Pleiotropic effects of obesity-susceptibility loci on metabolic traits: a meta-analysis of up to 37,874 individuals

Abstract

Aims/hypothesis Genetic pleiotropy may contribute to the clustering of obesity and metabolic conditions. We assessed whether genetic variants that are robustly associated with BMI and waist-to-hip ratio (WHR) also influence metabolic and cardiovascular traits, independently of obesity-related traits, in meta-analyses of up to 37,874 individuals from six European population-based studies. Methods We examined associations of 32 BMI and 14 WHR loci, individually and combined in two genetic predisposition scores (GPSs), with glycaemic traits, blood lipids and BP, with and without adjusting for BMI and/or WHR. Results We observed significant associations of BMIincreasing alleles at five BMI loci with lower levels of 2 h glucose (RBJ [also known as DNAJC27], QPTCL: effect sizes −0.068 and −0.107 SD, respectively), HDL-cholesterol (SLC39A8: −0.065 SD, MTCH2: −0.039 SD), and diastolic BP (SLC39A8: −0.069 SD), and higher and lower levels of LDL-and total cholesterol (QPTCL: 0.041 and 0.042 SDs, respectively, FLJ35779 [also known as POC5]: −0.042 and −0.041 SDs, respectively) (all p<2.4×10 −4 ), independent of BMI. The WHR-increasing alleles at two WHR loci were significantly associated with higher proinsulin (GRB14: 0.069 SD) and lower fasting glucose levels (CPEB4: −0.049 SD), independent of BMI and WHR. A higher GPS-BMI was associated with lower systolic BP (−0.005 SD), diastolic BP (−0.006 SD) and 2 h glucose (−0.013 SD), while a higher GPS-WHR was associated with lower HDL-cholesterol Electronic supplementary material The online version of this article (doi:10.1007/s00125-013-2985-y) contains peer-reviewed but unedited supplementary material, which is available to authorised users. J. V. van Vliet-Ostaptchouk : M. M. van der Klauw :

Genetic susceptibility to type 2 diabetes and obesity: from genome-wide association studies to rare variants and beyond

“…GRB14/COBLL1 is a locus that has been shown to associate with a range of traits. [96], even after adjusting for obesity measures [97]. These associations are in concordance with a general pleiotropic metabolic risk profile affecting many of components of the metabolic syndrome in a clinically unfavourable direction.…”

“…A recent meta-analysis of data on up to~37,000 individuals systematically evaluated metabolic pleiotropic associations for BMI-and WHR-associated variants [97]. Analysis of individual variants revealed that some were associated with a metabolically unhealthy profile whereas others displayed more complex associations [97].…”

“…Analysis of individual variants revealed that some were associated with a metabolically unhealthy profile whereas others displayed more complex associations [97]. Genetic risk scores, generated by adding the number of risk alleles for each individual, indicated that a high score based on WHR-associated variants had an adverse effect on serum lipid levels even after adjusting for adiposity.…”

“…Genetic risk scores, generated by adding the number of risk alleles for each individual, indicated that a high score based on WHR-associated variants had an adverse effect on serum lipid levels even after adjusting for adiposity. Interestingly, analyses of the genetic risk score based on BMI-associated alleles showed a generally metabolically unhealthy profile, which was abolished after adjustment for BMI, yet analyses adjusted for BMI revealed paradoxical associations with decreased plasma glucose at 2 h during an OGTT and with decreased systolic and diastolic BP for increasing number of BMI-increasing alleles [97]. Up-coming studies with even larger sample sizes will explore relationships between associations of specific loci and a range of metabolic traits and will probably elucidate further distinct mechanisms in adipose tissue function and physiological relationships between metabolic variables.…”

Genetically predicted body mass index and Alzheimer's disease–related phenotypes in three large samples: Mendelian randomization analyses

“…For each individual i we calculated BMI polygenic scores, using an additive genetic model, as the sum across k SNPs of the product of the β weight for the effect of that SNP on BMI by the individual’s allele count for that SNP: $${\mathrm{GRS}}_{\mathrm{normali}}={\Sigma }_{\mathrm{k}=1}^{\#\mathrm{SNPs}}{\beta }_{\mathrm{normalk}}\cdot {normal\text{allele count}}_{normali.k}$$ In exploratory analyses, we assigned each gene to one of four functional categories to generate mechanism-specific subscores after a literature review in PubMed: adipogenesis (adipocyte differentiation and fat accumulation, e.g. rs3817334 (MTCH2) with HDL-cholesterol levels[21]), appetite (regulation of appetite and food intake, e.g. rs10767664 (BDNF) with total caloric intake[22]), cardiopulmonary factors (cardiomyogenesis, oxidative stress response and cardiac remodeling, e.g.…”