Pleiotropy of genetic variants on obesity and smoking phenotypes: Results from the Oncoarray Project of The International Lung Cancer Consortium

Brain region-specific roles of brain-derived neurotrophic factor in social stress-induced depressive-like behavior

Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response. Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region-specific, particularly involving the corticolimbic system, including the ventral tegmental area, nucleus accumbens, prefrontal cortex, amygdala, and hippocampus. Determining how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress psychopathology. In this review, we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of depression. We focused on associated molecular pathways and neural circuits, with special attention to the brain-derived neurotrophic factor-tropomyosin receptor kinase B signaling pathway and the ventral tegmental area-nucleus accumbens dopamine circuit. We determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature, severity, and duration of stress, especially in the above-mentioned brain regions of the corticolimbic system. Therefore, BDNF might be a biological indicator regulating stress-related processes in various brain regions.

Transcriptional Patterns in Stages of Alzheimer's Disease Are Cell-Type-Specific and Partially Converge with the Effects of Alcohol Use Disorder in Humans

Advances in single-cell technologies have led to the discovery and characterization of new brain cell types, which in turn lead to a better understanding of the pathogenesis of Alzheimer's disease (AD). Here, we present a detailed analysis of single-nucleus (sn)RNA-seq data for three stages of AD from middle temporal gyrus and compare it with snRNA-seq data from the prefrontal cortices from individuals with alcohol use disorder (AUD). We observed a significant decrease in both inhibitory and excitatory neurons, in general agreement with previous reports. We observed several cell-type-specific gene expressions and pathway dysregulations that delineate AD stages. Endothelial and vascular leptomeningeal cells showed the greatest degree of gene expression changes. Cell-type-specific evidence of neurodegeneration was seen in multiple neuronal cell types particularly in somatostatin and Layer 5 extratelencephalic neurons, among others. Evidence of inflammatory responses was seen in non-neuronal cells, particularly in intermediate and advanced AD. We observed common perturbations in AD and AUD, particularly in pathways, like transcription, translation, apoptosis, autophagy, calcium signaling, neuroinflammation, and phosphorylation, that imply shared transcriptional pathogenic mechanisms and support the role of excessive alcohol intake in AD progression. Major AUD gene markers form and perturb a network of genes significantly associated with intermediate and advanced AD. Master regulator analysis from AUD gene markers revealed significant correlation with advanced AD of transcription factors that have implications in intellectual disability, neuroinflammation, and other neurodegenerative conditions, further suggesting a shared nexus of transcriptional changes between AD and AUD.

Early developmental risks for tobacco addiction: A probabilistic epigenesis framework

Considerable progress has been made in elucidating the relationships between early life psychobiological and environmental risk factors and the development of tobacco addiction. However, a comprehensive understanding of the heterogeneity in tobacco addiction phenotypes requires integrating research findings. The probabilistic epigenesis meta-theory offers a valuable framework for this integration, considering systemic, multilevel, developmental, and evolutionary perspectives. In this paper, we critically review relevant research on early developmental risks associated with tobacco addiction and highlight the integrative heuristic value of the probabilistic epigenesis framework for this research. For this, we propose a four-level systems approach as an initial step towards integration, analyzing complex interactions among different levels of influence. Additionally, we explore a coaction approach to examine key interactions between early risk factors. Moreover, we introduce developmental pathways to understand interindividual differences in tobacco addiction risk during development. This integrative approach holds promise for advancing our understanding of tobacco addiction etiology and informing potentially effective intervention strategies.

Early-life exposure to tobacco and childhood adiposity: Identifying windows of susceptibility

BACKGROUND

Early-life exposure to tobacco is associated with obesity, but the most susceptible developmental periods are unknown.

OBJECTIVE

To explore windows of susceptibility in a cohort of 568 mother-child pairs.

METHODS

We measured seven measures of tobacco exposure (five self-reported and two biomarkers) spanning from pre-conception to age 5 years. Mothers self-reported active smoking (pre-conception, 17 weeks, and delivery) and household smokers (5 and 18 months postnatally). Cotinine was measured in maternal urine (27 weeks) and child urine (5 years). Adiposity (fat mass percentage) was measured at birth and 5 years via air displacement plethysmography. Using a multiple informant approach, we tested whether adiposity (5 years) and changes in adiposity (from birth to 5 years) differed by the seven measures of tobacco exposure.

RESULTS

The associations may depend on timing. For example, only pre-conception (β = 3.1%; 95% CI: 1.0-5.1) and late gestation (β = 4.0%; 95% CI: 0.4-7.6) exposures influenced adiposity accretion from birth to 5 years (p for interaction = 0.01). Early infancy exposure was also associated with 1.7% higher adiposity at 5 years (95% CI: 0.1-3.2). Mid-pregnancy and early childhood exposures did not influence adiposity.

CONCLUSIONS

Pre-conception, late gestation, and early infancy exposures to tobacco may have the greatest impact on childhood adiposity.

Deciphering associations between three RNA splicing-related genetic variants and lung cancer risk

Limited efforts have been made in assessing the effect of genome-wide profiling of RNA splicing-related variation on lung cancer risk. In the present study, we first identified RNA splicing-related genetic variants linked to lung cancer in a genome-wide profiling analysis and then conducted a two-stage (discovery and replication) association study in populations of European ancestry. Discovery and validation were conducted sequentially with a total of 29,266 cases and 56,450 controls from both the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium as well as the OncoArray database. For those variants identified as significant in the two datasets, we further performed stratified analyses by smoking status and histological type and investigated their effects on gene expression and potential regulatory mechanisms. We identified three genetic variants significantly associated with lung cancer risk: rs329118 in JADE2 (P = 8.80E-09), rs2285521 in GGA2 (P = 4.43E-08), and rs198459 in MYRF (P = 1.60E-06). The combined effects of all three SNPs were more evident in lung squamous cell carcinomas (P = 1.81E-08, P = 6.21E-08, and P = 7.93E-04, respectively) than in lung adenocarcinomas and in ever smokers (P = 9.80E-05, P = 2.70E-04, and P = 2.90E-05, respectively) than in never smokers. Gene expression quantitative trait analysis suggested a role for the SNPs in regulating transcriptional expression of the corresponding target genes. In conclusion, we report that three RNA splicing-related genetic variants contribute to lung cancer susceptibility in European populations. However, additional validation is needed, and specific splicing mechanisms of the target genes underlying the observed associations also warrants further exploration.

Cataloging the potential SNPs (single nucleotide polymorphisms) associated with quantitative traits, viz. BMI (body mass index), IQ (intelligence quotient) and BP (blood pressure): an updated review

Background

Single nucleotide polymorphism (SNP) variants are abundant, persistent and widely distributed across the genome and are frequently linked to the development of genetic diseases. Identifying SNPs that underpin complex diseases can aid scientists in the discovery of disease-related genes by allowing for early detection, effective medication and eventually disease prevention.

Main body

Various SNP or polymorphism-based studies were used to categorize different SNPs potentially related to three quantitative traits: body mass index (BMI), intelligence quotient (IQ) and blood pressure, and then uncovered common SNPs for these three traits. We employed SNPedia, RefSNP Report, GWAS Catalog, Gene Cards (Data Bases), PubMed and Google Scholar search engines to find relevant material on SNPs associated with three quantitative traits. As a result, we detected three common SNPs for all three quantitative traits in global populations: SNP rs6265 of the BDNF gene on chromosome 11p14.1, SNP rs131070325 of the SL39A8 gene on chromosome 4p24 and SNP rs4680 of the COMT gene on chromosome 22q11.21.

Conclusion

In our review, we focused on the prevalent SNPs and gene expression activities that influence these three quantitative traits. These SNPs have been used to detect and map complex, common illnesses in communities for homogeneity testing and pharmacogenetic studies. High blood pressure, diabetes and heart disease, as well as BMI, schizophrenia and IQ, can all be predicted using common SNPs. Finally, the results of our work can be used to find common SNPs and genes that regulate these three quantitative features across the genome.

Genotype-expression interactions for BDNF across human brain regions

Background

Genetic variations in brain-derived neurotrophic factor (BDNF) are associated with various psychiatric disorders including depression, obsessive-compulsive disorder, substance use disorders, and schizophrenia; altered gene expression triggered by these genetic variants may serve to create these phenotypes. But genotype-expression interactions for this gene have not been well-studied across brain regions relevant for psychiatric disorders.

Results

At false discovery rate (FDR) of 10% (q < 0.1), a total of 61 SNPs were associated with BDNF expression in cerebellum (n = 209), 55 SNPs in cortex (n = 205), 48 SNPs in nucleus accumbens (n = 202), 47 SNPs in caudate (n = 194), and 58 SNPs in cerebellar hemisphere (n = 175). We identified a set of 30 SNPs in 2 haplotype blocks that were associated with alterations in expression for each of these 5 regions. The first haplotype block included variants associated in the literature with panic disorders (rs16917204), addiction (rs11030104), bipolar disorder (rs16917237/rs2049045), and obsessive-compulsive disorder (rs6265). Likewise, variants in the second haplotype block have been previously associated with disorders such as nicotine addiction, major depressive disorder (rs988748), and epilepsy (rs6484320/rs7103411).

Conclusions

This work supports the association of variants within BDNF for expression changes in these key brain regions that may contribute to common behavioral phenotypes for disorders of compulsion, impulsivity, and addiction. These SNPs should be further investigated as possible therapeutic and diagnostic targets to aid in management of these and other psychiatric disorders.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07525-1.

A powerful and versatile colocalization test

Transcriptome-wide association studies (TWAS and PrediXcan) have been increasingly applied to detect associations between genetically predicted gene expressions and GWAS traits, which may suggest, however do not completely determine, causal genes for GWAS traits, due to the likely violation of their imposed strong assumptions for causal inference. Testing colocalization moves it closer to establishing causal relationships: if a GWAS trait and a gene's expression share the same associated SNP, it may suggest a regulatory (and thus putative causal) role of the SNP mediated through the gene on the GWAS trait. Accordingly, it is of interest to develop and apply various colocalization testing approaches. The existing approaches may each have some severe limitations. For instance, some methods test the null hypothesis that there is colocalization, which is not ideal because often the null hypothesis cannot be rejected simply due to limited statistical power (with too small sample sizes). Some other methods arbitrarily restrict the maximum number of causal SNPs in a locus, which may lead to loss of power in the presence of wide-spread allelic heterogeneity. Importantly, most methods cannot be applied to either GWAS/eQTL summary statistics or cases with more than two possibly correlated traits. Here we present a simple and general approach based on conditional analysis of a locus on multiple traits, overcoming the above and other shortcomings of the existing methods. We demonstrate that, compared with other methods, our new method can be applied to a wider range of scenarios and often perform better. We showcase its applications to both simulated and real data, including a large-scale Alzheimer's disease GWAS summary dataset and a gene expression dataset, and a large-scale blood lipid GWAS summary association dataset. An R package "jointsum" implementing the proposed method is publicly available at github.

Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

The genomics era has brought useful tools to dissect the genetic architecture of complex traits. Here we propose a multivariate reaction norm model (MRNM) to tackle genotype–covariate (G–C) correlation and interaction problems. We apply MRNM to the UK Biobank data in analysis of body mass index using smoking quantity as a covariate, finding a highly significant G–C correlation, but only weak evidence for G–C interaction. In contrast, G–C interaction estimates are inflated in existing methods. It is also notable that there is significant heterogeneity in the estimated residual variances (i.e., variances not attributable to factors in the model) across different covariate levels, i.e., residual–covariate (R–C) interaction. We also show that the residual variances estimated by standard additive models can be inflated in the presence of G–C and/or R–C interactions. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses.

Complex traits are often influenced by genetic and non-genetic factors (such as environmental exposures), which are themselves interconnected. Here, the authors develop a method for disentangling genotype–covariate correlation and interaction, and investigate their effects on estimating statistical genetic parameters.

GWAS of smoking behaviour in 165,436 Japanese people reveals seven new loci and shared genetic architecture

Cigarette smoking is a risk factor for a wide range of human diseases¹. To investigate the genetic components associated with smoking behaviours in the Japanese population, we conducted a genome-wide association study of four smoking-related traits using up to 165,436 individuals. In total, we identified seven new loci, including three loci associated with the number of cigarettes per day (EPHX2-CLU, RET and CUX2-ALDH2), three loci associated with smoking initiation (DLC1, CXCL12-TMEM72-AS1 and GALR1-SALL3) and LINC01793-MIR4432HG, associated with the age of smoking initiation. Of these, three loci (LINC01793-MIR4432HG, CXCL12-TMEM72-AS1 and GALR1-SALL3) were found by conducting an additional sex-stratified genome-wide association study. This additional analysis showed heterogeneity of effects between sexes. The cross-sex linkage disequilibrium score regression^2,3 analysis also indicated that the genetic component of smoking initiation was significantly different between the sexes. Cross-trait linkage disequilibrium score regression analysis and trait-relevant tissue analysis showed that the number of cigarettes per day has a specific genetic background distinct from those of the other three smoking behaviours. We also report 11 diseases that share genetic basis with smoking behaviours. Although the current study should be carefully considered owing to the lack of replication samples, our findings characterized the genetic architecture of smoking behaviours. Further studies in East Asian populations are warranted to confirm our findings.

Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults

In addition to the health hazards posed individually by cigarette smoking and obesity, the combination of these conditions poses a particular impairment to health. Genetic factors have been shown to influence both traits and, to understand the connection between these conditions, we examined both the observed and genetic relationship between adiposity (an electrical impedance measure of body mass index (BMI)) and cigarettes smoked per day (CPD) in a large sample of current, former, and never smokers in the United Kingdom. In former smokers, BMI was positively associated with cigarettes formerly smoked; further, the genetic factors related to a greater number of cigarettes smoked were also responsible for a higher BMI. In current smokers, there was a positive association between BMI and number of cigarettes smoked, though this relationship did not appear to be influenced by similar genetic factors. We found a positive genetic relationship between smoking in current/former smokers and BMI in never smokers (who would be unmarred by the effects of nicotine). In addition to CPD, in current smokers, we looked at two variables, time from waking to first cigarette and difficulty not smoking for a day, that may align better with cigarette and food 'craving.' However, these smoking measures provided mixed findings with respect to their relationship with BMI. Overall, the positive relationships between the genetic factors that influence CPD in smokers and the genetic factors that influence BMI in former and never smokers point to common biological influences behind smoking and obesity.