Worldwide patterns of haplotype diversity at 9p21.3, a locus associated with type 2 diabetes and coronary heart disease

A 100 kb region on 9p21.3 harbors two major disease susceptibility loci: one for type 2 diabetes (T2D) and one for coronary heart disease (CHD). The single nucleotide polymorphisms (SNPs) associated with these two diseases in Europeans reside on two adjacent haplotype blocks with independent effects on disease. To help delimit the regions that likely harbor the disease-causing variants in populations of non-European origin, we studied the haplotype diversity and allelic history of the 9p21.3 region using 938 unrelated individuals from 51 populations (Human Genome Diversity Panel). We used SNP data from Illumina's 650Y SNP arrays supplemented with five additional SNPs within the region of interest. Haplotype frequencies were analyzed with the EM algorithm implemented in PLINK. For the T2D locus, the TT risk haplotype of SNPs rs10811661 and rs10757283 was present at similar frequencies in all global populations, while a shared 6-SNP haplotype that carries the protective C allele of rs10811661 was found at a frequency of 2.9% in Africans and 41.3% in East Asians and was associated with low haplotype diversity. For the CHD locus, all populations shared a core risk haplotype spanning >17.5 kb, which shows dramatic increase in frequency between African (11.5%) and Middle Eastern (63.7%) populations. Interestingly, two SNPs (rs2891168 and rs10757278) tagging this CHD risk haplotype are most strongly associated with CHD disease status according to independent clinical fine-mapping studies. The large variation in linkage disequilibrium patterns identified between the populations demonstrates the importance of allelic background data when selecting SNPs for replication in global populations. Intriguingly, the protective allele for T2D and the risk allele for CHD show an increase in frequency in non-Africans compared to Africans, implying different population histories for these two adjacent disease loci

Combined Effects of Thrombosis Pathway Gene Variants Predict Cardiovascular Events

The genetic background of complex diseases is proposed to consist of several low-penetrance risk loci. Addressing this complexity likely requires both large sample size and simultaneous analysis of different predisposing variants. We investigated the role of four thrombosis genes: coagulation factor V (F5), intercellular adhesion molecule 1 (ICAM1), protein C (PROC), and thrombomodulin (THBD) in cardiovascular diseases. Single allelic gene variants and their pair-wise combinations were analyzed in two independently sampled population cohorts from Finland. From among 14,140 FINRISK participants (FINRISK-92, n = 5,999 and FINRISK-97, n = 8,141), we selected for genotyping a sample of 2,222, including 528 incident cardiovascular disease (CVD) cases and random subcohorts totaling 786. To cover all known common haplotypes (>10%), 54 single nucleotide polymorphisms (SNPs) were genotyped. Classification-tree analysis identified 11 SNPs that were further analyzed in Cox's proportional hazard model as single variants and pair-wise combinations. Multiple testing was controlled by use of two independent cohorts and with false-discovery rate. Several CVD risk variants were identified: In women, the combination of F5 rs7542281 × THBD rs1042580, together with three single F5 SNPs, was associated with CVD events. Among men, PROC rs1041296, when combined with either ICAM1 rs5030341 or F5 rs2269648, was associated with total mortality. As a single variant, PROC rs1401296, together with the F5 Leiden mutation, was associated with ischemic stroke events. Our strategy to combine the classification-tree analysis with more traditional genetic models was successful in identifying SNPs—acting either in combination or as single variants—predisposing to CVD, and produced consistent results in two independent cohorts. These results suggest that variants in these four thrombosis genes contribute to arterial cardiovascular events at population level

Risk Alleles of USF1 Gene Predict Cardiovascular Disease of Women in Two Prospective Studies

Upstream transcription factor 1 (USF1) is a ubiquitously expressed transcription factor controlling several critical genes in lipid and glucose metabolism. Of some 40 genes regulated by USF1, several are involved in the molecular pathogenesis of cardiovascular disease (CVD). Although the USF1 gene has been shown to have a critical role in the etiology of familial combined hyperlipidemia, which predisposes to early CVD, the gene's potential role as a risk factor for CVD events at the population level has not been established. Here we report the results from a prospective genetic–epidemiological study of the association between the USF1 variants, CVD, and mortality in two large Finnish cohorts. Haplotype-tagging single nucleotide polymorphisms exposing all common allelic variants of USF1 were genotyped in a prospective case-cohort design with two distinct cohorts followed up during 1992–2001 and 1997–2003. The total number of follow-up years was 112,435 in 14,140 individuals, of which 2,225 were selected for genotyping based on the case-cohort study strategy. After adjustment for conventional risk factors, we observed an association of USF1 with CVD and mortality among females. In combined analysis of the two cohorts, female carriers of a USF1 risk haplotype had a 2-fold risk of a CVD event (hazard ratio [HR] 2.02; 95% confidence interval [CI] 1.16–3.53; p = 0.01) and an increased risk of all-cause mortality (HR 2.52; 95% CI 1.46–4.35; p = 0.0009). A putative protective haplotype of USF1 was also identified. Our study shows how a gene identified in exceptional families proves to be important also at the population level, implying that allelic variants of USF1 significantly influence the prospective risk of CVD and even all-cause mortality in females

Common Genetic Variation Near Melatonin Receptor 1A Gene Linked to Job-Related Exhaustion in Shift Workers

Study Objectives: Tolerance to shift work varies; only some shift workers suffer from disturbed sleep, fatigue, and job-related exhaustion. Our aim was to explore molecular genetic risk factors for intolerance to shift work. Methods: We assessed intolerance to shift work with job-related exhaustion symptoms in shift workers using the emotional exhaustion subscale of the Maslach Burnout Inventory-General Survey, and carried out a genome-wide association study (GWAS) using Illumina's Human610-Quad BeadChip (n = 176). The most significant findings were further studied in three groups of Finnish shift workers (n = 577). We assessed methylation in blood cells with the Illumina HumanMethylation450K BeadChip, and examined gene expression levels in the publicly available eGWAS Mayo data. Results: The second strongest signal identified in the GWAS (p = 2.3 x 10E-6) was replicated in two of the replication studies with p Conclusions: These findings suggest that a variant near MTNR1A may be associated with job-related exhaustion in shift workers. The risk variant may exert its effect via epigenetic mechanisms, potentially leading to reduced melatonin signaling in the brain. These results could indicate a link between melatonin signaling, a key circadian regulatory mechanism, and tolerance to shift work.Peer reviewe

Metabonomic, transcriptomic, and genomic variation of a population cohort

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Extending the Minimum Information About BIobank Data Sharing Terminology to Describe Samples, Sample Donors, and Events

Introduction: The Minimum Information About BIobank data Sharing (MIABIS) was initiated in 2012. MIABIS aims to create a common biobank terminology to facilitate data sharing in biobanks and sample collections. The MIABIS Core terminology consists of three components describing biobanks, sample collections, and studies, in which information on samples and sample donors is provided at aggregated form. However, there is also a need to describe samples and sample donors at an individual level to allow more elaborate queries on available biobank samples and data. Therefore the MIABIS terminology has now been extended with components describing samples and sample donors at an individual level. Materials and Methods: The components were defined according to specific scope and use cases by a large group of experts, and through several cycles of reviews, according to the new MIABIS governance model of BBMRI-ERIC (Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium). The guiding principles applied in developing these components included the following terms: model should consider only samples of human origin, model should be applicable to all types of samples and all sample donors, and model should describe the current status of samples stored in a given biobank. Results: A minimal set of standard attributes for defining samples and sample donors is presented here. We added an "event" component to describe attributes that are not directly describing samples or sample donors but are tightly related to them. To better utilize the generic data model, we suggest a procedure by which interoperability can be promoted, using specific MIABIS profiles. Discussion: The MIABIS sample and donor component extensions and the new generic data model complement the existing MIABIS Core 2.0 components, and substantially increase the potential usability of this terminology for better describing biobank samples and sample donors. They also support the use of individual level data about samples and sample donors to obtain accurate and detailed biobank availability queries

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ing glucose, insulin, and C-peptide, and more favorable cardiovascular risk profile compared to the complement set of subjects with T2DM. OSA also revealed 33 families with the lowest average fasting insulin that had increased evidence for linkage at a second locus (MLS = 3.45 at 128 cM; uncorrected p = 0.017) coincident with quantitative trait locus linkage analysis results for fasting and 2-hour insulin in subjects without T2DM. Conclusions: These results suggest two diabetes susceptibility loci on chromosome 6q that may affect subsets of individuals with a milder form of T2DM

Gender differences in genetic risk profiles for cardiovascular disease

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An Immune Response Network Associated with Blood Lipid Levels

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