Future of osteoporosis genetics: enhancing genome-wide association studies

Identification of key genes in bovine muscle development by co-expression analysis

Background

Skeletal muscle is not only an important tissue involved in exercise and metabolism, but also an important part of livestock and poultry meat products. Its growth and development determines the output and quality of meat to a certain extent, and has an important impact on the economic benefits of animal husbandry. Skeletal muscle development is a complex regulatory network process, and its molecular mechanism needs to be further studied.

Method

We used a weighted co-expression network (WGCNA) and single gene set enrichment analysis (GSEA) to study the RNA-seq data set of bovine tissue differential expression analysis, and the core genes and functional enrichment pathways closely related to muscle tissue development were screened. Finally, the accuracy of the analysis results was verified by tissue expression profile detection and bovine skeletal muscle satellite cell differentiation model in vitro (BSMSCs).

Results

In this study, Atp2a1, Tmod4, Lmod3, Ryr1 and Mybpc2 were identified as marker genes in muscle tissue, which are mainly involved in glycolysis/gluconeogenesis, AMPK pathway and insulin pathway. The assay results showed that these five genes were highly expressed in muscle tissue and positively correlated with the differentiation of bovine BSMSCs.

Conclusions

In this study, several muscle tissue characteristic genes were excavated, which may play an important role in muscle development and provide new insights for bovine molecular genetic breeding.

Development and validation of a gene signature predicting the risk of postmenopausal osteoporosis

AIMS

We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism.

METHODS

Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell's concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature.

RESULTS

We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that RAB2A and FYCO1 were amplified in clinical samples.

CONCLUSION

The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of RAB2A and FYCO1 underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring.Cite this article: Bone Joint Res 2022;11(8):548-560.

Relationship between rs7586085, GALNT3 and CCDC170 gene polymorphisms and the risk of osteoporosis among the Chinese Han population

Osteoporosis (OP) has plagued many women for years, and bone density loss is an indicator of OP. The purpose of this study was to evaluate the relationship between the polymorphism of the rs7586085, CCDC170 and GALNT3 gene polymorphisms and the risk of OP in the Chinese Han population. Using the Agena MassArray method, we identified six candidate SNPs on chromosomes 2 and 6 in 515 patients with OP and 511 healthy controls. Genetic model analysis was performed to evaluate the significant association between variation and OP risk, and meanwhile, the multiple tests were corrected by false discovery rate (FDR). Haploview 4.2 was used for haplotype analysis. In stratified analysis of BMI ˃ 24, rs7586085, rs6726821, rs6710518, rs1346004, and rs1038304 were associated with the risk of OP based on the results of genetic models among females even after the correction of FDR (qd < 0.05). In people at age ≤ 60 years, rs1038304 was associated with an increased risk of OP under genetic models after the correction of FDR (qd < 0.05). Our study reported that GALNT3 and CCDC170 gene polymorphisms and rs7586085 are the effective risk factors for OP in the Chinese Han population.

Understanding Musculoskeletal Disorders Through Next-Generation Sequencing

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An insight into musculoskeletal disorders through advancements in next-generation sequencing (NGS) promises to maximize benefits and improve outcomes through improved genetic diagnosis.

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The primary use of whole exome sequencing (WES) for musculoskeletal disorders is to identify functionally relevant variants.

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The current evidence has shown the superiority of NGS over conventional genotyping for identifying novel and rare genetic variants in patients with musculoskeletal disorders, due to its high throughput and low cost.

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Genes identified in patients with scoliosis, osteoporosis, osteoarthritis, and osteogenesis imperfecta using NGS technologies are listed for further reference.

Genetic association study identified a 20 kb regulatory element in WLS associated with osteoporosis and bone mineral density in Han Chinese

Previous studies have linked the WNT pathway and human skeleton formation; therefore, genes related to WNT might contribute to the onset and development of osteoporosis. In this study, we investigated the potential genetic association of WLS, which encodes an important mediator in the WNT pathway, with osteoporosis and its related quantitative traits in a sample of 6,620 individuals from Han Chinese population. A two-stage approach, with a discovery stage with 859 cases and 1,690 controls and a validation stage with 1,039 cases and 3,032 controls, was applied in the study. Forty SNPs were genotyped in the discovery stage. The intronic SNP rs2566752 was identified to be significantly associated with osteoporosis (OR_discovery = 0.78, P_discovery = 3.73 × 10⁻⁵; OR_validation = 0.80, P_validation = 1.96 × 10⁻⁵). Two SNPs surrounding rs2566752 (in addition to this SNP itself) were identified to be associated with bone mineral density. In addition, we have identified a 20 kb peak region of H3K27Ac histone mark enrichment between rs2772304 and rs2566752. Our study suggested that WLS is an important locus for osteoporosis and its related quantitative phenotypes in Han Chinese population. Additional sequencing-based studies are needed to investigate the genetic architecture of this regulatory region and its relationship with osteoporosis-related phenotypes.

A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease

Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi)genomic factors from diverse omics data; and (iii) Until present, the combination of more than two omics datasets has been poorly explored. Current integration approaches are not sufficient to address all of these challenges together. We proposed a novel integrative analysis framework by incorporating sparse model, multivariate analysis, Gaussian graphical model, and network analysis to address these three challenges simultaneously. Based on this strategy, we performed a systemic analysis for glioblastoma multiforme (GBM) integrating genome-wide gene expression, DNA methylation, and miRNA expression data. We identified three regulatory modules of genomic factors associated with GBM survival time and revealed a global regulatory pattern for GBM by combining the three modules, with respect to the common regulatory factors. Our method can not only identify disease-associated dysregulated genomic factors from different omics, but more importantly, it can incorporate the information from publicly accessible databases and omics datasets to infer a comprehensive interaction map of all these dysregulated genomic factors. Our work represents an innovative approach to enhance our understanding of molecular genomic mechanisms underlying human complex diseases.

Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for BMD Variation

Integration of multiple profiling data and construction of functional gene networks may provide additional insights into the molecular mechanisms of complex diseases. Osteoporosis is a worldwide public health problem, but the complex gene-gene interactions, post-transcriptional modifications and regulation of functional networks are still unclear. To gain a comprehensive understanding of osteoporosis etiology, transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing were performed simultaneously in 5 high hip BMD (Bone Mineral Density) subjects and 5 low hip BMD subjects. SPIA (Signaling Pathway Impact Analysis) and PCST (Prize Collecting Steiner Tree) algorithm were used to perform pathway-enrichment analysis and construct the interaction networks. Through integrating the transcriptomic and epigenomic data, firstly we identified 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) which showed the consistent association evidence from both gene expression and methylation data; secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status. In conclusion, the integration of multiple layers of omics can yield in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology.

Identification of common regulators of genes in co-expression networks affecting muscle and meat properties

Understanding the genetic contributions behind skeletal muscle composition and metabolism is of great interest in medicine and agriculture. Attempts to dissect these complex traits combine genome-wide genotyping, expression data analyses and network analyses. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of co-expression, which can be linked to phenotypes by correlation analysis of trait values and the module eigengenes, i.e. the first principal component of a given module. Network hub genes and regulators of the genes in the modules are likely to play an important role in the emergence of respective traits. In order to detect common regulators of genes in modules showing association with meat quality traits, we identified eQTL for each of these genes, including the highly connected hub genes. Additionally, the module eigengene values were used for association analyses in order to derive a joint eQTL for the respective module. Thereby major sites of orchestrated regulation of genes within trait-associated modules were detected as hotspots of eQTL of many genes of a module and of its eigengene. These sites harbor likely common regulators of genes in the modules. We exemplarily showed the consistent impact of candidate common regulators on the expression of members of respective modules by RNAi knockdown experiments. In fact, Cxcr7 was identified and validated as a regulator of genes in a module, which is involved in the function of defense response in muscle cells. Zfp36l2 was confirmed as a regulator of genes of a module related to cell death or apoptosis pathways. The integration of eQTL in module networks enabled to interpret the differentially-regulated genes from a systems perspective. By integrating genome-wide genomic and transcriptomic data, employing co-expression and eQTL analyses, the study revealed likely regulators that are involved in the fine-tuning and synchronization of genes with trait-associated expression.

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Patient bone mineral density (BMD) predicts the likelihood of osteoporotic fracture. While substantial progress has been made toward elucidating the genetic determinants of BMD, our understanding of the factors involved remains incomplete. Here, using a systems genetics approach in the mouse, we predicted that bicaudal C homolog 1 (Bicc1), which encodes an RNA-binding protein, is responsible for a BMD quantitative trait locus (QTL) located on murine chromosome 10. Consistent with this prediction, mice heterozygous for a null allele of Bicc1 had low BMD. We used a coexpression network-based approach to determine how Bicc1 influences BMD. Based on this analysis, we inferred that Bicc1 was involved in osteoblast differentiation and that polycystic kidney disease 2 (Pkd2) was a downstream target of Bicc1. Knock down of Bicc1 and Pkd2 impaired osteoblastogenesis, and Bicc1 deficiency-dependent osteoblast defects were rescued by Pkd2 overexpression. Last, in 2 human BMD genome-wide association (GWAS) meta-analyses, we identified SNPs in BICC1 and PKD2 that were associated with BMD. These results, in both mice and humans, identify Bicc1 as a genetic determinant of osteoblastogenesis and BMD and suggest that it does so by regulating Pkd2 transcript levels.

Clinical impact of recent genetic discoveries in osteoporosis

Osteoporotic fracture carries an enormous public health burden in terms of mortality and morbidity. Current approaches to identify individuals at high risk for fracture are based on assessment of bone mineral density and presence of other osteoporosis risk factors. Bone mineral density and susceptibility to osteoporotic fractures are highly heritable, and over 60 loci have been robustly associated with one or both traits through genome-wide association studies carried out over the past 7 years. In this review, we discuss opportunities and challenges for incorporating these genetic discoveries into strategies to prevent osteoporotic fracture and translating new insights obtained from these discoveries into development of new therapeutic targets.

Systems genetic analysis of osteoblast-lineage cells

The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected “hub” genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types.

The osteoblast-lineage consists of a range of cells from osteogenic precursors that mature into bone-forming osteoblasts to osteocytes that are entombed in bone. Each cell in the lineage serves a number of distinct and critical roles in the growth and maintenance of the skeleton, as well as many extra-skeletal functions. Over the last decade, many of the major regulatory pathways governing the differentiation and activity of these cells have been discovered. In contrast, little is known regarding the composition or function of gene networks within the lineage. The goal of this study was to increase our understanding of how genes are organized into networks in osteoblasts. Towards this goal, we used microarray gene expression profiles from bone to identify a group of genes that formed a network specific to the osteoblast-lineage. We used the knowledge of this network to identify novel genes that are important for regulating various aspects of osteoblast function. These data improve our understanding of the gene networks operative in cells of the osteoblast-lineage.

Systems genetics: a novel approach to dissect the genetic basis of osteoporosis

From the early 1990s to the middle of the last decade, the search for genes influencing osteoporosis proved difficult with few successes. However, over the last 5 years this has begun to change with the introduction of genome-wide association (GWA) studies. In this short period of time, GWA studies have significantly accelerated the pace of gene discovery, leading to the identification of nearly 100 independent associations for osteoporosis-related traits. However, GWA does not specifically pinpoint causal genes or provide functional context for associations. Thus, there is a need for approaches that provide systems-level insight on how associated variants influence cellular function, downstream gene networks, and ultimately disease. In this review we discuss the emerging field of "systems genetics" and how it is being used in combination with and independent of GWA to improve our understanding of the molecular mechanisms involved in bone fragility.

Bridging global gene expression candidates in first trimester placentas with susceptibility loci from linkage studies of preeclampsia

Preeclampsia is as a leading cause of maternal and perinatal morbidity and mortality. Prevention, early identification, and individualized treatments may become feasible if reliable early biomarkers can be developed. Towards a systems biology framework, this review synthesizes prior linkage studies and genome scans of preeclampsia with candidates identified in a global gene expression microarray analysis of chorionic villus sampling (CVS) specimens from women who subsequently developed preeclampsia. Nearly 40% of these CVS candidate genes occurred in previously identified susceptibility loci for preeclampsia. Integration of genetic epidemiologic and functional gene expression data could help to elucidate preeclampsia as a complex disease resulting from multiple maternal and fetal/placental factors that each contributes a greater or lesser effect. These loci and related candidate genes are set to substantially improve insights into the first trimester pathogenesis of this pregnancy disorder.

-1227C>T polymorphism in the pleiotrophin gene promoter influences bone mineral density in postmenopausal women

Our gene expression microarray data of primary cultures of osteoblasts revealed that the expression of the pleiotrophin (PTN) gene is decreased in osteoporosis. PTN is involved in osteoblasts' proliferation and differentiation, response to mechanical stimuli and cross-talk with Wnt signaling. On the basis of these findings, we studied the PTN gene as a candidate gene for genetic susceptibility to osteoporosis. The aim of the study was to evaluate the association of two PTN gene promoter polymorphisms with osteoporotic phenotype in postmenopausal women. 530 postmenopausal women, 480 without and 50 with hip fracture, were genotyped for the presence of PTN gene promoter polymorphisms -1734C>T (rs161335) and -1227C>T (rs321198). Three common haplotypes, CC (14.2%), CT (42.8%) and TC (42.9%), were inferred. Bone mineral densities (BMDs) at lumbar spine and (contralateral) hip were measured. In non-osteoporotic postmenopausal women without hip fracture, the association of -1227C>T and CT haplotype with lumbar spine BMD was shown (p=0.014 and 0.014). No other significant association of the studied genotypes and haplotypes in the PTN gene promoter with BMDs was found. Comparing age-matched postmenopausal women with and without hip fractures, no differences in frequency distributions of the studied genotypes and haplotypes was shown. For the first time we have shown that, in postmenopausal women, the PTN gene promoter polymorphism -1227C>T and CT haplotype could contribute to the genetic background of osteoporosis, but these findings need further functional and clinical confirmation.

The genetics of bone loss: challenges and prospects

CONTEXT

A strong genetic influence on bone mineral density has been long established, and modern genotyping technologies have generated a flurry of new discoveries about the genetic determinants of bone mineral density (BMD) measured at a single time point. However, much less is known about the genetics of age-related bone loss. Identifying bone loss-related genes may provide new routes for therapeutic intervention and osteoporosis prevention.

EVIDENCE ACQUISITION

A review of published peer-reviewed literature on the genetics of bone loss was performed. Relevant studies were summarized, most of which were drawn from the period 1990-2010.

EVIDENCE SYNTHESIS

Although bone loss is a challenging phenotype, available evidence supports a substantial genetic contribution. Some of the genes identified from recent genome-wide association studies of cross-sectional BMD are attractive candidate genes for bone loss, most notably genes in the nuclear factor κB and estrogen endocrine pathways. New insights into the biology of skeletal development and regulation of bone turnover have inspired new hypotheses about genetic regulation of bone loss and may provide new directions for identifying genes associated with bone loss.

CONCLUSIONS

Although recent genome-wide association and candidate gene studies have begun to identify genes that influence BMD, efforts to identify susceptibility genes specific for bone loss have proceeded more slowly. Nevertheless, clues are beginning to emerge on where to look, and as population studies accumulate, there is hope that important bone loss susceptibility genes will soon be identified.

Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits.

Genetics and calcium nephrolithiasis

Calcium nephrolithiasis is one of the most prevalent uronephrologic disorders in the western countries. Studies in families and twins evidenced a genetic predisposition to calcium nephrolithiasis. Family-based or case-control studies of single-candidate genes evidenced the possible involvement of calcium-sensing receptor (CASR), vitamin D receptor (VDR), and osteopontin (OPN) gene polymorphisms in stone formation. The only high-throughput genome-wide association study identified claudin 14 (CLDN14) gene as a possible major gene of nephrolithiasis. Specific phenotypes were related with these genes: CASR gene in normocitraturic patients, VDR gene in hypocitraturic patients with severe clinical course, and CLDN14 gene in hypercalciuric patients. The pathogenetic weight of these genes remains unclear, but an alteration of their expression may occur in stone formers. Technological skills, accurate clinical examination, and a detailed phenotype description are the basis to get new insight about the genetic basis of nephrolithiasis.