Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma

Liu, Yuanlong; Brossard, Myriam; Sarnowski, Chloé; Vaysse, Amaury; Moffatt, Miriam F.; Margaritte‐Jeannin, Patricia; Llinares-López, Felipe; Dizier, Marie‐Hélène; Lathrop, Mark; Cookson, Woc; Demenais, Florence

doi:10.1038/s41598-017-01058-y

Cited by 17 publications

(16 citation statements)

References 40 publications

(51 reference statements)

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“…Using the network analysis, we also identified APP hitherto not associated with asthma as important hub gene in regulatory network associated with NE tissue. APP gene encoding the amyloid beta precursor protein is known as a major player in Alzheimer's disease (AD) that have immune and inflammatory components ( 32 ). Recent studies also suggested a link between APP and asthma genes ( 32 , 33 ).…”

Section: Discussionmentioning

confidence: 99%

“…APP gene encoding the amyloid beta precursor protein is known as a major player in Alzheimer's disease (AD) that have immune and inflammatory components ( 32 ). Recent studies also suggested a link between APP and asthma genes ( 32 , 33 ). It is revealed that APP was potentially associated with airway hyperresponsiveness through the interaction with a disintegrin and metalloproteinase ( ADAM33 ) ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

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Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network

Wang¹,

Li²,

Cao³

et al. 2018

Mol Med Report

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Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The aim of the present study was to screen critical differentially expressed genes (DEGs) involved in asthma in children. Gene expression in different tissues was compared between asthmatic children and healthy control subjects in order to identify DEGs associated with asthma. Protein-protein interaction (PPI) networks were constructed for the DEGs and weighted gene co-expression network analysis methods were used to further determine the functional modules associated with DEGs in different tissue samples. In addition, the gene co-expression network was constructed. Gene Ontology function analysis and pathway analysis were conducted to identify critical DEGs. The results identified numerous DEGs from the different tissue samples, including 1,662 DEGs from nasal-epithelium tissue samples, 572 DEGs from peripheral blood (PB) samples and 146 DEGs from PB mononuclear cells samples. In the PPI network, F-box only protein 6 (FBXO6), histone deacetylase 1 (HDAC1) and amyloid β precursor protein (APP) were hub genes and served an important role in the process of asthma. In addition, proliferating cell nuclear antigen (PCNA), integrin α-4 (ITGA4), catenin α-1 (CTNNA1), nuclear factor-κB1 (NF-κB1) and mechanistic target of rapamycin (MTOR) may be critical DEGs involved in the progression of asthma in children. These results suggested that FBXO6, HDAC1 and APP may interact with PCNA, ITGA4, CTNNA1, NF-κB1 and mTOR in the progression of asthma in children.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network

Wang¹,

Li²,

Cao³

et al. 2018

Mol Med Report

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show abstract

“…Network‐based analysis has also been used to amplify marker signals from existing GWAS studies. Liu et al (2017) identified genes that contributed significantly to childhood‐onset asthma by searching for consistent gene modules between two large GWAS datasets.…”

Section: Introductionmentioning

confidence: 99%

SNPeffect: identifying functional roles of SNPs using metabolic networks

Sarkar

Maranas

2020

The Plant Journal

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Summary Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome‐wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single‐nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype‐to‐phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme‐coding genes. To this end, we also constructed a genome‐scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth‐determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

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“…Although a number of prior studies have integrated GWAS data with networks [16][17][18][19][20][21][22], the majority of these studies tended to focus on single diseases or phenotypes [23][24][25][26]. The potential for improved insights by integrating GWAS data with protein-protein interaction networks in particular, comes from the finding, that the protein products of genes within GWAS loci physically interact with each other more than expected by chance [27].…”

Section: Introductionmentioning

confidence: 99%

Protein-Protein interactions uncover candidate ‘core genes’ within omnigenic disease networks

et al. 2020

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Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancerwhere we identify 109 candidate core genes.

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Network-assisted analysis of GWAS data identifies a functionally-relevant gene module for childhood-onset asthma

Cited by 17 publications

References 40 publications

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network

SNPeffect: identifying functional roles of SNPs using metabolic networks

Protein-Protein interactions uncover candidate ‘core genes’ within omnigenic disease networks

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