Central abdominal fat is a strong risk factor for diabetes and cardiovascular disease. To identify common variants influencing central abdominal fat, we conducted a two-stage genome-wide association ...analysis for waist circumference (WC). In total, three loci reached genome-wide significance. In stage 1, 31,373 individuals of Caucasian descent from eight cohort studies confirmed the role of FTO and MC4R and identified one novel locus associated with WC in the neurexin 3 gene NRXN3 (rs10146997, p = 6.4×10-7). The association with NRXN3 was confirmed in stage 2 by combining stage 1 results with those from 38,641 participants in the GIANT consortium (p = 0.009 in GIANT only, p = 5.3×10-8 for combined analysis, n = 70,014). Mean WC increase per copy of the G allele was 0.0498 z-score units (0.65 cm). This SNP was also associated with body mass index (BMI) p = 7.4×10-6, 0.024 z-score units (0.10 kg/m2) per copy of the G allele and the risk of obesity (odds ratio 1.13, 95% CI 1.07-1.19; p = 3.2×10-5 per copy of the G allele). The NRXN3 gene has been previously implicated in addiction and reward behavior, lending further evidence that common forms of obesity may be a central nervous system-mediated disorder. Our findings establish that common variants in NRXN3 are associated with WC, BMI, and obesity.
A Common Polymorphism in the Complement Factor H Gene Is Associated With Increased Risk of Myocardial Infarction: The Rotterdam Study
Isabella Kardys, Caroline C. W. Klaver, Dominiek D. G. Despriet, ...Arthur A. B. Bergen, André G. Uitterlinden, Albert Hofman, Ben A. Oostra, Cornelia M. Van Duijn, Paulus T. V. M. de Jong, Jacqueline C. M. Witteman
The Tyr402His polymorphism of an inhibitory gene of the alternative complement cascade complement factor H (CFH) was determined in 5,520 subjects from the Rotterdam study age 55 years and over without history of coronary heart disease. Homozygotes had a multivariate adjusted hazards ratio of 1.77 (95% confidence interval 1.23 to 2.55) for myocardial infarction. These results suggest that the CFH gene determines susceptibility to myocardial infarction, and this finding underscores the importance of the alternative complement system in cardiovascular disease.
This study was designed to investigate the association between a common polymorphism (Tyr402His, rs1061170) in the complement factor H (CFH) gene and risk of coronary heart disease.
The evidence that inflammation is an important mechanism in atherogenesis is growing. C-reactive protein (CRP), complement factors, and complement regulatory factors have all been linked to coronary heart disease. The CFH gene is an important regulator of the alternative complement cascade. We investigated its association with coronary heart disease.
The study was embedded in the Rotterdam Study, a prospective population-based study among men and women aged 55 years and over. A total of 5,520 participants without history of coronary heart disease was genotyped for the Tyr402His polymorphism of the CFH gene. Cox proportional hazards analysis was used to determine risk of myocardial infarction for Tyr402His genotypes.
Mean age among participants was 69.5 years (SD 9.1 years). The overall frequency of the His allele was 36%; genotype frequencies were 41%, 45%, and 14% for TyrTyr, TyrHis, and HisHis, respectively. During a mean follow-up period of 8.4 years, 226 myocardial infarctions occurred. After adjustment for age, gender, established cardiovascular risk factors, and CRP level, HisHis homozygotes had a hazard ratio of 1.77 (95% confidence interval 1.23 to 2.55) for myocardial infarction. Total cholesterol level, diabetes mellitus, and smoking modified the effect. The Tyr402His polymorphism was not associated with established cardiovascular risk factors or CRP level.
Our data suggest that the CFH gene determines susceptibility to myocardial infarction. This finding underscores the importance of the alternative complement system in cardiovascular disease.
We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional ...theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We separately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts.
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