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1.
Bioinformatics ; 2021 Feb 24.
Artigo em Inglês | MEDLINE | ID: mdl-33624746

RESUMO

MOTIVATION: Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information. RESULTS: We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1. We performed 10 simulations per gene. The eQTL-h2 (i.e., the proportion of variation explained by the eQTLs) was set at 1%, 2%, and 5%. We found E-MAGMA outperforms other gene-based approaches across a range of simulated parameters (e.g. the number of identified causal genes). When applied to genome-wide association summary statistics for five neuropsychiatric disorders, E-MAGMA identified more putative candidate causal genes compared to other eQTL-based approaches. By integrating tissue-specific eQTL information, these results show E-MAGMA will help to identify novel candidate causal genes from genome-wide association summary statistics and thereby improve the understanding of the biological basis of complex disorders. AVAILABILITY: A tutorial and input files are made available in a github repository: https://github.com/eskederks/eMAGMA-tutorial. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

2.
Genome Biol ; 22(1): 49, 2021 Jan 26.
Artigo em Inglês | MEDLINE | ID: mdl-33499903

RESUMO

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

3.
Nat Commun ; 11(1): 6397, 2020 12 16.
Artigo em Inglês | MEDLINE | ID: mdl-33328453

RESUMO

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).


Assuntos
/genética , Interações Hospedeiro-Patógeno/genética , Proteínas/genética , /fisiologia , Sistema ABO de Grupos Sanguíneos/metabolismo , Aptâmeros de Peptídeos/sangue , Aptâmeros de Peptídeos/metabolismo , Coagulação Sanguínea , Sistemas de Liberação de Medicamentos , Feminino , Regulação da Expressão Gênica , Fatores Celulares Derivados do Hospedeiro/metabolismo , Humanos , Internet , Masculino , Pessoa de Meia-Idade , Locos de Características Quantitativas/genética
4.
Artigo em Inglês | MEDLINE | ID: mdl-33369091

RESUMO

Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to substance use, however, the functional genes and biological mechanisms remain poorly understood. The discovery and characterization of risk genes can be facilitated by the integration of genome-wide association data and gene expression data across biologically relevant tissues and/or cell types to identify genes whose expression is altered by DNA sequence variation (expression quantitative trait loci; eQTLs). The integration of gene expression data can be extended to the study of genetic co-expression, under the biologically valid assumption that genes form co-expression networks to influence the manifestation of a disease or trait. Here, we integrate genome-wide association data with gene expression data from 13 brain tissues to identify candidate risk genes for 8 substance use phenotypes. We then test for the enrichment of candidate risk genes within tissue-specific gene co-expression networks to identify modules (or groups) of functionally related genes whose dysregulation is associated with variation in substance use. We identified eight gene modules in brain that were enriched with gene-based association signals for substance use phenotypes. For example, a single module of 40 co-expressed genes was enriched with gene-based associations for drinks per week and biological pathways involved in GABA synthesis, release, reuptake and degradation. Our study demonstrates the utility of eQTL and gene co-expression analysis to uncover novel biological mechanisms for substance use traits.

5.
Nat Genet ; 52(11): 1239-1246, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33020666

RESUMO

Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

7.
Cancer Res ; 80(20): 4346-4354, 2020 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-32907841

RESUMO

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR ≤ 0.05, including seven previously reported genes (INHBA, SMC2, ABO, PDX1, RCCD1, CFDP1, and PGAP3) and six novel genes not yet reported for pancreatic cancer risk [6q27: SFT2D1 OR (95% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: MTMR6 OR (95% CI), 0.78 (0.70-0.88); 14q24.3: ACOT2 OR (95% CI), 1.35 (1.17-1.56); 17q12: STARD3 OR (95% CI), 6.49 (2.96-14.27); 17q21.1: GSDMB OR (95% CI), 1.94 (1.45-2.58); and 20p13: ADAM33 OR (95% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (SFT2D1, MTMR6, ACOT2, STARD3, GSDMB, ADAM33, SMC2, RCCD1, CFDP1, and PGAP3) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.

8.
Science ; 369(6509)2020 09 11.
Artigo em Inglês | MEDLINE | ID: mdl-32913072

RESUMO

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.


Assuntos
Regulação da Expressão Gênica , Expressão Gênica , Caracteres Sexuais , Cromossomos Humanos X/genética , Doença/genética , Epigênese Genética , Feminino , Variação Genética , Estudo de Associação Genômica Ampla , Humanos , Masculino , Especificidade de Órgãos , Regiões Promotoras Genéticas , Locos de Características Quantitativas , Fatores Sexuais
9.
PeerJ ; 8: e9554, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32765967

RESUMO

The development of explanatory models of protein sequence evolution has broad implications for our understanding of cellular biology, population history, and disease etiology. Here we analyze the GTEx transcriptome resource to quantify the effect of the transcriptome on protein sequence evolution in a multi-tissue framework. We find substantial variation among the central nervous system tissues in the effect of expression variance on evolutionary rate, with highly variable genes in the cortex showing significantly greater purifying selection than highly variable genes in subcortical regions (Mann-Whitney U p = 1.4 × 10-4). The remaining tissues cluster in observed expression correlation with evolutionary rate, enabling evolutionary analysis of genes in diverse physiological systems, including digestive, reproductive, and immune systems. Importantly, the tissue in which a gene attains its maximum expression variance significantly varies (p = 5.55 × 10-284) with evolutionary rate, suggesting a tissue-anchored model of protein sequence evolution. Using a large-scale reference resource, we show that the tissue-anchored model provides a transcriptome-based approach to predicting the primary affected tissue of developmental disorders. Using gradient boosted regression trees to model evolutionary rate under a range of model parameters, selected features explain up to 62% of the variation in evolutionary rate and provide additional support for the tissue model. Finally, we investigate several methodological implications, including the importance of evolutionary-rate-aware gene expression imputation models using genetic data for improved search for disease-associated genes in transcriptome-wide association studies. Collectively, this study presents a comprehensive transcriptome-based analysis of a range of factors that may constrain molecular evolution and proposes a novel framework for the study of gene function and disease mechanism.

10.
bioRxiv ; 2020 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-32637948

RESUMO

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid 'in silico' assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

11.
Nat Metab ; 2(6): 487-498, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32694732

RESUMO

Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions1-3. Here, using the debiased sparse partial correlation (DSPC) method3, we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. This function depends on the evolutionarily conserved uncharacterized domain (DUF2054) and promotes cell proliferation under cholesterol depletion. Notably, c12orf49 depletion in zebrafish blocks dietary lipid clearance in vivo, mimicking the phenotype of mbtps1 mutants. Finally, in an electronic health record (EHR)-linked DNA biobank, C12orf49 is associated with hyperlipidaemia through phenome analysis. Altogether, our findings reveal a conserved role for C12orf49 in cholesterol and lipid homeostasis and provide a platform to identify unknown components of other metabolic pathways.

12.
Alzheimers Res Ther ; 12(1): 43, 2020 04 16.
Artigo em Inglês | MEDLINE | ID: mdl-32299494

RESUMO

INTRODUCTION: Genome-wide association studies (GWAS) have successfully identified multiple independent genetic loci that harbour variants associated with Alzheimer's disease, but the exact causal genes and biological pathways are largely unknown. METHODS: To prioritise likely causal genes associated with Alzheimer's disease, we used S-PrediXcan to integrate expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression (GTEx) study and CommonMind Consortium (CMC) with Alzheimer's disease GWAS summary statistics. We meta-analysed the GTEx results using S-MultiXcan, prioritised disease-implicated loci using a computational fine-mapping approach, and performed a biological pathway analysis on the gene-based results. RESULTS: We identified 126 tissue-specific gene-based associations across 48 GTEx tissues, targeting 50 unique genes. Meta-analysis of the tissue-specific associations identified 73 genes whose expression was associated with Alzheimer's disease. Additional analyses in the dorsolateral prefrontal cortex from the CMC identified 12 significant associations, 8 of which also had a significant association in GTEx tissues. Fine-mapping of causal gene sets prioritised gene candidates in 10 Alzheimer's disease loci with strong evidence for causality. Biological pathway analyses of the meta-analysed GTEx data and CMC data identified a significant enrichment of Alzheimer's disease association signals in plasma lipoprotein clearance, in addition to multiple immune-related pathways. CONCLUSIONS: Gene expression data from brain and peripheral tissues can improve power to detect regulatory variation underlying Alzheimer's disease. However, the associations in peripheral tissues may reflect tissue-shared regulatory variation for a gene. Therefore, future functional studies should be performed to validate the biological meaning of these associations and whether they represent new pathogenic tissues.

13.
Genet Med ; 22(7): 1191-1200, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32296164

RESUMO

PURPOSE: The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease. METHODS: We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort. RESULTS: GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan. CONCLUSION: Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

14.
Clin Cancer Res ; 26(12): 2891-2897, 2020 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-32122921

RESUMO

PURPOSE: Cytarabine is an effective treatment for AML with associated toxicities including treatment related mortality (TRM). The purpose is to determine the clinical relevance of SNPs identified through the use of HapMap lymphoblastoid cell-based models, in predicting cytarabine response and toxicity in AML. EXPERIMENTAL DESIGN: We tested clinical significance of SNPs associated with cytarabine sensitivity in children with AML treated on Children's Oncology Group regimens (CCG 2941/2961). Endpoints included overall survival (OS), event-free survival (EFS), and TRM. Patients who received bone marrow transplant were excluded. We tested 124 SNPs associated with cytarabine sensitivity in HapMap cell lines in 348 children to determine whether any associated with treatment outcomes. In addition, we tested five SNPs previously associated with TRM in children with AML in our independent dataset of 385 children. RESULTS: Homozygous variant genotypes of rs2025501 and rs6661575 had increased in vitro cellular sensitivity to cytarabine and were associated with increased TRM. TRM was particularly increased in children with variant genotype randomized to high-dose cytarabine (rs2025501: P = 0.0024 and rs6661575 P = 0.0188). In analysis of previously reported SNPs, only the variant genotype rs17202778 C/C was significantly associated with TRM (P < 0.0001). CONCLUSIONS: We report clinical importance of two SNPs not previously associated with cytarabine toxicity. Moreover, we confirm that SNP rs17202778 significantly impacts TRM in pediatric AML. Cytarabine sensitivity genotypes may predict TRM and could be used to stratify to standard versus high-dose cytarabine regimens, warranting further study in prospective AML trials.

15.
Nat Med ; 26(1): 98-109, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31932796

RESUMO

Discovery of genotype-phenotype relationships remains a major challenge in clinical medicine. Here, we combined three sources of phenotypic data to uncover a new mechanism for rare and common diseases resulting from collagen secretion deficits. Using a zebrafish genetic screen, we identified the ric1 gene as being essential for skeletal biology. Using a gene-based phenome-wide association study (PheWAS) in the EHR-linked BioVU biobank, we show that reduced genetically determined expression of RIC1 is associated with musculoskeletal and dental conditions. Whole-exome sequencing identified individuals homozygous-by-descent for a rare variant in RIC1 and, through a guided clinical re-evaluation, it was discovered that they share signs with the BioVU-associated phenome. We named this new Mendelian syndrome CATIFA (cleft lip, cataract, tooth abnormality, intellectual disability, facial dysmorphism, attention-deficit hyperactivity disorder) and revealed further disease mechanisms. This gene-based, PheWAS-guided approach can accelerate the discovery of clinically relevant disease phenome and associated biological mechanisms.


Assuntos
Anormalidades Múltiplas/patologia , Bancos de Espécimes Biológicos , Fatores de Troca do Nucleotídeo Guanina/genética , Fenômica , Proteínas de Peixe-Zebra/genética , Animais , Comportamento Animal , Condrócitos/patologia , Condrócitos/ultraestrutura , Modelos Animais de Doenças , Matriz Extracelular/metabolismo , Fibroblastos/metabolismo , Fibroblastos/patologia , Fibroblastos/ultraestrutura , Humanos , Modelos Biológicos , Sistema Musculoesquelético/patologia , Osteogênese , Fenótipo , Pró-Colágeno/metabolismo , Transporte Proteico , Via Secretória , Síndrome , Peixe-Zebra
17.
Drug Alcohol Depend ; 206: 107703, 2020 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-31785998

RESUMO

BACKGROUND: Little is known about the functional mechanisms through which genetic loci associated with substance use traits ascertain their effect. This study aims to identify and functionally annotate loci associated with substance use traits based on their role in genetic regulation of gene expression. METHODS: We evaluated expression Quantitative Trait Loci (eQTLs) from 13 brain regions and whole blood of the Genotype-Tissue Expression (GTEx) database, and from whole blood of the Depression Genes and Networks (DGN) database. The role of single eQTLs was examined for six substance use traits: alcohol consumption (N = 537,349), cigarettes per day (CPD; N = 263,954), former vs. current smoker (N = 312,821), age of smoking initiation (N = 262,990), ever smoker (N = 632,802), and cocaine dependence (N = 4,769). Subsequently, we conducted a gene level analysis of gene expression on these substance use traits using S-PrediXcan. RESULTS: Using an FDR-adjusted p-value <0.05 we found 2,976 novel candidate genetic loci for substance use traits, and identified genes and tissues through which these loci potentially exert their effects. Using S-PrediXcan, we identified significantly associated genes for all substance traits. DISCUSSION: Annotating genes based on transcriptomic regulation improves the identification and functional characterization of candidate loci and genes for substance use traits.


Assuntos
Usuários de Drogas/psicologia , Regulação da Expressão Gênica/genética , Predisposição Genética para Doença/genética , Locos de Características Quantitativas/genética , Transtornos Relacionados ao Uso de Substâncias/genética , Sangue/metabolismo , Encéfalo/metabolismo , Perfilação da Expressão Gênica , Humanos , Metanálise como Assunto , Fenótipo , Transtornos Relacionados ao Uso de Substâncias/psicologia , Transcriptoma/genética
18.
Nat Ecol Evol ; 3(11): 1598-1606, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31591491

RESUMO

Sequencing DNA derived from archaic bones has enabled genetic comparison of Neanderthals and anatomically modern humans (AMHs), and revealed that they interbred. However, interpreting what genetic differences imply about their phenotypic differences remains challenging. Here, we introduce an approach for identifying divergent gene regulation between archaic hominins, such as Neanderthals, and AMH sequences, and find 766 genes that are likely to have been divergently regulated (DR) by Neanderthal haplotypes that do not remain in AMHs. DR genes include many involved in phenotypes known to differ between Neanderthals and AMHs, such as the structure of the rib cage and supraorbital ridge development. They are also enriched for genes associated with spontaneous abortion, polycystic ovary syndrome, myocardial infarction and melanoma. Phenotypes associated with modern human variation in these genes' regulation in ~23,000 biobank patients further support their involvement in immune and cardiovascular phenotypes. Comparing DR genes between two Neanderthals and a Denisovan revealed divergence in the immune system and in genes associated with skeletal and dental morphology that are consistent with the archaeological record. These results establish differences in gene regulatory architecture between AMHs and archaic hominins, and provide an avenue for exploring phenotypic differences between archaic groups from genomic information alone.


Assuntos
Hominidae , Homem de Neandertal , Animais , Feminino , Genoma Humano , Haplótipos , Humanos , Fenótipo
19.
PLoS Genet ; 15(7): e1008245, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31306407

RESUMO

Major depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of individual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages tissue-specific eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits.


Assuntos
Transtorno Depressivo Maior/genética , Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes , Estudo de Associação Genômica Ampla/métodos , Química Encefálica , Complemento C4a/genética , Regulação da Expressão Gênica , Humanos , Especificidade de Órgãos , Locos de Características Quantitativas , Análise de Sequência de RNA
20.
Nat Genet ; 51(6): 933-940, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31086352

RESUMO

The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression2-4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.


Assuntos
Perfilação da Expressão Gênica , Estudos de Associação Genética , Predisposição Genética para Doença , Transtornos Mentais/genética , Transtornos Mentais/psicologia , Transcriptoma , Algoritmos , Biologia Computacional/métodos , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Estudos de Associação Genética/métodos , Estudo de Associação Genômica Ampla , Humanos , Transtornos Mentais/diagnóstico , Especificidade de Órgãos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Característica Quantitativa Herdável
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