Anders Hamsten

Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (n_discovery = 52 654 and n_replication = 14 774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (P_combined = 1.8 × 10⁻¹¹), HLA-DRB1/DRB5 rs660895 on Chr6p21 (P_combined = 1.5 × 10⁻¹⁰) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (P_combined = 1.2 × 10⁻¹²²). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology.

(1) Background: In a previous study, we found that two phenotypes related to platelet reactivity, measured with the PFA-100 system, were highly heritable. The aim of the present study was to identify genetic determinants that influence the variability of these phenotypes: closure time of collagen-ADP (Col-ADP) and of collagen-epinephrine (Col-Epi). (2) Methods: As part of the GAIT-2 (Genetic Analysis of Idiopathic Thrombophilia (2) Project, 935 individuals from 35 large Spanish families were studied. A genome-wide association study (GWAS) with ≈ 10 M single nucleotide polymorphisms (SNPs) was carried out with Col-ADP and Col-Epi phenotypes. (3) Results: The study yielded significant genetic signals that mapped to the ABO locus. After adjusting both phenotypes for the ABO genotype, these signals disappeared. After adjusting for von Willebrand factor (VWF) or for coagulation factor VIII (FVIII), the significant signals disappeared totally for Col-Epi phenotype but only partially for Col-ADP phenotype. (4) Conclusion: Our results suggest that the ABO locus exerts the main genetic influence on PFA-100 phenotypes. However, while the effect of the ABO locus on Col-Epi phenotype is mediated through VWF and/or FVIII, the effect of the ABO locus on Col-ADP phenotype is partly produced through VWF and/or FVIII, and partly through other mechanisms.

1. SUMMARY: Despite recognizing aging as risk factor of human diseases, little is still known about the molecular traits of biological age and mortality risk. To identify age-associated proteins circulating human blood, we screened 156 subjects aged 50-92 years using an exploratory and multiplexed affinity proteomics approach. We corroborated the top age-associated protein profile (adjusted P < 0.001) in eight additional study sets (N = 4,044 individuals), and confirmed a consistent age-associated increase (P = 6.61 × 10 ^-6 ) by meta-analysis. Applying antibody validation determined circulating histidine-rich glycoprotein (HRG) as the target, and we observed that sequence variants influenced the antibodies ability to bind to the protein. Profiles of circulating HRG were associated to several clinical traits and predicted the risk of mortality during a follow-up period of 8.5 years (IQR = 7.7-9.3 years) after blood sampling (HR = 1.25 per SD; 95% CI = 1.12-1.39; P = 7.41 × 10 ^-5 ). In conclusion, our affinity proteomics analysis found associations between the molecular traits of circulating HRG with age and all-cause mortality. This suggests that the profiles of multi-purpose protein HRG could serve as an accessible indicator of physiological processes related to aging.

Cardiovascular disease (CVD) is a major cause of morbidity and mortality worldwide, particularly in individuals with diabetes. The current study objective was to determine the circulating metabolite profiles associated with the risk of future cardiovascular events, with emphasis on diabetes status. Nontargeted metabolomics analysis was performed by LC–HRMS in combination with targeted quantification of eicosanoids and endocannabinoids. Plasma from 375 individuals from the IMPROVE pan-European cohort was included in a case-control study design. Following data processing, the three metabolite data sets were concatenated to produce a single data set of 267 identified metabolites. Factor analysis identified six factors that described 26.6% of the variability in the given set of predictors. An association with cardiovascular events was only observed for one factor following adjustment (p = 0.026). From this factor, we identified a free fatty acid signature (n = 10 lipids, including saturated, monounsaturated, and polyunsaturated fatty acids) that was associated with lower risk of future cardiovascular events in nondiabetics only (OR = 0.65, 0.27–0.80 95% CI, p = 0.030), whereas no association was observed among diabetic individuals. These observations support the hypothesis that increased levels of circulating omega-6 and omega-3 fatty acids are associated with protective effects against future cardiovascular events. However, these effects were only observed in the nondiabetic population, further highlighting the need for patient stratification in clinical investigations.

Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PROCARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences. Many of our common diseases are driven by complex interactions between multiple genetic factors. Disease-specific, genome-wide association studies have been the prominent tool for cataloging such factors, by studying the genetic variation of a gene in a population and its association with the disease. However, these studies often fail to capture interactions between genes despite their importance. Interactions are notoriously difficult to investigate, because testing the large number of possible interactions using contemporary statistical methods requires very large sample sizes and computational resources. We have taken a step forward by developing a new statistical methodology that significantly reduces these requirements, making the study of interactions more feasible. We show that our methodology makes it possible to study interactions on a large scale without compromising the statistical accuracy. We further demonstrate the utility of our methodology on data relating to coronary artery disease and discover three distinct interactions that might provides new clues to the pathophysiology. The study of genetic interactions have the potential to link disease genes together into disease networks that provide a more detailed description of the interaction between genes and how it drives the disease.

Chronic obstructive pulmonary disease (COPD) independently associates with an increased risk of coronary artery disease (CAD), but it has not been fully investigated whether this co-morbidity involves shared pathophysiological mechanisms. To identify potential common pathways across the two diseases, we tested all recently published single nucleotide polymorphisms (SNPs) associated with human lung function (spirometry) for association with carotid intima-media thickness (cIMT) in 3,378 subjects with multiple CAD risk factors, and for association with CAD in a case-control study of 5,775 CAD cases and 7,265 controls. SNPs rs2865531, located in the CFDP1 gene, and rs9978142, located in the KCNE2 gene, were significantly associated with CAD. In addition, SNP rs9978142 and SNP rs3995090 located in the HTR4 gene, were associated with average and maximal cIMT measures. Genetic risk scores combining the most robustly spirometry–associated SNPs from the literature were modestly associated with CAD, (odds ratio (OR) (95% confidence interval (CI₉₅) = 1.06 (1.03, 1.09); P-value = 1.5×10⁻⁴, per allele). In conclusion, our study suggests that some genetic loci implicated in determining human lung function also influence cIMT and susceptibility to CAD. The present results should help elucidate the molecular underpinnings of the co-morbidity observed across COPD and CAD.

Background—: Estimates of the heritability of plasma fibrinogen concentration, an established predictor of cardiovascular disease, range from 34% to 50%. Genetic variants so far identified by genome-wide association studies explain only a small proportion (90 000 subjects of European ancestry, the first genome-wide association meta-analysis of fibrinogen levels in 7 studies in blacks totaling 8289 samples, and a genome-wide association study in Hispanics totaling 1366 samples. Evaluation for association of single-nucleotide polymorphisms with clinical outcomes included a total of 40 695 cases and 85 582 controls for coronary artery disease, 4752 cases and 24 030 controls for stroke, and 3208 cases and 46 167 controls for venous thromboembolism. Overall, we identified 24 genome-wide significant ( P −8 ) independent signals in 23 loci, including 15 novel associations, together accounting for 3.7% of plasma fibrinogen variation. Gene-set enrichment analysis highlighted key roles in fibrinogen regulation for the 3 structural fibrinogen genes and pathways related to inflammation, adipocytokines, and thyrotrophin-releasing hormone signaling. Whereas lead single-nucleotide polymorphisms in a few loci were significantly associated with coronary artery disease, the combined effect of all 24 fibrinogen-associated lead single-nucleotide polymorphisms was not significant for coronary artery disease, stroke, or venous thromboembolism. Conclusions—: We identify 23 robustly associated fibrinogen loci, 15 of which are new. Clinical outcome analysis of these loci does not support a causal relationship between circulating levels of fibrinogen and coronary artery disease, stroke, or venous thromboembolism.

OBJECTIVE Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10⁻⁸). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10⁻⁴), improved β-cell function (P = 1.1 × 10⁻⁵), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10⁻⁶). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

Recent genome-wide association (GWA) analyses have identified common single nucleotide polymorphisms (SNPs) that are associated with obesity. However, the reported genetic variation in obesity explains only a minor fraction of the total genetic variation expected to be present in the population. Thus many genetic variants controlling obesity remain to be identified. The aim of this study was to use GWA followed by multiple stepwise validations to identify additional genes associated with obesity. We performed a GWA analysis in 164 morbidly obese subjects (BMI:body mass index > 40 kg/m2) and 163 Swedish subjects (> 45 years) who had always been lean. The 700 SNPs displaying the strongest association with obesity in the GWA were analyzed in a second cohort comprising 460 morbidly obese subjects and 247 consistently lean Swedish adults. 23 SNPs remained significantly associated with obesity (nominal P< 0.05) and were in a step-wise manner followed up in five additional cohorts from Sweden, France, and Germany together comprising 4214 obese and 5417 lean or population-based control individuals. Three samples, n = 4133, were used to investigate the population-based associations with BMI. Gene expression in abdominal subcutaneous adipose tissue in relation to obesity was investigated for14 adults. Potassium channel, calcium activated, large conductance, subfamily M, alpha member (KCNMA1) rs2116830*G and BDNF rs988712*G were associated with obesity in five of six investigated case-control cohorts. In meta-analysis of 4838 obese and 5827 control subjects we obtained genome-wide significant allelic association with obesity for KCNMA1 rs2116830*G with P = 2.82 × 10-10 and an odds ratio (OR) based on cases vs controls of 1.26 [95% C.I. 1.12-1.41] and for BDNF rs988712*G with P = 5.2 × 10-17and an OR of 1.36 [95% C.I. 1.20-1.55]. KCNMA1 rs2116830*G was not associated with BMI in the population-based samples. Adipose tissue (P = 0.0001) and fat cell (P = 0.04) expression of KCNMA1 was increased in obesity. We have identified KCNMA1 as a new susceptibility locus for obesity, and confirmed the association of the BDNF locus at the genome-wide significant level.

Thrombosis in the arterial or venous vascular systems is preceded by a complex interplay between environmental and genetic factors, and it is the underlying cause of several common complex diseases. The genome-wide association approach has proved successful in identifying loci associated with cardiovascular disease and related risk factors. However, much work remains to define the culprit genes and causal variants as well as the mechanisms whereby they influence disease development and progression. In-depth studies of previously identified disease-associated loci are expected to improve our understanding of the pathophysiology of cardiovascular disease and identify novel targets for treatment. Here, we review the advances made in the past year in the field of atherothrombosis and thrombophilia, with the focus placed on results emerging from genome-wide association studies on coronary artery disease, ischemic stroke, venous thromboembolism, and intermediate traits associated with these disease entities.