Pathways to smoking behaviours: biological insights from the Tobacco and Genetics Consortium meta-analysis.

By running gene and pathway analyses for several smoking behaviours in the Tobacco and Genetics Consortium (TAG) sample of 74 053 individuals, 21 genes and several chains of biological pathways were implicated. Analyses were carried out using the HYbrid Set-based Test (HYST) as implemented in the Knowledge-based mining system for Genome-wide Genetic studies software. Fifteen genes are novel and were not detected with the single nucleotide polymorphism-based approach in the original TAG analysis. For quantity smoked, 14 genes passed the false discovery rate of 0.05 (corrected for multiple testing), with the top association signal located at the IREB2 gene (P=1.57E-37). Three genomic loci were significantly associated with ever smoked. The top signal is located at the noncoding antisense RNA transcript BDNF-AS (P=6.25E-07) on 11p14. The SLC25A21 gene (P=2.09E-08) yielded the top association signal in the analysis of smoking cessation. The 19q13 noncoding RNA locus exceeded the genome-wide significance in the analysis of age at initiation (P=1.33E-06). Pathways belonging to the Neuronal system pathways, harbouring the nicotinic acetylcholine receptor genes expressing the α (CHRNA 1-9), ß (CHRNB 1-4), γ, δ and ɛ subunits, yielded the smallest P-values in the pathway analysis of the quantity smoked (lowest P=4.90E-42). Additionally, pathways belonging to 'a subway map of cancer pathways' regulating the cell cycle, mitotic DNA replication, axon growth and synaptic plasticity were found significantly enriched for genetic variants in ever smokers relative to never smokers (lowest P=1.61E-07). In addition, these pathways were also significantly associated with the quantity smoked (lowest P=4.28E-17). Our results shed light on one of the world's leading causes of preventable death and open a path to potential therapeutic targets. These results are informative in decoding the biological bases of other disease traits, such as depression and cancers, with which smoking shares genetic vulnerabilities.

Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium.

Cannabis is the most widely produced and consumed illicit psychoactive substance worldwide. Occasional cannabis use can progress to frequent use, abuse and dependence with all known adverse physical, psychological and social consequences. Individual differences in cannabis initiation are heritable (40-48%). The International Cannabis Consortium was established with the aim to identify genetic risk variants of cannabis use. We conducted a meta-analysis of genome-wide association data of 13 cohorts (N=32 330) and four replication samples (N=5627). In addition, we performed a gene-based test of association, estimated single-nucleotide polymorphism (SNP)-based heritability and explored the genetic correlation between lifetime cannabis use and cigarette use using LD score regression. No individual SNPs reached genome-wide significance. Nonetheless, gene-based tests identified four genes significantly associated with lifetime cannabis use: NCAM1, CADM2, SCOC and KCNT2. Previous studies reported associations of NCAM1 with cigarette smoking and other substance use, and those of CADM2 with body mass index, processing speed and autism disorders, which are phenotypes previously reported to be associated with cannabis use. Furthermore, we showed that, combined across the genome, all common SNPs explained 13-20% (P<0.001) of the liability of lifetime cannabis use. Finally, there was a strong genetic correlation (rg=0.83; P=1.85 × 10(-8)) between lifetime cannabis use and lifetime cigarette smoking implying that the SNP effect sizes of the two traits are highly correlated. This is the largest meta-analysis of cannabis GWA studies to date, revealing important new insights into the genetic pathways of lifetime cannabis use. Future functional studies should explore the impact of the identified genes on the biological mechanisms of cannabis use.

Polygenic dissection of major depression clinical heterogeneity.

The molecular mechanisms underlying major depressive disorder (MDD) are largely unknown. Limited success of previous genetics studies may be attributable to heterogeneity of MDD, aggregating biologically different subtypes. We examined the polygenic features of MDD and two common clinical subtypes (typical and atypical) defined by symptom profiles in a large sample of adults with established diagnoses. Data were from 1530 patients of the Netherlands Study of Depression and Anxiety (NESDA) and 1700 controls mainly from the Netherlands Twin Register (NTR). Diagnoses of MDD and its subtypes were based on DSM-IV symptoms. Genetic overlap of MDD and subtypes with psychiatric (MDD, bipolar disorder, schizophrenia) and metabolic (body mass index (BMI), C-reactive protein, triglycerides) traits was evaluated via genomic profile risk scores (GPRS) generated from meta-analysis results of large international consortia. Single nucleotide polymorphism (SNP)-heritability of MDD and subtypes was also estimated. MDD was associated with psychiatric GPRS, while no association was found for GPRS of metabolic traits. MDD subtypes had differential polygenic signatures: typical was strongly associated with schizophrenia GPRS (odds ratio (OR)=1.54, P=7.8e-9), while atypical was additionally associated with BMI (OR=1.29, P=2.7e-4) and triglycerides (OR=1.21, P=0.006) GPRS. Similar results were found when only the highly discriminatory symptoms of appetite/weight were used to define subtypes. SNP-heritability was 32% for MDD, 38% and 43% for subtypes with, respectively, decreased (typical) and increased (atypical) appetite/weight. In conclusion, MDD subtypes are characterized by partially distinct polygenic liabilities and may represent more homogeneous phenotypes. Disentangling MDD heterogeneity may help the psychiatric field moving forward in the search for molecular roots of depression.

Further confirmation of the association between anxiety and CTNND2: replication in humans.

The rat genome sequencing and mapping consortium found evidence for an association between the catenin-δ2 gene (CTNND2) and anxious behaviour. We replicated these results in humans by carrying out a genetic association test in patients with panic disorder, social phobia, generalized anxiety disorder and/or agoraphobia (N = 1714) and controls (N = 4125). We further explored the association between CTNND2 and other psychiatric disorders based on publicly available genome-wide association results. A gene-based test showed that single nucleotide polymorphisms (SNPs) in CTNND2 have a significantly increased signal (P < 1e(-5) ) and decreased P-values. Single nucleotide polymorphism rs1012176 showed the strongest association with any anxiety disorder (odds ratio: 0.8128, SE = 0.063, P = 0.00099), but this effect was not significant after correction for multiple testing. In available genome-wide association results from the Psychiatric Genomics Consortium we found that SNPs in CTNND2 collectively showed an increased signal for schizophrenia (P < 1e(-5) ) and major depressive disorder (P < 1e(-5) ), but not for bipolar disorder. These signals remained significant after correction for potential confounders. The association between CTNND2 and anxiety was not strong enough to be picked up in the current generation of human genome-wide analyses, indicating the usefulness of and need for animal genetic studies to identify candidate genes for further study in human samples.

Signal transducer and activator of transcription and the risk of rheumatoid arthritis and thyroid autoimmune disorders.

OBJECTIVES: The signal transducer and activator of transcription 4 (STAT4) gene localised on chromosome 2q32.2-q32.3 is known to be essential for mediating responses to interleukin 12 in lymphocytes and regulating the differentiation of T helper cells. The aim of this study was to investigate the role of the STAT4 gene in susceptibility to rheumatoid arthritis (RA) and autoimmune thyroid diseases (AITDs) in Tunisian case control studies. METHODS: Genotyping of STAT4 rs7574865 single nucleotide polymorphism (SNP) was performed in 140 patients affected with RA, 159 patients affected with AITDs and 200 healthy controls using TaqMan® allelic discrimination assay. Data were analysed by χ2-test, genotype relative risk (GRR) and odds ratio (OR). RESULTS: Our results revealed that frequencies of the T allele and the T/T genotype were significantly higher among RA patients compared to controls (p=0.008; p=0.003, respectively). However, no significant associations with the risk of autoimmune thyroid diseases were detected. Moreover, the stratification of RA patients subgroups revealed a significant association of both T allele and T/T genotype in patients presented erosion (p=0.003; p=0.004, respectively) as well as anti-cyclic peptides-negative RA (ACPA-) (p=0.002; p=0.0003, respectively). Furthermore, genotypic association was found according to the absence of rheumatoid factor antibody (RF) (p=0.0014). But, no significant differences in allele and genotype frequencies of STAT4 rs7574865 polymorphism were detected according to the presence of another autoimmune disease, nodules and in HLA-DRB1*04 and HLA-DRB1*0404 positive subgroups. CONCLUSIONS: Our results support involvement of the STAT4 gene in the genetic susceptibility to RA but not to AITDs in the Tunisian population.