Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study.

Importance: Understanding antidepressant mechanisms could help design more effective and tolerated treatments. Objective: Identify DNA methylation (DNAm) changes associated with antidepressant exposure. Design: Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS. Setting: Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR. Participants: Participants with DNAm data and self-report/prescription derived antidepressant exposure. Main Outcomes and Measures: Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions. Results: The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ). Conclusions and Relevance: Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments. 3 Key Points: Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

Cardiovascular disease, psychiatric diagnosis and sex differences in the multistep hypothesis of amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Amyotrophic lateral sclerosis (ALS) risk increases with age, and a linear log-incidence and log-age relationship is interpreted to suggest that five to six factors are involved in disease onset. The factors remain unidentified, except that fewer steps are predicted for those carrying a known ALS-causing mutation. METHODS: Men with a psychiatric disorder or cardiovascular disease (CVD) diagnosis have an increased relative risk of ALS. Using the Danish population registries and ALS diagnosis years 1980 to 2017, we tested whether these factors would decrease the predicted steps to disease. RESULTS: Consistent with previous reports, we find a linear log-incidence and log-age ALS-onset relationship (n = 4385, regression coefficient b = 4.6, 95% confidence interval [CI]: 4.3-4.9, R2  = 0.99). This did not differ when considering ALS cases with a prior psychiatric diagnosis (n = 391, b = 4.6, 95% CI: 4.0-5.1) Surprisingly, it was higher (+1.5 steps, P = 2.3 × 10-5 ) for those with a prior CVD diagnosis (n = 901, b = 6.1, 95% CI: 5.4-6.8). To control for competing risk of death, a test to investigate if this effect was maintained in those with CVD in the population demonstrated an increased baseline risk and fewer steps to disease (b = 1.8, 95% CI: 1.2-2.3, P = 4.6 × 10-21 ), which consistent with a positive association of CVD and ALS. Assessing sex differences, our data and meta-analyses (n = 22 495) support half a step fewer for men (-0.4, 95% CI: ±0.24, P = 0.00031) without support for contributing differences explained by menopause. CONCLUSIONS: Any factor associated with ALS disease onset may be relevant for understanding disease pathogenesis and/or counselling. Modelling disease incidence with age demonstrates some insight into relevant risk factors; however, the outcome can differ if competing risks are considered.

Brain age predicts mortality.

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, 'brain-predicted age', derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.

Embracing polygenicity: a review of methods and tools for psychiatric genetics research.

The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

A DNA methylation biomarker of alcohol consumption.

The lack of reliable measures of alcohol intake is a major obstacle to the diagnosis and treatment of alcohol-related diseases. Epigenetic modifications such as DNA methylation may provide novel biomarkers of alcohol use. To examine this possibility, we performed an epigenome-wide association study of methylation of cytosine-phosphate-guanine dinucleotide (CpG) sites in relation to alcohol intake in 13 population-based cohorts (ntotal=13 317; 54% women; mean age across cohorts 42-76 years) using whole blood (9643 European and 2423 African ancestries) or monocyte-derived DNA (588 European, 263 African and 400 Hispanic ancestry) samples. We performed meta-analysis and variable selection in whole-blood samples of people of European ancestry (n=6926) and identified 144 CpGs that provided substantial discrimination (area under the curve=0.90-0.99) for current heavy alcohol intake (⩾42 g per day in men and ⩾28 g per day in women) in four replication cohorts. The ancestry-stratified meta-analysis in whole blood identified 328 (9643 European ancestry samples) and 165 (2423 African ancestry samples) alcohol-related CpGs at Bonferroni-adjusted P<1 × 10-7. Analysis of the monocyte-derived DNA (n=1251) identified 62 alcohol-related CpGs at P<1 × 10-7. In whole-blood samples of people of European ancestry, we detected differential methylation in two neurotransmitter receptor genes, the γ-Aminobutyric acid-A receptor delta and γ-aminobutyric acid B receptor subunit 1; their differential methylation was associated with expression levels of a number of genes involved in immune function. In conclusion, we have identified a robust alcohol-related DNA methylation signature and shown the potential utility of DNA methylation as a clinically useful diagnostic test to detect current heavy alcohol consumption.

A direct test of the diathesis-stress model for depression.

The diathesis-stress theory for depression states that the effects of stress on the depression risk are dependent on the diathesis or vulnerability, implying multiplicative interactive effects on the liability scale. We used polygenic risk scores for major depressive disorder (MDD) calculated from the results of the most recent analysis from the Psychiatric Genomics Consortium as a direct measure of the vulnerability for depression in a sample of 5221 individuals from 3083 families. In the same we also had measures of stressful life events and social support and a depression symptom score, as well as DSM-IV MDD diagnoses for most individuals. In order to estimate the variance in depression explained by the genetic vulnerability, the stressors and their interactions, we fitted linear mixed models controlling for relatedness for the whole sample as well as stratified by sex. We show a significant interaction of the polygenic risk scores with personal life events (0.12% of variance explained, P-value=0.0076) contributing positively to the risk of depression. Additionally, our results suggest possible differences in the aetiology of depression between women and men. In conclusion, our findings point to an extra risk for individuals with combined vulnerability and high number of reported personal life events beyond what would be expected from the additive contributions of these factors to the liability for depression, supporting the multiplicative diathesis-stress model for this disease.

Extra-motor abnormalities in amyotrophic lateral sclerosis: another layer of heterogeneity.

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease defined by the presence of muscle weakness. The motor features of disease are heterogeneous in site of onset and progression. There are also extra-motor features in some patients. The genetic basis for extra-motor features is uncertain. The heterogeneity of ALS is an issue for clinical trials. Areas covered: This paper reviews the range and prevalence of extra-motor features associated with ALS, and highlights the current information about genetic associations with extra-motor features. Expert commentary: There are extra-motor features of ALS, but these are not found in all patients. The most common is cognitive abnormality. More data is required to ascertain whether extra-motor features arise with progression of disease. Extra-motor features are reported in patients with a range of causative genetic mutations, but are not found in all patients with these mutations. Further studies are required of the heterogeneity of ALS, and genotype/phenotype correlations are required, taking note of extra-motor features.

Rare DNA variants in the brain-derived neurotrophic factor gene increase risk for attention-deficit hyperactivity disorder: a next-generation sequencing study.

Attention-deficit hyperactivity disorder (ADHD) is a prevalent and highly heritable disorder of childhood with negative lifetime outcomes. Although candidate gene and genome-wide association studies have identified promising common variant signals, these explain only a fraction of the heritability of ADHD. The observation that rare structural variants confer substantial risk to psychiatric disorders suggests that rare variants might explain a portion of the missing heritability for ADHD. Here we believe we performed the first large-scale next-generation targeted sequencing study of ADHD in 152 child and adolescent cases and 188 controls across an a priori set of 117 genes. A multi-marker gene-level analysis of rare (<1% frequency) single-nucleotide variants (SNVs) revealed that the gene encoding brain-derived neurotrophic factor (BDNF) was associated with ADHD at Bonferroni corrected levels. Sanger sequencing confirmed the existence of all novel rare BDNF variants. Our results implicate BDNF as a genetic risk factor for ADHD, potentially by virtue of its critical role in neurodevelopment and synaptic plasticity.

Meta-analysis of genome-wide association studies of anxiety disorders.

Anxiety disorders (ADs), namely generalized AD, panic disorder and phobias, are common, etiologically complex conditions with a partially genetic basis. Despite differing on diagnostic definitions based on clinical presentation, ADs likely represent various expressions of an underlying common diathesis of abnormal regulation of basic threat-response systems. We conducted genome-wide association analyses in nine samples of European ancestry from seven large, independent studies. To identify genetic variants contributing to genetic susceptibility shared across interview-generated DSM-based ADs, we applied two phenotypic approaches: (1) comparisons between categorical AD cases and supernormal controls, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. We used logistic and linear regression, respectively, to analyze the association between these phenotypes and genome-wide single nucleotide polymorphisms. Meta-analysis for each phenotype combined results across the nine samples for over 18 000 unrelated individuals. Each meta-analysis identified a different genome-wide significant region, with the following markers showing the strongest association: for case-control contrasts, rs1709393 located in an uncharacterized non-coding RNA locus on chromosomal band 3q12.3 (P=1.65 × 10(-8)); for FS, rs1067327 within CAMKMT encoding the calmodulin-lysine N-methyltransferase on chromosomal band 2p21 (P=2.86 × 10(-9)). Independent replication and further exploration of these findings are needed to more fully understand the role of these variants in risk and expression of ADs.

High loading of polygenic risk in cases with chronic schizophrenia.

Genomic risk profile scores (GRPSs) have been shown to predict case-control status of schizophrenia (SCZ), albeit with varying sensitivity and specificity. The extent to which this variability in prediction accuracy is related to differences in sampling strategies is unknown. Danish population-based registers and Neonatal Biobanks were used to identify two independent incident data sets (denoted target and replication) comprising together 1861 cases with SCZ and 1706 controls. A third data set was a German prevalent sample with diagnoses assigned to 1773 SCZ cases and 2161 controls based on clinical interviews. GRPSs were calculated based on the genome-wide association results from the largest SCZ meta-analysis yet conducted. As measures of genetic risk prediction, Nagelkerke pseudo-R(2) and variance explained on the liability scale were calculated. GRPS for SCZ showed positive correlations with the number of psychiatric admissions across all P-value thresholds in both the incident and prevalent samples. In permutation-based test, Nagelkerke pseudo-R(2) values derived from samples enriched for frequently admitted cases were found to be significantly higher than for the full data sets (Ptarget=0.017, Preplication=0.04). Oversampling of frequently admitted cases further resulted in a higher proportion of variance explained on the liability scale (improvementtarget= 50%; improvementreplication= 162%). GRPSs are significantly correlated with chronicity of SCZ. Oversampling of cases with a high number of admissions significantly increased the amount of variance in liability explained by GRPS. This suggests that at least part of the effect of common single-nucleotide polymorphisms is on the deteriorative course of illness.

Evidence of CNIH3 involvement in opioid dependence.

Opioid dependence, a severe addictive disorder and major societal problem, has been demonstrated to be moderately heritable. We conducted a genome-wide association study in Comorbidity and Trauma Study data comparing opioid-dependent daily injectors (N=1167) with opioid misusers who never progressed to daily injection (N=161). The strongest associations, observed for CNIH3 single-nucleotide polymorphisms (SNPs), were confirmed in two independent samples, the Yale-Penn genetic studies of opioid, cocaine and alcohol dependence and the Study of Addiction: Genetics and Environment, which both contain non-dependent opioid misusers and opioid-dependent individuals. Meta-analyses found five genome-wide significant CNIH3 SNPs. The A allele of rs10799590, the most highly associated SNP, was robustly protective (P=4.30E-9; odds ratio 0.64 (95% confidence interval 0.55-0.74)). Epigenetic annotation predicts that this SNP is functional in fetal brain. Neuroimaging data from the Duke Neurogenetics Study (N=312) provide evidence of this SNP's in vivo functionality; rs10799590 A allele carriers displayed significantly greater right amygdala habituation to threat-related facial expressions, a phenotype associated with resilience to psychopathology. Computational genetic analyses of physical dependence on morphine across 23 mouse strains yielded significant correlations for haplotypes in CNIH3 and functionally related genes. These convergent findings support CNIH3 involvement in the pathophysiology of opioid dependence, complementing prior studies implicating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate system.

The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25,000 subjects.

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884,105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120,000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status.

Quantitative genetics of disease traits.

John James authored two key papers on the theory of risk to relatives for binary disease traits and the relationship between parameters on the observed binary scale and an unobserved scale of liability (James Annals of Human Genetics, 1971; 35: 47; Reich, James and Morris Annals of Human Genetics, 1972; 36: 163). These two papers are John James' most cited papers (198 and 328 citations, November 2014). They have been influential in human genetics and have recently gained renewed popularity because of their relevance to the estimation of quantitative genetics parameters for disease traits using SNP data. In this review, we summarize the two early papers and put them into context. We show recent extensions of the theory for ascertained case-control data and review recent applications in human genetics.

Novel directions for G × E analysis in psychiatry.

G × E in psychiatry may explain why environmental risk factors have big impact in some individuals but not in others, and conversely why relatives that are genetically at risk for disease do not all develop disease. Here we discuss two novel methods that use an aggregate genome-wide measure of genetic risk to detect G × E and estimate its effect in the population using data currently available and data we anticipate will be available in the near future. The first method exploits summary statistics from large-scale genome-wide association studies ignorant of the environmental conditions and detects G × E in an out-of-sample risk-profiling framework. The second method relies on larger samples and is based on a mixed linear model framework. It estimates variance explained directly from single nucleotide polymorphisms and environmental measures. Both methods have great potential to improve public health interventions focusing on risk-based screening that is informed by both genetic and environmental risk factors.

The association between family history of mental disorders and general cognitive ability.

There is an emerging literature linking cognitive ability with a wide range of psychiatric disorders. These findings have led to the hypothesis that diminished 'cognitive reserve' is a causal risk factor for psychiatric disorders. However, it is also feasible that a family history of mental disorders may confound this relationship, by contributing to both a slight impairment in cognitive ability, and an increased risk of psychiatric disorder. On the basis of a large, population-based sample of young adult male conscripts (n=160 608), we examined whether the presence of a family history of a range of mental disorders was associated with cognitive ability, as tested by the Børge Priens Prøve. In those with no individual-level history of mental disorder, a family-level history of a mental disorder was associated with a slight reduction in cognitive ability. In general, this pattern was found regardless of the nature of the psychiatric disorder in the family. Our study suggests that shared familial factors may underpin both cognitive ability and the risk of a wide range of psychiatric disorders. Convergent evidence from epidemiology and genetics suggests that shared genetic factors underpin an unexpectedly diverse range of psychiatric disorders. On the basis of the findings of the current study, we speculate that these same shared genetic factors also contribute to general cognitive ability.

Genetic predisposition to schizophrenia associated with increased use of cannabis.

Cannabis is the most commonly used illicit drug worldwide. With debate surrounding the legalization and control of use, investigating its health risks has become a pressing area of research. One established association is that between cannabis use and schizophrenia, a debilitating psychiatric disorder affecting ~1% of the population over their lifetime. Although considerable evidence implicates cannabis use as a component cause of schizophrenia, it remains unclear whether this is entirely due to cannabis directly raising risk of psychosis, or whether the same genes that increases psychosis risk may also increase risk of cannabis use. In a sample of 2082 healthy individuals, we show an association between an individual's burden of schizophrenia risk alleles and use of cannabis. This was significant both for comparing those who have ever versus never used cannabis (P=2.6 × 10(-4)), and for quantity of use within users (P=3.0 × 10(-3)). Although directly predicting only a small amount of the variance in cannabis use, these findings suggest that part of the association between schizophrenia and cannabis is due to a shared genetic aetiology. This form of gene-environment correlation is an important consideration when calculating the impact of environmental risk factors, including cannabis use.

Monozygotic twins affected with major depressive disorder have greater variance in methylation than their unaffected co-twin.

Our understanding of major depressive disorder (MDD) has focused on the influence of genetic variation and environmental risk factors. Growing evidence suggests the additional role of epigenetic mechanisms influencing susceptibility for complex traits. DNA sequence within discordant monozygotic twin (MZT) pairs is virtually identical; thus, they represent a powerful design for studying the contribution of epigenetic factors to disease liability. The aim of this study was to investigate whether specific methylation profiles in white blood cells could contribute to the aetiology of MDD. Participants were drawn from the Queensland Twin Registry and comprised 12 MZT pairs discordant for MDD and 12 MZT pairs concordant for no MDD and low neuroticism. Bisulphite treatment and genome-wide interrogation of differentially methylated CpG sites using the Illumina Human Methylation 450 BeadChip were performed in WBC-derived DNA. No overall difference in mean global methylation between cases and their unaffected co-twins was found; however, the differences in females was significant (P=0.005). The difference in variance across all probes between affected and unaffected twins was highly significant (P<2.2 × 10⁻¹6), with 52.4% of probes having higher variance in cases (binomial P-value<2.2 × 10⁻¹6). No significant differences in methylation were observed between discordant MZT pairs and their matched concordant MZT (permutation minimum P=0.11) at any individual probe. Larger samples are likely to be needed to identify true associations between methylation differences at specific CpG sites.

The neuroprogressive nature of major depressive disorder: pathways to disease evolution and resistance, and therapeutic implications.

In some patients with major depressive disorder (MDD), individual illness characteristics appear consistent with those of a neuroprogressive illness. Features of neuroprogression include poorer symptomatic, treatment and functional outcomes in patients with earlier disease onset and increased number and length of depressive episodes. In such patients, longer and more frequent depressive episodes appear to increase vulnerability for further episodes, precipitating an accelerating and progressive illness course leading to functional decline. Evidence from clinical, biochemical and neuroimaging studies appear to support this model and are informing novel therapeutic approaches. This paper reviews current knowledge of the neuroprogressive processes that may occur in MDD, including structural brain consequences and potential molecular mechanisms including the role of neurotransmitter systems, inflammatory, oxidative and nitrosative stress pathways, neurotrophins and regulation of neurogenesis, cortisol and the hypothalamic-pituitary-adrenal axis modulation, mitochondrial dysfunction and epigenetic and dietary influences. Evidence-based novel treatments informed by this knowledge are discussed.