Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.
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Cited
Public
- Authors
- Huckins, Laura M; Dobbyn, Amanda; Ruderfer, Douglas M; Hoffman, Gabriel; Wang, Weiqing; Pardiñas, Antonio F; Rajagopal, Veera M; Als, Thomas D; T Nguyen, Hoang; Girdhar, Kiran; Boocock, James; Roussos, Panos; Fromer, Menachem; Kramer, Robin; Domenici, Enrico; Gamazon, Eric R; Purcell, Shaun; CommonMind Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium; iPSYCH-GEMS Schizophrenia Working Group; Demontis, Ditte; Børglum, Anders D; Walters, James T R; O'Donovan, Michael C; Sullivan, Patrick; Owen, Michael J; Devlin, Bernie; Sieberts, Solveig K; Cox, Nancy J; Im, Hae Kyung; Sklar, Pamela; Stahl, Eli A
- Year
- 2019
- Journal
- Nature genetics
- PMID
- 30911161
- DOI
- 10.1038/s41588-019-0364-4
- PMCID
- PMC7034316
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
Replication of DLPFC prediction models in independent data.Measured gene expression (ROSMAP RNA-seq) was compared to predicted genetically-regulated gene expression for CMC DLPFC and 12 GTeX predictor databases. Replication R2 values are significantly higher for the DLPFC than for the 12 GTEX brain expression models.A. Distribution of RR2 values of CMC DLPFC predictors in ROSMAP data. Mean RR2 = 0.056. 47.7% of genes have RR2 >= 0.01. Boxplots show mean, quartiles,; whiskers show full range of data.B. Distribution of RR2 values of 12 GTeX predictors in ROSMAP data.Table of sample sizes and p-val thresholds for CMC DLPFC and GTeX data. Number of samples, number of genes in the prediXcan model and number of eGenes are all significantly correlated with predictor performance in ROSMAP data (spearman correlation test).
SCZ associations resultsA) 413 genes are associated with SCZ across 12 brain tissues. Each point represents one gene-tissue pair.B) 67 genes remain significant outside the MHC after stepwise conditional analysisC) Number of genome-wide significant loci, outside the MHC region, identified in each brain region. These trends are partly driven by differences in power between brain regions.Abbreviations are as follows; CB- Cerebellum; CX- Cortex; FL- Frontal Cortex; DLPFC- Dorso-lateral pre-frontal cortex; CB HEMI- Cerebellar Hemisphere; HIP- Hippocampus; PIT- Pituitary Gland; HTH- Hypothalamus; NAB- Nucleus Accumbens (Basal Ganglia); PUT- Putamen (Basal Ganglia); CAU- Caudate (Basal Ganglia); CNG- Anterior Cingulate Cortex
SCZ-associated genes are co-expressed throughout development and across brain regionsA) Brain tissues selected for each of four brainspan regions. Brainspan includes 525 samples from 43 unique individuals. Region 1: IPC, V1C, ITC, OFC, STC, A1C; Region 2:S1C, M1C, DFC, VFC, MFC; Region 3:HIP, AMY, STR; Region 4: CBAverage clustering coefficients were calculated for all pairs of SCZ-associated genes, and compared to average clustering coefficients for 100,000 permuted gene networks to obtain empirical significance levels.
Gene expression patterns for SCZ-associated genes cluster into four groups, relating to distinct spatiotemporal expression.Brain regions are shown in figure 3a.A. 29 genes are expressed in the early-mid pre-natal period (4-24 post-conception weeks)B. 15 genes are expressed throughout development; subclusters correspond to either specific expression in region 4, or expression across the brainC. Ten genes are expressed in the late-prenatal (25-38pcw) and post-natal periodD. 12 genes are expressed in the late pre-natal period (25-39pcw)
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| 40 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Replication of gene expression prediction models in independent data | Equation 1: RR2 calculation. |
| 41 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Replication of gene expression prediction models in independent data | Where: |
| 42 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Replication of gene expression prediction models in independent data | A small number of genes (158) had very low predictive accuracy and were removed from further… |
| 43 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Replication of gene expression prediction models in independent data | Prediction accuracy was also assessed for 11 publicly available GTEx neurological predictor… |
| 44 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Replication of gene expression prediction models in independent data | To estimate trans-ancestral prediction accuracy, GREX was calculated for 162 African-American… |
| 45 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Extension to Summary Statistics | Transcriptomic Imputation may be applied to summary statistics instead of raw data, in instances… |
| 46 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Extension to Summary Statistics | We assessed concordance between CMC DLPFC transcriptomic imputation results using summary-statistics… |
| 47 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Extension to Summary Statistics | Concordance was also tested for the same nine European PGC-SCZ cohorts, across 12 neurological GTEx… |
| 48 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Application to Schizophrenia — Dataset Collection | We obtained 53 discovery cohorts for this study, including 40,299 SCZ cases and 65,264 controls… |
| 49 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Application to Schizophrenia — Dataset Collection | 50/53 datasets included individuals of European ancestry, while three datasets include individuals… |
| 50 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Application to Schizophrenia — Dataset Collection | Access to dosage data was available for 44/52 PGC-SCZ cohorts. The remaining PGC cohorts, and the… |
| 51 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Application to Schizophrenia — Dataset Collection | Additionally, we tested for replication of our CMC DLPFC associations in an independent dataset of… |
| 52 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Transcriptomic Imputation and association testing | Transcriptomic Imputation was carried out individually for each case-control PGC-SCZ cohort with… |
| 53 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Transcriptomic Imputation and association testing | For the eight PGC cohorts with no available dosage data, the three PGC trio-based analyses, and the… |
| 54 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Meta-analysis | Meta-analysis was carried out across all 53 cohorts using METAL83. Cochran’s Q test for… |
| 55 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Meta-analysis | Effect sizes and direction of effect quoted in this manuscript refer to changes in predicted… |
| 56 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Identifying independent associations | We identified a number of genomic regions which contained multiple gene associations and/or genes… |
| 57 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Identifying independent associations | Equation 2: Effective Sample Size, Neff Neff=4(1Ncases+1Ncontrols) |
| 58 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Identifying independent associations | Forward stepwise conditional analysis of all significant genes was carried out using joint linear… |
| 59 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Identifying independent associations | We calculated effect sizes and odds ratios for SCZ-associated genes by adjusting “CoCo” betas to… |
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In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. | 2021 | 34417470 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Functional implications of polygenic risk for schizophrenia in human neurons. | Michael Deans PJ et al. | — | 2026 | → |
| Multi-omics approaches to major psychiatric disorders. | Oraki Kohshour M et al. | — | 2026 | → |
| A computational genetic- and transcriptomics-based study nominates drug repurposing candidates for the treatment of chronic pain. | Cote AC et al. | — | 2025 | → |
| Analysis of Genomic and Transcriptomic Data Revealed Key Genes and Processes in the Development of Major Depressive Disorder. | Ivanov SM et al. | — | 2025 | → |
| Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment. | Seah C et al. | — | 2025 | → |
| Insights from a methylome-wide association study of antidepressant exposure. | Davyson E et al. | — | 2025 | → |
| Integrating rare variant genetics and brain transcriptome data implicates novel schizophrenia putative risk genes. | Han S et al. | — | 2025 | → |
| Leveraging genomic and transcriptomic data of diverse ancestry to uncover mechanisms of psychiatric risk in the adult and developing brain. | Jajoo A et al. | — | 2025 | → |
| Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics. | Abe H et al. | — | 2025 | → |
| Mendelian Randomization-Based Discovery of Novel Protein Biomarkers and Drug Targets in Colorectal Cancer: Validation Through Prognostic Modeling, Single-Cell Analysis, and In Vitro Cell Experiments. | Bao X et al. | — | 2025 | → |
| Mendelian Randomization Reveals Causalities Between DNA Methylation and Schizophrenia. | Wang D et al. | — | 2025 | → |
| Multi-ancestral genome-wide association study of clinically defined nicotine dependence reveals strong genetic correlations with other substance use disorders and health-related traits. | Johnson EC et al. | — | 2025 | → |
| Protein arginine methyltransferase 7 linked to schizophrenia through regulation of neural progenitor cell proliferation and differentiation. | Shen T et al. | — | 2025 | → |
| The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol. | Berthold N et al. | — | 2025 | → |
| Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis. | Zhang R et al. | — | 2024 | → |
| Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework. | He D et al. | — | 2024 | → |
| A miR-137-Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing. | Pergola G et al. | — | 2024 | → |
| CLOZAPINE-RELATED BRAIN<i>NRN1</i>EXPRESSION PATTERNS ARE ASSOCIATED WITH METHYLATION AND GENETIC VARIANTS IN SCHIZOPHRENIA | Almodóvar-Payá C et al. | — | 2024 | — |
| Co-expression of prepulse inhibition and Schizophrenia genes in the mouse and human brain. | Garrett L et al. | — | 2024 | → |
| Decreased CNNM2 expression in prefrontal cortex affects sensorimotor gating function, cognition, dendritic spine morphogenesis and risk of schizophrenia. | Zhou DY et al. | — | 2024 | → |
| Gene expression imputation provides clinical and biological insights into treatment-resistant schizophrenia polygenic risk. | Prohens L et al. | — | 2024 | → |
| Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. | Xu X et al. | — | 2024 | → |
| Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders. | Luo J et al. | — | 2024 | → |
| Genomic insights into the comorbidity between type 2 diabetes and schizophrenia. | Arruda AL et al. | — | 2024 | → |
| Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. | Nam Y et al. | — | 2024 | → |
| Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction. | Pain O et al. | — | 2024 | → |
| Network-wide risk convergence in gene co-expression identifies reproducible genetic hubs of schizophrenia risk. | Borcuk C et al. | — | 2024 | → |
| Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study. | Choi E et al. | — | 2024 | → |
| The schizophrenia syndrome, circa 2024: What we know and how that informs its nature. | Tandon R et al. | — | 2024 | → |
| Transcriptional risk scores in Alzheimer's disease: From pathology to cognition. | Pyun JM et al. | — | 2024 | → |
| Cerebellar Functional Dysconnectivity in Drug-Naïve Patients With First-Episode Schizophrenia. | Cao H et al. | — | 2023 | → |
| Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions. | Pergola G et al. | — | 2023 | → |
| Dysfunction of cAMP-Protein Kinase A-Calcium Signaling Axis in Striatal Medium Spiny Neurons: A Role in Schizophrenia and Huntington's Disease Neuropathology. | Fjodorova M et al. | — | 2023 | → |
| Human forebrain organoid-based multi-omics analyses of PCCB as a schizophrenia associated gene linked to GABAergic pathways. | Zhang W et al. | — | 2023 | → |
| Identification and functional analysis of circulating extrachromosomal circular DNA in schizophrenia implicate its negative effect on the disorder. | Xiang X et al. | — | 2023 | → |
| Induction of dopaminergic neurons for neuronal subtype-specific modeling of psychiatric disease risk. | Powell SK et al. | — | 2023 | → |
| Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. | Hicks EM et al. | — | 2023 | → |
| Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways. | Pergola G et al. | — | 2023 | → |
| Mapping anorexia nervosa genes to clinical phenotypes. | Johnson JS et al. | — | 2023 | → |
| Mendelian Randomization Study Using Dopaminergic Neuron-Specific eQTL Identifies Novel Risk Genes for Schizophrenia. | Dang X et al. | — | 2023 | → |
| Prioritization of potential causative genes for schizophrenia in placenta. | Ursini G et al. | — | 2023 | → |
| Rediscovering tandem repeat variation in schizophrenia: challenges and opportunities. | Birnbaum R | — | 2023 | → |
| Spatiotemporal expression patterns of anxiety disorder-associated genes. | Karunakaran KB et al. | — | 2023 | → |
| Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders. | Seah C et al. | — | 2023 | → |
| The genetic architecture of schizophrenia: review of large-scale genetic studies. | Kato H et al. | — | 2023 | → |
| A conditional gene-based association framework integrating isoform-level eQTL data reveals new susceptibility genes for schizophrenia. | Li X et al. | — | 2022 | → |
| An epigenetic association analysis of childhood trauma in psychosis reveals possible overlap with methylation changes associated with PTSD. | Løkhammer S et al. | — | 2022 | → |
| Bioinformatics and network-based approaches for determining pathways, signature molecules, and drug substances connected to genetic basis of schizophrenia etiology. | Khan U et al. | — | 2022 | → |
| Bioinformatics detection of modulators controlling splicing factor-dependent intron retention in the human brain. | Chen SX et al. | — | 2022 | → |
| Gene co-expression architecture in peripheral blood in a cohort of remitted first-episode schizophrenia patients. | Rodríguez N et al. | — | 2022 | → |
| Genetic control of RNA splicing and its distinct role in complex trait variation. | Qi T et al. | — | 2022 | → |
| Identification of genetic loci that overlap between schizophrenia and metabolic syndrome. | Lv H et al. | — | 2022 | → |
| Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population. | Ding K et al. | — | 2022 | → |
| Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder. | Wingo TS et al. | — | 2022 | → |
| Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction. | Guan F et al. | — | 2022 | → |
| In vivo study sheds new light on the dendritic spine pathology hypothesis of schizophrenia. | Li W et al. | — | 2022 | → |
| Joint-Tissue Integrative Analysis Identified Hundreds of Schizophrenia Risk Genes. | Wu Y et al. | — | 2022 | → |
| Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization. | Jiang L et al. | — | 2022 | → |
| Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. | Highland HM et al. | — | 2022 | → |
| Shared mechanisms across the major psychiatric and neurodegenerative diseases. | Wingo TS et al. | — | 2022 | → |
| The shared genetic basis of mood instability and psychiatric disorders: A cross-trait genome-wide association analysis. | Hindley G et al. | — | 2022 | → |
| Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes. | Li L et al. | — | 2022 | → |
| Transcriptome-wide association study reveals increased neuronal FLT3 expression is associated with Tourette's syndrome. | Liao C et al. | — | 2022 | → |
| Upper cortical layer-driven network impairment in schizophrenia. | Batiuk MY et al. | — | 2022 | → |
| What genes are differentially expressed in individuals with schizophrenia? A systematic review. | Merikangas AK et al. | — | 2022 | → |
| A longitudinal study of gene expression in first-episode schizophrenia; exploring relapse mechanisms by co-expression analysis in peripheral blood. | Gassó P et al. | — | 2021 | → |
| Applying stem cells and CRISPR engineering to uncover the etiology of schizophrenia. | Michael Deans PJ et al. | — | 2021 | → |
| A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary statistics. | Gleason KJ et al. | — | 2021 | → |
| Brain-immune interactions in neuropsychiatric disorders: Lessons from transcriptome studies for molecular targeting. | Afridi R et al. | — | 2021 | → |
| Brain proteome-wide association study implicates novel proteins in depression pathogenesis. | Wingo TS et al. | — | 2021 | → |
| D-cysteine is an endogenous regulator of neural progenitor cell dynamics in the mammalian brain. | Semenza ER et al. | — | 2021 | → |
| DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways. | Nguyen TH et al. | — | 2021 | → |
| Delineating the Genetic Component of Gene Expression in Major Depression. | Dall'Aglio L et al. | — | 2021 | → |
| Dream: powerful differential expression analysis for repeated measures designs. | Hoffman GE et al. | — | 2021 | → |
| Engineering <i>in vitro</i> human neural tissue analogs by 3D bioprinting and electrostimulation. | Warren D et al. | — | 2021 | → |
| Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study. | Tavares V et al. | — | 2021 | → |
| Evidence of an interaction between <i>FXR1</i> and <i>GSK3β</i> polymorphisms on levels of Negative Symptoms of Schizophrenia and their response to antipsychotics. | Rampino A et al. | — | 2021 | → |
| Gene Expression Analysis in Three Posttraumatic Stress Disorder Cohorts Implicates Inflammation and Innate Immunity Pathways and Uncovers Shared Genetic Risk With Major Depressive Disorder. | Garrett ME et al. | — | 2021 | → |
| Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders. | Pei G et al. | — | 2021 | → |
| Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci. | Chen HH et al. | — | 2021 | → |
| Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans. | Bigdeli TB et al. | — | 2021 | → |
| Genome-wide association study followed by trans-ancestry meta-analysis identify 17 new risk loci for schizophrenia. | Liu J et al. | — | 2021 | → |
| Glyoxalase 1 Confers Susceptibility to Schizophrenia: From Genetic Variants to Phenotypes of Neural Function. | Yin J et al. | — | 2021 | → |
| Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning. | Chang M et al. | — | 2021 | → |
| Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways. | Damena D et al. | — | 2021 | → |
| Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer's disease pathogenesis. | Wingo AP et al. | — | 2021 | → |
| Integrative Analyses Followed by Functional Characterization Reveal TMEM180 as a Schizophrenia Risk Gene. | Wang JY et al. | — | 2021 | → |
| kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes. | Cao C et al. | — | 2021 | → |
| Low-Level Brain Somatic Mutations Are Implicated in Schizophrenia. | Kim MH et al. | — | 2021 | → |
| Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. | Kapoor M et al. | — | 2021 | → |
| Neurodevelopmental Disorders in Patients With Complex Phenotypes and Potential Complex Genetic Basis Involving Non-Coding Genes, and Double CNVs. | Servetti M et al. | — | 2021 | → |
| Publicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia. | Dobrindt K et al. | — | 2021 | → |
| Shaping the Trans-Scale Properties of Schizophrenia <i>via</i> Cerebral Alterations on Magnetic Resonance Imaging and Single-Nucleotide Polymorphisms of Coding and Non-Coding Regions. | Zhao SW et al. | — | 2021 | → |
| SNX29, a new susceptibility gene shared with major mental disorders in Han Chinese population. | Chen JH et al. | — | 2021 | → |
| Transcriptome-wide association study reveals two genes that influence mismatch negativity. | Bhat A et al. | — | 2021 | → |
| Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders. | Hernandez LM et al. | — | 2021 | → |
| Analysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts. | Huckins LM et al. | — | 2020 | → |
| Analysis of GWAS-Derived Schizophrenia Genes for Links to Ischemia-Hypoxia Response of the Brain. | Schmidt-Kastner R et al. | — | 2020 | → |
| An update on the role of common genetic variation underlying substance use disorders. | Johnson EC et al. | — | 2020 | → |
| A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits. | Dahl A et al. | — | 2020 | → |
| A role for TGFβ signalling in medium spiny neuron differentiation of human pluripotent stem cells. | Fjodorova M et al. | — | 2020 | → |
| A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia. | Hall LS et al. | — | 2020 | → |
| Blood-based multi-tissue gene expression inference with Bayesian ridge regression. | Xu W et al. | — | 2020 | → |
| Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. | Matoba N et al. | — | 2020 | → |
| Complexities of Understanding Function from CKD-Associated DNA Variants. | Lin J et al. | — | 2020 | → |
| Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics. | Bovijn J et al. | — | 2020 | → |
| Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification. | Barbeira AN et al. | — | 2020 | → |
| Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease. | Wright GEB et al. | — | 2020 | → |
| Genetic Influences on Disease Subtypes. | Dahl A et al. | — | 2020 | → |
| Identification of ALK in Thinness. | Orthofer M et al. | — | 2020 | → |
| Identification of relevant hub genes for early intervention at gene coexpression modules with altered predicted expression in schizophrenia. | Rodriguez-López J et al. | — | 2020 | → |
| Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder. | Meng Q et al. | — | 2020 | → |
| Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells. | Powell SK et al. | — | 2020 | → |
| Massively parallel techniques for cataloguing the regulome of the human brain. | Townsley KG et al. | — | 2020 | → |
| Meta-Analysis of Transcriptomic Data of Dorsolateral Prefrontal Cortex and of Peripheral Blood Mononuclear Cells Identifies Altered Pathways in Schizophrenia. | Petralia MC et al. | — | 2020 | → |
| Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation. | Dang H et al. | — | 2020 | → |
| Modeling the complex genetic architectures of brain disease. | Fernando MB et al. | — | 2020 | → |
| Molecular mechanisms of psychiatric diseases. | Blokhin IO et al. | — | 2020 | → |
| mTADA is a framework for identifying risk genes from de novo mutations in multiple traits. | Nguyen TH et al. | — | 2020 | → |
| Peripheral Blood Leukocyte RNA-Seq Identifies a Set of Genes Related to Abnormal Psychomotor Behavior Characteristics in Patients with Schizophrenia. | Zhang Y et al. | — | 2020 | → |
| Prefrontal cortex eQTLs/mQTLs enriched in genetic variants associated with alcohol use disorder and other diseases. | Lin H et al. | — | 2020 | → |
| Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk. | Jaffe AE et al. | — | 2020 | → |
| Quantifying genetic effects on disease mediated by assayed gene expression levels. | Yao DW et al. | — | 2020 | → |
| Recent advances in genetic studies of alcohol use disorders. | Gupta I et al. | — | 2020 | → |
| SZDB2.0: an updated comprehensive resource for schizophrenia research. | Wu Y et al. | — | 2020 | → |
| The genome-wide risk alleles for psychiatric disorders at 3p21.1 show convergent effects on mRNA expression, cognitive function, and mushroom dendritic spine. | Yang Z et al. | — | 2020 | → |
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| Genome-wide association study implicates CHRNA2 in cannabis use disorder. | Demontis D et al. | — | 2019 | → |
| Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits. | Gamazon ER et al. | — | 2019 | → |
| Synergistic effects of common schizophrenia risk variants. | Schrode N et al. | — | 2019 | → |
| Transcriptome association studies of neuropsychiatric traits in African Americans implicate <i>PRMT7</i> in schizophrenia. | Fiorica PN et al. | — | 2019 | → |
| Translational research identifies a metabolism pathway involved in first-episode of schizophrenia: Towards precision medicine. | Simonneau M | — | 2019 | → |
| Use of the epigenetic toolbox to contextualize common variants associated with schizophrenia risk . | Rajarajan P et al. | — | 2019 | → |