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1.
Carcinogenesis ; 44(10-11): 741-747, 2023 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-37769343

RESUMO

A large proportion of the heritability of pancreatic cancer risk remains elusive, and the contribution of specific mRNA splicing events to pancreatic cancer susceptibility has not been systematically evaluated. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies (Enet, LASSO and MCP) to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for pancreatic cancer risk by assessing 8275 pancreatic cancer cases and 6723 controls of European ancestry. Data from 305 subjects of whom the majority are of European descent in the Genotype-Tissue Expression Project (GTEx) were used and both cis-acting and promoter-enhancer interaction regions were considered to build these models. We identified nine splicing events of seven genes (ABO, UQCRC1, STARD3, ETAA1, CELA3B, LGR4 and SFT2D1) that showed an association of genetically predicted expression with pancreatic cancer risk at a false discovery rate ≤0.05. Of these genes, UQCRC1 and LGR4 have not yet been reported to be associated with pancreatic cancer risk. Fine-mapping analyses supported likely causal associations corresponding to six splicing events of three genes (P4HTM, ABO and PGAP3). Our study identified novel genes and splicing events associated with pancreatic cancer risk, which can improve our understanding of the etiology of this deadly malignancy.


Assuntos
Neoplasias Pancreáticas , Transcriptoma , Humanos , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Splicing de RNA , Neoplasias Pancreáticas/genética , Processamento Alternativo/genética , Polimorfismo de Nucleotídeo Único/genética , Antígenos de Superfície , Elastase Pancreática/genética
2.
OMICS ; 27(8): 372-380, 2023 08.
Artigo em Inglês | MEDLINE | ID: mdl-37486714

RESUMO

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy.


Assuntos
Neoplasias da Próstata , Transcriptoma , Masculino , Humanos , Transcriptoma/genética , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Processamento Alternativo/genética , Neoplasias da Próstata/genética , Polimorfismo de Nucleotídeo Único/genética
3.
Neurobiol Dis ; 184: 106209, 2023 08.
Artigo em Inglês | MEDLINE | ID: mdl-37354922

RESUMO

Alzheimer's disease (AD) is a common neurodegenerative disease in aging individuals. Alternative splicing is reported to be relevant to AD development while their roles in etiology of AD remain largely elusive. We performed a comprehensive splicing transcriptome-wide association study (spTWAS) using intronic excision expression genetic prediction models of 12 brain tissues developed through three modelling strategies, to identify candidate susceptibility splicing introns for AD risk. A total of 111,326 (46,828 proxy) cases and 677,663 controls of European ancestry were studied. We identified 343 associations of 233 splicing introns (143 genes) with AD risk after Bonferroni correction (0.05/136,884 = 3.65 × 10-7). Fine-mapping analyses supported 155 likely causal associations corresponding to 83 splicing introns of 55 genes. Eighteen causal splicing introns of 15 novel genes (EIF2D, WDR33, SAP130, BYSL, EPHB6, MRPL43, VEGFB, PPP1R13B, TLN2, CLUHP3, LRRC37A4P, CRHR1, LINC02210, ZNF45-AS1, and XPNPEP3) were identified for the first time to be related to AD susceptibility. Our study identified novel genes and splicing introns associated with AD risk, which can improve our understanding of the etiology of AD.


Assuntos
Doença de Alzheimer , Doenças Neurodegenerativas , Humanos , Doença de Alzheimer/genética , Doença de Alzheimer/metabolismo , Transcriptoma , Predisposição Genética para Doença/genética , Splicing de RNA , Polimorfismo de Nucleotídeo Único , Proteínas Repressoras/genética , Fatores de Transcrição Kruppel-Like/genética , Moléculas de Adesão Celular/genética
4.
Nat Commun ; 13(1): 6336, 2022 10 25.
Artigo em Inglês | MEDLINE | ID: mdl-36284135

RESUMO

Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a method, the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We apply SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium. Through simulation studies and analyses of genome-wide association study summary statistics for 24 complex traits, we show that SUMMIT improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. Finally, we conduct a case study of COVID-19 severity with SUMMIT and identify 11 likely causal genes associated with COVID-19 severity.


Assuntos
COVID-19 , Transcriptoma , Humanos , Estudo de Associação Genômica Ampla/métodos , COVID-19/genética , Locos de Características Quantitativas/genética , Herança Multifatorial , Polimorfismo de Nucleotídeo Único , Predisposição Genética para Doença/genética
5.
Genet Epidemiol ; 45(8): 848-859, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34255882

RESUMO

Transcriptome-wide association studies (TWAS) that integrate transcriptomic reference data and genome-wide association studies (GWAS) have successfully enhanced the discovery of candidate genes for many complex traits. However, existing methods may suffer from substantial power loss because they fail to effectively consider that expression of many genes tends to be consistent across tissues. Here we propose a computationally efficient testing method, referred to as Integrative Test for Associations via Cauchy Transformation (InTACT), that effectively combines information across multiple tissues and thus improves the power of identifying associated genes. Through simulation studies, we show that InTACT maintains high power while properly controls for Type 1 error rates. We applied InTACT to the largest GWAS of Alzheimer's disease (AD) to date and identified 227 genome-wide significant genes, of which 130 were not identified by benchmark methods, TWAS and MultiXcan. Importantly, InTACT identified five novel loci for AD. We implemented InTACT in publicly available software, "InTACT."


Assuntos
Estudo de Associação Genômica Ampla , Transcriptoma , Estudo de Associação Genômica Ampla/métodos , Humanos , Modelos Genéticos , Herança Multifatorial , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas
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