A common biological basis of obesity and nicotine addiction
Contains fulltext : 128630.pdf (Publisher’s version ) (Open Access) 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 …
A genomewide association study of smoking relapse in four European population-based samples.
OBJECTIVES: Genomewide association studies (GWAS) have identified clear evidence of genetic markers for nicotine dependence. Other smoking phenotypes have been tested, but the results are less consistent. The tendency to relapse versus the ability to maintain long-term abstinence has received little attention in genetic studies; thus, our aim was to provide a better biological understanding of this phenotype through the identification of genetic loci associated with smoking relapse. METHODS: We carried out a GWAS on data from two European population-based collections, including a total of 835 cases (relapsers) and 990 controls (abstainers). Top-ranked findings from the discovery phase were …
Meta-analysis and imputation refines the association of 15q25 with smoking quantity.
Smoking is a leading global cause of disease and mortality(1). We established the Oxford-GlaxoSmithKline study (Ox-GSK) to perform a genome-wide meta-analysis of SNP association with smoking-related behavioral traits. Our final data set included 41,150 individuals drawn from 20 disease, population and control cohorts. Our analysis confirmed an effect on smoking quantity at a locus on 15q25 (P = 9.45 x 10(-19)) that includes CHRNA5, CHRNA3 and CHRNB4, three genes encoding neuronal nicotinic acetylcholine receptor subunits. We used data from the 1000 Genomes project to investigate the region using imputation, which allowed for analysis of virtually all common SNPs in the region and offered a …
Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies
AbstractEating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use (genetic correlation [rg], twin-based=0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], …
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs
AM Vicente - Cross-Disorder Group of the Psychiatric Genomics Consortium Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in …
Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways
G.B. and S.N. acknowledge funding support for this work from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. P.H.L. is supported by US National Institute of Mental Health (NIMH) grant K99MH101367. Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an an…