Smoking is a leading cause of preventable death, causing approximately five million premature deaths world-wide each year 1, 2 . Evidence for genetic influence on smoking behaviour and nicotine dependence (ND) 3-8 has prompted a search for susceptibility genes. Furthermore, assessing the impact of sequence variants on smoking-related diseases is important for public health reasons 9, 10 . Smoking is the major risk factor for lung cancer (LC) [11][12][13][14] , and one of the main risk factors for peripheral arterial disease (PAD) [15][16][17] . We have identified a common variant in the nicotinic acetylcholine receptor gene cluster on chromosome 15q24 with an effect on smoking quantity, ND and the risk of two smoking-related diseases in populations of European descent. The variant has an effect on the number of cigarettes smoked per day in 15,771 smokers (P=6×10 −20 ). The same variant associated with ND in a previous genome-wide association study using low quantity smokers as controls (OR=1.3, P=1×10 −3 ) 18,19 , and with a similar approach we observe a highly significant association with ND (OR =1.40, P=7×10 −15 ). Comparison of LC (N=1,024) and PAD (N= 2,738) cases with about 30,000 population controls each showed that the variant confers risk of LC (OR=1.31, P=1.5×10 −8 ) and PAD (OR=1.19, P=1.4×10 −7 ). The findings highlight the role of nicotine addiction in the pathogenesis of other serious diseases and provide a case study of the role of active gene-environment correlation 20 in the pathogenesis of disease.To perform a genome-wide association (GWA) study of smoking quantity (SQ), we utilised questionnaire data limited to basic questions on smoking behaviour that were available for a large number of lifetime smokers. The GWA scan comprises 10,995 Icelandic smokers who Reprints and permissions information is available at www.nature.com/reprints.

Background Genome-wide association studies (GWAS) have been unable to identify variants linked to depression. We hypothesized that examining depressive symptoms and considering gene-environment interaction (G×E) might improve efficiency for gene discovery. We therefore conducted a GWAS and genome-wide environment interaction study (GWEIS) of depressive symptoms. Methods Using data from the SHARe cohort of the Women’s Health Initiative, comprising African Americans (n=7179) and Hispanics/Latinas (n=3138), we examined genetic main effects and G×E with stressful life events and social support. We also conducted a heritability analysis using genome-wide complex trait analysis (GCTA). Replication was attempted in four independent cohorts. Results No SNPs achieved genome-wide significance for main effects in either discovery sample. The top signals in African Americans were rs73531535 (located 20kb from GPR139, p=5.75×10−8) and rs75407252 (intronic to CACNA2D3, p=6.99×10−7). In Hispanics/Latinas, the top signals were rs2532087 (located 27kb from CD38, p=2.44×10−7) and rs4542757 (intronic to DCC, p=7.31×10−7). In the GWEIS with stressful life events, one interaction signal was genome-wide significant in African Americans (rs4652467; p=4.10×10−10; located 14kb from CEP350). This interaction was not observed in a smaller replication cohort. Although heritability estimates for depressive symptoms and stressful life events were each less than 10%, they were strongly genetically correlated (rG=0.95), suggesting that common variation underlying depressive symptoms and stressful life event exposure, though modest on their own, were highly overlapping in this sample. Conclusions Our results underscore the need for larger samples, more GWEIS, and greater investigation into genetic and environmental determinants of depressive symptoms in minorities.

No abstract

Based on this group of patients there is no evidence that the genes causing FHM and EA-2 represent major susceptibility loci for MV.

Although lithium preparations remain first-line treatment for bipolar disorder, risk for development of renal insufficiency may discourage their use. Estimating such risk could allow more informed decisions and facilitate development of prevention strategies. We utilized electronic health records from a large New England health-care system between 2006 and 2013 to identify patients aged 18 years or older with a lithium prescription. Renal insufficiency was identified using the presence of renal failure by ICD9 code or laboratory-confirmed glomerular filtration rate below 60 ml/min. Logistic regression was used to build a predictive model in a random two-thirds of the cohort, which was tested in the remaining one-third. Risks associated with aspects of pharmacotherapy were also examined in the full cohort. We identified 1445 adult lithium-treated patients with renal insufficiency, matched by risk set sampling 1 : 3 with 4306 lithium-exposed patients without renal insufficiency. In regression models, features associated with risk included older age, female sex, history of smoking, history of hypertension, overall burden of medical comorbidity, and diagnosis of schizophrenia or schizoaffective disorder (p<0.01 for all contrasts). The model yielded an area under the ROC curve exceeding 0.81 in an independent testing set, with 74% of renal insufficiency cases among the top two risk quintiles. Use of lithium more than once daily, lithium levels greater than 0.6 mEq/l, and use of first-generation antipsychotics were independently associated with risk. These results suggest the possibility of stratifying risk for renal failure among lithium-treated patients. Once-daily lithium dosing and maintaining lower lithium levels where possible may represent strategies for reducing risk.