Heritability Estimation of Reliable Connectomic Features*

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%

DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia

We performed a systematic analysis of blood DNA methylation profiles from 4,483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1,048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia

Regional brain volumes and antidepressant treatment resistance in major depressive disorder

Major depressive disorder (MDD) is a heritable and highly debilitating condition with antidepressants, first-line treatment, demonstrating low to modest response rates. No current biological mechanism substantially explains MDD but both neurostructural and neurochemical pathways have been suggested. Further explication of these may aid in identifying subgroups of MDD that are better defined by their aetiology. Specifically, genetic stratification provides an array of tools to do this, including the intermediate phenotype approach which was applied in this thesis. This thesis explores genetic overlap with regional brain volume and MDD and the genetic and non-genetic components of antidepressant response. The first study utilised the most recent published data from ENIGMA (Enhancing Neuroimaging Genetics through Meta-analysis) Consortium’s genome-wide association study (GWAS) of regional brain volume to examine shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. This was explored using linkage disequilibrium score regression (LDSC), polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX (Breaking Up Heterogeneous Mixture Based On Cross-locus correlations). Results indicated that hippocampal volume was positively genetically correlated with MDD (rg= 0.46, P= 0.02), although this did not survive multiple comparison testing. Additionally, there was evidence for genetic subgrouping in Generation Scotland: Scottish Family Health Study (GS:SFHS) MDD cases (P=0.00281), however, this was not replicated in two other independent samples. This study does not support a shared architecture for regional brain volumes and MDD, however, provided some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. To explore antidepressant treatment resistance, the second study utilised prescription data in (GS:SFHS) to define a measure of (a) treatment resistance (TR) and (b) stages of resistance (SR) by inferring antidepressant switching as non-response. GWAS were conducted separately for TR in GS:SFHS and the GENDEP (Genome-based Therapeutic Drugs for Depression) study and then meta-analysed (meta-analysis n=4,213, cases=358). For SR, a GWAS on GS:SFHS only was performed (n=3,452). Additionally, gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis were conducted. No significant locus, gene or gene-set was associated with TR or SR, however power analysis indicated that this analysis was underpowered. Pedigree-based correlations identified genetic overlap with psychological distress, schizotypy and mood disorder traits. Finally, the role of neuroticism, psychological resilience and coping styles in antidepressant resistance was investigated. Univariate, moderation and mediation models were applied using logistic regression and structural equation modelling techniques. In univariate models, neuroticism and emotion-orientated coping demonstrated significant negative association with antidepressant resistance, whereas resilience, task-orientated and avoidance-orientated coping demonstrated significant positive association. No moderation of the association between neuroticism and TR was detected and no mediating effect of coping styles was found. However, resilience was found to partially mediate the association between neuroticism and TR. Whilst the first study does not indicate a genetic overlap between regional brain volumes and MDD, it demonstrates the utility of the intermediate approach in complex disease. Antidepressant resistance was associated with neuroticism both genetically and phenotypically, indicating its role as an intermediate phenotype. Nonetheless, larger sample sizes are needed to adequately address the components of antidepressant resistance. Further work in antidepressant non-response may help to identify biological mechanisms responsible in MDD pathology and help stratify individuals into more tractable groups

Widespread white matter microstructural differences in schizophrenia across 4322 individuals:Results from the ENIGMA Schizophrenia DTI Working Group

The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.Molecular Psychiatry advance online publication, 17 October 2017; doi:10.1038/mp.2017.170

Exploration of Shared Genetic Architecture Between Subcortical Brain Volumes and Anorexia Nervosa

In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlations ranged from − 0.10 to 0.23 (all p > 0.05). There were some signs of an inverse concordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN

DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia

We performed a systematic analysis of blood DNA methylation profiles from 4,483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1,048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia