Autism candidate genes in cytogenetic regions of interest identified with a gene interaction network

2006 ◽  
Vol 16 ◽  
pp. S6
Author(s):  
W. Staal ◽  
L. Franke ◽  
J.A.S. Vorstman ◽  
H. van Engeland ◽  
C. Wijminga
Keyword(s):  
Candidate Genes ◽  
Interaction Network ◽  
Gene Interaction ◽  
Regions Of Interest ◽  
Gene Interaction Network

scholarly journals A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Marta Gòdia ◽  
Antonio Reverter ◽  
Rayner González-Prendes ◽  
Yuliaxis Ramayo-Caldas ◽  
Anna Castelló ◽  
...  
Keyword(s):  
Systems Biology ◽  
Candidate Genes ◽  
Molecular Basis ◽  
Sperm Quality ◽  
Interaction Network ◽  
Gene Interaction ◽  
Phenotypic Variance ◽  
Rna Seq ◽  
Gene Interaction Network ◽  
Sperm Traits

Abstract Background Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework. Results By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1, OAZ3, DNAJB8, TPPP2 and TNP1) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits. Conclusions By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

scholarly journals An integrative systems biology approach to identify the molecular basis of sperm quality in swine

2020 ◽  
Author(s):  
Marta Godia ◽  
Antonio Reverter ◽  
Rayner Gonzalez-Prendes ◽  
Yuliaxis Ramayo-Caldas ◽  
Anna Castello ◽  
...  
Keyword(s):  
Systems Biology ◽  
Candidate Genes ◽  
Molecular Basis ◽  
Sperm Quality ◽  
Interaction Network ◽  
Gene Interaction ◽  
Phenotypic Variance ◽  
Rna Seq ◽  
Gene Interaction Network ◽  
Sperm Traits

Abstract Background:Genetic pressure in animal breeding is sparking the interest to select for elite boars with higher sperm quality to maximize ejaculate doses and fertility rates. However, the molecular basis of sperm quality remains largely unexplored. In this study, we sought to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analyzing 25 sperm-related phenotypes using a systems biology approach that integrates GWAS and RNA-seq.Results:By GWAS, we identified 12 QTL regions associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 or ABCA1. By RNA-seq, we detected 6,128 significant correlations between sperm traits and gene RNA abundances. We built a gene interaction network with the GWAS and the RNA-seq data. To build a robust gene interaction network, only the pair-wise interactions present in both the genetic co-association and the RNA co-abundance network were kept. Moreover, we also included to the Final Network both the genes which RNA abundances correlated with more than 4 semen traits as well as the miRNAs interacting with the genes on the network. The Final Network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. We finally designed a panel of 73 SNPs provided from the GWAS, eGWAS and the Final Network, that explains between 5 to 36% of the phenotypic variance of the sperm traits.Conclusions:By means of a systems biology approach, we identified potential key genes affecting sperm quality. Furthermore, we propose a SNP panel that might explain a substantial part of the genetic variance for semen quality in swine and may thus be of interest for the pig breeding sector.

scholarly journals Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes

2009 ◽  
Vol 3 (Suppl 7) ◽  
pp. S75 ◽  
Author(s):  
Chien-Hsun Huang ◽  
Lei Cong ◽  
Jun Xie ◽  
Bo Qiao ◽  
Shaw-Hwa Lo ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Candidate Genes ◽  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hao Yu ◽  
Yang Liu ◽  
Chao Li ◽  
Jianhao Wang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Inflammatory Responses ◽  
Interaction Network ◽  
Gene Interaction ◽  
Bioinformatic Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Pathway Enrichment ◽  
Protein Protein Interaction

Background. Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods. The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results. A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion. The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.

scholarly journals A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

10.1186/gm404 ◽  
2012 ◽  
Vol 4 (12) ◽  
Author(s):  
Raymond J Louie ◽  
Jingyu Guo ◽  
John W Rodgers ◽  
Rick White ◽  
Najaf A Shah ◽  
...  
Keyword(s):  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network ◽  
Protein Biogenesis

Rheumatoid arthritis–associated DNA methylation sites in peripheral blood mononuclear cells

2018 ◽  
Vol 78 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Hong Zhu ◽  
Long-Fei Wu ◽  
Xing-Bo Mo ◽  
Xin Lu ◽  
Hui Tang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mrna Expression ◽  
Mononuclear Cells ◽  
Interaction Network ◽  
Gene Interaction ◽  
Jurkat Cells ◽  
Gene Interaction Network ◽  
Peripheral Blood Mononuclear ◽  
Inference Tests ◽  
Inducible Gene

ObjectivesTo identify novel DNA methylation sites significant for rheumatoid arthritis (RA) and comprehensively understand their underlying pathological mechanism.MethodsWe performed (1) genome-wide DNA methylation and mRNA expression profiling in peripheral blood mononuclear cells from RA patients and health controls; (2) correlation analysis and causal inference tests for DNA methylation and mRNA expression data; (3) differential methylation genes regulatory network construction; (4) validation tests of 10 differential methylation positions (DMPs) of interest and corresponding gene expressions; (5) correlation between PARP9 methylation and its mRNA expression level in Jurkat cells and T cells from patients with RA; (6) testing the pathological functions of PARP9 in Jurkat cells.ResultsA total of 1046 DNA methylation positions were associated with RA. The identified DMPs have regulatory effects on mRNA expressions. Causal inference tests identified six DNA methylation–mRNA–RA regulatory chains (eg, cg00959259-PARP9-RA). The identified DMPs and genes formed an interferon-inducible gene interaction network (eg, MX1, IFI44L, DTX3L and PARP9). Key DMPs and corresponding genes were validated their differences in additional samples. Methylation of PARP9 was correlated with mRNA level in Jurkat cells and T lymphocytes isolated from patients with RA. The PARP9 gene exerted significant effects on Jurkat cells (eg, cell cycle, cell proliferation, cell activation and expression of inflammatory factor IL-2).ConclusionsThis multistage study identified an interferon-inducible gene interaction network associated with RA and highlighted the importance of PARP9 gene in RA pathogenesis. The results enhanced our understanding of the important role of DNA methylation in pathology of RA.

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