Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Factors Associated to Vaccination against Influenza among Elderly in a Large Brazilian Metropolis

BACKGROUND:This study aimed to estimate coverage and identify factors associated to vaccination against influenza in the elderly population. METHODS:The study design was cross-sectional and population based. Data was collected in 2010 by the Health, Well-Being and Aging Study. Sample consisted of 1,341 community-dwelling elderly, in São Paulo, Brazil. Association between vaccination and covariates was evaluated by means of prevalence ratios estimated by Poisson regression models. RESULTS:Self-reported vaccination coverage was 74.2% (95% confidence interval: 71.3-76.9). Remaining physically active and having had recent interaction with health services, mainly with public units of healthcare, were the main incentives to increase vaccination coverage among the elderly; whereas lower age, living alone and absent interaction with health services were the main constraints to influenza vaccination at the community level. These covariates had already been reported to influence influenza vaccination of elders in previous years. CONCLUSION:Previous knowledge already available on the main constraints to influenza vaccination has not allowed to remove them. Influenza campaigns should be strengthened to increase vaccination coverage, especially in the group more reticent to vaccination. Instructing healthcare providers to recommend vaccine uptake is an important piece of this puzzle

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

AbstractWe conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.</jats:p