Smoking is a risk factor for most of the diseases leading in mortality1. We conducted genome-wide association (GWA) meta-analyses of smoking data within the ENGAGE consortium to search for common alleles associating with the number of cigarettes smoked per day (CPD) in smokers (N=31,266) and smoking initiation (N=46,481). We tested selected SNPs in a second stage (N=45,691 smokers), and assessed some in a third sample (N=9,040). Variants in three genomic regions associated with CPD (P< 5·10−8), including previously identified SNPs at 15q25 represented by rs1051730-A (0.80 CPD,P=2.4·10−69), and SNPs at 19q13 and 8p11, represented by rs4105144-C (0.39 CPD, P=2.2·10−12) and rs6474412-T (0.29 CPD,P= 1.4·10−8), respectively. Among the genes at the two novel loci, are genes encoding nicotine-metabolizing enzymes (CYP2A6 and CYP2B6), and nicotinic acetylcholine receptor subunits (CHRNB3 and CHRNA6) highlighted in previous studies of nicotine dependence2-3. Nominal associations with lung cancer were observed at both 8p11 (rs6474412-T,OR=1.09,P=0.04) and 19q13 (rs4105144-C,OR=1.12,P=0.0006).

The relation between BMI and diet appears to be mediated through dieting behaviors. Dietary counseling should focus on unhealthy dieting behaviors rather than only on direct advice on food use.

Sweet taste preferences are partly inherited. Chromosome 16p11.2 may harbor genetic variations that affect the consumption of sweet foods.

A cluster of three nicotinic acetylcholine receptor genes on chromosome 15 (CHRNA5/CHRNA3/CHRNB4) has been shown to be associated with nicotine dependence and smoking quantity. The aim of this study was to clarify whether the variation at this locus regulates nicotine intake among smokers by using the level of a metabolite of nicotine, cotinine, as an outcome. The number of cigarettes smoked per day (CPD) and immune-reactive serum cotinine level were determined in 516 daily smokers (age 30-75 years, 303 males) from the population-based Health2000 study. Association of 21 SNPs from a 100 kb region of chromosome 15 with cotinine and CPD was examined. SNP rs1051730 showed the strongest association to both measures. However, this SNP accounted for nearly a five-fold larger proportion of variance in cotinine levels than in CPD (R(2) 4.3% versus 0.9%). The effect size of the SNP was 0.30 for cotinine level, whereas it was 0.13 for CPD. Variation at CHRNA5/CHRNA3/CHRNB4 cluster influences nicotine level, measured as cotinine, more strongly than smoking quantity, measured by CPD, and appears thus to be involved in regulation of nicotine levels among smokers.

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Background: Sweet taste preferences are measured by several often correlated measures. Objective: We examined the relative proportions of genetic and environmental effects on sweet taste preference indicators and their mutual correlations. Design: A total of 663 female twins (324 complete pairs, 149 monozygous and 175 dizygous pairs) aged 17-80 y rated the liking and intensity of a 20% (wt/vol) sucrose solution, reported the liking and the use-frequency of 6 sweet foods (sweet desserts, sweets, sweet pastry, ice cream, hard candy, and chocolate), and completed a questionnaire on cravings of sweet foods. The estimated contributions of genetic factors, environmental factors shared by a twin pair, and environmental factors unique to each twin individual to the variance and covariance of the traits were obtained with the use of linear structural equation modeling.Results: Approximately half of the variation in liking for sweet solution and liking and use-frequency of sweet foods (49 -53%) was explained by genetic factors, whereas the rest of the variation was due to environmental factors unique to each twin individual. Sweet taste preference-related traits were correlated. Tetravariate modeling showed that the correlation between liking for the sweet solution and liking for sweet foods was due to genetic factors (genetic r ҃ 0.27). Correlations between liking, use-frequency, and craving for sweet foods were due to both genetic and unshared environmental factors. Conclusion: Detailed information on the associations between preference measures is an important intermediate goal in the determination of the genetic components affecting sweet taste preferences.

Smoking influences body weight such that smokers weigh less than non-smokers and smoking cessation often leads to weight increase. The relationship between body weight and smoking is partly explained by the effect of nicotine on appetite and metabolism. However, the brain reward system is involved in the control of the intake of both food and tobacco. We evaluated the effect of single-nucleotide polymorphisms (SNPs) affecting body mass index (BMI) on smoking behavior, and tested the 32 SNPs identified in a meta-analysis for association with two smoking phenotypes, smoking initiation (SI) and the number of cigarettes smoked per day (CPD) in an Icelandic sample (N=34 216 smokers). Combined according to their effect on BMI, the SNPs correlate with both SI (r=0.019, P=0.00054) and CPD (r=0.032, P=8.0 × 10−7). These findings replicate in a second large data set (N=127 274, thereof 76 242 smokers) for both SI (P=1.2 × 10−5) and CPD (P=9.3 × 10−5). Notably, the variant most strongly associated with BMI (rs1558902-A in FTO) did not associate with smoking behavior. The association with smoking behavior is not due to the effect of the SNPs on BMI. Our results strongly point to a common biological basis of the regulation of our appetite for tobacco and food, and thus the vulnerability to nicotine addiction and obesity.

The contribution of genetic factors to individual differences in food use was estimated in a large population-based twin cohort of young adults (22-to 27-year-old). Male and female twins (n=2009 complete twin pairs) evaluated use-frequencies of 24 food items using 5 categories (1=never -5=several times a day) in a postal questionnaire. Foods were categorized by factor analysis. Estimates of the relative proportions of additive genetic, shared environmental, and unshared environmental effects on the use-frequency of food items and factor scores were obtained by quantitative genetic modeling of twin data based on linear structural equations. Four factors of food use were identified: "healthy" foods, high-fat foods, sweet foods, and meats. The variance of the use-frequency of food items and food categories was explained by additive genetic and unshared environmental influences, whereas shared environmental factors did not contribute to food use. The average proportions of genetic effects on the total variance of the use-frequency of food items and food categories were 40% and 45%, respectively. Sex differences were observed in the magnitude of genetic influences for use-frequency of four food items (chocolate, other sweets, fried foods, and meat), and in genetic factors underlying the use of three (fresh vegetables, fruits, and cheeses) items. In conclusion, family environment does not appear to influence the food use of young adults and thus nutritional education should be targeted at this age group to support development of healthy eating patterns. In addition, the results illuminate the importance of the sex-specific genetic effects on food use.