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In patients with type 2 diabetes, sleep-disordered breathing is a widespread cause of deteriorated quality of life. However, robust prevalence estimates for sleep-disordered breathing in patients with type 2 diabetes are limited due to scarce data. We investigated sex differences in sleep-disordered breathing prevalence and its modulators in the DIACORE SDB substudy, a sample of outpatient type 2 diabetes. 721 participants were tested for sleep-disordered breathing using a two-channel sleep apnoea monitoring device. Patients were stratified according to the severity of sleep-disordered breathing, defined as an apnoea-hypopnoea index

Height, adiposity, and fat distribution differ in men and women and, in part, may explain sex differences in susceptibilities to complex diseases like cardiovascular disease. Genome-wide association studies (GWAS) of these traits have previously reported sexually dimorphic associations, yet studies have primarily been limited to interrogation of variants with genome wide significant main effects only. Because of these biological differences by sex and as there is growing interest in the study of gene-environment interactions in the context of GWAS in general, we conducted sex-specific meta-analyses of 9 phenotypes: height (HT), weight (WT), body mass index (BMI), waist circumference (WC), hip circumference (HIP), WC/HC ratio (WHR), WC adjusted for BMI (WCadjBMI), HC adjusted for BMI (HCadjBMI), and WHR adjusted for BMI (WHRadjBMI). In the discovery stage, we performed sex-specific meta-analyses of 46 GWAS, comprising 60,586 men and 73,137 women. Each study used an additive model to test up to ∼2.8M imputed SNPs for association with inverse-normal transformed phenotypes. From our first scan based on the sex-specific association P-values (P women , P men ) across all phenotypes, we selected 619 independent SNPs at a false discovery rate (FDR) of 5% to take forward to replication. We also conducted a second scan based on the P-value for sex difference (P sex-diff ) with better power to detect signals of opposite effect direction, yet we did not detect any signal at FDR of 5%. Follow-up of the 619 SNPs in up to 62,395 men and 74,657 women, many of which were genotyped on Metabochip, a custom Illumina iSelect array to which we submitted sex-specific SNPs, resulted in 205 loci with genome-wide significanct (P women or P men < 5x10 -8 ) p values in the combined discovery and follow-up analysis. For those 205 loci, we found 4 loci with significant (P sex-diff < 0.05/205) and 14 loci with suggestive (P sex-diff GRB14 , 1q41 , VEGFA , ADAMTS9), known anthropometric trait associations without any prior evidence for sexual-dimorphism ( 14q23.1, 3q21.3, 6q14.1, 4q12, 12q24.31, SEC16B, 17q21.32 , and 13q31.3 ), and novel sex-specific associations with anthropometric traits ( 5q11.2, 5q23.1, PPARG, 2q37.1, 17p11.2 , and 5q35.1 ). Interestingly, we found that our replicated loci for WHR/WC were enriched with markers with sex-differences, and that these genetic effects were uniformly stronger in women compared to men. Collectively, these results underscore the gain from sex-stratified GWAS in order to better pinpoint the genetics of complex traits and illustrate a sexually dimorphic genetic underpinning to some of these traits. Our results more globally emphasize the need to consider gene-environment interaction when searching for genes influencing risk to complex disease.

Obesity is a heritable trait and a risk factor for many common diseases such as type 2 diabetes, heart disease, and hypertension. We used a dense whole-genome scan of DNA samples from the Framingham Heart Study participants to identify a common genetic variant near the INSIG2 gene associated with obesity. We have replicated the finding in four separate samples composed of individuals of Western European ancestry, African Americans, and children. The obesity-predisposing genotype is present in 10% of individuals. Our study suggests that common genetic polymorphisms are important determinants of obesity.

Identification of genetic variants affecting complex traits such as obesity is confounded by many types of bias, especially when effect sizes are small. Given our findings of a positive association between rs7566605 and body mass index in four out of five separate samples, a false positive finding cannot be ruled out with certainty but seems unlikely. Meta-analyses of multiple large studies will help refine the estimate of the effects of rs7566605 on body mass index.

Item does not contain fulltext The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 x 10(-8)) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across approximately 115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.