Abstract
Primary Sjögren’s syndrome (pSS) is a complex autoimmune disorder. So far, genetic research in pSS has lagged far behind and the underlying biological mechanism is unclear. Further exploring existing genome-wide association study (GWAS) data is urgently expected to uncover disease-related gene combination patterns. Herein, we conducted a network-based analysis by integrating pSS GWAS in Han Chinese with a protein-protein interactions network to identify pSS candidate genes. After module detection and evaluation, 8 dense modules covering 40 genes were obtained for further functional annotation. Additional 31 MHC genes with significant gene-level P-values (sigMHC-gene) were also remained. The combined module genes and sigMHC-genes, a total of 71 genes, were denoted as pSS candidate genes. Of these pSS candidates, 14 genes had been reported to be associated with any of pSS, RA, and SLE, including STAT4, GTF2I, HLA-DPB1, HLA-DRB1, PTTG1, HLA-DQB1, MBL2, TAP2, CFLAR, NFKBIE, HLA-DRA, APOM, HLA-DQA2 and NOTCH4. This is the first report of the network-assisted analysis for pSS GWAS data to explore combined gene patterns associated with pSS. Our study suggests that network-assisted analysis is a useful approach to gaining further insights into the biology of associated genes and providing important clues for future research into pSS etiology.
Highlights
Sjögren’s syndrome (SS) is a chronic autoimmune disease characterized by exocrine gland dysfunction, the salivary and lacrimal glands, resulting in oral and ocular dryness[1]
In order to reduce the influence of sigMHC-genes on module searching and primarily focus on genes outside MHC, the sigMHC-genes were not set as seed nodes to search modules
To further mining the existing genetic data, network-assisted analysis of Primary Sjögren’s syndrome (pSS) genome-wide association studies (GWAS) in Han Chinese was performed in order to explore the joint effects of multiple genetic association signals on pSS and discover additional candidate genes associated with pSS pathogenesis
Summary
Sjögren’s syndrome (SS) is a chronic autoimmune disease characterized by exocrine gland dysfunction, the salivary and lacrimal glands, resulting in oral and ocular dryness[1]. The pSS GWASs have uncovered a few risk loci conferring susceptibility to pSS5,6 In spite of these successes, as with other complex diseases, GWAS analysis of pSS is limited by the use of a genome-wide significance cutoff SNP P-value of 5 × 10−8 needed for multiple testing correction[7]. The rationale behind network-assisted analysis is “guilt by association”[12], i.e. different causal genes for the same phenotypes often interact, either directly or via common interaction partners Along these lines, the present study applied a network-assisted method by integrating pSS GWAS data in Han Chinese with human PPI network to investigate whether a set of genes, whose protein products closely interact www.nature.com/scientificreports/. Our network-assisted analysis of pSS GWAS would facilitate the understanding of genetic mechanism of pSS
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