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Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co‑expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co‑expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co‑expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC‑related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated the genetic correlations between type 2 diabetes, obesity and dyslipidemia, and indicated that many genes have pleiotropic effects on them. However, these pleiotropic genes have not been well-defined. It is essential to identify pleiotropic genes using systematic approaches because systematically analyzing correlated traits is an effective way to enhance their statistical power. To identify potential pleiotropic genes for these three disorders, we performed a systematic analysis by incorporating GWAS (genome-wide associated study) datasets of six correlated traits related to type 2 diabetes, obesity and dyslipidemia using Meta-CCA (meta-analysis using canonical correlation analysis). Meta-CCA is an emerging method to systematically identify potential pleiotropic genes using GWAS summary statistics of multiple correlated traits. 2,720 genes were identified as significant genes after multiple testing (Bonferroni corrected p value < 0.05). Further, to refine the identified genes, we tested their relationship to the six correlated traits using VEGAS-2 (versatile gene-based association study-2). Only the genes significantly associated (Bonferroni corrected p value < 0.05) with more than one trait were kept. Finally, 25 genes (including two confirmed pleiotropic genes and eleven novel pleiotropic genes) were identified as potential pleiotropic genes. They were enriched in 5 pathways including the statin pathway and the PPAR (peroxisome proliferator-activated receptor) Alpha pathway. In summary, our study identified potential pleiotropic genes and pathways of type 2 diabetes, obesity and dyslipidemia, which may shed light on the common biological etiology and pathogenesis of these three disorders and provide promising insights for new therapies. [ABSTRACT FROM AUTHOR]

There are co-morbidity between osteoporosis (OP) and rheumatoid arthritis (RA). Some genetic risk factors have been identified for these two phenotypes respectively in previous research; however, they accounted for only a small portion of the underlying total genetic variances. Here, we sought to identify additional common genetic loci associated with OP and/or RA. The conditional false discovery rate (cFDR) approach allows detection of additional genetic factors (those respective ones as well as common pleiotropic ones) for the two associated phenotypes. We collected and analyzed summary statistics provided by large, multi-center GWAS studies of FNK (femoral neck) BMD (a major risk factor for osteoporosis) (n = 53,236) and RA (n = 80,799). The conditional quantile-quantile (Q-Q) plots can assess the enrichment of SNPs related to FNK BMD and RA, respectively. Furthermore, we identified shared loci between FNK BMD and RA using conjunction cFDR (ccFDR). We found strong enrichment of p-values in FNK BMD when conditional Q-Q was done on RA and vice versa. We identified 30 novel OP-RA associated pleiotropic loci that have not been reported in previous OP or RA GWAS, 18 of which located in the MHC (major histocompatibility complex) region previously reported to play an important role in immune system and bone health. We identified some specific novel polygenic factors for OP and RA respectively, and identified 30 novel OP-RA associated pleiotropic loci. These discovery findings may offer novel pathobiological insights, and suggest new targets and pathways for drug development in OP and RA patients. [ABSTRACT FROM AUTHOR]