System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes.

Genome-wide association studies (GWASs) have discovered >50 risk loci for type 1 diabetes (T1D). However, those variations only have modest effects on the genetic risk of T1D. In recent years, accumulated studies have suggested that gene-gene interactions might explain part of the missing heritability. The purpose of our research was to identify potential and novel risk genes for T1D by systematically considering the gene-gene interactions through network analyses. We carried out a novel system network analysis of summary GWAS statistics jointly with transcriptomic gene expression data to identify some of the missing heritability for T1D using weighted gene co-expression network analysis (WGCNA). Using WGCNA, seven modules for 1852 nominally significant (P ≤ 0.05) GWAS genes were identified by analyzing microarray data for gene expression profile. One module (tagged as green module) showed significant association (P ≤ 0.05) between the module eigengenes and the trait. This module also displayed a high correlation (r = 0.45, P ≤ 0.05) between module membership (MM) and gene significant (GS), which indicated that the green module of co-expressed genes is of significant biological importance for T1D status. By further describing the module content and topology, the green module revealed a significant enrichment in the "regulation of immune response" (GO:0050776), which is a crucially important pathway in T1D development. Our findings demonstrated a module and several core genes that act as essential components in the etiology of T1D possibly via the regulation of immune response, which may enhance our fundamental knowledge of the underlying molecular mechanisms for T1D.

Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis.

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.

Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach.

BACKGROUND: Both type 2 diabetes (T2D) and Alzheimer's disease (AD) occur commonly in the aging populations and T2D has been considered as an important risk factor for AD. The heritability of both diseases is estimated to be over 50%. However, common pleiotropic single-nucleotide polymorphisms (SNPs)/loci have not been well-defined. The aim of this study is to analyze two large public accessible GWAS datasets to identify novel common genetic loci for T2D and/or AD. METHODS AND MATERIALS: The recently developed novel conditional false discovery rate (cFDR) approach was used to analyze the summary GWAS datasets from International Genomics of Alzheimer's Project (IGAP) and Diabetes Genetics Replication And Meta-analysis (DIAGRAM) to identify novel susceptibility genes for AD and T2D. RESULTS: We identified 78 SNPs (including 58 novel SNPs) that were associated with AD in Europeans conditional on T2D (cFDR<0.05). 66 T2D SNPs (including 40 novel SNPs) were identified by conditioning on SNPs association with AD (cFDR<0.05). A conjunction-cFDR (ccFDR) analysis detected 8 pleiotropic SNPs with a significance threshold of ccFDR<0.05 for both AD and T2D, of which 5 SNPs (rs6982393, rs4734295, rs7812465, rs10510109, rs2421016) were novel findings. Furthermore, among the 8 SNPs annotated at 6 different genes, 3 corresponding genes TP53INP1, TOMM40 and C8orf38 were related to mitochondrial dysfunction, critically involved in oxidative stress, which potentially contribute to the etiology of both AD and T2D. CONCLUSION: Our study provided evidence for shared genetic loci between T2D and AD in European subjects by using cFDR and ccFDR analyses. These results may provide novel insight into the etiology and potential therapeutic targets of T2D and/or AD.

Identification of novel genetic loci for osteoporosis and/or rheumatoid arthritis using cFDR approach.

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.