Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations.

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

The Polygenic and Monogenic Basis of Blood Traits and Diseases.

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

The Rotterdam Study. Design update and major findings between 2020 and 2024.

The Rotterdam Study is a population-based cohort study, started in 1990 in the district of Ommoord in the city of Rotterdam, the Netherlands, with the aim to describe the prevalence and incidence, unravel the etiology, and identify targets for prediction, prevention or intervention of multifactorial diseases in mid-life and elderly. The study currently includes 17,931 participants (overall response rate 65%), aged 40 years and over, who are examined in-person every 3 to 5 years in a dedicated research facility, and who are followed-up continuously through automated linkage with health care providers, both regionally and nationally. Research within the Rotterdam Study is carried out along two axes. First, research lines are oriented around diseases and clinical conditions, which are reflective of medical specializations. Second, cross-cutting research lines transverse these clinical demarcations allowing for inter- and multidisciplinary research. These research lines generally reflect subdomains within epidemiology. This paper describes recent methodological updates and main findings from each of these research lines. Also, future perspective for coming years highlighted.

Predicting amyloid-beta pathology in the general population.

INTRODUCTION: Reliable models to predict amyloid beta (Aß) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease. METHODS: We developed Aß prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500). RESULTS: The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia, and subjective and objective measures of cognition, walking duration and sleep behavior, was validated in the independent Rotterdam Study with higher accuracy (AUC = 0.85 [0.81-0.89]). Yet, the improvement relative to a model including only age and APOE ε4 was marginal. DISCUSSION: Aß prediction models including inexpensive and non-invasive measures were successfully applied to a general population-derived sample more representative of typical older non-demented adults.

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1-0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.

Design, implementation and initial findings of COVID-19 research in the Rotterdam Study: leveraging existing infrastructure for population-based investigations on an emerging disease.

The Rotterdam Study is an ongoing prospective, population-based cohort study that started in 1989 in the city of Rotterdam, the Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. It focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. In response to the COVID-19 pandemic, a substudy was designed and embedded within the Rotterdam Study. On the 20th of April, 2020, all living non-institutionalized participants of the Rotterdam Study (n = 8732) were invited to participate in this sub-study by filling out a series of questionnaires administered over a period of 8 months. These questionnaires included questions on COVID-19 related symptoms and risk factors, characterization of lifestyle and mental health changes, and determination of health care seeking and health care avoiding behavior during the pandemic. As of May 2021, the questionnaire had been sent out repeatedly for a total of six times with an overall response rate of 76%. This article provides an overview of the rationale, design, and implementation of this sub-study nested within the Rotterdam Study. Finally, initial results on participant characteristics and prevalence of COVID-19 in this community-dwelling population are shown.

Ascertainment of cancer in longitudinal research: The concordance between the Rotterdam Study and the Netherlands Cancer Registry.

Complete and accurate registration of cancer is needed to provide reliable data on cancer incidence and to investigate aetiology. Such data can be derived from national cancer registries, but also from large population-based cohort studies. Yet, the concordance and discordance between these two data sources remain unknown. We evaluated completeness and accuracy of cancer registration by studying the concordance between the population-based Rotterdam Study (RS) and the Netherlands Cancer Registry (NCR) between 1989 and 2012 using the independent case ascertainment method. We compared all incident cancers in participants of the RS (aged ≥45 years) to registered cancers in the NCR in the same persons based on the date of diagnosis and the International Classification of Diseases (ICD) code. In total, 2,977 unique incident cancers among 2,685 persons were registered. Two hundred eighty-eight cancers (9.7%) were coded by the RS that were not present in the NCR. These were mostly nonpathology-confirmed lung and haematological cancers. Furthermore, 116 cancers were coded by the NCR, but not by the RS (3.9%), of which 20.7% were breast cancers. Regarding pathology-confirmed cancer diagnoses, completeness was >95% in both registries. Eighty per cent of the cancers registered in both registries were coded with the same date of diagnosis and ICD code. Of the remaining cancers, 344 (14.5%) were misclassified with regard to date of diagnosis and 72 (3.0%) with regard to ICD code. Our findings indicate that multiple sources on cancer are complementary and should be combined to ensure reliable data on cancer incidence.

Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis.

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium.

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10-6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Objectives, design and main findings until 2020 from the Rotterdam Study.

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.

Mendelian randomization analysis does not support causal associations of birth weight with hypertension risk and blood pressure in adulthood.

Epidemiology studies suggested that low birthweight was associated with a higher risk of hypertension in later life. However, little is known about the causality of such associations. In our study, we evaluated the causal association of low birthweight with adulthood hypertension following a standard analytic protocol using the study-level data of 183,433 participants from 60 studies (CHARGE-BIG consortium), as well as that with blood pressure using publicly available summary-level genome-wide association data from EGG consortium of 153,781 participants, ICBP consortium and UK Biobank cohort together of 757,601 participants. We used seven SNPs as the instrumental variable in the study-level analysis and 47 SNPs in the summary-level analysis. In the study-level analyses, decreased birthweight was associated with a higher risk of hypertension in adults (the odds ratio per 1 standard deviation (SD) lower birthweight, 1.22; 95% CI 1.16 to 1.28), while no association was found between genetically instrumented birthweight and hypertension risk (instrumental odds ratio for causal effect per 1 SD lower birthweight, 0.97; 95% CI 0.68 to 1.41). Such results were consistent with that from the summary-level analyses, where the genetically determined low birthweight was not associated with blood pressure measurements either. One SD lower genetically determined birthweight was not associated with systolic blood pressure (ß = - 0.76, 95% CI - 2.45 to 1.08 mmHg), 0.06 mmHg lower diastolic blood pressure (ß = - 0.06, 95% CI - 0.93 to 0.87 mmHg), or pulse pressure (ß = - 0.65, 95% CI - 1.38 to 0.69 mmHg, all p > 0.05). Our findings suggest that the inverse association of birthweight with hypertension risk from observational studies was not supported by large Mendelian randomization analyses.

Macronutrient intake and frailty: the Rotterdam Study.

PURPOSE: To investigate the longitudinal association between the macronutrient composition of the diet and frailty. METHODS: Data were obtained from 5205 Dutch middle-aged and older adults participating in the Rotterdam Study. Frailty was measured using a frailty index based on the accumulation of 38 health-related deficits, score between 0 and 100, and a higher score indicating more frailty. Frailty was assessed at baseline and 11 years later (range of 23 years). Macronutrient intake was assessed using food-frequency questionnaires. The association between macronutrients and frailty over time was evaluated using multivariable linear regression, adjusted for the frailty index at baseline, energy intake, and other relevant confounders. All analyses were performed in strata of BMI. RESULTS: Median frailty index score was 13.8 points (IQR 9.6; 19.1) at baseline and increased by a median of 2.3 points (IQR - 2.0; 7.6) after 11 years. Overall, we found no significant associations between intake of carbohydrates or fat and frailty over time. We did observe a significant positive association between an iso-energetic intake of 10 g protein and frailty over time (ß 0.31 (95% CI 0.06; 0.55)) which was mainly driven by animal protein (ß 0.31 (95% CI 0.07; 0.56)). It did not depend on whether it was substituted fat or carbohydrates. CONCLUSIONS: Our findings suggest that a reduction in the intake of animal protein may improve the overall health status over time in a relatively healthy population. More research is needed on the optimal macronutrient composition of the diet and frailty in more vulnerable populations.

Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies.

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called 'missing heritability'. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51-62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36-38%). Hence, cross-study heterogeneity contributes to the missing heritability.

Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis.

Circulating blood cell counts and indices are important indicators of hematopoietic function and a number of clinical parameters, such as blood oxygen-carrying capacity, inflammation, and hemostasis. By performing whole-exome sequence association analyses of hematologic quantitative traits in 15,459 community-dwelling individuals, followed by in silico replication in up to 52,024 independent samples, we identified two previously undescribed coding variants associated with lower platelet count: a common missense variant in CPS1 (rs1047891, MAF = 0.33, discovery + replication p = 6.38 × 10(-10)) and a rare synonymous variant in GFI1B (rs150813342, MAF = 0.009, discovery + replication p = 1.79 × 10(-27)). By performing CRISPR/Cas9 genome editing in hematopoietic cell lines and follow-up targeted knockdown experiments in primary human hematopoietic stem and progenitor cells, we demonstrate an alternative splicing mechanism by which the GFI1B rs150813342 variant suppresses formation of a GFI1B isoform that preferentially promotes megakaryocyte differentiation and platelet production. These results demonstrate how unbiased studies of natural variation in blood cell traits can provide insight into the regulation of human hematopoiesis.

Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases.

White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of âˆ¼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.

Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry.

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006-0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.

Sedentary time assessed by actigraphy and mortality: The Rotterdam Study.

Research suggests that sedentary behavior is a risk factor for mortality. However, most studies rely on questionnaires, which are prone to reporting error. We examined the association between sedentary time assessed by actigraphy and mortality among 1839 participants, aged 45-98years, from the prospective population-based Rotterdam Study, enrolled between 2004 and 2007. Participants wore an actigraph around the wrist for seven days. Sedentary time was evaluated continuously, per 1h/day increase, and categorically in three groups (<8, 8-11, ≥11h/day). The lowest category was used as reference. Mortality risks were examined using Cox proportional hazard models, adjusted for confounders and biological risk factors. We examined the association between sedentary behavior and mortality over and beyond other activity measures (including physical activity (PA) and activities of daily living (ADL)) in a final model. During 11years of follow-up (median: 7.5years, interquartile range: 6.6-8.3years), 212 participants (11.5%) died. In the multivariable model, the hazard ratio (HR) and 95% confidence interval (95% CI) per 1 more hour/day sedentary time was 1.09 (1.00, 1.18). The HR (95% CI) after adjustment for PA and ADL was 1.04 (0.96, 1.13). Participants sedentary for ≥11h/day had a higher mortality risk (HR: 1.80, 95% CI: 1.14, 2.84) than those sedentary <8h/day, in the multivariable model. After adjusting for PA and ADL, this association was clearly attenuated (HR: 1.50, 95% CI: 0.93, 2.41). In conclusion, our study suggests that sedentary behavior is a risk factor for mortality. Further investigation is needed to examine whether this association is distinct from the effect of other measures of activity.

Adherence to the 2015 Dutch dietary guidelines and risk of non-communicable diseases and mortality in the Rotterdam Study.

We aimed to evaluate the criterion validity of the 2015 food-based Dutch dietary guidelines, which were formulated based on evidence on the relation between diet and major chronic diseases. We studied 9701 participants of the Rotterdam Study, a population-based prospective cohort in individuals aged 45 years and over [median 64.1 years (95%-range 49.0-82.8)]. Dietary intake was assessed at baseline with a food-frequency questionnaire. For all participants, we examined adherence (yes/no) to fourteen items of the guidelines: vegetables (≥200 g/day), fruit (≥200 g/day), whole-grains (≥90 g/day), legumes (≥135 g/week), nuts (≥15 g/day), dairy (≥350 g/day), fish (≥100 g/week), tea (≥450 mL/day), ratio whole-grains:total grains (≥50%), ratio unsaturated fats and oils:total fats (≥50%), red and processed meat (<300 g/week), sugar-containing beverages (≤150 mL/day), alcohol (≤10 g/day) and salt (≤6 g/day). Total adherence was calculated as sum-score of the adherence to the individual items (0-14). Information on disease incidence and all-cause mortality during a median follow-up period of 13.5 years (range 0-27.0) was obtained from data collected at our research center and from medical records. Using Cox proportional-hazards models adjusted for confounders, we observed every additional component adhered to was associated with a 3% lower mortality risk (HR 0.97, 95% CI 0.95; 0.98), lower risk of stroke (HR 0.95, 95% CI 0.92; 0.99), chronic obstructive pulmonary disease (HR 0.94, 95% CI 0.91; 0.98), colorectal cancer (HR 0.90, 95% CI 0.84; 0.96), and depression (HR 0.97, 95% CI 0.95; 0.999), but not with incidence of coronary heart disease, type 2 diabetes, heart failure, lung cancer, breast cancer, or dementia. These associations were not driven by any of the individual dietary components. To conclude, adherence to the Dutch dietary guidelines was associated with a lower mortality risk and a lower risk of developing some but not all of the chronic diseases on which the guidelines were based.

Physical Activity Types and Coronary Heart Disease Risk in Middle-Aged and Elderly Persons: The Rotterdam Study.

Physical activity is associated with decreased risk of coronary heart disease (CHD). The specific physical activity types that provide beneficial effects in an older population remain unclear. We assessed the association of total physical activity, walking, cycling, domestic work, sports, and gardening with CHD by using Cox proportional hazard models among 5,901 participants aged >55 (median age, 67) years from the prospective population-based Rotterdam Study, enrolled between 1997 and 2001. Activities were categorized into tertiles, and the lowest tertiles were used as reference. In the multivariable model, we adjusted for age, sex, smoking, alcohol consumption, education, diet, and other physical activity types. During 15 years of follow-up (median, 10.3 (interquartile range, 8.0-11.8) years), 642 participants (10.9%) experienced a CHD event. In the multivariable model, the respective hazard ratios for the medium and high categories compared with the low category were 0.79 (95% confidence interval CI): 0.66, 0.96) and 0.71 (95% CI: 0.58, 0.87) for total physical activity, 0.76 (95% CI: 0.63, 0.92) and 0.70 (95% CI: 0.57, 0.88) for cycling, and 0.81 (95% CI: 0.66, 0.98) and 0.71 (95% CI: 0.56, 0.90) for domestic work. Walking, sports, and gardening were not associated with CHD. In conclusion, in this long-term follow-up study of older adults, domestic work and cycling were associated with reduced CHD risk. Physical activity should be promoted in this population with the aim to prevent CHD.

Trans-ethnic meta-analysis of white blood cell phenotypes.

White blood cell (WBC) count is a common clinical measure used as a predictor of certain aspects of human health, including immunity and infection status. WBC count is also a complex trait that varies among individuals and ancestry groups. Differences in linkage disequilibrium structure and heterogeneity in allelic effects are expected to play a role in the associations observed between populations. Prior genome-wide association study (GWAS) meta-analyses have identified genomic loci associated with WBC and its subtypes, but much of the heritability of these phenotypes remains unexplained. Using GWAS summary statistics for over 50 000 individuals from three diverse populations (Japanese, African-American and European ancestry), a Bayesian model methodology was employed to account for heterogeneity between ancestry groups. This approach was used to perform a trans-ethnic meta-analysis of total WBC, neutrophil and monocyte counts. Ten previously known associations were replicated and six new loci were identified, including several regions harboring genes related to inflammation and immune cell function. Ninety-five percent credible interval regions were calculated to narrow the association signals and fine-map the putatively causal variants within loci. Finally, a conditional analysis was performed on the most significant SNPs identified by the trans-ethnic meta-analysis (MA), and nine secondary signals within loci previously associated with WBC or its subtypes were identified. This work illustrates the potential of trans-ethnic analysis and ascribes a critical role to multi-ethnic cohorts and consortia in exploring complex phenotypes with respect to variants that lie outside the European-biased GWAS pool.