Biology subjects

Rietschel, M.

Publications and source records attributed to Rietschel, M..

At least 19 recordsLinked to original sources

bioRxiv2025

Dynamic Epigenetic Changes During Antidepressant Pharmacotherapy in Major Depressive Disorder

Although antidepressants remain the main pharmacological treatment for major depressive disorder (MDD), therapeutic response varies, highlighting the need for molecular markers that predict treatment response, as well as insights into the biological processes underlying antidepressant efficacy. Candidate-gene and cross-sectional epigenome-wide association studies (EWAS) have reported DNA methylation signatures associated with antidepressant response; however, findings remain inconsistent. To date, no longitudinal EWAS has examined methylation trajectories across multiple time points during antidepressant treatment in MDD. Within the Early Medication Change trial, DNA methylation data was generated for 162 patients with MDD (81 responders, 81 non-responders) and 48 matched healthy controls. Patients were assessed at four times across eight-weeks of standardized antidepressant treatment, while controls were assessed twice. Differentially methylated positions (DMPs) and regions (DMRs) were identified using longitudinal EWAS models and the comb-p algorithm. Baseline methylation levels were associated with depressive symptom severity at day 28 and day 56 through two and six DMRs, respectively (e.g., TNRC6C, CAT), whereas no single DMP reached significance. Longitudinal analyses identified one DMP associated with improvement in depressive symptoms (YLMP1) and three DMRs showing methylation changes over time (e.g., GPR126, PM20D1). Combined patient-control analyses revealed additional DMPs and DMRs associated with diagnosis and temporal effects. This study provides first longitudinal evidence of regionally coordinated DNA methylation changes during antidepressant pharmacotherapy, revealing alterations in genes involved in neuroplasticity and inflammatory processes that are associated with clinical response. Replication and functional validation will be essential to determine their relevance for personalized antidepressant treatment.

genomics↗

bioRxiv2024

A multi-omics and cell type-specific characterization of the ventral striatum in human cocaine use disorder

Epigenome, transcriptome, and proteome analyses of postmortem brains have revealed initial molecular insights into cocaine use disorder (CUD). However, the inter-relationship between these -omics and the contribution of individual cell types remain largely unknown. We present an in-depth analysis of molecular changes in the ventral striatum in CUD at multi-omics and single-cell resolution. Integrative multi-omics analyses of microRNA-seq, RNA-seq, and proteomics datasets in 41 individuals and single-nuclei RNA-seq in a subset of 16 individuals revealed conserved deregulation of metabolic pathways, oxidative phosphorylation, and glutamatergic signaling. Cell type-specific analyses identified inverse metabolic pathway deregulation patterns in glial and neuronal cells, notably in astrocytes and medium spiny neurons (MSNs). Characterizing astrocyte-neuron crosstalk revealed altered glutamatergic and cell adhesion signaling in CUD. By applying a comprehensive multi-omics analytical framework, our study provides novel insights into CUD-associated molecular changes in the ventral striatum, suggesting astrocytes, MSNs, and their crosstalk as particularly perturbed in CUD.

neuroscience↗

bioRxiv2022

Single molecule long-read real-time amplicon-based sequencing of CYP2D6: a proof-of-concept with hybrid haplotypes

CYP2D6 is a widely expressed human xenobiotic metabolizing enzyme, best known for its role in the hepatic phase I cytochrome P450 enzyme system, where it metabolizes [~]20% of medications. It is also expressed in other organs including the brain, where its potential role in physiology and mental health traits and disorders is under further investigation. Owing to the presence of homologous pseudogenes in the CYP2D locus and transposable repeat elements in the intergenic regions, the gene encoding the CYP2D6 enzyme, CYP2D6, is one of the most hypervariable known human genes - with more than 165 core haplotypes. Haplotypes include structural variants, with a subtype of these known as hybrid haplotypes or fusion genes comprising part of CYP2D6 and part of its adjacent pseudogene, CYP2D7. The fusion genes are particularly challenging to identify. High fidelity (HiFi) single molecule real-time (SMRT) long-read sequencing can cover whole CYP2D6 haplotypes in a single continuous sequence, and is therefore ideal for structural variant detection. In addition, it is highly accurate and suitable for novel haplotype identification, which is necessary as new CYP2D6 haplotypes are continuously being discovered, and many more likely remain to be identified in relatively understudied populations such as Indigenous Peoples. The aim of the present work was to develop an efficient and accurate HiFi SMRT amplicon-based method capable of detecting the full range of CYP2D6 haplotypes including fusion genes. We report proof-of-concept for 24 amplicons including three positive controls, aligned to fusion gene haplotypes, with prior cross-validation data. Amplicons with CYP2D7-D6 fusion genes, including positive controls, aligned to the *13 subhaplotypes predicted (*13F, *13A2) with 100% accuracy, with the exception of one that aligned at 99.9%. Alignment of the *68 was 100% and above 99.9% to the CYP2D6*68 partial sequences EU5300606 and JF307779, respectively. The best alignments for the remaining CYP2D6-2D7 fusion genes were [≥]99.7% (to 3 significant figures). Lower percentage alignment for CYP2D6-2D7 fusion genes may reflect imperfect PCR optimization and/or the possibility that we may have haplotypes not yet in public databases. Further work on these is in progress. Moreover, we have adapted this method for non-hybrid haplotypes. This technique could therefore suffice for the characterization of the full range of CYP2D6 haplotypes. The method that we have developed could be extended to other complex loci and to other species in a multiplexed high throughput assay.

genomics↗

bioRxiv2021

Connection Failure: Differences in White Matter Microstructure are associated with 5-HTTLPR but not with Risk-Seeking for Losses

In a previous study (Neukam, Kroemer et al. 2018), we found for the 5-HTTLPR genotype higher risk-seeking for losses in S/S vs. L/L carrier, which could not be explained by acutely changed central serotonin levels. This finding alternatively may be the result of reduced top-down control from the frontal cortex due to altered signal pathways involving the amygdala and ventral striatum. The serotonergic system is known to be involved in neurodevelopment and neuroplasticity. Therefore, the aim of this study was to investigate whether structural differences in white matter can explain the differences in risk-seeking behaviour that we observed in our previous study and whether 5-HTTLPR groups differ in their white matter microstructure. These differences can be detected using diffusion-weighted imaging (DWI). We assumed lower structural connectivity in S/S compared to L/L carrier, and a negative relationship between risk-seeking for losses and connectivity. We used DWI to compute diffusion parameters for the frontostriatal and uncinate tract in 175 individuals (39 S/S, 80 S/L, 56 L/L). Results showed no significant relationship between diffusion parameters and risk-seeking for losses. Furthermore, we did not find significant differences in diffusion parameters of the S/S vs. L/L group. There were only group differences in the frontostriatal tract showing stronger structural connectivity in the S/L group, which is also reflected in the whole brain approach. Therefore, the data do not support our hypothesis that the association between 5- HTTLPR and risk-seeking for losses is related to differences in white matter pathways implicated in decision-making.

neuroscience↗

bioRxiv2020

Meta-analysis of epigenome-wide associations between DNA methylation at birth and childhood cognitive skills.

Cognitive skills are a strong predictor of a wide range of later life outcomes. Genetic and epigenetic associations across the genome explain some of the variation in general cognitive abilities in the general population and it is plausible that epigenetic associations might arise from prenatal environmental exposures and/or genetic variation early in life. We investigated the association between cord blood DNA methylation at birth and cognitive skills assessed in children from eight pregnancy cohorts (N=2196-3798) within the Pregnancy And Childhood Epigenetics (PACE) Consortium across overall, verbal and non-verbal cognitive scores. The associations at single CpG sites were weak for all of the cognitive domains investigated. One region near DUSP22 on chromosome 6 was associated with non-verbal cognition in a model adjusted for maternal IQ. We conclude that there is little evidence to support the idea that cord blood DNA methylation at single CpGs can predict cognitive skills and further studies are needed to confirm regional differences.

developmental biology↗

bioRxiv2020

"The Heidelberg Five" Personality Dimensions: Genome-wide Associations, Polygenic Risk for Neuroticism, and Psychopathology 20 Years after Assessment

The HeiDE study ( Heidelberger Langzeitstudie zu Risikofaktoren und Diagnose chronischer Erkrankungen) is a longitudinal population-based study that started in the 1990s and, at baseline, assessed an array of health-related personality questionnaires in 5 133 individuals. Five latent personality dimensions (The Heidelberg Five) were identified and interpreted as Emotional Lability (ELAB), Lack of Behavioral Control (LBCN), Type A Behavior (TYAB), Locus of Control over Disease (LOCC), and Psychoticism (PSYC). A subset of participants (n=3 268; after quality control) were genotyped on whole-genome arrays at follow-up. To further characterize The Heidelberg Five, we analyzed genomic underpinnings, their relations to the genetic basis of the Big Five trait Neuroticism, and longitudinal associations with lifetime psychiatric symptoms. SNP-based heritability was significant for ELAB (34%) and LBCN (29%). Five separate genome-wide association studies (GWAS) using factor scores on personality dimensions as phenotypes were conducted, only the phenotype PSYC yielded a genome-wide significant finding (p<5x10-8, top SNP rs138223660). Gene-based analyses identified significant findings for ELAB (Integrin Subunit Beta 5), TYAB (Coiled-coil Domain Containing 83), and PSYC (Nuclear Receptor Subfamily 1 Group H Member 4). Polygenic risk scores for Neuroticism, phenotypically related to ELAB, were associated with ELAB, but not with the remaining Heidelberg Five. Longitudinally, all personality dimensions were related to depressive symptoms at follow-up, with ELAB, LBCN, and PSYC also associated with lifetime anxiety symptoms. These results highlight the clinical importance of health-related personality traits, and identify LBCN as a heritable "executive function" personality trait.

genomics↗

bioRxiv2020

Expanding the Genetic Architecture of Nicotine Dependence and its Shared Genetics with Multiple Traits: Findings from the Nicotine Dependence GenOmics (iNDiGO) Consortium

Cigarette smoking is the leading cause of preventable morbidity and mortality. Knowledge is evolving on genetics underlying initiation, regular smoking, nicotine dependence (ND), and cessation. We performed a genome-wide association study using the Fagerstrom Test for ND (FTND) in 58,000 smokers of European or African ancestry. Five genome-wide significant loci, including two novel loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416) were identified, and loci reported for other smoking traits were extended to ND. Using the heaviness of smoking index (HSI) in the UK Biobank (N=33,791), rs2714700 was consistently associated, but rs1862416 was not associated, likely reflecting ND features not captured by the HSI. Both variants were cis-eQTLs (rs2714700 for MAGI2-AS3 in hippocampus, rs1862416 for TENM2 in lung), and expression of genes spanning ND-associated variants was enriched in cerebellum. SNP-based heritability of ND was 8.6%, and ND was genetically correlated with 17 other smoking traits (rg=0.40-0.95) and co-morbidities. Our results emphasize the FTND as a composite phenotype that expands genetic knowledge of smoking, including loci specific to ND.

genomics↗

bioRxiv2019

Cross-validation of technologies for genotyping CYP2D6 and CYP2C19

BackgroundCYP2D6 and CYP2C19 are cytochrome P450 enzymes involved in the metabolism of many medications from multiple therapeutic classes. Associations between patterns of variants (known as haplotypes) in the genes encoding them (CYP2D6 and CYP2C19) and enzyme activities are well described. The genes in fact comprise 21% of biomarkers in drug labels. Despite this, genotyping is not common, partly attributable to its challenging nature (CYP2D6 having >100 haplotypes, including those with sequence from an adjacent pseudogene, and gene duplications). We cross-validated different methodologies for identifying haplotypes in these genes against each other. MethodsNinety-two samples with a variety of CYP2D6 and CYP2C19 genotypes according to prior AmpliChip CYP450 and TaqMan CYP2C19*17 data were selected from the Genome-based therapeutic drugs for depression (GENDEP) study. Genotyping was performed with TaqMan copy number variant (CNV) and single nucleotide variant (SNV) analysis, the next generation sequencing-based Ion S5 AmpliSeq Pharmacogenomics Panel, PharmacoScan, long-range polymerase chain reaction (L-PCR) followed by amplicon analysis, and Agena for CYP2C19. Variant pattern to haplotype translation was automated. ResultsThe inter-platform concordance for CYP2C19 was high (up to 100% for available data). For CYP2D6, the IonS5-PharmacoScan concordance was 94% for a range of variants tested apart from those with at least one extra copy of a CYP2D6 gene (occurring at a frequency of 3.8%, 33/853), or those with substantial sequence derived from pseudogene, known as hybrids (3%, 26/853). ConclusionsInter-platform concordance for CYP2C19 was high, and, moreover, the Ion S5 and PharmacoScan data were 100% concordant with that from a TaqMan CYP2C19*2 assay. We have also demonstrated feasibility of using an NGS platform for genotyping CYP2D6 and CYP2C19, with automated data interpretation methodology. This points the way to a method of making CYP2D6 and CYP2C19 genotyping more readily accessible.

genetics↗

bioRxiv2019

Longitudinal transcriptome-wide gene expression analysis of sleep deprivation treatment showsinvolvement of circadian genes and immune pathways

BackgroundTherapeutic sleep deprivation (SD) rapidly induces robust, transient antidepressant effects in a large proportion of major mood disorder patients suffering from a depressive episode, but underlying biological factors remain poorly understood. Research suggests that these patients may have altered circadian molecular genetic clocks and that SD functions through resetting dysregulated genes; additional factors may be involved, warranting further investigation. Leveraging advances in microarray technology enabling the transcriptome-wide assessment of gene expression, this study aimed to examine gene expression changes accompanying SD and recovery sleep in patients suffering from an episode of depression. MethodsPatients (N=78) and controls (N=15) underwent SD, with blood taken at the same time of day before, after one night of SD and after recovery sleep. A transcriptome-wide gene-by-gene approach was used, with a targeted look also taken at circadian genes. Furthermore, gene set enrichment, and longitudinal gene set analyses including the time point after recovery sleep, were conducted. ResultsCircadian genes were significantly affected by SD, with patterns suggesting that molecular clocks of responders and non-responders, as well as patients and controls respond differently to chronobiologic stimuli. Notably, gene set analyses revealed a strong widespread effect of SD on pathways involved in immune function and inflammatory response, such as those involved in cytokine and especially in interleukin signalling. Longitudinal gene set analyses showed that in responders these pathways were upregulated after SD; in non-responders, little response was observed. ConclusionsOur findings emphasize the close relationship between circadian, immune and sleep systems and their link to etiology of depression at the transcriptomic level.

epidemiology↗

bioRxiv2019

Generative network models identify biological mechanisms of altered structural brain connectivity in schizophrenia

BackgroundAlterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms leading to those alterations remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of the formation of altered structural brain networks.\n\nMethodsWe evaluated different generative network models to investigate the formation of structural brain networks in healthy controls (n=152), schizophrenia patients (n=66) and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated the association of these factors to cognition and to polygenic risk for schizophrenia.\n\nResultsStructural brain networks can be best accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation for all groups analyzed. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. The model parameter for spatial constraint was correlated with the polygenic risk for schizophrenia and predicted reduced cognitive performance.\n\nConclusionsOur results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to normal brain development as well as altered connectomes in schizophrenia. Spatial constraints were linked to the genetic risk for schizophrenia and general cognitive functioning, thereby providing insights into their biological basis and behavioral relevance.

neuroscience↗

bioRxiv2019

Conditional GWAS analysis identifies putative disorder-specific SNPs for psychiatric disorders

Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium - schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genom-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. In addition, we identified a number of variants that approached genome-wide significance in the original GWAS and have larger conditional effect sizes after conditioning on the other disorders. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

genetics↗

bioRxiv2018

Bipolar multiplex families have an increased burden of common risk variants for psychiatric disorders

Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families as well as 438 subjects from an independent, sporadic BD case-control cohort were analysed. Polygenic risk scores (PRS) for BD, schizophrenia, and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had significantly higher PRS for all three psychiatric disorders than the independent controls, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and sporadic BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses, therefore, demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. The PRS explained only part of the observed phenotypic variance and rare variants might have also contributed to disease development.

genetics↗

bioRxiv2018

Assessing 42 inflammatory markers in 321 control subjects and 887 major depressive disorder cases: BMI and other confounders and overall predictive ability for current depression

Inflammatory markers such as cytokines represent potential biomarkers for major depressive disorder (MDD). Many, generally small studies have examined the role of single markers and found significant associations. We assessed 42 inflammatory markers, namely cytokines, in the blood of 321 control subjects and 887 MDD cases. We tested whether individual inflammatory marker levels were significantly affected by MDD case/control status, current episode, or current depression severity, co-varying for age, sex, body mass index (BMI), smoking, current antidepressant use, ethnicity, assay batch and study effects. We further used machine learning algorithms to investigate if we could use our data to blindly discriminate MDD patients, or those in a current episode. We found broad and powerful influences of confounding factors on log-protein levels. Notably, IL-6 levels were very strongly influenced by BMI (p = 1.37 x 10-43, variance explained = 18%), while Interleukin-16 was the most significant predictor of current depressive episode (p = 0.003, variance explained = 0.9%, q < 0.1). No single inflammatory marker predicted MDD case/control status when a subject was not in a depressed episode, nor did any predict depression severity. Machine learning results revealed that using inflammatory marker data with clinical confounder information significantly increased precision for differentiating MDD patients who were in an episode. To conclude, a wide panel of inflammatory markers alongside clinical information may aid in predicting the onset of symptoms, but no single inflammatory protein is likely to represent a clinically useful biomarker for MDD diagnosis or prognosis. We note that the potential influence of physical health related and population stratification related confounders on inflammatory biomarker studies in psychiatry is considerable.

neuroscience↗

bioRxiv2018

Response to Therapeutic Sleep Deprivation: A Naturalistic Study of Clinical and Genetic Factors and Post-Treatment Depressive Symptom Trajectory

Research has shown that therapeutic sleep deprivation (SD) has rapid antidepressant effects in the majority of depressed patients. Investigation of factors preceding and accompanying these effects may facilitate the identification of the underlying biological mechanisms. This exploratory study aimed to examine clinical and genetic factors predicting response to SD and determine the impact of SD on illness course. Mood and tiredness during SD were also assessed via visual analogue scales (VAS). Depressed inpatients (n = 78) and healthy controls (n = 15) underwent ~36hrs of SD. Response to SD was defined as a score of [≤]2 on the Clinical Global Impression Scale for Global Improvement. Depressive symptom trajectories were evaluated for up to a month using self/expert ratings. Impact of genetic burden was calculated using polygenic risk scores for major depressive disorder. 72% of patients responded to SD. Responders and nonresponders did not differ in baseline self/expert depression symptom ratings, but mood subjectively measured by VAS scale differed. Response was associated with lower age (p = 0.007) and later age at life-time disease onset (p = 0.003). Higher genetic burden of depression was observed in non-responders than healthy controls. Up to a month post-SD, depressive symptoms decreased in both patients groups, but more in responders, in whom effects were sustained. The present findings suggest that re-examining SD with a greater focus on biological mechanisms will lead to better understanding of mechanisms of depression.

epidemiology↗

bioRxiv2018

Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: meta-analysis of data from genome-wide association studies

Cytochrome (CYP) P450 enzymes have a primary role in antidepressant metabolism and variants in these polymorphic genes are targets for pharmacogenetic investigation. This is the first meta-analysis to investigate how CYP2C19 polymorphisms predict citalopram/escitalopram efficacy and side effects.\n\nCYP2C19 phenotypes comprise poor metabolizers (PM), intermediate and intermediate+ metabolizers (IM; IM+), extensive and extensive+ metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) in Caucasians. These polymorphisms were genotyped or imputed from genome-wide data in four samples treated with citalopram or escitalopram (GENDEP, STAR*D, GenPod, PGRN-AMPS). Treatment efficacy was percentage symptom improvement and remission. Side effect data were available at weeks 2-4, 6 and 9 in three of the investigated samples. A fixed-effects meta-analysis was performed using EM as the reference group.\n\nAnalysis of 2558 patients for efficacy and 2037 patients for side effects showed that PMs had higher symptom improvement (SMD=0.43, CI=0.19-0.66) and higher remission rates (OR=1.55, CI=1.23-1.96) compared to EMs. At weeks 2-4, PMs showed higher risk of gastro-intestinal (OR=1.26, CI=1.08-1.47), neurological (OR=1.28, CI=1.07-1.53) and sexual side effects (OR=1.52, CI=1.23-1.87; week 6 values similar). No difference was seen at week 9 or in total side effect burden. PMs did not have higher risk of dropout at week 4 compared to EMs. Antidepressant dose was not different among CYP2C19 groups.\n\nCYP2C19 polymorphisms may provide helpful information for guiding citalopram/escitalopram treatment, despite PMs are relatively rare among Caucasians (~2%).

genetics↗

bioRxiv2017

Higher genetic risk for schizophrenia is associated with living in urban and populated areas

Social stress in urban life has been proposed as an environmental risk factor associated with the increased prevalence of schizophrenia in urban compared to rural areas. However, the potential genetic contributions to this relationship have been largely ignored.\n\nUsing a community-based sample of 15,544 adults living in Australia, we found higher genetic loading for schizophrenia in participants living in more densely populated (p-value=5.69*10-5) or less remote areas (p-value=0.003). Mendelian Randomization suggested that high schizophrenia genetic risk is a causal factor in chosing to live in denser (p-value=0.046) and less remote areas (p-value=0.044).\n\nOur results support the hypothesis of selective migration to more urban environments by people at higher genetic risk for schizophrenia and suggest a need to refine the social stress model for schizophrenia by including genetic influences on where people choose to live.

genetics↗

bioRxiv2017

Genome-wide Association Study of Clinical Features in the Schizophrenia Psychiatric Genomics Consortium: Confirmation of Polygenic Effect on Negative Symptoms

Schizophrenia is a clinically heterogeneous disorder. Proposed revisions in DSM - 5 included dimensional measurement of different symptom domains. We sought to identify common genetic variants influencing these dimensions, and confirm a previous association between polygenic risk of schizophrenia and the severity of negative symptoms. The Psychiatric Genomics Consortium study of schizophrenia comprised 8,432 cases of European ancestry with available clinical phenotype data. Symptoms averaged over the course of illness were assessed using the OPCRIT, PANSS, LDPS, SCAN, SCID, and CASH. Factor analyses of each constituent PGC study identified positive, negative, manic, and depressive symptom dimensions. We examined the relationship between the resultant symptom dimensions and aggregate polygenic risk scores indexing risk of schizophrenia. We performed genome - wide association study (GWAS) of each quantitative traits using linear regression and adjusting for significant effects of sex and ancestry. The negative symptom factor was significantly associated with polygene risk scores for schizophrenia, confirming a previous, suggestive finding by our group in a smaller sample, though explaining only a small fraction of the variance. In subsequent GWAS, we observed the strongest evidence of association for the positive and negative symptom factors, with SNPs in RFX8 on 2q11.2 (P = 6.27x10-8) and upstream of WDR72 / UNC13C on 15q21.3 (P = 7.59x10-8), respectively. We report evidence of association of novel modifier loci for schizophrenia, though no single locus attained established genome - wide significance criteria. As this may have been due to insufficient statistical power, follow - up in additional samples is warranted. Importantly, we replicated our previous finding that polygenic risk explains at least some of the variance in negative symptoms, a core illness dimension.

genetics↗

bioRxiv2017

Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

genomics↗