of-art approach, with a 3 orders of magnitude lower runtime. We also found that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications. We extend our findings to real datasets by analyzing the Population Architecture using Genomics and Epidemiology (PAGE) Study dataset with 51,000 samples and 2 million shared segments on Chromosome 1, resulting in the extraction of 39 million local IBD clusters. We demonstrate the power of our approach by recovering signals of rare genetic variation in the Whole-Exome Sequence data of 200,000 individuals in the UK Biobank. We provide an efficient implementation to enable clustering at scale for IBD mapping for various populations and scenarios.Supplementary Information: The code, along with supplementary methods and figures are available at https://github.com/roohy/localIBDClustering.

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).

Background Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. Results We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. Conclusions Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Animal studies have revealed gut microbial and metabolic pathways of blood pressure (BP) regulation, yet few epidemiological studies have collected microbiota and metabolomics data in the same individuals. In a population-based, Chinese cohort who did not report antihypertension medication use (30–69 years, 54% women), thus minimizing BP treatment effects, we examined multivariable-adjusted (eg, diet, physical activity, smoking, kidney function), cross-sectional associations between measures of gut microbiota (16S rRNA [ribosomal ribonucleic acid], N=1003), and plasma metabolome (liquid chromatography-mass spectrometry, N=434) with systolic (SBP, mean [SD]=126.0 [17.4] mm Hg) and diastolic BP (DBP [80.7 (10.7) mm Hg]). We found that the overall microbial community assessed by principal coordinate analysis varied by SBP and DBP (permutational multivariate ANOVA P P P

Background: There is increasing evidence that sodium consumption alters the gut microbiota and host metabolome in murine models and small studies in humans. However, there is a lack of population-based studies that capture large variations in sodium consumption as well as potassium consumption. Objective: We examined the associations of energy-adjusted dietary sodium (milligrams/kilocalorie), potassium, and sodium-to-potassium (Na/K) ratio with the microbiota and plasma metabolome in a well-characterized Chinese cohort with habitual excessive sodium and deficient potassium consumption. Methods: We estimated dietary intakes from 3 consecutive validated 24-h recalls and household inventories. In 2833 adults (18-80 y old, 51.2% females), we analyzed microbial (genus-level 16S ribosomal RNA) between-person diversity, using distance-based redundancy analysis (dbRDA), and within-person diversity and taxa abundance using linear regression, accounting for geographic variation in both. In a subsample (n = 392), we analyzed the overall metabolome (dbRDA) and individual metabolites (linear regression). P values for specific taxa and metabolites were false discovery rate adjusted (q-value). Results: Sodium, potassium, and Na/K ratio were associated with microbial between-person diversity (dbRDA P < 0.01) and several specific taxa with large geographic variation, including pathogenic Staphylococcus and Moraxellaceae, and SCFA-producing Phascolarctobacterium and Lachnospiraceae (q-value < 0.05). For example, sodium and Na/K ratio were positively associated with Staphylococcus and Moraxellaceae in Liaoning, whereas potassium was positively associated with 2 genera from Lachnospiraceae in Shanghai. Additionally, sodium, potassium, and Na/K ratio were associated with the overall metabolome (dbRDA P ≤ 0.01) and several individual metabolites, including butyrate/isobutyrate and gut-derived phenolics such as 1,2,3-benzenetriol sulfate, which was negatively associated with sodium in Guizhou (q-value < 0.05). Conclusions: Our findings suggest that sodium and potassium consumption is associated with taxa and metabolites that have been implicated in cardiometabolic health, providing insights into the potential roles of gut microbiota and host metabolites in the pathogenesis of sodium- and potassium-associated diseases. More studies are needed to confirm our results.

Introduction: Although thiazide diuretics are common therapies for the treatment of hypertension, evidence suggests that they are also associated with elevated blood lipid concentrations after their initiation. Pharmacogenomics studies could help identify potential biological pathways underlying this phenomenon, but few well-powered studies have been conducted. Methods: In participants of European ancestry from the UK Biobank, we conducted a genome-wide association study (GWAS) to examine whether common variants (minor allele frequency [MAF] >1%) modified the effect of thiazide diuretic use on three lipid measures: low density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) concentrations (mmol/L). TG concentrations were natural log transformed, and LDL-C concentrations were adjusted for statin use. Given the conflicting evidence about the impact of loop diuretics on blood lipid concentrations, unexposed participants were defined as those taking neither thiazides nor loop diuretics. GWAS of UK10K plus 1000-Genomes Phase 3 imputed data were performed using SAIGE, adjusting for age, sex, fasting status, body mass index, study center, and the first 10 ancestral principal components. Results: Approximately 7.1% (n=27,971) of participants (n=395,058; mean age=57 years; 54% female [n=214,334]) used thiazides at study baseline. The thiazide-LDL-C GWAS (n=9,680,686 variants) identified one chromosome 19 locus harboring several genome-wide significant variants that modified the effect of thiazide diuretic use on LDL-C. The lead variant rs2199576 (MAF = 0.18) had an effect estimate (standard error) of 0.159 (0.013) among participants exposed to thiazides, and an effect estimate of 0.083 (0.003) among participants unexposed to any diuretic, yielding a significant variant-by-thiazide interaction effect estimate of 0.076 (0.013) ( p = 1.62 x 10 -9 ). These effect estimates are comparable to previously identified main effects of blood lipids loci. The rs2199576 variant resides near the PVRL2 locus, a cholesterol-responsive gene that is located within 100 kb of TOMM40 and the APO cluster, which have both been associated with blood lipid concentrations and Alzheimer’s disease. Conclusion: Our results may provide insights into pathways that influence the adverse effects of thiazide diuretic use and highlight the large sample sizes needed to detect modest pharmacogenetic effects.

Introduction: Despite decades of research linking obesity with cardiovascular disease (CVD) and its risk factors, major research gaps remain, with few studies evaluating the often-described but poorly understood heterogeneity in CVD risk observed within obese populations. One plausible, but largely unexplored source of heterogeneity in obesity-associated CVD risk is the impact of body fat distribution (central versus overall obesity), obesity duration and severity. Longitudinally assessed anthropometric measures and GWAS derived from these exposures may improve understanding of functional properties of loci associated with obesity and downstream influences on CVD. Methods: We respond to these research gaps by using 25 years of Atherosclerosis Risk in Communities (N=14,514, mean baseline age=54 years; 55% Female; 25.73% African-American) data and latent class mixed models to derive four longitudinal measures of obesity distribution, duration, and severity, operationalized as obesity subtypes. Genome wide association studies on the subclasses were conducted using logistic multi-variate regression models to test for associations between variants and BMI. Meta-analyses across race and sex were conducted in METAL. Functional properties of single nucleotide polymorphisms (SNP) were assessed in FUMA. Results: The four obesity subclasses were: decline (4.1%), stable/slow decline (67.8%) [referent], moderate increase (24.6%), and rapid increase (3.6%). The lead SNP from the decline group was known, on chromosome 7 (rs1196508, beta (SE) = -0.44 (0.089), p = 6.84x10 -7 , minor allele frequency (MAF) = 0.36), and nearest to a tyrosine phosphatase receptor, PTPRZ1 , in the central nervous system. We combined moderate and rapid increase for analyses; they shared a common top, unknown, SNP on chromosome 5 (rs76956550, beta (SE) = -0.37 (0.074), MAF=0.12), nearest to PGAM5P1 . The top SNP within the rapid increase group was unknown, on chromosome 14 (rs580888, beta(SE) = -0.57 (0.11), MAF=0.35), and nearest to the OTX2 gene, a transcription factor involved in production of dopaminergic neurons. Mutations of this gene are associated with pituitary hormone deficiency. Conclusion: Our GWAS of obesity subclasses demonstrated unique genetic and functional properties between groups, supporting the contention that CVD risk may differ across obese individuals. We intend to replicate our novel association of OTX2 with BMI, waist-to-hip ratio, and waist circumference in people who rapidly gained weight in other large longitudinal cohorts.

Introduction: Experimental and observational evidence suggests that aberrant branched chain amino acid (BCAA; leucine, isoleucine, and valine) metabolism promotes insulin resistance and downstream disease, prompting interest in BCAA catabolism as a treatment target. Most published research has evaluated the three BCAAs in combination, although each BCAA may harbor distinct phenotypic effects, as valine is glucogenic, leucine is ketogenic, and isoleucine is glucogenic and ketogenic. Methods: Because studies examining individual phenotypic effects of BCAAs that account for the effects of other BCAAs have been limited by high correlation between BCAAs and strong confounding by dietary patterns and shared genetic loci, we applied a novel causal inference method developed to address these limitations: multivariable Mendelian randomization (MVMR). In a study of UK Biobank European ancestry participants, we examined 1,468 phenotypes that included metabolic, cardiovascular, cancer, cognitive, hematopoietic, and musculoskeletal domains. Polygenic risk score (PRS) instrumental variables were constructed using genotypic and circulating BCAA phenotypic data in European ancestry UK Biobank participants and the Crosspred method was used to correct for overfitting. Significant independent causal effects were defined as estimates with P- values< 1.1x10 -5 [0.05/(1,468 phenotypes*3 BCAAs)]. Results: The n=97,469 participants with measured BCAAs were on average middle aged (mean age=57), female (54%), and overweight [mean body mass index (BMI)=27]. Each BCAA PRS strongly predicted its circulating concentrations ( P- value-100 ). MVMR methods did not identify any phenotypes for which all three BCAAs showed evidence of a directionally consistent and significant independent causal effect. However, there were numerous instances where BCAAs harbored independent and directionally inconsistent statistically significant effects. For example, significant positive causal effects for valine independent of isoleucine and leucine and significant negative causal effects for leucine independent of valine and isoleucine were identified for BMI, glycated hemoglobin, and type 2 diabetes; no significant independent causal effects for isoleucine with BMI, glycated hemoglobin, and type 2 diabetes were identified. In contrast, models without control for the effects of other BCAAs suggested that isoleucine, leucine and valine each had significant positive causal effects on BMI, glycated hemoglobin, and type 2 diabetes. When examining other phenotypes using MVMR, heterogeneity in the effects of individual BCAAs was detected for cardiovascular and cognitive phenotypes, among others. Conclusion: BCAAs may harbor distinct and opposing effects on metabolism, health, and disease, motivating further studies that examine the independent effects of these essential amino acids.

Introduction: Modifiable risk factors (including cardiovascular traits) are associated with dementia risk. However, it is unknown which risk factors have the greatest influence on dementia rates in the United States, or how that varies by race/ethnicity. Objective: For all Americans and separately by race and ethnicity, we calculated population attributable fractions (PAFs) of dementia for 12 potentially modifiable risk factors: low education, hearing loss, traumatic brain injury, hypertension, excessive alcohol consumption, obesity, smoking, depression, social isolation, physical inactivity, diabetes, and air pollution. Methods: Assuming a causal relationship between risk factors and dementia, we calculated PAFs for dementia using relative risks and prevalence estimates for each risk factor. We used relative risks from recent meta-analyses and prevalence estimates from nationally representative cross-sectional survey data (e.g., NHANES). We estimated prevalence separately by race/ethnicity. We calculated the PAF (for all Americans and separately by race and ethnicity) for each dementia risk factor and a combined PAF accounting for correlations between exposures. Results: In the total population, 42.6% of dementia cases were attributable to the 12 risk factors we considered. The estimated PAF of these risk factors was greater for Black and Hispanic Americans (47.5% and 47.3%, respectively) than it was for White and Asian Americans (41.3% and 37.8%, respectively). The greatest fractions of dementia cases were attributable to key cardiovascular traits: hypertension (20.2%), obesity (20.9%), and physical inactivity (20.1%). These factors were highest within each racial and ethnic group, though the proportions varied (Figure 1). Conclusions: A large fraction of dementia cases are attributable to potentially modifiable factors, especially for Black and Hispanic Americans. Further research is needed to test whether intervening on these factors reduces dementia risk.

Summary Despite a lifetime prevalence of at least 5%, developmental stuttering, characterized by prolongations, blocks, and repetitions of speech sounds, remains a largely idiopathic speech disorder. Family, twin, and segregation studies overwhelmingly support a strong genetic influence on stuttering risk; however, its complex mode of inheritance combined with thus-far underpowered genetic studies contribute to the challenge of identifying and reproducing genes implicated in developmental stuttering susceptibility. We conducted a trans-ancestry genome-wide association study (GWAS) and meta-analysis of developmental stuttering in two primary datasets: The International Stuttering Project comprising 1,345 clinically ascertained cases from multiple global sites and 6,759 matched population controls from the biobank at Vanderbilt University Medical Center (VUMC), and 785 self-reported stuttering cases and 7,572 controls ascertained from The National Longitudinal Study of Adolescent to Adult Health (Add Health). Meta-analysis of these genome-wide association studies identified a genome-wide significant (GWS) signal for clinically reported developmental stuttering in the general population: a protective variant in the intronic or genic upstream region of SSUH2 (rs113284510, protective allele frequency = 7.49%, Z = −5.576, p = 2.46 × 10−8) that acts as an expression quantitative trait locus (eQTL) in esophagus-muscularis tissue by reducing its gene expression. In addition, we identified 15 loci reaching suggestive significance (p < 5 × 10−6). This foundational population-based genetic study of a common speech disorder reports the findings of a clinically ascertained study of developmental stuttering and highlights the need for further research., Despite overwhelming evidence in support of strong genetic influence, the genetic etiology of developmental stuttering has remained elusive. This study reveals genome-wide significant and suggestively significant signals for clinically ascertained stuttering cases in the general population, laying essential groundwork for further research into identification of common stuttering susceptibility variants.

Mitigating age-related disease and disability presents challenges. Physical activity (PA) may be influential for prolonging health and functioning, warranting characterization of its patterns over the life course in population-based data. With the availability of up to three self-reported assessments of past year leisure-time PA (LTPA) over multiple decades in 15,036 participants (26% African American; 55% women; mean baseline age=54; median follow-up=23 years) from the Atherosclerosis Risk in Communities (ARIC) Study sampled from four U.S. communities, race-sex-stratified trajectories of average weekly intensity (metabolic equivalent of task (MET)), duration (hours), and energy expenditure or volume (MET-h) of LTPA were developed from age 45 to 90 using joint models to accommodate expected non-ignorable attrition. Declines in weekly LTPA intensity, duration, and volume from age 70 to 90 were observed in white women (2.9 to 1.2 MET; 2.5 to 0.6 h; 11.1 to 2.6 MET-h), white men (2.5 to 1.0 MET; 3.5 to 1.8 h; 15.5 to 6.4 MET-h), African American women (2.5 to 2.4 MET; 0.8 to 0.1 h; 6.7 to 6.0 MET-h), and African American men (2.3 to 1.4 MET; 1.5 to 0.6 h; 8.0 to 2.3 MET-h). These data reveal population-wide shifts towards less active lifestyles in older adulthood.

Background Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. Results We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. Conclusion This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Objective This study aimed to understand how an increase in abdominal adiposity relative to overall adiposity is associated with blood pressure (BP) change. Methods A sex-stratified mixed linear model was used to examine the association (95% CI) between annual changes in waist circumference (WC) and systolic blood pressure and diastolic blood pressure, estimated from two to eight repeated measures across the 1993-2015 China Health and Nutrition Survey, among 5,742 men and 5,972 women (18-66 years) with no history of antihypertension medication use. Results The association between annual WC change and BP change remained statistically significant but was attenuated after controlling for annual BMI change, regardless of baseline abdominal obesity or overweight status. Each 10-cm annual WC gain in men and women was associated with a 0.98-mm Hg (95% CI: 0.61-1.35) and a 0.97-mm Hg (95% CI: 0.62-1.32) annual increase in systolic blood pressure and a 1.13-mm Hg (95% CI: 0.87-1.38) and a 0.74-mm Hg (95% CI: 0.51-0.97) annual increase in diastolic blood pressure, respectively, independent of annual BMI change. Conclusions WC gain may elevate BP even in the absence of BMI gain. BP management that addresses only BMI gain could overlook individuals at risk of elevated BP who have increased WC but not BMI.

The first phase of Chile's Law of Food Labelling and Advertising showed important declines in the sugar content of packaged foods, but it is unknown whether the law led to an increase in nonnutritive sweetener (NNS) intake, particularly among preschool children.Estimate the changes in preschoolers' NNS intake after the first phase of the Chilean law.We used 24-h dietary recalls collected in 2016 (pre-law) and 2017 (post-law) from a cohort of preschoolers (n = 875). The primary caretaker was the respondent of the recalls. Information on NNS was obtained from nutrition facts panels collected annually and linked to dietary data. We used logistic regression to estimate the changes in the proportion of preschoolers who consume NNS and two-part models to estimate the changes in mean intake. We determined the percentage of children that surpassed the acceptable daily intake (ADI) of each NNS using the National Cancer Institute method.The proportion of consumers of at least one NNS increased from 77.9% to 92.0% (p-value 0.01). The mean intake increased for sucralose, aspartame, acesulfame-K and steviol glycosides (+20.3, +15.1, +6.1 and +3.3 mg/day, respectively). In addition, NNS dietary sources changed for sucralose and steviol glycosides, becoming industrialized juices and dairy beverages more relevant while tabletop NNS became less relevant. None of the children surpassed the ADI.NNS intake increased in preschoolers after the first phase of a national policy that promoted sugar reformulation.

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization (QRS duration) and repolarization (JT interval). Abnormalities of the QT interval are associated with potentially fatal ventricular arrhythmia. We conducted genome-wide multi-ancestry analyses in >250,000 individuals and identified 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identified associations with Mendelian disease genes. Enrichments were observed in established pathways for QT and JT, with new genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast, connective tissue components and processes for cell growth and extracellular matrix interactions were significantly enriched for QRS. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlighted potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.

BackgroundGroups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks via a process called IBD mapping. Clustering algorithms play an important role in finding these groups. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare clustering algorithms in terms of statistical power. We also investigated the effectiveness of common clustering metrics as replacements for statistical power.ResultsWe simulated 3.4 million clusters across 850 experiments with varying cluster counts, false-positive, and false-negative rates. Infomap and Markov Clustering (MCL) community detection methods have high statistical power in most of the graphs, compared to greedy methods such as Louvain and Leiden. We demonstrate that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications, though they can help with simulating realistic benchmarks. We extend our findings to real datasets by analyzing 3 populations in the Population Architecture using Genomics and Epidemiology (PAGE) Study with 51,000 members and 2 million shared segments on Chromosome 1, resulting in the extraction of 39 million local IBD clusters across three different populations in PAGE. We used cluster properties derived in PAGE to increase the accuracy of our simulations and comparison.ConclusionsMarkov Clustering produces a 30% increase in statistical power compared to the current state-of-art approach, while reducing runtime by 3 orders of magnitude; making it computationally tractable in modern large-scale genetic datasets. We provide an efficient implementation to enable clustering at scale for IBD mapping and poplation-based linkage for various populations and scenarios.

The ability to identify segments of genomes identical-by-descent (IBD) is a part of standard workflows in both statistical and population genetics. However, traditional methods for finding local IBD across all pairs of individuals scale poorly leading to a lack of adoption in very large-scale datasets. Here, we present iLASH, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals. We apply iLASH to the PAGE dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, identifying IBD segments in ~3 minutes per chromosome compared to over 6 days for a state-of-the-art algorithm. iLASH enables efficient analysis of very large-scale datasets, as we demonstrate by computing IBD across the UK Biobank (~500,000 individuals), detecting 12.9 billion pairwise connections., Traditional methods to identify genomic regions identical-by-descent (IBD) do not scale well to biobank-level datasets. Here, the authors describe a new IBD algorithm, iLASH, which uses LocAlity-Sensitive Hashing to provide rapid IBD estimation when applied to the PAGE and UK Biobank datasets.