nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia.
Brief Bioinform
; 22(4)2021 07 20.
Article
de En
| MEDLINE
| ID: mdl-33230537
ABSTRACT
MOTIVATION Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account. RESULTS:
We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Schizophrénie
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Logiciel
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Prédisposition génétique à une maladie
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Polymorphisme de nucléotide simple
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Locus de caractère quantitatif
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Réseaux de régulation génique
Type d'étude:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limites:
Female
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Humans
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Male
Langue:
En
Journal:
Brief Bioinform
Sujet du journal:
BIOLOGIA
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INFORMATICA MEDICA
Année:
2021
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
ENGLAND
/
ESCOCIA
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GB
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GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
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SCOTLAND
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UK
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UNITED KINGDOM