Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, non-coding variants from which pinpointing causal genes remains challenging. Here, we combined data from 718,734 individuals to discover rare and low-frequency (MAF<5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which eight in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2, ZNF169) newly implicated in human obesity, two (MC4R, KSR2) previously observed in extreme obesity, and two variants in GIPR. Effect sizes of rare variants are ~10 times larger than of common variants, with the largest effect observed in carriers of an MC4R stop-codon (p.Tyr35Ter, MAF=0.01%), weighing ~7kg more than non-carriers. Pathway analyses confirmed enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically-supported therapeutic targets to treat obesity.
In this study, the authors analyzed whether chronotypes, sleep duration, and sleep sufficiency are associated with cardiovascular diseases and type 2 diabetes by using the National FINRISK Study 2007 data (N = 6258), being a representative sample of the population aged 25 to 74 living in five areas of Finland. Health status assessments and laboratory measurements from the participants (N = 4589) of the DILGOM substudy were used for the detailed analysis of chronotype. Evening types had a 2.5-fold odds ratio for type 2 diabetes (p < .01) as compared with morning types, the association being independent of sleep duration and sleep sufficiency. Evening types had a 1.3-fold odds ratio for arterial hypertension (p < .05 after controlling for sleep duration or sleep sufficiency), a faster resting heart rate and a lower systolic blood pressure (both p < .01), and lower levels of serum total cholesterol and low-density lipoprotein cholesterol (both p < .0001) than morning types. There were significant 1.2- to 1.4-fold odds ratios for arterial hypertension among those with long or short sleep durations or reduced sleep sufficiency. To conclude, the behavioral trait towards eveningness is suggested to predispose individuals to type 2 diabetes in particular, whereas compromised sleep is robustly associated with arterial hypertension.
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.
Aim: To assess the behavioral effects of receiving personal genetic information, using apoE genotypes as a tool for promoting lifestyle changes. apoE was chosen because it has a significant impact on lipid metabolism and cholesterol absorption, both factors in cardiovascular disease. Methods: This study was a 1-year intervention study of healthy adults aged 20-67 years (n = 107). Their behavioral changes were measured by diet (e.g., fat quality, as well as consumption of vegetables, berries, fruits, and fatty and sugary foods), alcohol consumption, and exercise. Health and taste attitudes were assessed with the Health and Taste Attitude Scales (HTAS). The measurements were performed 4 times: at baseline (T0), as well as 10 weeks (T1), 6 months (T2), and 12 months after baseline (T3). These behavioral effects were assessed for three groups: a high-risk (Ɛ4+; n = 16), a low-risk (Ɛ4-; n = 35), and a control group (n = 56). Results: Personal genetic information affected health behavior. Dietary fat quality improved more in the Ɛ4+ group than in the Ɛ4- and control groups after personal, genotype-based health advice. This change differed significantly between the Ɛ4+ and the control group (p < 0.05), but only for a short time. Conclusion: Personal genetic information, based on apoE, may affect dietary fat quality. More research is required to determine how to utilize genotype-based health information and how to efficiently achieve long-term changes in the prevention of lifestyle-related diseases.
Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
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