David Stacey

Multimodal imaging of a tescalcin (TESC)-regulating polymorphism (rs7294919)-specific effects on hippocampal gray matter structure

In two large genome-wide association studies, an intergenic single-nucleotide polymorphism (SNP; rs7294919) involved in TESC gene regulation has been associated with hippocampus volume. Further characterization of neurobiological effects of the TESC gene is warranted using multimodal brain-wide structural and functional imaging. Voxel-based morphometry (VBM8) was used in two large, well-characterized samples of healthy individuals of West-European ancestry (Münster sample, N=503; SHIP-TREND, N=721) to analyze associations between rs7294919 and local gray matter volume. In subsamples, white matter fiber structure was investigated using diffusion tensor imaging (DTI) and limbic responsiveness was measured by means of functional magnetic resonance imaging (fMRI) during facial emotion processing (N=220 and N=264, respectively). Furthermore, gene x environment (G × E) interaction and gene x gene interaction with SNPs from genes previously found to be associated with hippocampal size (FKBP5, Reelin, IL-6, TNF-α, BDNF and 5-HTTLPR/rs25531) were explored. We demonstrated highly significant effects of rs7294919 on hippocampal gray matter volumes in both samples. In whole-brain analyses, no other brain areas except the hippocampal formation and adjacent temporal structures were associated with rs7294919. There were no genotype effects on DTI and fMRI results, including functional connectivity measures. No G × E interaction with childhood maltreatment was found in both samples. However, an interaction between rs7294919 and rs2299403 in the Reelin gene was found that withstood correction for multiple comparisons. We conclude that rs7294919 exerts highly robust and regionally specific effects on hippocampal gray matter structures, but not on other neuropsychiatrically relevant imaging markers. The biological interaction between TESC and RELN pointing to a neurodevelopmental origin of the observed findings warrants further mechanistic investigations.

Consequences Of Natural Perturbations In The Human Plasma Proteome

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Genomic atlas of the human plasma proteome.

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with genexa0expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.A genetic atlas of the human plasma proteome, comprising 1,927 genetic associations with 1,478 proteins, identifies causes of disease and potential drug targets.

Interaction between childhood maltreatment on immunogenetic risk in depression: Discovery and replication in clinical case-control samples

Major depressive disorder (MDD) is a prevalent disorder with moderate heritability. Both MDD and interpersonal adversity, including childhood maltreatment, have been consistently associated with elevated inflammatory markers. We investigated interaction between exposure to childhood maltreatment and extensive genetic variation within the inflammation pathway (CRP, IL1b, IL-6, IL11, TNF, TNFR1, and TNFR2) in relation to depression diagnosis. The discovery RADIANT sample included 262 cases with recurrent DSM-IV/ICD-10 MDD, and 288 unaffected controls. The replication Münster cohort included 277 cases with DSM-IV MDD, and 316 unaffected controls. We identified twenty-five single nucleotide polymorphisms (SNPs) following multiple testing correction that interacted with childhood maltreatment to predict depression in the discovery cohort. Seven SNPs representing independent signals (rs1818879, rs1041981, rs4149576, rs616645, rs17882988, rs1061622, and rs3093077) were taken forward for replication. Meta-analyses of the two samples presented evidence for interaction with rs1818879 (IL6) (RD=0.059, SE=0.016, p<0.001), with the replication Münster sample approaching statistical significance in analyses restricted to recurrent MDD and controls following correction for multiple testing (q=0.066). The CRP locus (rs3093077) showed a similar level of evidence for interaction in the meta-analysis (RD=0.092, SE=0.029, p=0.002), but less compelling evidence in the replication sample alone (recurrent MDD q=0.198; all MDD q=0.126). Here we present evidence suggestive of interaction with childhood maltreatment for novel loci in IL-6 (rs1818879) and CRP (rs3093077), increasing risk of depression. Replication is needed by independent groups, targeting these specific variants and interaction with childhood maltreatment on depression risk.

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci

Abstract Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the ‘Prioritization of candidate causal Genes at Molecular QTLs’ (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of ‘true positive’ causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

Targeted proteomic analysis of cognitive dysfunction in remitted major depressive disorder: Opportunities of multi-omics approaches towards predictive, preventive, and personalized psychiatry

In order to accelerate the understanding of pathophysiological mechanisms and clinical biomarker discovery and in psychiatry, approaches that integrate multiple -omics platforms are needed. We introduce a workflow that investigates a narrowly defined psychiatric phenotype, makes use of the potent and cost-effective discovery technology of gene expression microarrays, applies Weighted Gene Co-Expression Network Analysis (WGCNA) to better capture complex and polygenic traits, and finally explores gene expression findings on the proteomic level using targeted mass-spectrometry (MS) technologies. To illustrate the effectiveness of the workflow, we present a proteomic analysis of peripheral blood plasma from patients remitted major depressive disorder (MDD) who experience ongoing cognitive deficits. We show that co-expression patterns previous detected on the transcript level could be replicated for plasma proteins, as could the module eigengene correlation with cognitive performance. Further, we demonstrate that functional analysis of multi-omics data has the potential to point to cellular mechanisms and candidate biomarkers for cognitive dysfunction in MDD, implicating cell cycle regulation by cyclin D3 (CCND3), regulation of protein processing in the endoplasmatic reticulum by Thioredoxin domain-containing protein 5 (TXND5), and modulation of inflammatory cytokines by Tripartite Motif Containing 26 (TRI26).nnnSIGNIFICANCEnThis paper discusses how data from multiple -omics platforms can be integrated to accelerate biomarker discovery in psychiatry. Using the phenotype of cognitive impairment in remitted major depressive disorder (MDD) as an example, we show that the application of a systems biology approach - weighted gene co-expression network analysis (WGCNA) - in the discovery phase, and targeted proteomic follow-up of results, provides a structured avenue towards uncovering novel candidate markers and pathways for personalized clinical psychiatry.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

A gene co-expression module implicating the mitochondrial electron transport chain is associated with long-term response to lithium treatment in bipolar affective disorder

Lithium is the first-line treatment for bipolar affective disorder (BPAD) but two-thirds of patients respond only partially or not at all. The reasons for this high variability in lithium response are not well understood. Transcriptome-wide profiling, which tests the interface between genes and the environment, represents a viable means of exploring the molecular mechanisms underlying lithium response variability. Thus, in the present study we performed co-expression network analyses of whole-blood-derived RNA-seq data from nu2009=u200950 lithium-treated BPAD patients. Lithium response was assessed using the well-validated ALDA scale, which we used to define both a continuous and a dichotomous measure. We identified a nominally significant correlation between a co-expression module comprising 46 genes and lithium response represented as a continuous (i.e., scale ranging 0–10) phenotype (coru2009=u2009−0.299, pu2009=u20090.035). Forty-three of these 46 genes had reduced mRNA expression levels in better lithium responders relative to poorer responders, and the central regulators of this module were all mitochondrially-encoded (MT-ND1, MT-ATP6, MT-CYB). Accordingly, enrichment analyses indicated that genes involved in mitochondrial functioning were heavily over-represented in this module, specifically highlighting the electron transport chain (ETC) and oxidative phosphorylation (OXPHOS) as affected processes. Disrupted ETC and OXPHOS activity have previously been implicated in the pathophysiology of BPAD. Our data adds to previous evidence suggesting that a normalisation of these processes could be central to lithium’s mode of action, and could underlie a favourable therapeutic response.

Genome-Wide Gene Expression Signature Of Depression

Background Many studies have attempted to identify the molecular signature of depression. The results, however, are inconsistent. The small sample sizes, choice of tissue/cell types and suboptimal statistical methods are the major limitations that might contribute to this inconsistency. In our study, to identify the whole blood transcriptome signature of geriatric depression, we utilized two large population cohorts aged over 65 -The Sydney Memory and Aging Study, MAS (N=521) and The Older Australian Twin Study, OATS (N=324) as discovery and replication cohorts, respectively. Major Depression was assessed according to DSM-IV criteria. Methods The genome-wide gene expression data were obtained using Illumina HT-12 v4. After quality control and pre-processing, the application of stringent filtering criteria (by detection p-value (p Results The eigengenes of two modules were associated with the depression phenotype (p=0.01 and p=0.02). Closer inspection of the modules of interest revealed that only 37 out of 82 genes in one module and 17 out of 64 genes in another module were significantly associated with the phenotype of depression. Correlational analyses of individual genes within the depression relevant modules revealed that 8 out of 37 significant genes in one module were protein coding genes, involved in various translational, metabolic and immune processes (PCYOX1L, RPL14, MCTS1, GIMAP7, NDUFB9, BOLA2, EIF3M, RPL7A). The second module contained 5 protein coding genes out of 17 associated with depression, the known molecular functions of which include catalytic, enzyme regulation, transcription factor and translational regulation activities (PRCP, POLR2J2, ATF4, TAOK3, EIF2B5). The two top genes from both modules are known to be involved in metabolic process regulation by reactive oxygen species (PCYOX1L, prenylcysteine oxidase-like (p Discussion Our results support the oxidative stress hypothesis of depression and provide new insights into pathophysiological mechanisms of geriatric depression. In addition, we will present the results from pathway analyses (Ingenuity Pathway Analysis software, IPA) performed on genes identified as relevant to depression in this study. To bridge genotype with whole blood transcriptome and identify genomic loci that influence the identified gene expression signature of depression, we will present results from genome-wide eQTL analyses, which are ongoing.

Transcriptome Signature of Depression

Abstract Previous studies aiming to identify the transcriptome signature of depression show inconsistent results with low replicability at the single gene level in both brain and peripheral tissues. The complexity of the depression phenotype may contribute to this inconsistency. An advance in this field could be made by utilising systems biology approach. In this study, we analysed whole blood transcriptomes of 521 elderly people from the general population (The Sydney Memory and Aging Study, MAS) to identify molecular networks involved in geriatric depression. Depression was assessed according to DSM-IV criteria yielding both categorical and continuous depression phenotypes. Pre-processing of 47,323 probes (Illumina HT-12 v4) resulted in the 11,018 top-varying genes for downstream analyses. Pre-processing included maximum likelihood estimation (MLE) background correction, variance-stabilising (VST) transformation, quantile normalisation and filtering by detection p-value (p Using WGCNA package (R) we constructed the co-expression network consisting of 29 modules, two of which were associated with depressive symptoms. Both modules showed a highly significant correlation between module membership (MM) and gene significance (GS) measures (r=0.55, p=1e-07, r=0.4, p=0.0011), indicating that genes in these modules are highly significantly associated with depression. Further analysis showed that 37 and 17 genes respectively from the two “depression modules” are significantly associated with depressive symptoms (p