C.C. Minica

Investigating the possible causal association of smoking with depression and anxiety using Mendelian randomisation meta-analysis: the CARTA consortium

Objectives To investigate whether associations of smoking with depression and anxiety are likely to be causal, using a Mendelian randomisation approach. Design Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730) as a proxy for smoking heaviness, and observational meta-analyses of the associations of smoking status and smoking heaviness with depression, anxiety and psychological distress. Participants Current, former and never smokers of European ancestry aged ≥16 years from 25 studies in the Consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA). Primary outcome measures Binary definitions of depression, anxiety and psychological distress assessed by clinical interview, symptom scales or self-reported recall of clinician diagnosis. Results The analytic sample included up to 58 176 never smokers, 37 428 former smokers and 32 028 current smokers (total N=127 632). In observational analyses, current smokers had 1.85 times greater odds of depression (95% CI 1.65 to 2.07), 1.71 times greater odds of anxiety (95% CI 1.54 to 1.90) and 1.69 times greater odds of psychological distress (95% CI 1.56 to 1.83) than never smokers. Former smokers also had greater odds of depression, anxiety and psychological distress than never smokers. There was evidence for positive associations of smoking heaviness with depression, anxiety and psychological distress (ORs per cigarette per day: 1.03 (95% CI 1.02 to 1.04), 1.03 (95% CI 1.02 to 1.04) and 1.02 (95% CI 1.02 to 1.03) respectively). In Mendelian randomisation analyses, there was no strong evidence that the minor allele of rs16969968/rs1051730 was associated with depression (OR=1.00, 95% CI 0.95 to 1.05), anxiety (OR=1.02, 95% CI 0.97 to 1.07) or psychological distress (OR=1.02, 95% CI 0.98 to 1.06) in current smokers. Results were similar for former smokers. Conclusions Findings from Mendelian randomisation analyses do not support a causal role of smoking heaviness in the development of depression and anxiety.

Sex Differences in Genetic Architecture of Complex Phenotypes

We examined sex differences in familial resemblance for a broad range of behavioral, psychiatric and health related phenotypes (122 complex traits) in children and adults. There is a renewed interest in the importance of genotype by sex interaction in, for example, genome-wide association (GWA) studies of complex phenotypes. If different genes play a role across sex, GWA studies should consider the effect of genetic variants separately in men and women, which affects statistical power. Twin and family studies offer an opportunity to compare resemblance between opposite-sex family members to the resemblance between same-sex relatives, thereby presenting a test of quantitative and qualitative sex differences in the genetic architecture of complex traits. We analyzed data on lifestyle, personality, psychiatric disorder, health, growth, development and metabolic traits in dizygotic (DZ) same-sex and opposite-sex twins, as these siblings are perfectly matched for age and prenatal exposures. Sample size varied from slightly over 300 subjects for measures of brain function such as EEG power to over 30,000 subjects for childhood psychopathology and birth weight. For most phenotypes, sample sizes were large, with an average sample size of 9027 individuals. By testing whether the resemblance in DZ opposite-sex pairs is the same as in DZ same-sex pairs, we obtain evidence for genetic qualitative sex-differences in the genetic architecture of complex traits for 4% of phenotypes. We conclude that for most traits that were examined, the current evidence is that same the genes are operating in men and women.

Genetic Relationship between Schizophrenia and Nicotine Dependence

It is well known that most schizophrenia patients smoke cigarettes. There are different hypotheses postulating the underlying mechanisms of this comorbidity. We used summary statistics from large meta-analyses of plasma cotinine concentration (COT), Fagerström test for nicotine dependence (FTND) and schizophrenia to examine the genetic relationship between these traits. We found that schizophrenia risk scores calculated at P-value thresholds of 5 × 10−3 and larger predicted FTND and cigarettes smoked per day (CPD), suggesting that genes most significantly associated with schizophrenia were not associated with FTND/CPD, consistent with the self-medication hypothesis. The COT risk scores predicted schizophrenia diagnosis at P-values of 5 × 10−3 and smaller, implying that genes most significantly associated with COT were associated with schizophrenia. These results implicated that schizophrenia and FTND/CPD/COT shared some genetic liability. Based on this shared liability, we identified multiple long non-coding RNAs and RNA binding protein genes (DA376252, BX089737, LOC101927273, LINC01029, LOC101928622, HY157071, DA902558, RBFOX1 and TINCR), protein modification genes (MANBA, UBE2D3, and RANGAP1) and energy production genes (XYLB, MTRF1 and ENOX1) that were associated with both conditions. Further analyses revealed that these shared genes were enriched in calcium signaling, long-term potentiation and neuroactive ligand-receptor interaction pathways that played a critical role in cognitive functions and neuronal plasticity.

Sandwich corrected standard errors in family-based genome-wide association studies

Given the availability of genotype and phenotype data collected in family members, the question arises which estimator ensures the most optimal use of such data in genome-wide scans. Using simulations, we compared the Unweighted Least Squares (ULS) and Maximum Likelihood (ML) procedures. The former is implemented in Plink and uses a sandwich correction to correct the standard errors for model misspecification of ignoring the clustering. The latter is implemented by fast linear mixed procedures and models explicitly the familial resemblance. However, as it commits to a background model limited to additive genetic and unshared environmental effects, it employs a misspecified model for traits with a shared environmental component. We considered the performance of the two procedures in terms of type I and type II error rates, with correct and incorrect model specification in ML. For traits characterized by moderate to large familial resemblance, using an ML procedure with a correctly specified model for the conditional familial covariance matrix should be the strategy of choice. The potential loss in power encountered by the sandwich corrected ULS procedure does not outweigh its computational convenience. Furthermore, the ML procedure was quite robust under model misspecification in the simulated settings and appreciably more powerful than the sandwich corrected ULS procedure. However, to correct for the effects of model misspecification in ML in circumstances other than those considered here, we propose to use a sandwich correction. We show that the sandwich correction can be formulated in terms of the fast ML method.

Genetic association in multivariate phenotypic data: power in five models.

This article concerns the power of various data analytic strategies to detect the effect of a single genetic variant (GV) in multivariate data. We simulated exactly fitting monozygotic and dizygotic phenotypic data according to single and two common factor models, and simplex models. We calculated the power to detect the GV in twin 1 data in an ANOVA of phenotypic sum scores, in a MANOVA, and in exploratory factor analysis (EFA), in which the common factors are regressed on the genetic variant. We also report power in the full twin model, and power of the single phenotype ANOVA. The results indicate that (1) if the GV affects all phenotypes, the sum score ANOVA and the EFA are most powerful, while the MANOVA is less powerful. Increasing phenotypic correlations further decreases the power of the MANOVA; and (2) if the GV affects only a subset of the phenotypes, the EFA or the MANOVA are most powerful, while sum score ANOVA is less powerful. In this case, an increase in phenotypic correlations may enhance the power of MANOVA and EFA. If the effect of the GV is modeled directly on the phenotypes in the EFA, the power of the EFA is approximately equal to the power of the MANOVA.

Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2.

Genome-wide association studies (GWAS) of complex behavioural phenotypes such as cigarette smoking typically employ self-report phenotypes. However, precise biomarker phenotypes may afford greater statistical power and identify novel variants. Here we report the results of a GWAS meta-analysis of levels of cotinine, the primary metabolite of nicotine, in 4,548 daily smokers of European ancestry. We identified a locus close to UGT2B10 at 4q13.2 (minimum p = 5.89 × 10−10 for rs114612145), which was consequently replicated. This variant is in high linkage disequilibrium with a known functional variant in the UGT2B10 gene which is associated with reduced nicotine and cotinine glucuronidation activity, but intriguingly is not associated with nicotine intake. Additionally, we observed association between multiple variants within the 15q25.1 region and cotinine levels, all located within the CHRNA5-A3-B4 gene cluster or adjacent genes, consistent with previous much larger GWAS using self-report measures of smoking quantity. These results clearly illustrate the increase in power afforded by using precise biomarker measures in GWAS. Perhaps more importantly however, they also highlight that biomarkers do not always mark the phenotype of interest. The use of metabolite data as a proxy for environmental exposures should be carefully considered in the context of individual differences in metabolic pathways.

Association studies of genomic variants with treatment response to risperidone, clozapine, quetiapine and chlorpromazine in the Chinese Han population.

Schizophrenia is a widespread mental disease with a prevalence of about 1% in the world population. Continuous long-term treatment is required to maintain social functioning and prevent symptom relapse of schizophrenia patients. However, there are considerable individual differences in response to the antipsychotic drugs. There is a pressing need to identify more drug-response-related markers. But most pharmacogenomics of schizophrenia have typically focused on a few candidate genes in small sample size. In this study, 995 subjects were selected for discovering the drug-response-related markers. A total of 77 single-nucleotide polymorphisms of 25 genes have been investigated for four commonly used antipsychotic drugs in China: risperidone, clozapine, quetiapine, and chlorpromazine. Significant associations with treatment response for several genes, such as CYP2D6, CYP2C19, COMT, ABCB1, DRD3 and HTR2C have been verified in our study. Also, we found several new candidate genes (TNIK, RELN, NOTCH4 and SLC6A2) and combinations (haplotype rs1544325–rs5993883–rs6269–rs4818 in COMT) that are associated with treatment response to the four drugs. Also, multivariate interactions analysis demonstrated the combination of rs6269 in COMT and rs3813929 in HTR2C may work as a predictor to improve the clinical antipsychotic response. So our study is of great significance to improve current knowledge on the pharmacogenomics of schizophrenia, thus promoting the implementation of personalized medicine in schizophrenia.The Pharmacogenomics Journal advance online publication, 18 August 2015; doi:10.1038/tpj.2015.61

MZ twin pairs or MZ singletons in population family-based GWAS? More power in pairs

Family-based genome-wide association studies (GWAS) involve testing the genetic association of (many) genetic variants with the phenotype of interest, while taking into account the relatedness among family members. Occasionally in family-based GWAS, including monozygotic (MZ) twins, the data from one MZ twin are dropped, thus reducing the MZ pairs to singletons (for example, Loukola et al., Lowe et al., Parsons et al. and Psychosis Endophenotypes International Consortium et al.).

Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence

Cigarette smoking is a leading cause of preventable mortality worldwide. Nicotine dependence, which reduces the likelihood of quitting smoking, is a heritable trait with firmly established associations with sequence variants in nicotine acetylcholine receptor genes and at other loci. To search for additional loci, we conducted a genome-wide association study (GWAS) meta-analysis of nicotine dependence, totaling 38,602 smokers (28,677 Europeans/European Americans and 9925 African Americans) across 15 studies. In this largest-ever GWAS meta-analysis for nicotine dependence and the largest-ever cross-ancestry GWAS meta-analysis for any smoking phenotype, we reconfirmed the well-known CHRNA5-CHRNA3-CHRNB4 genes and further yielded a novel association in the DNA methyltransferase gene DNMT3B. The intronic DNMT3B rs910083-C allele (frequency=44–77%) was associated with increased risk of nicotine dependence at P=3.7 × 10−8 (odds ratio (OR)=1.06 and 95% confidence interval (CI)=1.04–1.07 for severe vs mild dependence). The association was independently confirmed in the UK Biobank (N=48,931) using heavy vs never smoking as a proxy phenotype (P=3.6 × 10−4, OR=1.05, and 95% CI=1.02–1.08). Rs910083-C is also associated with increased risk of squamous cell lung carcinoma in the International Lung Cancer Consortium (N=60,586, meta-analysis P=0.0095, OR=1.05, and 95% CI=1.01–1.09). Moreover, rs910083-C was implicated as a cis-methylation quantitative trait locus (QTL) variant associated with higher DNMT3B methylation in fetal brain (N=166, P=2.3 × 10−26) and a cis-expression QTL variant associated with higher DNMT3B expression in adult cerebellum from the Genotype-Tissue Expression project (N=103, P=3.0 × 10−6) and the independent Brain eQTL Almanac (N=134, P=0.028). This novel DNMT3B cis-acting QTL variant highlights the importance of genetically influenced regulation in brain on the risks of nicotine dependence, heavy smoking and consequent lung cancer.

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), β (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 worlds 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.