13 research outputs found
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
OTTO: a new strategy to extract mental disease-relevant combinations of GWAS hits from individuals.
Despite high heritability of schizophrenia, genome-wide association studies (GWAS) have not yet revealed distinct combinations of single-nucleotide polymorphisms (SNPs), relevant for mental disease-related, quantifiable behavioral phenotypes. Here we propose an individual-based model to use genome-wide significant markers for extracting first genetic signatures of such behavioral continua. 'OTTO' (old Germanic=heritage) marks an individual characterized by a prominent phenotype and a particular load of phenotype-associated risk SNPs derived from GWAS that likely contributed to the development of his personal mental illness. This load of risk SNPs is shared by a small squad of 'similars' scattered under the genetically and phenotypically extremely heterogeneous umbrella of a schizophrenia end point diagnosis and to a variable degree also by healthy subjects. In a discovery sample of >1000 deeply phenotyped schizophrenia patients and several independent replication samples, including the general population, a gradual increase in the severity of 'OTTO's phenotype' expression is observed with an increasing share of 'OTTO's risk SNPs', as exemplified here by autistic and affective phenotypes. These data suggest a model in which the genetic contribution to dimensional behavioral traits can be extracted from combinations of GWAS SNPs derived from individuals with prominent phenotypes. Even though still in the 'model phase' owing to a world-wide lack of sufficiently powered, deeply phenotyped replication samples, the OTTO approach constitutes a conceptually novel strategy to delineate biological subcategories of mental diseases starting from GWAS findings and individual subjects.peerReviewe
Fibroblast Growth Factor 14 Modulates the Neurogenesis of Granule Neurons in the Adult Dentate Gyrus
Common variants on 8p12 and 1q24.2 confer risk of schizophrenia
To access publisher full text version of this article. Please click on the hyperlink in Additional Links field.Schizophrenia is a severe mental disorder affecting ∼1% of the world population, with heritability of up to 80%. To identify new common genetic risk factors, we performed a genome-wide association study (GWAS) in the Han Chinese population. The discovery sample set consisted of 3,750 individuals with schizophrenia and 6,468 healthy controls (1,578 cases and 1,592 controls from northern Han Chinese, 1,238 cases and 2,856 controls from central Han Chinese, and 934 cases and 2,020 controls from the southern Han Chinese). We further analyzed the strongest association signals in an additional independent cohort of 4,383 cases and 4,539 controls from the Han Chinese population. Meta-analysis identified common SNPs that associated with schizophrenia with genome-wide significance on 8p12 (rs16887244, P = 1.27 × 10(-10)) and 1q24.2 (rs10489202, P = 9.50 × 10(-9)). Our findings provide new insights into the pathogenesis of schizophrenia.973 Program
2010CB529600
2009AA022701
2006AA02A407
Natural Science Foundation of China
81130022
81121001
31000553
Foundation for the Author of National Excellent Doctoral Dissertation of China 201026
Program for New Century Excellent Talents in University NCET-09-0550
Shanghai Municipal Health Bureau 2008095
Shanghai Changning Health Bureau 2008406002
Shanghai Municipal Commission 09DJ1400601
National Key Technology RD Program 2006BAI05A09
Shanghai Leading Academic Discipline Project 20