A genome-wide association study meta-analysis of clinical fracture in 10,012 African American women

Looking back on 30+ years of the Johnston County Osteoarthritis Project while looking forward with the Johnston County Health Study: A narrative review

Over the last 30 years, knowledge of the epidemiology of osteoarthritis (OA) has dramatically advanced, and Osteoarthritis and Cartilage has been on the forefront of disseminating research findings from large OA cohort studies, including the Johnston County OA Project (JoCoOA). The JoCoOA is a population-based, prospective longitudinal cohort that began roughly 30 years ago with a key focus on understanding prevalence, incidence, and progression of OA, as well as its risk factors, in a predominantly rural population of Black and White adults 45+ years old in a county in the southeastern United States. Selected OA results that will be discussed in this review include racial differences, lifetime risk, biomarkers, mortality, and OA risk factors. The new Johnston County Health Study will also be introduced. This new cohort study of OA and comorbid conditions builds upon current OA knowledge and JoCoOA infrastructure and is designed to reflect changes in demographics and urbanization in the county and the region.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Deciphering the chromatin spatial organization landscapes during BMMSC differentiation

The differentiation imbalance in bone marrow mesenchymal stem cells (BMMSCs) is critical for the development of bone density diseases as the population ages. BMMSCs are precursor cells for osteoblasts and adipocytes; however, the chromatin organization landscapes during BMMSC differentiation remain elusive. In this study, we systematically delineate the four-dimensional (4D) genome and dynamic epigenetic atlas of BMMSCs by RNA sequencing (RNA-seq), assay for transposase-accessible chromatin sequencing (ATAC-seq), and high-throughput chromosome conformation capture (Hi-C). The structure analyses reveal 17.5% common and 28.5%-30% specific loops among BMMSCs, osteoblasts, and adipocytes. The subsequent correlation of genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) data with multi-omics analysis reveal 274 genes and 3634 single nucleotide polymorphisms (SNPs) associated with bone degeneration and osteoporosis (OP). We hypothesize that SNP mutations affect transcription factor (TF) binding sites, thereby affecting changes in gene expression. Furthermore, 26 motifs, 260 TFs, and 291 SNPs are identified to affect the eQTL. Among these genes, DAAM2, TIMP2, and TMEM241 were found to be essential for diseases such as bone degeneration and OP and may serve as potential drug targets.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Associations of osteoprotegerin (OPG) TNFRSF11B gene polymorphisms with risk of fractures in older adult populations: meta-analysis of genetic and genome-wide association studies

The meta-analysis of osteoprotegerin (OPG) (TNFRSF11B) polymorphisms from genetic association studies and genome-wide association studies was performed in order to test the hypothesis of association between OPG polymorphisms and fracture. The findings showed a significant 13% to 37% protective effect of OPG on fractures in postmenopausal women (PSM) (rs2073618), overall, ≥ 60y and Western subjects (rs3134069 and rs3134070).

PURPOSE

Fractures in older people usually result from compromised bone integrity. The multifactorial aetiology of fractures includes both genetic and environmental factors. Inconsistency of reported associations of osteoprotegerin (OPG) (TNFRSF11B) polymorphisms with fracture in the older adult population warranted a meta-analysis to determine more precise estimates.

METHODS

We searched for all available literature on OPG (TNFRSF11B) and fracture. Four polymorphisms were examined, one exonic (rs2073618) and three intronic (rs3134069, rs3134070 and rs3102735). The first two intron polymorphisms were combined (OPGI: osteoprotegerin intron) on account of complete linkage disequilibrium. Risks were estimated with odds ratios (ORs) and 95% confidence intervals (CIs) using the allele-genotype model that included variant (var), wild-type (wt) and heterozygote (het). Multiple comparisons were Bonferroni-corrected. We used meta-regression to examine sources of heterogeneity. Zero heterogeneity (homogeneity: I² = 0%) and high significance (P^a < 0.00001) were the criteria for strength of evidence. Significant outcomes were subjected to sensitivity analysis and publication bias assessment.

RESULTS

From 13 articles (11 genetic association and two genome-wide), this meta-analysis generated five significant pooled ORs, all indicating reduced risks (ORs 0.44-0.87). Of the five, four highly significant comparisons (P^a ≤ 0.00001-0.002) survived the Bonferroni correction, one in rs2073618 het model of the postmenopausal women (OR 0.87, 95% CI 0.81-0.92, I² = 0%) and the other three in OPGI wt model of the overall analysis, ≥ 60 y and Western subjects (ORs 0.63-0.71, 95% CI 0.47-0.86, I² = 97-99%). These findings were consistent, had high significance and high statistical power and were robust and without evidence of publication bias. Four covariates (year of publication, study quality, fracture type/site and sample size) were the sources of heterogeneity in the OPGI overall outcomes (P^a = 0.0001-0.03).

CONCLUSION

Evidence showed that the OPG (TNFRSF11B) polymorphisms reduced the risk for fracture in older adults, particularly protective among postmenopausal women, ≥ 60 y and Western subjects.

A multiethnic whole genome sequencing study to identify novel loci for bone mineral density

At present, there have only been a few DNA sequencing-based studies to explore the genetic determinants of bone mineral density (BMD). We carried out the largest whole genome sequencing analysis to date for femoral neck and spine BMD (n = 4981), with one of the highest average sequencing depths implemented thus far at 22×, in a multiethnic sample (58% Caucasian and 42% African American) from the Louisiana Osteoporosis Study (LOS). The LOS samples were combined with summary statistics from the GEFOS consortium and several independent samples of various ethnicities to perform GWAS meta-analysis (n = 44 506). We identified 31 and 30 genomic risk loci for femoral neck and spine BMD, respectively. The findings substantiate many previously reported susceptibility loci (e.g. WNT16 and ESR1) and reveal several others that are either novel or have not been widely replicated in GWAS for BMD, including two for femoral neck (IGF2 and ZNF423) and one for spine (SIPA1). Although we were not able to uncover ethnicity specific differences in the genetic determinants of BMD, we did identify several loci which demonstrated sex-specific associations, including two for women (PDE4D and PIGN) and three for men (TRAF3IP2, NFIB and LYSMD4). Gene-based rare variant association testing detected MAML2, a regulator of the Notch signaling pathway, which has not previously been suggested, for association with spine BMD. The findings provide novel insights into the pathophysiological mechanisms of osteoporosis.

The association of OPG polymorphisms with risk of osteoporotic fractures: A systematic review and meta-analysis

BACKGROUND

Subjects with low bone mineral density and osteoporosis are more likely to suffer osteoporotic fractures during their lifetime. Polymorphisms in osteoprotegerin (OPG) gene are found to be associated with low bone mineral density and osteoporosis risk but their association with fracture risk is inconclusive. Here, we performed a meta-analysis to investigate the relationship between OPG polymorphisms with susceptibility to osteoporotic fractures.

METHODS

Eligible studies investigating the association between common OPG polymorphisms (A164G, T245G, T950C, and G1181C) and risk of osteoporotic fracture were retrieved from PubMed, EMBASE, Web of Science, and the Cochrane Library. Odds ratio (OR) and the 95% confidence interval (CI) were calculated in the allelic, dominant, recessive, and homozygous model. Subgroup analyses of vertebral fractures, Caucasians, and postmenopausal women were also performed.

RESULTS

A total of 14 studies comprising 5459 fracture cases and 9860 non-fracture controls were included. A163G was associated with fracture risk in dominant (OR = 1.29, 95%CI 1.11-1.50), recessive (OR = 1.64, 95%CI 1.10-2.44), and homozygous model (OR = 1.73, 95%CI 1.16-2.59). T245G was significantly correlated with susceptibility to fractures in all genetic models. Subjects with CC genotype of T950C had a reduced risk of fracture compared to those with CT or TT genotypes (OR = 0.81, 95%CI 0.70-0.94, P = .004). Subgroup analysis showed that A163G and T245G but not T950C and G1181C were associated with vertebral fracture risk.

CONCLUSION

OPG A163G and T245G polymorphisms were risk factors of osteoporotic fractures while T950C had a protective role. These polymorphisms can be used as predictive markers of fractures.

Genetic ancestry and skeletal toxicities among childhood acute lymphoblastic leukemia patients in the DFCI 05-001 cohort

Hispanic children have a higher incidence of acute lymphoblastic leukemia (ALL) and inferior treatment outcomes relative to non-Hispanic White children. We previously reported that Hispanic children with ALL had lower risk of fracture and osteonecrosis. To unravel the genetic root of such ethnic differences, we genotyped 449 patients from the DFCI 05-001 cohort and analyzed their ancestry. Patients with discordant clinical and genetic ancestral groups were reclassified, and those with unknown ancestry were reassigned on the basis of genetic estimates. Both clinical and genetic ancestries were analyzed in relation to risk of bone toxicities and survival outcomes. Consistent with clinically reported race/ethnicity, genetically defined Hispanic and Black patients had significantly lower risk of fracture (Hispanic: subdistribution hazard ratio [SHR], 0.42; 95% confidence interval [CI], 0.22-0.81; P = .01; Black: SHR, 0.28; 95% CI, 0.10-0.75; P = .01), and osteonecrosis (Hispanic: SHR, 0.12; 95% CI, 0.02-0.93; P = .04; Black: SHR, 0.24; 95% CI, 0.08-0.78; P = .02). The lower risk was driven by African but not Native American or Asian ancestry. In addition, patients with a higher percentage of Native American ancestry had significantly poorer overall survival and event-free survival. Our study revealed that the lower risk of bone toxicities among Black and Hispanic children treated for ALL was attributed, in part, to the percentage of African ancestry in their genetic admixture. The findings provide suggestive evidence for the protective effects of genetic factors associated with African decent against bone damage caused by ALL treatment and clues for future studies to identify underlying biological mechanisms.

Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

Osteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.

A Meta-Analysis of the Transferability of Bone Mineral Density Genetic Loci Associations From European to African Ancestry Populations

Genetic studies of bone mineral density (BMD) largely have been conducted in European populations. We therefore conducted a meta-analysis of six independent African ancestry cohorts to determine whether previously reported BMD loci identified in European populations were transferable to African ancestry populations. We included nearly 5000 individuals with both genetic data and assessments of BMD. Genotype imputation was conducted using the 1000G reference panel. We assessed single-nucleotide polymorphism (SNP) associations with femoral neck and lumbar spine BMD in each cohort separately, then combined results in fixed effects (or random effects if study heterogeneity was high, I2 index >60) inverse variance weighted meta-analyses. In secondary analyses, we conducted locus-based analyses of rare variants using SKAT-O. Mean age ranged from 12 to 68 years. One cohort included only men and another cohort included only women; the proportion of women in the other four cohorts ranged from 52% to 63%. Of 56 BMD loci tested, one locus, 6q25 (C6orf97, p = 8.87 × 10-4 ), was associated with lumbar spine BMD and two loci, 7q21 (SLC25A13, p = 2.84 × 10-4 ) and 7q31 (WNT16, p = 2.96 × 10-5 ), were associated with femoral neck BMD. Effects were in the same direction as previously reported in European ancestry studies and met a Bonferroni-adjusted p value threshold, the criteria for transferability to African ancestry populations. We also found associations that met locus-specific Bonferroni-adjusted p value thresholds in 11q13 (LRP5, p < 2.23 × 10-4 ), 11q14 (DCDC5, p < 5.35 × 10-5 ), and 17p13 (SMG6, p < 6.78 × 10-5 ) that were not tagged by European ancestry index SNPs. Rare single-nucleotide variants in AKAP11 (p = 2.32 × 10-2 ), MBL2 (p = 4.09 × 10-2 ), MEPE (p = 3.15 × 10-2 ), SLC25A13 (p = 3.03 × 10-2 ), STARD3NL (p = 3.35 × 10-2 ), and TNFRSF11A (p = 3.18 × 10-3 ) were also associated with BMD. The majority of known BMD loci were not transferable. Larger genetic studies of BMD in African ancestry populations will be needed to overcome limitations in statistical power and to identify both other loci that are transferable across populations and novel population-specific variants. © 2020 American Society for Bone and Mineral Research (ASBMR).

Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations

Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.

Analyzing Genome-Wide Association Study Dataset Highlights Immune Pathways in Lip Bone Mineral Density

Osteoporosis is a common complex human disease. Until now, large-scale genome-wide association studies (GWAS) using single genetic variant have reported some novel osteoporosis susceptibility variants. However, these risk variants only explain a small proportion of osteoporosis genetic risk, and most genetic risk is largely unknown. Interestingly, the pathway analysis method has been used in investigation of osteoporosis mechanisms and reported some novel pathways. Until now, it remains unclear whether there are other risk pathways involved in BMD. Here, we selected a lip BMD GWAS with 301,019 SNPs in 5,858 Europeans, and conducted a gene-based analysis (SET SCREEN TEST) and a pathway-based analysis (WebGestalt). On the gene level, BMD susceptibility genes reported by previous GWAS were identified to be the top 10 significant signals. On the pathway level, we identified 27 significant KEGG pathways. Three immune pathways including T cell receptor signaling pathway (hsa04660), complement and coagulation cascades (hsa04610), and intestinal immune network for IgA production (hsa04672) are ranked the top three significant signals. Evidence from the PubMed and Google Scholar databases further supports our findings. In summary, our findings provide complementary information to these nine risk pathways.

Shambhala: a platform-agnostic data harmonizer for gene expression data

Background

Harmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple human gene expression datasets obtained using different experimental methods and platforms of microarray hybridization and RNA sequencing.

Results

Unlike previously published methods enabling good quality data harmonization for only two datasets, Shambhala allows conversion of multiple datasets into the universal form suitable for further comparisons. Shambhala harmonization is based on the calibration of gene expression profiles using the auxiliary standardization dataset. Each profile is transformed to make it similar to the output of microarray hybridization platform Affymetrix Human Gene. This platform was chosen because it has the biggest number of human gene expression profiles deposited in public databases. We evaluated Shambhala ability to retain biologically important features after harmonization. The same four biological samples taken in multiple replicates were profiled independently using three and four different experimental platforms, respectively, then Shambhala-harmonized and investigated by hierarchical clustering.

Conclusion

Our results showed that unlike other frequently used methods: quantile normalization and DESeq/DESeq2 normalization, Shambhala harmonization was the only method supporting sample-specific and platform-independent biologically meaningful clustering for the data obtained from multiple experimental platforms.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2641-8) contains supplementary material, which is available to authorized users.

Recent Advances in the Genetics of Fractures in Osteoporosis

Genetic susceptibility, together with old age, female sex, and low bone mineral density (BMD) are amongst the strongest determinants of fracture risk. Tmost recent large-scale genome-wide association study (GWAS) meta-analysis has yielded fifteen loci. This review focuses on the advances in the research of genetic determinants of fracture risk. We first discuss the genetic architecture of fracture risk, touching upon different methods and overall findings. We then discuss in a second paragraph the most recent advances in the field and focus on the genetics of fracture risk and also of other endophenotypes closely related to fracture risk such as bone mineral density (BMD). Application of state-of-the-art methodology such as Mendelian randzation in fracture GWAS are reviewed. The final part of this review touches upon potential future directions in genetic research of osteoporotic fractures.

Genetic effects on bone health

PURPOSE OF REVIEW

In recent years, the lower costs of arrays and sequencing technologies, and the better availability of data from genome-wide association studies (GWASs) have led to more reports on genetic factors that are associated with bone health. However, there remains the need for a summary of the newly identified genetic targets that are associated with bone metabolism, and the status of their functional characterization.

RECENT FINDINGS

GWASs revealed dozens of novel genetic loci that are associated with bone mineral density (BMD). Some of these targets have been functionally characterized, although the vast majority have not. Glypican 6, a membrane surface proteoglycan involved in cellular growth control and differentiation, was identified as a novel determinant of BMD and represents a possible drug target for treatment of osteoporosis. Pathway analysis also showed that cell-growth pathways and the SMAD proteins associated with low BMD.

SUMMARY

Hits that were significantly associated with BMD in different studies represent likely candidates (e.g. SOST, WNT16, ESR1 and RANKL) for functional characterization and development of osteoporosis treatments. Indeed, currently available treatment for osteoporosis (antibody against RANKL) appeared a significant target in four recent GWAS studies indicating their applicability and importance for future treatment development.