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BackgroundMultimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown.MethodsSummary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases.ResultsLDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (PConclusionsThis study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

Low bone mineral density (BMD) is a risk factor of osteoporotic fracture (OF). Peripheral blood monocytes (PBM) can differentiate into osteoclasts to resorb bone. It was known that PBM-expressed Anxa2 protein is associated with BMD, and extracellular Anxa2 protein promotes osteoclastogenesis. This study aimed to test 1) whether Anxa2 protein level in PBM differs significantly between subjects with OF and without fracture history (NF); 2) whether Anxa2 level in plasma is associated with BMD; 3) how Anxa2 protein at various concentrations would affect osteoblastic activity in vitro. All the study subjects were Chinese Han elderly. Firstly, Anxa2 protein in PBM was identified and quantitated by LC-MS/MS and compared between 45 OF cases and 42 healthy controls. Secondly, plasma Anxa2 protein level was quantitated by ELISA and compared between unrelated subjects with extremely low vs. high hip BMD (0.63±0.10 vs. 1.05±0.10 g/cm2, n = 75). Furthermore, in vitro functional assay was utilized to test the effects of extracellular Anxa2 protein on osteoblastic growth. We found that Anxa2 protein expression in PBM was significantly up-regulated in OF vs. NF subjects (fold change [FC)] = 1.16, P

Gelsolin (GSN) protein, expressed in circulating monocytes, was previously reported to be associated with osteoporosis in both Chinese and Caucasian women. This study aims to test if plasma GSN protein level is associated with hip bone mineral density (BMD) in Chinese population. Based on two study Groups containing 6,308 old Chinese, we adopted extreme sampling scheme and selected 3 independent samples (Subgroups 1-3) for discovery, replication, and validation purposes. We tested plasma GSN concentration, and analyzed whether plasma GSN level differs between subjects with extremely low vs. high hip BMD. In Group 1 (N = 1,860), the plasma GSN level increased in the female with low BMD, which was discovered in the Subgroup 1 (N = 42, p = 0.093) and replicated in the Subgroup 2 (N = 39, p = 0.095). With more extreme sampling for the Subgroup 3 from the Group 2 (N = 4,448), the difference of plasma GSN level in the female with low BMD vs. high BMD is more significant (N = 45, p = 0.037). After the subjects were pooled from Subgroups 2 and 3, the difference in plasma GSN between low and high BMD subjects became even more significant (p = 0.016). The plasma GSN level was negatively correlated with total hip BMD (r = -0.26, p = 0.033). We concluded that plasma GSN was associated with hip BMD in Chinese postmenopausal women and plasma GSN might be a potential risk biomarker for osteoporosis.

OBJECTIVE:Systemic lupus erythematosus (SLE) is a complex auto-immune disease. Gene expression studies have been conducted to identify SLE-related genes in various types of samples. It is unknown whether there are common marker genes significant for SLE but independent of sample types, which may have potentials for follow-up translational research. The aim of this study is to identify common marker genes across various sample types for SLE. METHODS:Based on four public microarray gene expression datasets for SLE covering three representative types of blood-born samples (monocyte; peripheral blood mononuclear cell, PBMC; whole blood), we utilized three statistics (fold-change, FC; t-test p value; false discovery rate adjusted p value) to scrutinize genes simultaneously regulated with SLE across various sample types. For common marker genes, we conducted the Gene Ontology enrichment analysis and Protein-Protein Interaction analysis to gain insights into their functions. RESULTS:We identified 10 common marker genes associated with SLE (IFI6, IFI27, IFI44L, OAS1, OAS2, EIF2AK2, PLSCR1, STAT1, RNASE2, and GSTO1). Significant up-regulation of IFI6, IFI27, and IFI44L with SLE was observed in all the studied sample types, though the FC was most striking in monocyte, compared with PBMC and whole blood (8.82-251.66 vs. 3.73-74.05 vs. 1.19-1.87). Eight of the above 10 genes, except RNASE2 and GSTO1, interact with each other and with known SLE susceptibility genes, participate in immune response, RNA and protein catabolism, and cell death. CONCLUSION:Our data suggest that there exist common marker genes across various sample types for SLE. The 10 common marker genes, identified herein, deserve follow-up studies to dissert their potentials as diagnostic or therapeutic markers to predict SLE or treatment response.

Rheumatoid arthritis (RA) is a complex autoimmune disease. Using a gene-based association research strategy, the present study aims to detect unknown susceptibility to RA and to address the ethnic differences in genetic susceptibility to RA between European and Asian populations.Gene-based association analyses were performed with KGG 2.5 by using publicly available large RA datasets (14,361 RA cases and 43,923 controls of European subjects, 4,873 RA cases and 17,642 controls of Asian Subjects). For the newly identified RA-associated genes, gene set enrichment analyses and protein-protein interactions analyses were carried out with DAVID and STRING version 10.0, respectively. Differential expression verification was conducted using 4 GEO datasets. The expression levels of three selected 'highly verified' genes were measured by ELISA among our in-house RA cases and controls.A total of 221 RA-associated genes were newly identified by gene-based association study, including 71'overlapped', 76 'European-specific' and 74 'Asian-specific' genes. Among them, 105 genes had significant differential expressions between RA patients and health controls at least in one dataset, especially for 20 genes including 11 'overlapped' (ABCF1, FLOT1, HLA-F, IER3, TUBB, ZKSCAN4, BTN3A3, HSP90AB1, CUTA, BRD2, HLA-DMA), 5 'European-specific' (PHTF1, RPS18, BAK1, TNFRSF14, SUOX) and 4 'Asian-specific' (RNASET2, HFE, BTN2A2, MAPK13) genes whose differential expressions were significant at least in three datasets. The protein expressions of two selected genes FLOT1 (P value = 1.70E-02) and HLA-DMA (P value = 4.70E-02) in plasma were significantly different in our in-house samples.Our study identified 221 novel RA-associated genes and especially highlighted the importance of 20 candidate genes on RA. The results addressed ethnic genetic background differences for RA susceptibility between European and Asian populations and detected a long list of overlapped or ethnic specific RA genes. The study not only greatly increases our understanding of genetic susceptibility to RA, but also provides important insights into the ethno-genetic homogeneity and heterogeneity of RA in both ethnicities.

Genetic factors contribute to the variation of bone mineral density (BMD), which is a major risk factor of osteoporosis. The aim of this study was to identify more "novel" genes for BMD. Based on the publicly available SNP-based P values, we performed an initial gene-based analysis in a total of 32,961 individuals. Furthermore, we performed differential expression, pathway and protein-protein interaction analyses to find supplementary evidence to support the significance of the identified genes. About 21,695 genes for femoral neck (FN)-BMD and 21,683 genes for lumbar spine (LS)-BMD were analyzed using gene-based association analysis. A total of 35 FN-BMD associated genes and 53 LS-BMD associated genes were identified (P < 2.3×10(-6)) after Bonferroni correction. Among them, 64 genes have not been reported in previous SNP-based genome-wide association studies. Differential expression analysis further supported the significant associations of 14 genes with FN-BMD and 19 genes with LS-BMD. Especially, WNT3 and WNT9B in the Wnt signaling pathway for FN-BMD were further supported by pathway analysis and protein-protein interaction analysis. The present study took the advantage of gene-based association method to perform a supplementary analysis of the GWAS dataset and found some BMD-associated genes. The evidence taken together supported the importance of Wnt signaling pathway genes in determining osteoporosis. Our findings provided more insights into the genetic basis of osteoporosis.

Rheumatic diseases have some common symptoms. Extensive gene expression studies, accumulated thus far, have successfully identified signature molecules for each rheumatic disease, individually. However, whether there exist shared factors across rheumatic diseases has yet to be tested.We collected and utilized 6 public microarray datasets covering 4 types of representative rheumatic diseases including rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, and osteoarthritis. Then we detected overlaps of differentially expressed genes across datasets and performed a meta-analysis aiming at identifying common differentially expressed genes that discriminate between pathological cases and normal controls. To further gain insights into the functions of the identified common differentially expressed genes, we conducted gene ontology enrichment analysis and protein-protein interaction analysis.We identified a total of eight differentially expressed genes (TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, PRF1), each associated with at least 3 of the 4 studied rheumatic diseases. Meta-analysis warranted the significance of the eight genes and highlighted the general significance of four genes (CX3CR1, LY96, TLR5, and PRF1). Protein-protein interaction and gene ontology enrichment analyses indicated that the eight genes interact with each other to exert functions related to immune response and immune regulation.The findings support that there exist common factors underlying rheumatic diseases. For rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis and osteoarthritis diseases, those common factors include TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, and PRF1. In-depth studies on these common factors may provide keys to understanding the pathogenesis and developing intervention strategies for rheumatic diseases.

Systemic lupus erythematosus (SLE) is a serious prototype autoimmune disease characterized by chronic inflammation, auto-antibody production and multi-organ damage. Recent association studies have identified a long list of loci that were associated with SLE with relatively high statistical power. However, most of them only established the statistical associations of genetic markers and SLE at the DNA level without supporting evidence of functional relevance. Here, using publically available datasets, we performed integrative analyses (gene relationship across implicated loci analysis, differential gene expression analysis and functional annotation clustering analysis) and combined with expression quantitative trait loci (eQTLs) results to dissect functional mechanisms underlying the associations for SLE. We found that 14 SNPs, which were significantly associated with SLE in previous studies, have cis-regulation effects on four eQTL genes (HLA-DQA1, HLA-DQB1, HLA-DQB2, and IRF5) that were also differentially expressed in SLE-related cell groups. The functional evidence, taken together, suggested the functional mechanisms underlying the associations of 14 SNPs and SLE. The study may serve as an example of mining publically available datasets and results in validation of significant disease-association results. Utilization of public data resources for integrative analyses may provide novel insights into the molecular genetic mechanisms underlying human diseases.

Femoral neck geometric parameters (FNGPs), such as periosteal diameter (W), cross-sectional area (CSA), cortical thickness (CT), buckling ratio (BR), and section modulus (Z), are highly genetically correlated with body lean mass. However, the specific SNPs/genes shared by these phenotypes are largely unknown.To identify the specific SNPs/genes shared between FNGPs and appendicular lean mass (ALM), we performed an initial bivariate genome-wide association study (GWAS) by scanning ∼690,000 SNPs in 1,627 unrelated Han Chinese adults (802 males and 825 females) and a follow-up replicate study in 2,286 unrelated US Caucasians.We identified 13 interesting SNPs that may be important for both FNGPs and ALM. Two SNPs, rs681900 located in the HK2 (hexokinase 2) gene and rs11859916 in the UMOD (uromodulin) gene, were bivariately associated with FNGPs and ALM (p = 7.58×10(-6) for ALM-BR and p = 2.93×10(-6) for ALM-W, respectively). The associations were then replicated in Caucasians, with corresponding p values of 0.024 for rs681900 and 0.047 for rs11859916. Meta-analyses yielded combined p values of 3.05×10(-6) and 2.31×10(-6) for rs681900 and rs11859916, respectively. Our findings are consistent with previous biological studies that implicated HK2 and UMOD in both FNGPs and ALM. Our study also identified a group of 11 contiguous SNPs, which spanned a region of ∼130 kb, were bivariately associated with FNGPs and ALM, with p values ranging from 3.06×10(-7) to 4.60×10(-6) for ALM-BR. The region contained two neighboring miRNA coding genes, MIR873 (MicroRNA873) and MIR876 (MicroRNA876).Our study implicated HK2, UMOD, MIR873 and MIR876, as pleiotropic genes underlying variation of both FNGPs and ALM, thus suggesting their important functional roles in co-regulating both FNGPs and ALM.

Osteoporotic hip fracture (HF) is a serious global public health problem associated with high morbidity and mortality. Hip bone size (BS) has been identified as one of key measurable risk factors for HF, independent of bone mineral density (BMD). Hip BS is highly genetically determined, but genetic factors underlying BS variation are still poorly defined. Here, we performed an initial genome-wide copy number variation (CNV) association analysis for hip BS in 1,627 Chinese Han subjects using Affymetrix GeneChip Human Mapping SNP 6.0 Array and a follow-up replicate study in 2,286 unrelated US Caucasians sample. We found that a copy number polymorphism (CNP267) located at chromosome 2q12.2 was significantly associated with hip BS in both initial Chinese and replicate Caucasian samples with p values of 4.73E-03 and 5.66E-03, respectively. An important candidate gene, four and a half LIM domains 2 (FHL2), was detected at the downstream of CNP267, which plays important roles in bone metabolism by binding to several bone formation regulator, such as insulin-like growth factor-binding protein 5 (IGFBP-5) and androgen receptor (AR). Our findings suggest that CNP267 region may be associated with hip BS which might influence the FHL2 gene downstream.

Mitochondria play a central role in ATP production and energy metabolism. Previous studies suggest that common variants in mtDNA are associated with several common complex diseases, including obesity. To test the hypothesis that common mtDNA variants influence obesity-related phenotypes, including BMI and body fat mass, we genotyped a total of 445 mtSNPs across the whole mitochondrial genome in a large sample of 2,286 unrelated Caucasian subjects. 72 of these 445 mtSNPs passed quality control criteria, and were used for subsequent analyses. We also classified all subjects into nine common European haplogroups. Association analyses were conducted for both BMI and body fat mass with single mtSNPs and mtDNA haplogroups. Two mtSNPs, mt4823 and mt8873 were detected to be significantly associated with body fat mass, with adjusted P values of 4.94 × 10⁻³ and 4.58 × 10⁻², respectively. The minor alleles mt4823 C and mt8873 A were associated with reduced fat mass values and the effect size (β) was estimated to be 3.52 and 3.18, respectively. These two mtSNPs also achieved nominally significant levels for association with BMI. For haplogroup analyses, we found that haplogroup X was strongly associated with both BMI (adjusted P = 8.31 × 10⁻³) and body fat mass (adjusted P = 5.67×10⁻⁴) Subjects classified as haplogroup X had lower BMI and fat mass values, with the β estimated to be 2.86 and 6.03, respectively. Our findings suggest that common variants in mitochondria might play a role in variations of body fat mass. Further molecular and functional studies will be needed to clarify the potential mechanism.