In Silico Post Genome-Wide Association Studies Analysis of C-Reactive Protein Loci Suggests an Important Role for Interferons

Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

A genome-wide association meta-analysis implicates Hedgehog and Notch signaling in Dupuytren's disease

Dupuytren's disease (DD) is a highly heritable fibrotic disorder of the hand with incompletely understood etiology. A number of genetic loci, including Wnt signaling members, have been previously identified. Our overall aim was to identify novel genetic loci, to prioritize genes within the loci for functional studies, and to assess genetic correlation with associated disorders. We performed a meta-analysis of six DD genome-wide association studies from three European countries and extensive bioinformatic follow-up analyses. Leveraging 11,320 cases and 47,023 controls, we identified 85 genome-wide significant single nucleotide polymorphisms in 56 loci, of which 11 were novel, explaining 13.3-38.1% of disease variance. Gene prioritization implicated the Hedgehog and Notch signaling pathways. We also identified a significant genetic correlation with frozen shoulder. The pathways identified highlight the potential for new therapeutic targets and provide a basis for additional mechanistic studies for a common disorder that can severely impact hand function.

A genome-wide association study of 24-hour urinary excretion of endocrine disrupting chemicals

Ubiquitous exposure to environmental endocrine disrupting chemicals (EDCs) instigates a major public health problem, but much remains unknown on the inter-individual differences in metabolism and excretion of EDCs. To examine this we performed a two-stage genome-wide association study (GWAS) for 24-hour urinary excretions of four parabens, two bisphenols, and nine phthalate metabolites. Results showed five genome-wide significant (p-value < 5x10-8) and replicated single nucleotide polymorphisms (SNPs) representing four independent signals that associated with mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) and mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP). Three of the four signals were located on chromosome 10 in a locus harboring the cytochrome P450 (CYP) genes CYP2C9, CYP2C58P, and CYP2C19 (rs117529685, pMECPP = 5.38x10-25; rs117033379, pMECPP = 1.96x10-19; rs4918798, pMECPP = 4.01x10-71; rs7895726, pMEHHP = 1.37x10-15, r2 with rs4918798 = 0.93). The other signal was on chromosome 6 close to the solute carrier (SLC) genes SLC17A1, SLC17A3, SLC17A4, and SCGN (rs1359232, pMECPP = 7.6x10-16). These four SNPs explained a substantial part (8.3 % - 9.2 %) of the variance in MECPP in the replication cohort. Bioinformatics analyses supported a likely causal role of CYP2C9 and SLC17A1 in metabolism and excretion of MECPP and MEHHP. Our results provide biological insights into mechanisms of phthalate metabolism and excretion with a likely causal role for CYP2C9 and SLC17A1.

The pentraxin family in autoimmune disease

The pentraxins represent a family of multifunctional proteins composed of long and short pentamers. The latter includes serum amyloid P component (SAP) and C-reactive protein (CRP) whereas the former includes neuronal PTX1 and PTX2 (NPTX1 and NPTX2, respectively), PTX3 and PTX4. These serve as a bridge between adaptive immunity and innate immunity and a link between inflammation and immunity. Similarities and differences between long and short pentamers are examined and their roles in autoimmune disease are discussed. Increased CRP and PTX3 could indicate the activity of rheumatoid arthritis, systemic lupus erythematosus or other autoimmune diseases. Mechanistically, CRP and PTX3 may predict target organ injury, regulate bone metabolic immunity and maintain homeostasis as well as participate in vascular endothelial remodeling. Interestingly, PTX3 is pleiotropic, being involved in inflammation and tissue repair. Given the therapeutic potential of PTX3 and CRP, targeting these factors to exert a beneficial effect is the focus of research efforts. Unfortunately, studies on NPTX1, NPTX2, PTX4 and SAP are scarce and more research is clearly needed to elaborate their potential roles in autoimmune disease.

Relationship between CRP gene polymorphisms and ischemic stroke risk: A systematic review and meta-analysis

Ischemic stroke (IS), usually caused due to an abrupt blockage of an artery, is the leading cause of disability and the second leading cause of death worldwide. The association of the C-reactive protein (CRP) gene (s3093059 T/C and rs1205 C/T) polymorphisms and IS susceptibility has been widely studied, but the results remain inconsistent. Our study aimed to assess the association between CRP gene (s3093059 T/C and rs1205 C/T) polymorphisms and IS risk. PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, and WanFang databases were searched up to April 2022 to identify eligible studies. The Newcastle-Ottawa scale (NOS) score was calculated to assess study quality. The odd ratios (ORs) with a 95% confidence interval (CI) were calculated to assess the association between CRP gene (rs3093059 T/C and rs1205 C/T) polymorphisms and IS risk. Eighteen case–control studies with 6339 cases and 29580 controls were identified. We found that CRP (s3093059 T/C and rs1205 C/T) polymorphism was not significantly associated with the risk of IS in any genetic model (recessive model: OR 1.00, 95% CI 0.79–1.26; OR 1.06, 95% CI 0.90–1.25). When stratified analysis by country, genotype method, source of controls, and NOS score, still no statistically significant association was found. Our study indicated that the CRP (rs3093059 T/C and rs1205 C/T) polymorphisms were not associated with the susceptibility to IS.

Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation

Recent genome-wide association studies uncovered part of blood pressure’s heritability. However, there is still a vast gap between genetics and biology that needs to be bridged. Here, we followed up blood pressure genome-wide summary statistics of over 750,000 individuals, leveraging comprehensive epigenomic and transcriptomic data from blood with a follow-up in cardiovascular tissues to prioritise likely causal genes and underlying blood pressure mechanisms. We first prioritised genes based on coding consequences, multilayer molecular associations, blood pressure-associated expression levels, and coregulation evidence. Next, we followed up the prioritised genes in multilayer studies of genomics, epigenomics, and transcriptomics, functional enrichment, and their potential suitability as drug targets. Our analyses yielded 1880 likely causal genes for blood pressure, tens of which are targets of the available licensed drugs. We identified 34 novel genes for blood pressure, supported by more than one source of biological evidence. Twenty-eight (82%) of these new genes were successfully replicated by transcriptome-wide association analyses in a large independent cohort (n = ~220,000). We also found a substantial mediating role for epigenetic regulation of the prioritised genes. Our results provide new insights into genetic regulation of blood pressure in terms of likely causal genes and involved biological pathways offering opportunities for future translation into clinical practice.

Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma

Background: Primary open-angle glaucoma (POAG) is the most prevalent glaucoma subtype, but its exact etiology is still unknown. In this study, we aimed to prioritize the most likely ‘causal’ genes and identify functional characteristics and underlying biological pathways of POAG candidate genes. Methods: We used the results of a large POAG genome-wide association analysis study from GERA and UK Biobank cohorts. First, we performed systematic gene-prioritization analyses based on: (i) nearest genes; (ii) nonsynonymous single-nucleotide polymorphisms; (iii) co-regulation analysis; (iv) transcriptome-wide association studies; and (v) epigenomic data. Next, we performed functional enrichment analyses to find overrepresented functional pathways and tissues. Results: We identified 142 prioritized genes, of which 64 were novel for POAG. BICC1, AFAP1, and ABCA1 were the most highly prioritized genes based on four or more lines of evidence. The most significant pathways were related to extracellular matrix turnover, transforming growth factor-β, blood vessel development, and retinoic acid receptor signaling. Ocular tissues such as sclera and trabecular meshwork showed enrichment in prioritized gene expression (>1.5 fold). We found pleiotropy of POAG with intraocular pressure and optic-disc parameters, as well as genetic correlation with hypertension and diabetes-related eye disease. Conclusions: Our findings contribute to a better understanding of the molecular mechanisms underlying glaucoma pathogenesis and have prioritized many novel candidate genes for functional follow-up studies.

Genetic Determinants of Serum Calcification Propensity and Cardiovascular Outcomes in the General Population

Background:

Serum calciprotein particle maturation time (T50), a measure of vascular calcification propensity, is associated with cardiovascular morbidity and mortality. We aimed to identify genetic loci associated with serum T50 and study their association with cardiovascular disease and mortality.

Methods:

We performed a genome-wide association study of serum T50 in 2,739 individuals of European descent participating in the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study, followed by a two-sample Mendelian randomization (MR) study to examine causal effects of T50 on cardiovascular outcomes. Finally, we examined associations between T50 loci and cardiovascular outcomes in 8,566 community-dwelling participants in the Rotterdam study.

Results:

We identified three independent genome-wide significant single nucleotide polymorphism (SNPs) in the AHSG gene encoding fetuin-A: rs4917 (p = 1.72 × 10−101), rs2077119 (p = 3.34 × 10−18), and rs9870756 (p = 3.10 × 10−8), together explaining 18.3% of variation in serum T50. MR did not demonstrate a causal effect of T50 on cardiovascular outcomes in the general population. Patient-level analyses revealed that the minor allele of rs9870756, which explained 9.1% of variation in T50, was associated with a primary composite endpoint of all-cause mortality or cardiovascular disease [odds ratio (95% CI) 1.14 (1.01–1.28)] and all-cause mortality alone [1.14 (1.00–1.31)]. The other variants were not associated with clinical outcomes. In patients with type 2 diabetes or chronic kidney disease, the association between rs9870756 and the primary composite endpoint was stronger [OR 1.40 (1.06–1.84), relative excess risk due to interaction 0.54 (0.01–1.08)].

Conclusions:

We identified three SNPs in the AHSG gene that explained 18.3% of variability in serum T50 levels. Only one SNP was associated with cardiovascular outcomes, particularly in individuals with type 2 diabetes or chronic kidney disease.

Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour

Age at first sexual intercourse and age at first birth have implications for health and evolutionary fitness. In this genome-wide association study (age at first sexual intercourse, N = 387,338; age at first birth, N = 542,901), we identify 371 single-nucleotide polymorphisms, 11 sex-specific, with a 5-6% polygenic score prediction. Heritability of age at first birth shifted from 9% [CI = 4-14%] for women born in 1940 to 22% [CI = 19-25%] for those born in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility and spermatid differentiation. Our findings suggest that polycystic ovarian syndrome may lead to later age at first birth, linking with infertility. Late age at first birth is associated with parental longevity and reduced incidence of type 2 diabetes and cardiovascular disease. Higher childhood socioeconomic circumstances and those in the highest polygenic score decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, understanding longevity and guiding experimentation into mechanisms of infertility.

C-Reactive Protein Levels in Systemic Lupus Erythematosus Are Modulated by the Interferon Gene Signature and CRP Gene Polymorphism rs1205

Objectives

Patients with systemic lupus erythematosus (SLE) often display modest elevations of C-reactive protein (CRP) despite raised disease activity and increased interleukin (IL-) 6. We asked to what extent IL-6 levels, the CRP polymorphism rs1205, and the type I interferon (IFN) gene signature affects the basal CRP levels in patients with SLE during a quiescent phase of the disease.

Methods

CRP and IL-6 were analyzed in plasma from 57 patients meeting established classification criteria for SLE. The CRP polymorphism rs1205 was assessed and gene expression analyzed including four type I IFN-regulated genes (IGS).

Results

CRP was increased in patients with detectable IL-6 levels (p=0.001) and decreased among IGS-positive subjects (p=0.033). A multiple linear regression model revealed IL-6 to have a positive association with CRP levels, whereas both IGS-positivity and CRP genotype (rs1205) AA/GA were negatively associated with CRP-levels.

Conclusion

Our data offer an explanation to the modest CRP levels seen in viral infections and IFN-α driven autoimmunity and corroborate prior observations showing an IFN-α dependent downregulation of CRP. The latter observation, together with the fact that the CRP-lowering polymorphism rs1205 is overrepresented in human SLE, could explain low basal CRP and inadequate CRP-responses among patients with active SLE.

The effects of bariatric surgery on clinical profile, DNA methylation, and ageing in severely obese patients

BACKGROUND

Severe obesity is a growing, worldwide burden and conventional therapies including radical change of diet and/or increased physical activity have limited results. Bariatric surgery has been proposed as an alternative therapy showing promising results. It leads to substantial weight loss and improvement of comorbidities such as type 2 diabetes. Increased adiposity is associated with changes in epigenetic profile, including DNA methylation. We investigated the effect of bariatric surgery on clinical profile, DNA methylation, and biological age estimated using Horvath's epigenetic clock.

RESULTS

To determine the impact of bariatric surgery and subsequent weight loss on clinical traits, a cohort of 40 severely obese individuals (BMI = 30-73 kg/m²) was examined at the time of surgery and at three follow-up visits, i.e., 3, 6, and 12 months after surgery. The majority of the individuals were women (65%) and the mean age at surgery was 45.1 ± 8.1 years. We observed a significant decrease over time in BMI, fasting glucose, HbA1c, HOMA-IR, insulin, total cholesterol, triglycerides, LDL and free fatty acids levels, and a significant small increase in HDL levels (all p values < 0.05). Epigenome-wide association analysis revealed 4857 differentially methylated CpG sites 12 months after surgery (at Bonferroni-corrected p value < 1.09 × 10^-7). Including BMI change in the model decreased the number of significantly differentially methylated CpG sites by 51%. Gene set enrichment analysis identified overrepresentation of multiple processes including regulation of transcription, RNA metabolic, and biosynthetic processes in the cell. Bariatric surgery in severely obese patients resulted in a decrease in both biological age and epigenetic age acceleration (EAA) (mean = - 0.92, p value = 0.039).

CONCLUSIONS

Our study shows that bariatric surgery leads to substantial BMI decrease and improvement of clinical outcomes observed 12 months after surgery. These changes explained part of the association between bariatric surgery and DNA methylation. We also observed a small, but significant improvement of biological age. These epigenetic changes may be modifiable by environmental lifestyle factors and could be used as potential biomarkers for obesity and in the future for obesity related comorbidities.

Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression

Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations (r_g ± SE) of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia were 0.79 ± 0.07, 0.24 ± 0.08, 0.53 ± 0.09 and 0.57 ± 0.05, respectively. From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general.

The genetics of depression: successful genome-wide association studies introduce new challenges

The recent successful genome-wide association studies (GWASs) for depression have yielded more than 80 replicated loci and brought back the excitement that had evaporated during the years of negative GWAS findings. The identified loci provide anchors to explore their relevance for depression, but this comes with new challenges. Using the watershed model of genotype-phenotype relationships as a conceptual aid and recent genetic findings on other complex phenotypes, we discuss why it took so long and identify seven future challenges. The biggest challenge involves the identification of causal mechanisms since GWAS associations merely flag genomic regions without a direct link to underlying biological function. Furthermore, the genetic association with the index phenotype may also be part of a more extensive causal pathway (e.g., from variant to comorbid condition) or be due to indirect influences via intermediate traits located in the causal pathways to the final outcome. This challenge is highly relevant for depression because even its narrow definition of major depressive disorder captures a heterogeneous set of phenotypes which are often measured by even more broadly defined operational definitions consisting of a few questions (minimal phenotyping). Here, Mendelian randomization and future discovery of additional genetic variants for depression and related phenotypes will be of great help. In addition, reduction of phenotypic heterogeneity may also be worthwhile. Other challenges include detecting rare variants, determining the genetic architecture of depression, closing the "heritability gap", and realizing the potential for personalized treatment. Along the way, we identify pertinent open questions that, when addressed, will advance the field.

Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7, TNXB, LRP12, LOC283335, SEPT9, and AKT2, and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA. Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.

Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74 Collapse

Key Words Collapse

MESH Headings

• Blood Pressure
• Cohort Studies
• Genetic Predisposition to Disease
• Genome-Wide Association Study
• Heart Diseases/genetics
• Heart Diseases/physiopathology
• Heart Rate
• Humans
• Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels/genetics
• Muscle Proteins/genetics
• Polymorphism, Single Nucleotide
• Potassium Channels/genetics
• Quantitative Trait Loci
• RGS Proteins/genetics
• Risk Factors
• White People/genetics

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Grants

• R01 HL120393 NHLBI NIH HHS
• U01 HL130114 NHLBI NIH HHS
• P01 GM099568 NIGMS NIH HHS
• UL1 TR000124 NCATS NIH HHS
• R01 HL105756 NHLBI NIH HHS
• S10 OD020069 NIH HHS
• K23 HL114724 NHLBI NIH HHS
• R01 HL116747 NHLBI NIH HHS
• P30 DK063491 NIDDK NIH HHS
• I01 BX002558 BLRD VA
• MC_UU_12019/1 Medical Research Council
• UL1 TR001881 NCATS NIH HHS
• P2C HD050924 NICHD NIH HHS

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Affiliation(s)

Ilja M. Nolte Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
M. Loretto Munoz Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Vinicius Tragante Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands
Azmeraw T. Amare Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands Department of Epidemiology, School of Medicine, University of Adelaide, Adelaide, South Australia 5005, Australia College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar 6000, Ethiopia
Rick Jansen Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Ahmad Vaez Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
Benedikt von der Heyde Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden
Christy L. Avery Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Joshua C. Bis Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA
Bram Dierckx Department of Child and Adolescent Psychiatry/Psychology, Department of Child and Adolescent Psychiatry, PO Box 2060, Rotterdam 3000 CB, The Netherlands The Generation R Study Group, Erasmus MC, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Jenny van Dongen Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands
Stephanie M. Gogarten Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Philippe Goyette Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8
Jussi Hernesniemi Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland Department of Cardiology, Heart Hospital, Tampere University Hospital, Tampere 33521, Finland
Ville Huikari Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland
Shih-Jen Hwang Framingham Heart Study, Framingham, Massachusetts 01702, USA Population Sciences Branch, National Heart, Lung and Blood Institute, Bethesda, Maryland 20892, USA
Deepali Jaju Department of Clinical Physiology, Sultan Qaboos University Hospital, Muscat—Al Khoudh 123, Sultanate of Oman
Kathleen F. Kerr Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Alexander Kluttig Institute of Medical Epidemiology, Biostatistics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale) 06097, Germany
Bouwe P. Krijthe Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Jitender Kumar Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden
Sander W. van der Laan Laboratory of Experimental Cardiology, Department of Heart and Lung, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
Leo-Pekka Lyytikäinen Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland
Adam X. Maihofer Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Arpi Minassian Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Peter J. van der Most Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Martina Müller-Nurasyid Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany Department of Medicine, University Hospital Munich, Ludwig-Maximilians-University, Munich 80539, Germany DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany
Michel Nivard Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Erika Salvi Department of Health Sciences, University of Milano, Milano 20122, Italy
James D. Stewart Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Julian F. Thayer Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, Ohio 43210, USA
Niek Verweij Department of Cardiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Andrew Wong MRC Unit for Lifelong Health and Ageing, University College London, 33 Bedford Place, London WC1B 5JU, UK
Delilah Zabaneh Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK University College London Genetics Institute, University College London, London WC1E 6BT, UK
Mohammad H. Zafarmand Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, Amsterdam 1105 AZ, The Netherlands Department of Obstetrics and Gynaecology, Academic Medical Centre, University of Amsterdam, Amsterdam 1105 AZ, The Netherlands
Abdel Abdellaoui Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Sulayma Albarwani Department of Physiology, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat Al-Khoudh 123, Sultanate of Oman
Christine Albert Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
Alvaro Alonso Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA
Foram Ashar McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
Juha Auvinen Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland Unit of Primary Health Care, Oulu University Hospital, Oulu 90220, Finland
Tomas Axelsson Department of Medical Sciences, Molecular Medicine, Uppsala University, Uppsala 75237, Sweden
Dewleen G. Baker Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Paul I. W. de Bakker Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
Matteo Barcella Department of Health Sciences, University of Milano, Milano 20122, Italy
Riad Bayoumi College of Medicine, Mohammed Bin Rashid University, PO Box 505055, Dubai Healthcare City, United Arab Emirates
Rob J. Bieringa Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Dorret Boomsma Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Gabrielle Boucher Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8
Annie R. Britton Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK
Ingrid Christophersen Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02114, USA Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Rud 1346, Norway
Andrea Dietrich Department of Child- and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
George B. Ehret Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA Cardiology, Department of Specialties of Internal Medicine, Geneva University Hospital, Geneva 1211, Switzerland
Patrick T. Ellinor Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02114, USA Cardiac Arrhythmia Service & Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Markku Eskola Department of Cardiology, Heart Hospital, Tampere University Hospital, Tampere 33521, Finland Department of Cardiology, University of Tampere School of Medicine, Tampere 33014, Finland
Janine F. Felix Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
John S. Floras University Health Network and Mount Sinai Hospital Division of Cardiology, Department of Medicine, University of Toronto, Ontario, Canada M5S Toronto General Research Institute, University Health Network, Toronto, Canada
Oscar H. Franco Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Peter Friberg Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
Maaike G. J. Gademan Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, Amsterdam 1105 AZ, The Netherlands
Mark A. Geyer Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA
Vilmantas Giedraitis Department of Public Health and Caring Sciences, Molecular Geriatrics, Uppsala University, Uppsala 75237, Sweden
Catharina A. Hartman Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Daiane Hemerich Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands CAPES Foundation, Ministry of Education of Brazil, Brasília DF 70040-020, Brazil
Albert Hofman Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Jouke-Jan Hottenga Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Heikki Huikuri Research Unit of Internal Medicine, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu 90220, Finland
Nina Hutri-Kähönen Department of Pediatrics, Tampere University Hospital, Tampere 33521, Finland Department of Pediatrics, University of Tampere School of Medicine, Tampere 33014, Finland
Xavier Jouven INSERM U970, Paris Descartes University, Paris 75006, France
Juhani Junttila Research Unit of Internal Medicine, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu 90220, Finland
Markus Juonala Department of Medicine, University of Turku, Turku 20520, Finland Division of Medicine, Turku University Hospital, Turku 20521, Finland
Antti M. Kiviniemi Research Unit of Internal Medicine, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu 90220, Finland
Jan A. Kors Department of Medical Informatics, Erasmus Medical Center, Rotterdam 3015 CE, The Netherlands
Meena Kumari Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK ISER, Essex University, Colchester, Essex CO4 3SQ, UK
Tatiana Kuznetsova Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven 3000, Belgium
Cathy C. Laurie Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Joop D. Lefrandt Department of Internal Medicine, Division of Vascular Medicine, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Yong Li Division of Genetic Epidemiology, Institute for Medical Biometry and Statistics, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg 79110, Germany
Yun Li Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Duanping Liao Division of Epidemiology, Department of Public Health Sciences, Penn State University College of Medicine, Hershey, Pennsylvania 17033, USA
Marian C. Limacher Division of Cardiovascular Medicine, University of Florida College of Medicine, Gainesville, Florida 32611, USA
Henry J. Lin Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California 90502, USA
Cecilia M. Lindgren Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford OX3 7BN, UK Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
Steven A. Lubitz Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02114, USA Cardiac Arrhythmia Service & Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Anubha Mahajan Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
Barbara McKnight Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
Henriette Meyer zu Schwabedissen Biopharmacy, Department Pharmaceutical Sciences, University of Basel, Basel CH-4056, Switzerland
Yuri Milaneschi Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Nina Mononen Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland
Andrew P. Morris Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK
Mike A. Nalls Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA
Gerjan Navis Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Melanie Neijts Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Kjell Nikus Department of Cardiology, Heart Hospital, Tampere University Hospital, Tampere 33521, Finland Department of Cardiology, University of Tampere, School of Medicine, Tampere 33014, Finland
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Daniel T. O'Connor Department of Medicine, University of California, San Diego, San Diego, California 92093, USA
Johan Ormel Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Siegfried Perz Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
Annette Peters DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany German Center for Diabetes Research, Neuherberg 85764, Germany
Bruce M. Psaty Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA Departments of Epidemiology and Health Services, University of Washington, Seattle, Washington 98195, USA Group Health Research Institute, Group Health Cooperative, Seattle, Washington 98101, USA
Olli T. Raitakari Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland
Victoria B. Risbrough Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Moritz F. Sinner Department of Medicine, University Hospital Munich, Ludwig-Maximilians-University, Munich 80539, Germany DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany
David Siscovick The New York Academy of Medicine, New York, New York 10029, USA
Johannes H. Smit Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Nicholas L. Smith Group Health Research Institute, Group Health Cooperative, Seattle, Washington 98101, USA Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington 98195, USA Seattle Epidemiologic Research and Information Center, Veterans Affairs Office of Research and Development, Seattle, Washington 98108, USA
Elsayed Z. Soliman Epidemiological Cardiology Research Center (EPICARE), Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
Nona Sotoodehnia Cardiovascular Health Research Unit, Division of Cardiology, Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington 98101, USA
Jan A. Staessen Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven 3000, Belgium
Phyllis K. Stein Heart Rate Variability Lab, Washington University School of Medicine, St Louis, Missouri 63108, USA
Adrienne M. Stilp Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Katarzyna Stolarz-Skrzypek First Department of Cardiology, Interventional Electrocardiology and Hypertension, Jagiellonian University Medical College, Cracow 31-008, Poland
Konstantin Strauch Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich 81377, Germany
Johan Sundström Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala 751 85, Sweden
Cees A. Swenne Department of Cardiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
Ann-Christine Syvänen Department of Medical Sciences, Molecular Medicine, Uppsala University, Uppsala 75237, Sweden
Jean-Claude Tardif Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8 Université de Montréal, Montreal, Quebec, Canada H3T IJ4
Kent D. Taylor Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90509, USA
Alexander Teumer Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
Timothy A. Thornton Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Lesley E. Tinker Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
André G. Uitterlinden Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands Department of Internal Medicine, Erasmus University Medical Center, Rotterdam 3015 CE, The Netherlands Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging NCHA), Leiden 2300 RC, The Netherlands
Jessica van Setten Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands
Andreas Voss Institute of Innovative Health Technologies—IGHT Jena Ernst-Abbe-Hochschule Jena, Jena 07745, Germany
Melanie Waldenberger Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany Research Unit of Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
Kirk C. Wilhelmsen Departments of Genetics and Neurology University of North Carolina, Chapel Hill, North Carolina 27599, USA The Renaissance Computing Institute, Chapel Hill, North Carolina 27599, USA
Gonneke Willemsen Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Quenna Wong Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Zhu-Ming Zhang Epidemiological Cardiology Research Center (EPICARE), Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA Department of Epidemiology & Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
Alan B. Zonderman Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland 21224, USA
Daniele Cusi Institute of Biomedical Technologies, CNR—Italian National Research Council, Milan 20090, Italy KOS Genetic SRL, Bresso (Milano) 20091, Italy
Michele K. Evans Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland 21224, USA
Halina K. Greiser German Cancer Research Centre, Division of Cancer Epidemiology, Heidelberg 69210, Germany
Pim van der Harst Department of Cardiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Mohammad Hassan Department of Physiology, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat Al-Khoudh 123, Sultanate of Oman
Erik Ingelsson Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
Marjo-Riitta Järvelin Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland Unit of Primary Health Care, Oulu University Hospital, Oulu 90220, Finland Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St Mary’s campus, Imperial College London, London W2 1PG, UK Biocenter Oulu University of Oulu, Oulu 90014, Finland
Stefan Kääb Department of Medicine, University Hospital Munich, Ludwig-Maximilians-University, Munich 80539, Germany DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany
Mika Kähönen Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland Department of Clinical Physiology, University of Tampere, School of Medicine, Tampere 33014, Finland
Mika Kivimaki Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK
Charles Kooperberg Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
Diana Kuh MRC Unit for Lifelong Health and Ageing, University College London, 33 Bedford Place, London WC1B 5JU, UK
Terho Lehtimäki Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland
Lars Lind Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala 751 85, Sweden
Caroline M. Nievergelt Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Chris J. O'Donnell Framingham Heart Study, Framingham, Massachusetts 01702, USA Population Sciences Branch, National Heart, Lung and Blood Institute, Bethesda, Maryland 20892, USA Cardiology Section, Boston Veteran’s Administration Healthcare, Boston, Maryland 02132, USA
Albertine J. Oldehinkel Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Brenda Penninx Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Alexander P. Reiner Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Harriëtte Riese Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Arie M. van Roon Department of Internal Medicine, Division of Vascular Medicine, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
John D. Rioux Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8 Université de Montréal, Montreal, Quebec, Canada H3T IJ4
Jerome I. Rotter Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90509, USA
Tamar Sofer Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Bruno H. Stricker Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands Inspectorate for Health Care, The Hague 2511 VX, The Netherlands
Henning Tiemeier Department of Child and Adolescent Psychiatry/Psychology, Department of Child and Adolescent Psychiatry, PO Box 2060, Rotterdam 3000 CB, The Netherlands Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Tanja G. M. Vrijkotte Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, Amsterdam 1105 AZ, The Netherlands
Folkert W. Asselbergs Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands Institute of Cardiovascular Science, University College London, 222 Euston Road, London NW1 2DA, UK Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht 3501 DG, The Netherlands
Bianca J. J. M. Brundel Department of Physiology, Institute for Cardiovascular Research, VU University Medical Center, De Boelelaan 1118, Amsterdam 1081 HV, The Netherlands
Susan R. Heckbert Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Eric A. Whitsel Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA Department of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Marcel den Hoed Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden
Harold Snieder Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Eco J. C. de Geus Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands

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Conditional eQTL analysis reveals allelic heterogeneity of gene expression

In recent years, multiple eQTL (expression quantitative trait loci) catalogs have become available that can help understand the functionality of complex trait-related single nucleotide polymorphisms (SNPs). In eQTL catalogs, gene expression is often strongly associated with multiple SNPs, which may reflect either one or multiple independent associations. Conditional eQTL analysis allows a distinction between dependent and independent eQTLs. We performed conditional eQTL analysis in 4,896 peripheral blood microarray gene expression samples. Our analysis showed that 35% of genes with a cis eQTL have at least two independent cis eQTLs; for several genes up to 13 independent cis eQTLs were identified. Also, 12% (671) of the independent cis eQTLs identified in conditional analyses were not significant in unconditional analyses. The number of GWAS catalog SNPs identified as eQTL in the conditional analyses increases with 24% as compared to unconditional analyses. We provide an online conditional cis eQTL mapping catalog for whole blood (https://eqtl.onderzoek.io/), which can be used to lookup eQTLs more accurately than in standard unconditional whole blood eQTL databases.

Genome-wide analysis identifies 12 loci influencing human reproductive behavior

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.

Association of C-Reactive Protein (rs1205) Gene Polymorphism with Susceptibility to Psoriasis in South Indian Tamils

INTRODUCTION

Psoriasis is a multi-factorial heritable T-helper Th-1/Th-17 mediated inflammatory disease, affecting the skin. It is associated with co-morbidities such as Cardiovascular Disease (CVD). C-Reactive Protein (CRP) is a good inflammatory marker. CRP rs1205 polymorphism is associated with circulating plasma CRP levels. Although there is association between the rs1205 Single Nucleotide Polymorphism (SNP) and CVD, there are no prior reports regarding the association of CRP rs1205 SNP with psoriasis susceptibility.

AIM

To study the association of the genetic variant rs1205 in the CRP gene with susceptibility to the disease and protein levels in South Indian Tamils with psoriasis.

MATERIALS AND METHODS

In this case-control genetic study, 300 cases of psoriasis and 300 age and gender matched controls were genotyped for CRP SNP rs1205 using Taq Man 5'allele discrimination assay at Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India from February 2014 to January 2016. Plasma high sensitivity (hs)-CRP levels were estimated by ELISA. Disease severity was assessed by Psoriasis Area Severity Index (PASI).

RESULTS

CRP genetic variation rs1205 was not associated with psoriasis risk in our South Indian Tamil population. However, the circulating levels of hs-CRP was significantly higher in patients with psoriasis, as compared with controls (p < 0.0001) and the protein levels were significantly associated with disease severity, as assessed by PASI scoring. No genotype was found significantly associated with PASI or CRP levels.

CONCLUSION

Our results suggest that plasma CRP levels are higher in patients with psoriasis and correlate with disease severity, whilst CRP rs1205 is not associated with susceptibility to psoriasis in South Indian Tamils.

Investigating the Causal Relationship of C-Reactive Protein with 32 Complex Somatic and Psychiatric Outcomes: A Large-Scale Cross-Consortium Mendelian Randomization Study

BACKGROUND

C-reactive protein (CRP) is associated with immune, cardiometabolic, and psychiatric traits and diseases. Yet it is inconclusive whether these associations are causal.

METHODS AND FINDINGS

We performed Mendelian randomization (MR) analyses using two genetic risk scores (GRSs) as instrumental variables (IVs). The first GRS consisted of four single nucleotide polymorphisms (SNPs) in the CRP gene (GRSCRP), and the second consisted of 18 SNPs that were significantly associated with CRP levels in the largest genome-wide association study (GWAS) to date (GRSGWAS). To optimize power, we used summary statistics from GWAS consortia and tested the association of these two GRSs with 32 complex somatic and psychiatric outcomes, with up to 123,865 participants per outcome from populations of European ancestry. We performed heterogeneity tests to disentangle the pleiotropic effect of IVs. A Bonferroni-corrected significance level of less than 0.0016 was considered statistically significant. An observed p-value equal to or less than 0.05 was considered nominally significant evidence for a potential causal association, yet to be confirmed. The strengths (F-statistics) of the IVs were 31.92-3,761.29 and 82.32-9,403.21 for GRSCRP and GRSGWAS, respectively. CRP GRSGWAS showed a statistically significant protective relationship of a 10% genetically elevated CRP level with the risk of schizophrenia (odds ratio [OR] 0.86 [95% CI 0.79-0.94]; p < 0.001). We validated this finding with individual-level genotype data from the schizophrenia GWAS (OR 0.96 [95% CI 0.94-0.98]; p < 1.72 × 10-6). Further, we found that a standardized CRP polygenic risk score (CRPPRS) at p-value thresholds of 1 × 10-4, 0.001, 0.01, 0.05, and 0.1 using individual-level data also showed a protective effect (OR < 1.00) against schizophrenia; the first CRPPRS (built of SNPs with p < 1 × 10-4) showed a statistically significant (p < 2.45 × 10-4) protective effect with an OR of 0.97 (95% CI 0.95-0.99). The CRP GRSGWAS showed that a 10% increase in genetically determined CRP level was significantly associated with coronary artery disease (OR 0.88 [95% CI 0.84-0.94]; p < 2.4 × 10-5) and was nominally associated with the risk of inflammatory bowel disease (OR 0.85 [95% CI 0.74-0.98]; p < 0.03), Crohn disease (OR 0.81 [95% CI 0.70-0.94]; p < 0.005), psoriatic arthritis (OR 1.36 [95% CI 1.00-1.84]; p < 0.049), knee osteoarthritis (OR 1.17 [95% CI 1.01-1.36]; p < 0.04), and bipolar disorder (OR 1.21 [95% CI 1.05-1.40]; p < 0.007) and with an increase of 0.72 (95% CI 0.11-1.34; p < 0.02) mm Hg in systolic blood pressure, 0.45 (95% CI 0.06-0.84; p < 0.02) mm Hg in diastolic blood pressure, 0.01 ml/min/1.73 m2 (95% CI 0.003-0.02; p < 0.005) in estimated glomerular filtration rate from serum creatinine, 0.01 g/dl (95% CI 0.0004-0.02; p < 0.04) in serum albumin level, and 0.03 g/dl (95% CI 0.008-0.05; p < 0.009) in serum protein level. However, after adjustment for heterogeneity, neither GRS showed a significant effect of CRP level (at p < 0.0016) on any of these outcomes, including coronary artery disease, nor on the other 20 complex outcomes studied. Our study has two potential limitations: the limited variance explained by our genetic instruments modeling CRP levels in blood and the unobserved bias introduced by the use of summary statistics in our MR analyses.

CONCLUSIONS

Genetically elevated CRP levels showed a significant potentially protective causal relationship with risk of schizophrenia. We observed nominal evidence at an observed p < 0.05 using either GRSCRP or GRSGWAS-with persistence after correction for heterogeneity-for a causal relationship of elevated CRP levels with psoriatic osteoarthritis, rheumatoid arthritis, knee osteoarthritis, systolic blood pressure, diastolic blood pressure, serum albumin, and bipolar disorder. These associations remain yet to be confirmed. We cannot verify any causal effect of CRP level on any of the other common somatic and neuropsychiatric outcomes investigated in the present study. This implies that interventions that lower CRP level are unlikely to result in decreased risk for the majority of common complex outcomes.

DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis

Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.

Mendelian randomization studies of biomarkers and type 2 diabetes

Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring

Background: We examined whether the effect of maternal smoking during pregnancy on birthweight of the offspring was mediated by smoking-induced changes to DNA methylation in cord blood.

Methods: First, we used cord blood of 129 Dutch children exposed to maternal smoking vs 126 unexposed to maternal and paternal smoking (53% male) participating in the GECKO Drenthe birth cohort. DNA methylation was measured using the Illumina HumanMethylation450 Beadchip. We performed an epigenome-wide association study for the association between maternal smoking and methylation followed by a mediation analysis of the top signals [false-discovery rate (FDR) < 0.05]. We adjusted both analyses for maternal age, education, pre-pregnancy BMI, offspring’s sex, gestational age and white blood cell composition. Secondly, in 175 exposed and 1248 unexposed newborns from two independent birth cohorts, we replicated and meta-analysed results of eight cytosine-phosphate-guanine (CpG) sites in the GFI1 gene, which showed the most robust mediation. Finally, we performed functional network and enrichment analysis.

Results: We found 35 differentially methylated CpGs (FDR < 0.05) in newborns exposed vs unexposed to smoking, of which 23 survived Bonferroni correction (P < 1 × 10^-7). These 23 CpGs mapped to eight genes: AHRR, GFI1, MYO1G, CYP1A1, NEUROG1, CNTNAP2, FRMD4A and LRP5. We observed partial confirmation as three of the eight CpGs in GFI1 replicated. These CpGs partly mediated the effect of maternal smoking on birthweight (Sobel P < 0.05) in meta-analysis of GECKO and the two replication cohorts. Differential methylation of these three GFI1 CpGs explained 12–19% of the 202 g lower birthweight in smoking mothers. Functional enrichment analysis pointed towards activation of cell-mediated immunity.

Conclusions: Maternal smoking during pregnancy was associated with cord blood methylation differences. We observed a potentially mediating role of methylation in the association between maternal smoking during pregnancy and birthweight of the offspring. Functional network analysis suggested a role in activating the immune system.