TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.
Am J Med Genet B Neuropsychiatr Genet
; 183(8): 454-463, 2020 12.
Article
em En
| MEDLINE
| ID: mdl-32954640
ABSTRACT
Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transtornos Psicóticos
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Marcadores Genéticos
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Biologia Computacional
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Herança Multifatorial
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Polimorfismo de Nucleotídeo Único
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Locos de Características Quantitativas
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Transcriptoma
Tipo de estudo:
Etiology_studies
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Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Am J Med Genet B Neuropsychiatr Genet
Assunto da revista:
GENETICA MEDICA
/
NEUROLOGIA
/
PSIQUIATRIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos