An Exome-Wide Association Study Identifies New Susceptibility Loci for Age of Smoking Initiation in African- and European-American Populations

Identification of a Novel Functional Non-synonymous Single Nucleotide Polymorphism in Frizzled Class Receptor 6 Gene for Involvement in Depressive Symptoms

Although numerous susceptibility loci for depression have been identified in recent years, their biological function and molecular mechanism remain largely unknown. By using an exome-wide association study for depressive symptoms assessed by the Center for Epidemiological Studies Depression (CES-D) score, we discovered a novel missense single nucleotide polymorphism (SNP), rs61753730 (Q152E), located in the fourth exon of the frizzled class receptor 6 gene (FZD6), which is a potential causal variant and is significantly associated with the CES-D score. Computer-based in silico analysis revealed that the protein configuration and stability, as well as the secondary structure of FZD6 differed greatly between the wild-type (WT) and Q152E mutant. We further found that rs61753730 significantly affected the luciferase activity and expression of FZD6 in an allele-specific way. Finally, we generated Fzd6-knockin (Fzd6-KI) mice with rs61753730 mutation using the CRISPR/Cas9 genome editing system and found that these mice presented greater immobility in the forced swimming test, less preference for sucrose in the sucrose preference test, as well as decreased center entries, center time, and distance traveled in the open filed test compared with WT mice after exposed to chronic social defeat stress. These results indicate the involvement of rs61753730 in depression. Taken together, our findings demonstrate that SNP rs61753730 is a novel functional variant and plays an important role in depressive symptoms.

Gene-based association analysis reveals involvement of LAMA5 and cell adhesion pathways in nicotine dependence in African- and European-American samples

Nicotine dependence (ND) is a chronic brain disorder that causes heavy social and economic burdens. Although many susceptibility genetic loci have been reported, they can explain only approximately 5%-10% of the genetic variance for the disease. To further explore the genetic etiology of ND, we genotyped 242 764 SNPs using an exome chip from both European-American (N = 1572) and African-American (N = 3371) samples. Gene-based association analysis revealed 29 genes associated significantly with ND. Of the genes in the AA sample, six (i.e., PKD1L2, LAMA5, MUC16, MROH5, ATP8B1, and FREM1) were replicated in the EA sample with p values ranging from 0.0031 to 0.0346. Subsequently, gene enrichment analysis revealed that cell adhesion-related pathways were significantly associated with ND in both the AA and EA samples. Considering that LAMA5 is the most significant gene in cell adhesion-related pathways, we did in vitro functional analysis of this gene, which showed that nicotine significantly suppressed its mRNA expression in HEK293T cells (p < 0.001). Further, our cell migration experiment showed that the migration rate was significantly different in wild-type and LAMA5-knockout (LAMA5-KO)-HEK293T cells. Importantly, nicotine-induced cell migration was abolished in LAMA5-KO cells. Taken together, these findings indicate that LAMA5, as well as cell adhesion-related pathways, play an important role in the etiology of smoking addiction, which warrants further investigation.

Prediction of Smoking Behavior From Single Nucleotide Polymorphisms With Machine Learning Approaches

Smoking is a complex behavior with a heritability as high as 50%. Given such a large genetic contribution, it provides an opportunity to prevent those individuals who are susceptible to smoking dependence from ever starting to smoke by predicting their inherited predisposition with their genomic profiles. Although previous studies have identified many susceptibility variants for smoking, they have limited power to predict smoking behavior. We applied the support vector machine (SVM) and random forest (RF) methods to build prediction models for smoking behavior. We first used 1,431 smokers and 1,503 non-smokers of African origin for model building with a 10-fold cross-validation and then tested the prediction models on an independent dataset consisting of 213 smokers and 224 non-smokers. The SVM model with 500 top single nucleotide polymorphisms (SNPs) selected using logistic regression (p<0.01) as the feature selection method achieved an area under the curve (AUC) of 0.691, 0.721, and 0.720 for the training, test, and independent test samples, respectively. The RF model with 500 top SNPs selected using logistic regression (p<0.01) achieved AUCs of 0.671, 0.665, and 0.667 for the training, test, and independent test samples, respectively. Finally, we used the combined logistic (p<0.01) and LASSO (λ=10^-3) regression to select features and the SVM algorithm for model building. The SVM model with 500 top SNPs achieved AUCs of 0.756, 0.776, and 0.897 for the training, test, and independent test samples, respectively. We conclude that machine learning methods are promising means to build predictive models for smoking.

Meta-analytic method reveal a significant association of theBDNF Val66Met variant with smoking persistence based on a large samples

Although numerous genetic studies have reported the link between Val66Met in BDNF gene with smoking, the findings remain controversial, mainly due to small-to-moderate sample sizes. The main aim of current investigation is to explore whether the variant of Val66Met has any genetic functions in the progress of smoking persistence. The Val-based dominant genetic model considering Val/* (namely, Val/Val + Val/Met) and Met/Met as two genotypes with comparison of the frequency of each genotype in current smokers and never smokers. There were seven genetic association articles including eight independent datasets with 10,160 participants were chosen in current meta-analytic investigation. In light of the potent effects of ethnicity on homogeneity across studies, we carried out separated meta-analyses according to the ancestry origin by using the wide-used tool of Comprehensive Meta-analysis software (V 2.0). Our meta-analyses results indicated that the Val66Met polymorphism was significantly linked with smoking persistence based on either all the chosen samples (N = 10,160; Random and fixed models: pooled OR = 1.23; 95% CI = 1.03–1.46; P value = 0.012) or Asian samples (N = 2,095; Fixed model: pooled OR = 1.25; 95% CI = 1.01–1.54; P value = 0.044; Random model: pooled OR = 1.25; 95% CI = 1.001–1.56; P value = 0.049). No significant clue of bias in publications or heterogeneity across studies was detected. Thus, we conclude that the Val66Met (rs6265) variant conveys genetic susceptibility to maintaining smoking, and smokers who carry Val/* genotypes have a higher possibility of maintaining smoking than those having Met/Met genotype.