O4.7. PLACENTAL GENE EXPRESSION, OBSTETRICAL HISTORY AND POLYGENIC RISK FOR SCHIZOPHRENIA

ABSTRACTOur group developed a transcriptome-based polygenic risk score (T-PRS) that uses common genetic variants to capture ‘depression-like’ shifts in cortical gene expression. Here, we mapped T-PRS onto diagnosis and symptom severity in major depressive disorder (MDD) cases and controls from the Psychiatric Genomics Consortium (PGC). To evaluate potential mechanisms, we further mapped T-PRS onto discrete measures of brain morphology and broad depression risk in healthy young adults. Genetic, self-report, and/or neuroimaging data were available in 29,340 PGC participants (59% women; 12,923 MDD cases, 16,417 controls) and 482 participants in the Duke Neurogenetics Study (DNS: 53% women; aged 19.8±1.2 years). T-PRS was computed from SNP data using PrediXcan to impute cortical expression levels of MDD-related genes from a previous post-mortem transcriptome meta-analysis. Sex-specific regressions were used to test effects of T-PRS on depression diagnosis, symptom severity, and Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index in the PGC and DNS samples, respectively. T-PRS did not predict depression diagnosis (OR=1.007, 95%CI=[0.997-1.018]); however, it correlated with symptom severity in men (rho=0.175, p=7.957×10−4) in one large PGC cohort (N=762, 48% men). In DNS, T-PRS was associated with smaller amygdala volume in women (β=-0.186, t=-3.478, p=.001) and less prefrontal gyrification (max≤-2.970, p≤.006) in both sexes. In men, prefrontal gyrification mediated an indirect effect of T-PRS on broad depression risk (b=.005, p=.029), indexed using self-reported family history of depression. Depression-like shifts in cortical gene expression predict symptom severity in men and may contribute to disease vulnerability through their effect on cortical gyrification.

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Gene expression elucidates functional impact of polygenic risk for schizophrenia

Nature Neuroscience ◽

10.1038/nn.4399 ◽

2016 ◽

Vol 19 (11) ◽

pp. 1442-1453 ◽

Cited By ~ 493

Author(s):

Menachem Fromer ◽

Panos Roussos ◽

Solveig K Sieberts ◽

Jessica S Johnson ◽

David H Kavanagh ◽

...

Keyword(s):

Gene Expression ◽

Functional Impact ◽

Polygenic Risk

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O11.1. MULTIPLE GENOMIC MEASURES OF SCHIZOPHRENIA RISK ARE RELATED TO CORTICAL THICKNESS IN TYPICALLY DEVELOPING YOUTH AND YOUNG ADULTS

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa028.061 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S27-S27

Author(s):

Maria Jalbrzikowski ◽

Lambertus Klei ◽

William Foran ◽

Beatriz Luna ◽

Bernie Devlin

Keyword(s):

Gene Expression ◽

Brain Development ◽

Cortical Thickness ◽

Principal Components ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Principal Component ◽

Typically Developing ◽

Risk Genes ◽

Polygenic Risk

Abstract Background The incidence of psychotic disorders increases in adolescence and young adulthood. Transition to a psychotic disorder is associated with atypical development of brain structures, specifically protracted developmental course. It is unknown how polygenic risk for schizophrenia and gene expression profiles of schizophrenia risk genes affect typical brain development. The goal of the current study is to examine relationships multiple genomic measures associated with schizophrenia risk and structural neuroimaging measures thickness in typically developing youth. Methods We combined structural neuroimaging and genetic data from three different cohorts of typically developing youth (N=994, 5–30 years old): the Philadelphia Neurodevelopmental Cohort, Pediatric Imaging Neurocognition and Genetics Study, and a locally collected sample at the University of Pittsburgh. All youth were free from psychiatric disorders and not taking psychiatric medications. We used Freesurfer to process the T1-weighted structural scans and calculate subcortical volumes, cortical thickness, and surface area measurements. After regressing out study, sex, ancestry eigenvectors, and grey matter signal-to-noise ratio, we ran principal components analysis on all neuroimaging measures (N=156). We calculated a schizophrenia polygenic risk score using genome-wide summary statistics from the Psychiatric Genome Consortium. Using a generalized linear model, each of the top five principal components was evaluated in relation to the risk score. We then used a computational method, Predixcan, to calculate expected gene expression profiles from the genotype data. We selected 125 genes that were associated with schizophrenia in a previous case-control comparison. Elastic net regression was used to determine significant associations between individual gene expression and the principal components. Results Schizophrenia polygenic risk was statistically associated with the 5th principal component (b=-0.10, p=0.001), which consisted of contributions from multiple measures of cortical thickness. Reduced cortical thickness in frontal and temporal regions was associated with increased genetic liability for schizophrenia. Increased cortical thickness in sensory-motor areas was associated with higher schizophrenia polygenic risk scores. This relationship remained when age was included as a predictor of interest and there were no statistically significant interactions between schizophrenia polygenic risk and age. Sixteen unique gene expression profiles were also associated with this principal component, significantly increasing the proportion of variance explained in this measure (from ~1% with the schizophrenia polygenic risk only to ~6% when including the additional gene expression measures). Many of the genes significantly associated with this principal component have important roles during early fetal brain development, including neuronal migration (e.g., SDCCAG8) and DNA repair (e.g., MLH1). Discussion These results suggest that that genetic risk for schizophrenia has a consistent influence on subtle, individual differences in a distinct spatial pattern of cortical thickness across typical development. This spatial pattern of cortical thickness is also associated with schizophrenia risk genes that have important functions during early brain development. Taken together, these findings suggest that increased genetic risk for schizophrenia is related to early subtle alterations during early brain development, setting up individuals with higher risk profiles to have a small biological vulnerability for later developing the illness.

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Novel polygenic risk score as a translational tool linking depression-related changes in the corticolimbic transcriptome with neural face processing and anhedonic symptoms

10.1101/556852 ◽

2019 ◽

Author(s):

Klara Mareckova ◽

Colin Hawco ◽

Fernanda C. Dos Santos ◽

Arin Bakht ◽

Navona Calarco ◽

...

Keyword(s):

Gene Expression ◽

Risk Score ◽

Clinical Sample ◽

Expression Patterns ◽

Neural Response ◽

Postmortem Brain ◽

Polygenic Risk Score ◽

Least Squares Regression ◽

Social Stimuli ◽

Polygenic Risk

ABSTRACTConvergent data from imaging and postmortem brain transcriptome studies implicate corticolimbic circuit (CLC) dysregulation in the pathophysiology of depression. To more directly bridge these lines of work, we generated a novel transcriptome-based polygenic risk score (T-PRS), capturing subtle shifts towards depression-like gene expression patterns in key CLC regions, and mapped this T-PRS onto brain function and related depressive symptoms in a non-clinical sample of 478 young adults (225 men; age 19.79+/−1.24) from the Duke Neurogenetics Study. First, T-PRS was generated based on common functional SNPs shifting CLC gene expression towards a depression-like state. Next, we used multivariate partial least squares regression to map T-PRS onto whole-brain activity patterns during perceptual processing of social stimuli (i.e., human faces). For validation, we conducted a comparative analysis with a PRS summarizing depression risk variants identified by the Psychiatric Genomics Consortium (PGC-PRS). Sex was modeled as moderating factor. We showed that T-PRS was associated with widespread reductions in neural response to neutral faces in women and to emotional faces and shapes in men (multivariate p<0.01). This female-specific reductions in neural response to neutral faces was also associated with PGC-PRS (multivariate p<0.03). Reduced reactivity to neutral faces was further associated with increased self-reported anhedonia. We conclude that women with functional alleles mimicking the postmortem transcriptomic CLC signature of depression have blunted neural activity to social stimuli, which may be expressed as higher anhedonia.

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Imputed Gene Expression Risk Scores: A Functionally Informed Component of Polygenic Risk

10.1101/2020.12.01.369462 ◽

2020 ◽

Author(s):

Oliver Pain ◽

Kylie P. Glanville ◽

Saskia Hagenaars ◽

Saskia Selzam ◽

Anna Fürtjes ◽

...

Keyword(s):

Gene Expression ◽

Genetic Risk ◽

Elastic Net ◽

Risk Scores ◽

Future Research ◽

Functional Genomic ◽

Predictive Utility ◽

Polygenic Risk ◽

Regulated Gene Expression ◽

Gene Expression Levels

AbstractBackgroundIntegration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results.MethodsThe predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study (TEDS). GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed SNP-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression.ResultsGeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for Height (p=0.023) to 4% for Rheumatoid Arthritis (p=5.9×10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalised genes.ConclusionGeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

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Novel polygenic risk score as a translational tool linking depression-related changes in the corticolimbic transcriptome with neural face processing and anhedonic symptoms

Translational Psychiatry ◽

10.1038/s41398-020-01093-w ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Klara Mareckova ◽

Colin Hawco ◽

Fernanda C. Dos Santos ◽

Arin Bakht ◽

Navona Calarco ◽

...

Keyword(s):

Gene Expression ◽

Risk Score ◽

Activity Patterns ◽

Expression Patterns ◽

Neural Response ◽

Postmortem Brain ◽

Polygenic Risk Score ◽

Least Squares Regression ◽

Social Stimuli ◽

Polygenic Risk

AbstractConvergent data from imaging and postmortem brain transcriptome studies implicate corticolimbic circuit (CLC) dysregulation in the pathophysiology of depression. To more directly bridge these lines of work, we generated a novel transcriptome-based polygenic risk score (T-PRS), capturing subtle shifts toward depression-like gene expression patterns in key CLC regions, and mapped this T-PRS onto brain function and related depressive symptoms in a nonclinical sample of 478 young adults (225 men; age 19.79 +/− 1.24) from the Duke Neurogenetics Study. First, T-PRS was generated based on common functional SNPs shifting CLC gene expression toward a depression-like state. Next, we used multivariate partial least squares regression to map T-PRS onto whole-brain activity patterns during perceptual processing of social stimuli (i.e., human faces). For validation, we conducted a comparative analysis with a PRS summarizing depression risk variants identified by the Psychiatric Genomics Consortium (PGC-PRS). Sex was modeled as moderating factor. We showed that T-PRS was associated with widespread reductions in neural response to neutral faces in women and to emotional faces and shapes in men (multivariate p < 0.01). This female-specific reductions in neural response to neutral faces was also associated with PGC-PRS (multivariate p < 0.03). Reduced reactivity to neutral faces was further associated with increased self-reported anhedonia. We conclude that women with functional alleles mimicking the postmortem transcriptomic CLC signature of depression have blunted neural activity to social stimuli, which may be expressed as higher anhedonia.

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Proof of concept: Molecular prediction of schizophrenia risk

10.1101/129213 ◽

2017 ◽

Cited By ~ 1

Author(s):

Anna R. Docherty ◽

Arden Moscati ◽

Daniel E. Adkins ◽

Gemma T. Wallace ◽

Guarav Kumar ◽

...

Keyword(s):

Gene Expression ◽

Genetic Risk ◽

Complement Component ◽

Classification And Regression Tree ◽

Risk Scores ◽

Prodromal Symptom ◽

Polygenic Risk Score ◽

Molecular Pathways ◽

Proof Of Concept ◽

Polygenic Risk

Key PointsQuestionTo what extent do global polygenic risk scores (PRS), molecular pathway-specific PRS, complement component (C4) gene expression, MHC loci, sex, and ancestry jointly contribute to risk for schizophrenia-spectrum disorders (SZ)?FindingsGlobal polygenic risk for schizophrenia, sex, and their interaction most robustly predict risk in a classification and regression tree model, with highest risk groups having 50/50 chance of SZ.MeaningPsychometric risk indicators, such as prodromal symptom assessments, may be enhanced by the examination of genetic risk metrics. Preliminary results suggest that of genetic risk metrics, global polygenic information has the most potential to significantly aide in the prediction of SZ.AbstractImportanceSchizophrenia (SZ) has a complex, heterogeneous symptom presentation with limited established associations between biological markers and illness onset. Many (gene) molecular pathways (MPs) are enriched for SZ signal, but it is still unclear how these MPs, global PRS, major histocompatibility complex (MHC) complement component (C4) gene expression, and MHC loci might jointly contribute to SZ and its clinical presentation. It is also unclear whether sex or ancestry interacts with these metrics to increase risk in certain individuals.ObjectiveTo examine multiple genetic metrics, sex, and their interactions as possible predictors of SZ risk. Genetic information could aid in the clinical prediction of risk, but it is still unclear which genetic metrics are most promising, and how sex interacts with genetic risk metrics.Design, Setting, and ParticipantsTo examine molecular risk in a proof-of-concept study, we used the Wellcome Trust case-control cohort and classified cases as a function of 1) polygenic risk score (PRS) for both whole genome and for 345 implicated molecular pathways, 2) predicted C4 expression, 3) SZ-relevant MHC loci, 4) sex, and 5) ancestry.Main Outcomes and MeasuresPRSs, C4 expression, SZ-relevant MHC loci, sex, and ancestry as joint risk factors for SZ.ResultsRecursive partitioning yielded 15 molecular risk classes and retained as significant psychosis classifiers only sex, genome-wide SZ polygenic risk, and one MP PRS. Sex was the most robust classifier in a stepwise regression, and there was a significant interaction of sex with SZ PRS on case status, suggesting males have a lower polygenic risk threshold. By down-sampling case proportion to 1% and 1.4% population base rates in males and females, respectively, high-risk subtypes defined by this model had roughly a 52% odds of developing SZ (individuals with SZ PRS elevated by 2.6 SDs; incidence = 51.8%).Conclusions and RelevanceThis proof-of-concept suggests that global SZ PRS, sex, and their interaction are robust predictors of risk and that males have a lower PRS threshold for onset. Implications for the integration of these metrics with psychometrically-identified risk are discussed.

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Imputed gene expression risk scores: a functionally informed component of polygenic risk

Human Molecular Genetics ◽

10.1093/hmg/ddab053 ◽

2021 ◽

Author(s):

Oliver Pain ◽

Kylie P Glanville ◽

Saskia Hagenaars ◽

Saskia Selzam ◽

Anna Fürtjes ◽

...

Keyword(s):

Gene Expression ◽

Genetic Risk ◽

Elastic Net ◽

Risk Scores ◽

Future Research ◽

Functional Genomic ◽

Predictive Utility ◽

Polygenic Risk ◽

Regulated Gene Expression ◽

Gene Expression Levels

Abstract Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression–genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10−8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

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Large-scale pathway-specific polygenic risk, transcriptomic community networks and functional inferences in Parkinson disease

10.1101/2020.05.05.079228 ◽

2020 ◽

Cited By ~ 1

Author(s):

S Bandres-Ciga ◽

S Saez-Atienzar ◽

JJ Kim ◽

MB Makarious ◽

F Faghri ◽

...

Keyword(s):

Gene Expression ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Parkinson Disease ◽

Gene Expression Data ◽

Large Scale ◽

Polygenic Risk Score ◽

Expression Data ◽

Polygenic Risk ◽

Cellular Context

ABSTRACTPolygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinson’s Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinson’s disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

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