Genome-wide interaction study of a proxy for stress-sensitivity and its prediction of major depressive disorder

Individual response to stress is correlated with neuroticism and is an important predictor of both neuroticism and the onset of major depressive disorder (MDD). Identification of the genetics underpinning individual differences in response to negative events (stress-sensitivity) may improve our understanding of the molecular pathways involved, and its association with stress-related illnesses. We sought to generate a proxy for stress-sensitivity through modelling the interaction between SNP allele and MDD status on neuroticism score in order to identify genetic variants that contribute to the higher neuroticism seen in individuals with a lifetime diagnosis of depression compared to unaffected individuals. Meta-analysis of genome-wide interaction studies (GWIS) in UK Biobank (N = 23,092) and Generation Scotland: Scottish Family Health Study (N = 7,155) identified no genome-wide significance SNP interactions. However, gene-based tests identified a genome-wide significant gene, ZNF366, a negative regulator of glucocorticoid receptor function implicated in alcohol dependence (p = 1.48x10-7; Bonferroni-corrected significance threshold p < 2.79x10-6). Using summary statistics from the stress-sensitivity term of the GWIS, SNP heritability for stress-sensitivity was estimated at 5.0%. In models fitting polygenic risk scores of both MDD and neuroticism derived from independent GWAS, we show that polygenic risk scores derived from the UK Biobank stress-sensitivity GWIS significantly improved the prediction of MDD in Generation Scotland. This study may improve interpretation of larger genome-wide association studies of MDD and other stress-related illnesses, and the understanding of the etiological mechanisms underpinning stress-sensitivity

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.Peer reviewe

Genome-wide by Environment Interaction Studies of Depressive Symptoms and Psychosocial Stress in UK Biobank and Generation Scotland

Stress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population-based cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77 x 10(-6)). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53-kb downstream PIWIL4; p = 4.95 x 10(-9); total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46 x 10(-8); dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00 x 10(-8); dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77 x 10(-6)). Polygenic risk scores (PRSs) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91 x 10(-3)). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum

Polygenic burden analysis of longitudinal clusters of psychopathological features in a cross-diagnostic group of individuals with severe mental illness.

Background Bipolar disorder (BD), schizophrenia (SZ) and schizoaffective disorder (SZA) can be disabling disorders associated with severe psychiatric symptomatology. Individual psychopathological features often overlap between these diagnostic groups and their severity can vary widely. More severe psychopathological features are generally associated with a less favorable outcome. Further, all three diseases are common complex genetic disorders with a polygenic genetic architecture in the majority of cases. The inherent heterogeneity with regard to disease severity has posed a significant challenge to both the study of the underlying disease mechanism and the clinical management. Therefore, stratification of cases into more homogeneous subgroups across diagnoses using both longitudinal clusters derived from psychometric data and genetic information could provide a means to identify individuals with higher risk for severe illness, mandating earlier and intensified clinical intervention. Methods Individuals included herein partake in an ongoing multisite cohort study across Germany and Austria (www.kfo241.de; www.PsyCourse.de). Participants were characterized at 4 time points over an 18-months period using a comprehensive phenotyping battery. The subsample used here totals 198 participants (46.9&plusmn;12.4 yrs; 46% female) with DSM-IV diagnoses of SZ, SZA or BD. Blood DNA samples were genotyped using Illumina&rsquo;s Infinium PsychArray and imputed using the 1000 genomes. SZ-PRS were calculated using PLINK 1.07. Effect sizes and p-values were determined with the PGC2 SZ summary results as discovery sample. A set of 67 longitudinally measured variables derived from the Positive and Negative Syndrome Scale (PANSS), the Inventory of Depressive Symptoms (IDS) and the Young Mania Rating Scale (YMRS) entered the cluster analyses. Factor analysis for mixed data (FAMD) was applied to compute abstract data dimensions, subsequently used to derive the longitudinal trajectories which then served as inputs for a k-mean clustering for longitudinal data. Identified clusters were employed in a linear regression model as predictive variables for SZ-PRS at 11 thresholds. Results Computed by FAMD, the strongest loadings were observed for PANSS and IDS on the first dimension and for IDS on the second dimension. Two clusters of longitudinal trajectories were identified in these dimensions: (A) individuals with continuously low scores on both PANSS and IDS (70.7%) and (B) individuals with consistently high scores on both PANSS and IDS (29.3%). Clusters differed significantly with regard to Global Assessment of Functioning (GAF; higher in (A); FDR-adjusted p-value=2.23x10-10), while there were no significant differences regarding sex, age, diagnoses, center, age at onset, family history or duration of illness. Cluster membership was not significantly associated with the SZ-PRS in either cluster. Discussion Although the results are preliminary and have to be interpreted with caution, the approach of longitudinal clustering to identify cross-diagnostic homogeneous subgroups of individuals appears to be feasible. The fact that more severe psychopathological features were not associated with increased genetic risk burden will also be interesting to explore further. &nbsp

Polygenic burden analysis of longitudinal clusters of quality of life and functioning in patients with severe mental illness.

Background Psychiatric illnesses such as bipolar disorder, schizophrenia and schizoaffective disorder are severe, disabling disorders associated with decreased quality of life (QOL) and functioning (Bobes, Garcia-Portilla, Bascaran, Saiz, &amp; Bouso&ntilde;o, 2007; Latalova, Prasko, Diveky, Kamaradova, &amp; Velartova, 2010; Merikangas et al., 2012). Stigmatization, co-morbidities, adverse effects of medications, care models with deficits in personal and social recovery needs and chronic symptoms due to treatment resistance are factors that can lead to severe reductions in quality of life and functioning (Kahn et al., 2015; Sum, Ho, &amp; Sim, 2015). In this study we aim to characterize patients with good and poor outcomes according to QOL and functioning scores. Using cluster analysis, we sought to identify longitudinal trajectories and investigate whether levels of QOL and functioning are associated with polygenic risk scores. Determining clusters of patients at higher risk of poorer outcomes is critical to provide early and effective interventions. Methods Longitudinal data was used from the Clinical Research Group 241 and PsyCourse studies in Germany. Participants were phenotyped using a comprehensive battery which included data on socio-demographics, history of illness, symptomatology, QOL and functioning. Data was collected at four equidistant time points over an 18-month period. The Infinium Psycharray from Illumina was used to genotype patients. Relevant questionnaire items (i.e. QOL, functioning scores, and socio-demographic data) were pre-selected and factor analysis for mixed data was applied to identify trends in the data. This allowed for the computation of abstract data dimensions which were used for calculation of longitudinal trajectories. These trajectories can be seen as a representation of the overall status of patients and both the overall level as well as the longitudinal change of this status were used as inputs for a k-mean clustering for longitudinal data (Genolini et al., 2013). This, in turn, resulted in the identification of three distinct subpopulations of patients. In a linear regression model we used clusters as predictive variables for polygenic risk scores at 11 thresholds. Results The dimension which explained the most variance was used for cluster analysis. This dimension was mainly driven by scores for self-satisfaction, life enjoyment, ability to cope with daily tasks, energy, and quality of life. In a sample of 198 patients, three clusters were observed; cluster A (39,4%) consisted of participants with the highest average scores for functioning and QOL, cluster B (33,8%) including participants with the lowest average scores for functioning and QOL, and cluster C (26,8%) consisting of participants who had great improvement in functioning and QOL scores over the course of the longitudinal study. Male patients were substantially overrepresented in cluster A and the inverse effect was observed in cluster B. No significant differences were seen for age of onset, age at interview, or duration of illness within the clusters. Polygenic risk scores at certain thresholds can be predicted by the clusters. In cluster B there was a trend for higher polygenic risk scores. Discussion Phenotypic data provide insight to target sufferers of severe mental illness with worse outcomes. Levels of functioning and QOL seem to be associated with polygenic risk scores. Further investigations are needed. &nbsp

Integrating polygenic allele burden information and phenomic data to characterize complex disease trajectories in severe mental illness.

Background Bipolar disorder (BD), schizoaffective disorder (SZA) and schizophrenia (SZ) are severe mental illnesses that share - at least in parts - psychopathological features and an underlying polygenic nature. One characteristic of all three diagnoses is the highly variable disease course and outcome. This heterogeneity is one of the biggest challenges in studying the underlying biological mechanisms. Therefore, defining more homogeneous subgroups across diagnoses is a promising approach. However, there are no clear criteria as how to define a &ldquo;good&rdquo; or &ldquo;poor&rdquo; course of illness as different domains can be considered such as psychopathology, cognitive performance, psychosocial functioning, or quality of life. We aim to integrate these domains and define longitudinal clusters of patients across diagnoses. Furthermore, we explore the characteristics of these clusters and the association of cluster membership with the individual load on schizophrenia polygenic risk scores (SZ-PRS). Methods Participants were selected from an ongoing longitudinal project carried out at several centers in Germany and Austria (www.kfo241.de; www.PsyCourse.de). We characterize patients at four time-points over an 18-month period with a comprehensive phenotyping battery. The selected sample comprised a total of 198 participants (age(SD)=46.93(12.43); 46% females) with a DSM-IV diagnosis of SZ, SZA or BD, who completed the entire study period. DNA samples were genotyped using the Illumina PsychChip and imputed using the 1000 Genomes Phase 3 reference panel. SZ-PRS were calculated for all individuals based on the PGC2 SZ summary results. Factor analysis for mixed data (FAMD) was applied to compute abstract data dimensions in a set of 117 longitudinally measured variables, i.a. on psychopathology, cognitive performance, functioning and quality of life. Longitudinal trajectories of patients on the first dimension were used as inputs for k-mean clustering for longitudinal data. This, in turn, resulted in the identification of three distinct clusters of patients, which we used as predictive variables for SZ-PRS at 11 p-value thresholds in a linear regression model. Results Strongest loadings on the first dimension computed by FAMD were observed for quality of life items, a global depression rating and level of functioning. Three clusters of longitudinal trajectories were identified on this dimension: A) patients who scored highly on the dimension across all time points (58.1%); B) patients with consistently low scores (26.3%); C) patients who improved from baseline to the last follow up (15.7%). There were no significant between-group differences regarding sex, age, diagnoses, center, age at onset, and duration of illness. Cluster membership was significantly associated with the SZ-PRS with highest polygenic burden in cluster B. Discussion Although the reported results are preliminary and therefore have to be interpreted with caution, the approach of longitudinal clustering in order to identify cross-diagnostic, homogeneous subgroups of patients for genetic studies is promising. The next steps will be refinement of clusters by taking more than one dimension from the FAMD into account, verification of cluster solutions in an external dataset, and exploration of associations with other biological markers. &nbsp

A high affinity RIM binding protein Aplip1 interaction prevents the formation of ectopic axonal active zones

Synaptic vesicles SVs fuse at active zones AZs covered by a protein scaffold, at Drosophila synapses comprised of ELKS family member Bruchpilot BRP and RIM binding protein RBP . We here demonstrate axonal co transport of BRP and RBP using intravital live imaging, with both proteins co accumulating in axonal aggregates of several transport mutants. RBP, via its C terminal Src homology 3 SH3 domains, binds Aplip1 JIP1, a transport adaptor involved in kinesin dependent SV transport. We show in atomic detail that RBP C terminal SH3 domains bind a proline rich PxxP motif of Aplip1 JIP1 with submicromolar affinity. Pointmutating this PxxP motif provoked formation of ectopic AZ like structures at axonal membranes. Direct interactions between AZ proteins and transport adaptors seem to provide complex avidity and shield synaptic interaction surfaces of pre assembled scaffold protein transport complexes, thus, favouring physiological synaptic AZ assembly over premature assembly at axonal membrane