Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.

Background Elevated plasma levels of direct low‐density lipoprotein cholesterol (LDL‐C), small dense LDL‐C (sdLDL‐C), low‐density lipoprotein (LDL) triglycerides, triglycerides, triglyceride‐rich lipoprotein cholesterol, remnant lipoprotein particle cholesterol, and lipoprotein(a) have all been associated with incident atherosclerotic cardiovascular disease (ASCVD). Our goal was to assess which parameters were most strongly associated with ASCVD risk. Methods and Results Plasma total cholesterol, triglycerides, high‐density lipoprotein cholesterol, direct LDL‐C, sdLDL‐C, LDL triglycerides, remnant lipoprotein particle cholesterol, triglyceride‐rich lipoprotein cholesterol, and lipoprotein(a) were measured using standardized automated analysis (coefficients of variation, <5.0%) in samples from 3094 fasting subjects free of ASCVD. Of these subjects, 20.2% developed ASCVD over 16 years. On univariate analysis, all ASCVD risk factors were significantly associated with incident ASCVD, as well as the following specialized lipoprotein parameters: sdLDL‐C, LDL triglycerides, triglycerides, triglyceride‐rich lipoprotein cholesterol, remnant lipoprotein particle cholesterol, and direct LDL‐C. Only sdLDL‐C, direct LDL‐C, and lipoprotein(a) were significant on multivariate analysis and net reclassification after adjustment for standard risk factors (age, sex, hypertension, diabetes mellitus, smoking, total cholesterol, and high‐density lipoprotein cholesterol). Using the pooled cohort equation, many specialized lipoprotein parameters individually added significant information, but no parameter added significant information once sdLDL‐C (hazard ratio, 1.42; P <0.0001) was in the model. These results for sdLDL‐C were confirmed by adjusted discordance analysis versus calculated non–high‐density lipoprotein cholesterol, in contrast to LDL triglycerides. Conclusions sdLDL‐C, direct LDL‐C, and lipoprotein(a) all contributed significantly to ASCVD risk on multivariate analysis, but no parameter added significant risk information to the pooled cohort equation once sdLDL‐C was in the model. Our data indicate that small dense LDL is the most atherogenic lipoprotein parameter.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.