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Genetic risk factors for osteoporosis, a prevalent disease associated with aging, have been examined in many genome‐wide association studies (GWASs). A major challenge is to prioritize transcription‐regulatory GWAS‐derived variants that are likely to be functional. Given the critical role of epigenetics in gene regulation, we have used an unusual epigenetics‐based and transcription‐based approach to identify some of the credible regulatory single‐nucleotide polymorphisms (SNPs) relevant to osteoporosis from 38 reported bone mineral density (BMD) GWASs. Using Roadmap databases, we prioritized SNPs based upon their overlap with strong enhancer or promoter chromatin preferentially in osteoblasts relative to 12 heterologous cell culture types. We also required that these SNPs overlap open chromatin (Deoxyribonuclease I [DNaseI]‐hypersensitive sites) and DNA sequences predicted to bind to osteoblast‐relevant transcription factors in an allele‐specific manner. From >50,000 GWAS‐derived SNPs, we identified 14 novel and credible regulatory SNPs (Tier‐1 SNPs) for osteoporosis risk. Their associated genes, BICC1, LGR4, DAAM2, NPR3, or HMGA2, are involved in osteoblastogenesis or bone homeostasis and regulate cell signaling or enhancer function. Four of these genes are preferentially expressed in osteoblasts. BICC1, LGR4, and DAAM2 play important roles in canonical Wnt signaling, a pathway critical for bone formation and repair. The transcription factors predicted to bind to the Tier‐1 SNP‐containing DNA sequences also have bone‐related functions. We present evidence that some of the Tier‐1 SNPs exert their effects on BMD risk indirectly through little‐studied long noncoding RNA (lncRNA) genes, which may, in turn, control the nearby bone‐related protein‐encoding gene. Our study illustrates a method to identify novel BMD‐related causal regulatory SNPs for future study and to prioritize candidate regulatory GWAS‐derived SNPs, in general. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets.

As of August 27, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,863,363 and included 180,595 deaths, thus causing a serious public health crisis. Curbing the spread of Covid-19 is still urgently needed. Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1000 people, workplaces, tests done/1000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1000 people, mobility trends for places of residence (residential), retail and test capacity were the most significant risk factor for the new cases of COVID-19 in 23, 7, 6, 5, 4, 2, 1 and 1 states, respectively, and that active cases/1000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1000 people, transit stations, mobility trends (transit), tests done/1000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the most significant risk factor for the deaths of COVID-19 in 17, 10, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1 states, respectively. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

SUMMARY: By adopting the extension approaches of Mendelian randomization, we successfully detected and prioritized the potential causal risk factors for BMD traits, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases. INTRODUCTION: Osteoporosis (OP) is a common metabolic skeletal disease characterized by reduced bone mineral density (BMD). The identified SNPs for BMD can only explain approximately 10% of the variability, and very few causal factors have been identified so far. METHODS: The Mendelian randomization (MR) approach enables us to assess the potential causal effect of a risk factor on the outcome by using genetic IVs. By using extension methods of MR—multivariable MR (mvMR) and MR based on Bayesian model averaging (MR-BMA)—we intend to estimate the causal relationship between fifteen metabolic risk factors for BMD and try to prioritize the most potential causal risk factors for BMD. RESULTS: Our analysis identified three risk factors T2D, FG, and HCadjBMI for FN BMD; four risk factors FI, T2D, HCadjBMI, and WCadjBMI for FA BMD; and three risk factors FI, T2D, and HDL cholesterol for LS BMD, and all risk factors were causally associated with heel BMD except for triglycerides and WCadjBMI. Consistent with the mvMR results, MR-BMA confirmed those risk factors as top risk factors for each BMD trait individually. CONCLUSIONS: By combining MR approaches, we identified the potential causal risk factors for FN, FA, LS, and heel BMD individually and we also prioritized and ranked the potential causal risk factors for BMD, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases.

Objectives Clinical and epidemiological findings indicate an association between coronary artery disease (CAD) and low birth weight (BW). However, the mechanisms underlying this relationship are largely unknown. Here, we aimed to identify novel single-nucleotide polymorphisms (SNPs) associated with CAD, BW, and their shared pleiotropic loci, and to detect the potential causal relationship between CAD and BW. Methods We first applied a genetic pleiotropic conditional false discovery rate (cFDR) method to two independent genome-wide association studies (GWAS) summary statistics of CAD and BW to estimate the pleiotropic enrichment between them. Then, bi-directional Mendelian randomization (MR) analyses were performed to clarify the causal association between these two traits. Results By incorporating related traits into a conditional analysis framework, we observed the significant pleiotropic enrichment between CAD and BW. By applying the cFDR level of 0.05, 109 variants were detected for CAD, 203 for BW, and 26 pleiotropic variants for both traits. We identified 11 CAD- and/or BW-associated SNPs that showed more than three of the metabolic quantitative trait loci (metaQTL), protein QTL (pQTL), methylation QTL (meQTL), or expression QTL (eQTL) effects. The pleiotropic SNP rs10774625, located at ATXN2, showed metaQTL, pQTL, meQTL, and eQTL effects simultaneously. Using the bi-directional MR approach, we found a negative association from BW to CAD (odds ratio [OR] = 0.68, 95% confidence interval [CI]: 0.59 to 0.80, p = 1.57× 10-6). Conclusion We identified several pleiotropic loci between CAD and BW by leveraging GWAS results of related phenotypes and identified a potential causal relationship from BW to CAD. Our findings provide novel insights into the shared biological mechanisms and overlapping genetic heritability between CAD and BW.

Background: The machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffer from a significant bottleneck of requiring massive labeled data. We hypothesize that semi-supervised deep learning leveraging a small number of labeled data can provide a powerful alternative strategy.Method: We proposed a semi-supervised model based on mean teacher that provide pathological predictions at both patch-level and patient-level. We demonstrated the general utility of the model utilizing 13,111 whole slide images from 8,803 subjects gathered from 13 centers. We compared our proposed method with the prevailing supervised learning and six pathologists.Results: with a small amount of labeled training patches (~3,150 labeled, ~40,950 unlabeled or ~6,300 labeled,~37,800 unlabeled), the semi-supervised model performed significantly better than the supervised model (AUC: 0.90 ± 0.06 vs. 0.84 ± 0.07, P value = 0.02 or AUC: 0.98 ± 0.01 vs 0.92 ± 0.04, P value = 0.0004). Moreover, we found no significant difference between the supervised model using massive ~44,100 labeled patches and the semi-supervised model (~6,300 labeled, ~37,800 unlabeled) at patch-level diagnoses (AUC: 0.98 ± 0.01 vs 0.987 ± 0.01, P value = 0.134) and patient-level diagnoses (average AUC: 97.40% vs. 97.96%, P value = 0.117) . Our model was close to human pathologists (average AUC: 97.17% vs. 96.91%).Conclusions: We reported that semi-supervised learning can achieve excellent performance through a multi-center study. We thus suggested that semi-supervised learning has great potentials to build artificial intelligence (AI) platforms, which will dramatically reduce the cost of labeled data and greatly facilitate the development and application of AI in medical sciences.

Background Sarcopenia, a progressive loss of muscle mass and function with advancing age, is a prevalent condition among older adults. As most older people are too frail to do intensive exercise and vibration therapy has low risk and ease of participation, it may be more readily accepted by elderly individuals. However, it remains unclear whether vibration therapy would be effective among older adults with sarcopenia. This systematic review and meta-analysis examined the effect of vibration therapy including local vibration therapy and whole-body vibration therapy, for enhancing muscle mass, muscle strength and physical function in older people with sarcopenia. Methods A systematic literature search was conducted in March 2019 in the following 5 electronic databases: PubMed, CINAHL, Embase, PEDro, and the Cochrane Central Register of Controlled Trials, with no restriction of language or the year of publication. Randomized controlled trials and quasi-experimental studies examining effects of vibration therapy on muscle mass, muscle strength or physical function in older adults with sarcopenia were included in this systematic review. Two reviewers independently assessed the methodological quality of the selected studies. Results Of the 1972 identified studies, seven publications from six studies involving 223 participants were included in this systematic review. Five of them conducted whole-body vibration therapy, while two conducted local vibration therapy. A meta-analysis of randomized controlled studies indicated that muscle strength significantly increased after whole-body vibration therapy (SMD 0.69, 95% CI 0.28 to 1.11, I2 = 0%, P = 0.001) and local vibration therapy (SMD 3.78, 95% CI 2.29 to 5.28, P 2 = 0%, P 2 = 64%, P = 0.02, respectively). Conclusion Vibration therapy could be a prospective strategy for improving muscle strength and physical performance in older adults with sarcopenia. However, due to the limited number of the included studies, caution is needed when interpreting these results. More well-designed, large sample size studies should be conducted to further explore and validate the benefits of vibration therapy for this population.

Context Though genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with osteoporosis related traits, such as bone mineral density (BMD) and fracture, it remains a challenge to interpret their biological functions and underlying biological mechanisms. Objective Integrate diverse expression quantitative trait loci and splicing quantitative trait loci data with several powerful GWAS datasets to identify novel candidate genes associated with osteoporosis. Design, Setting, and Participants Here, we conducted a transcriptome-wide association study (TWAS) for total body BMD (TB-BMD) (n = 66 628 for discovery and 7697 for validation) and fracture (53 184 fracture cases and 373 611 controls for discovery and 37 857 cases and 227 116 controls for validation), respectively. We also conducted multi-SNP-based summarized mendelian randomization analysis to further validate our findings. Results In total, we detected 88 genes significantly associated with TB-BMD or fracture through expression or ribonucleic acid splicing. Summarized mendelian randomization analysis revealed that 78 of the significant genes may have potential causal effects on TB-BMD or fracture in at least 1 specific tissue. Among them, 64 genes have been reported in previous GWASs or TWASs for osteoporosis, such as ING3, CPED1, and WNT16, as well as 14 novel genes, such as DBF4B, GRN, TMUB2, and UNC93B1. Conclusions Overall, our findings provide novel insights into the pathogenesis mechanisms of osteoporosis and highlight the power of a TWAS to identify and prioritize potential causal genes.

IntroductionSeveral Mendelian randomization studies have been conducted, which identified multiple risk factors for Alzheimer’s disease (AD). However, they typically focus on a few pre-selected risk factors.MethodsTwo-sample Mendelian randomization (MR) study was used to systematically examine the potential causal associations of 1,054 risk factors/medical conditions and 28 drugs with the risk of late- onset AD. To correct for multiple comparisons, the false discovery rate was set at <0.05.ResultsThere were strong evidence of a causal association between glioma risk, reduced trunk fat-free mass, lower education levels, lower intelligence and a higher risk of AD. For 28 investigated treatments (such as antihypertensive drugs), we found limited evidence for their associations.ConclusionMR found robust evidence of causal associations between glioma, trunk fat-free and AD. Our study also confirms that higher educational attainment and higher intelligence are associated with a reduced risk of AD.

Background: Genome-wide association studies (GWASs) routinely identify loci associated with risk factors for osteoporosis. However, GWASs with relatively small sample sizes still lack sufficient power to ascertain the majority of genetic variants with small to modest effect size, which may together truly influence the phenotype. The loci identified only account for a small percentage of the heritability of osteoporosis. This study aims to identify novel genetic loci associated with DXA-derived femoral neck (FNK) bone mineral density (BMD) and quantitative ultrasound of the heel calcaneus estimated BMD (eBMD), and to detect shared/causal variants for the two traits, to assess whether the SNPs or putative causal SNPs associated with eBMD were also associated with FNK-BMD. Methods: Novel loci associated with eBMD and FNK-BMD were identified by the genetic pleiotropic conditional false discovery rate (cFDR) method. Shared putative causal variants between eBMD and FNK-BMD and putative causal SNPs for each trait were identified by the colocalization method. Mendelian randomization analysis addresses the causal relationship between eBMD/FNK-BMD and fracture. Results: We identified 9,500 (cFDR

Genetic risk factors for osteoporosis, a prevalent disease associated with aging, have been examined in many genome-wide association studies (GWAS). A major challenge is to prioritize transcription-regulatory GWAS-derived variants that are likely to be functional. Given the critical role of epigenetics in gene regulation, we have used an unusual epigenetics- and transcription-based approach to identify credible regulatory SNPs relevant to osteoporosis from 38 reported BMD GWAS. Using Roadmap databases, we prioritized SNPs based upon their overlap with strong enhancer or promoter chromatin preferentially in osteoblasts relative to 11 heterologous cell culture types. The selected SNPs also had to overlap open chromatin (DNaseI-hypersensitive sites) and DNA sequences predicted to bind to osteoblast-relevant transcription factors in an allele-specific manner. From >50,000 GWAS-derived SNPs, we identified 16 novel and credible regulatory SNPs (Tier-1 SNPs) for osteoporosis risk. Their associated genes, BICC1, LGR4, DAAM2, NPR3, or HMGA2, are involved in osteoblastogenesis or bone homeostasis and regulate cell signaling or enhancer function. Four of them are preferentially expressed in osteoblasts. BICC1, LGR4, and DAAM2 play important roles in canonical Wnt signaling, a pathway critical to bone formation and repair. The transcription factors that are predicted to bind to the Tier-1 SNP-containing DNA sequences also have bone-related functions. For the seven Tier-1 SNPs near the 5’ end of BICC1, examination of eQTL overlap and the distribution of BMD-increasing alleles suggests that at least one SNP in each of two clusters contributes to inherited osteoporosis risk. Our study not only illustrates a method that can be used to identify novel BMD-related causal regulatory SNPs for future study, but also reveals evidence that some of the Tier-1 SNPs exert their effects on BMD risk indirectly through little-studied noncoding RNA genes, which in turn may control the nearby bone-related protein-encoding gene.

Purpose Menopause is a crucial physiological transition during a woman’s life, and it occurs with growing risks of health issues like osteoporosis. To identify postmenopausal osteoporosis-related genes, we performed transcriptome-wide expression analyses for human peripheral blood monocytes (PBMs) using Affymetrix 1.0 ST arrays in 40 Caucasian postmenopausal women with discordant bone mineral density (BMD) levels. Methods We performed multiscale embedded gene coexpression network analysis (MEGENA) to study functionally orchestrating clusters of differentially expressed genes in the form of functional networks. Gene sets net correlations analysis (GSNCA) was applied to assess how the coexpression structure of a predefined gene set differs in high and low BMD groups. Bayesian network (BN) analysis was used to identify important regulation patterns between potential risk genes for osteoporosis. A small interfering ribonucleic acid (siRNA)-based gene silencing in vitro experiment was performed to validate the findings from BN analysis. Result MEGENA showed that the “T cell receptor signaling pathway” and the “osteoclast differentiation pathway” were significantly enriched in the identified compact network, which is significantly correlated with BMD variation. GSNCA revealed that the coexpression structure of the “Signaling by TGF-beta receptor complex pathway” is significantly different between the 2 BMD discordant groups; the hub genes in the postmenopausal low and high BMD group are FURIN and SMAD3 respectively. With siRNA in vitro experiments, we confirmed the regulation relationship of TGFBR2–SMAD7 and TGFBR1–SMURF2. Main Conclusion The present study suggests that biological signals involved in monocyte recruitment, monocyte/macrophage lineage development, osteoclast formation, and osteoclast differentiation might function together in PBMs that contribute to the pathogenesis of postmenopausal osteoporosis.

Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.

Coronary artery disease (CAD) is the leading cause of death worldwide, constituting more than one-fourth of global mortalities every year. Accurate semantic segmentation of each artery using invasive coronary angiography (ICA) is important for stenosis assessment and CAD diagnosis. However, due to the morphological similarity among different types of arteries, it is challenging for deep-learning-based models to generate semantic segmentation with an end-to-end approach. In this paper, we propose a multi-step semantic segmentation algorithm based on the analysis of arterial segments extracted from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlying arterial topology, position and pixel features, we construct a powerful coronary artery segment classifier based on support vector machine. Each arterial segment is classified into left coronary artery (LCA), left anterior descending (LAD) and other types of arterial segments. We tested the proposed method on a dataset with 225 ICAs and achieved artery classification accuracy of 70.33%. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs.

Osteoblasts are multifunctional bone cells, which play essential roles in bone formation, angiogenesis regulation, as well as maintenance of hematopoiesis. Although bothin vivoandin vitrostudies on mice have identified several potential osteoblast subtypes based on their different transition stages or biological responses to external stimuli, the categorization of primary osteoblast subtypesin vivoin humans has not yet been achieved. Here, we used single-cell RNA sequencing (scRNA-seq) to perform a systematic cellular taxonomy dissection of freshly isolated human osteoblasts. Based on the gene expression patterns and cell lineage reconstruction, we identified three distinct cell clusters including preosteoblasts, mature osteoblasts, and an undetermined rare osteoblast subpopulation. This novel subtype was mainly characterized by the nuclear receptor subfamily 4 group A member 1 and 2 (NR4A1 and NR4A2), and its existence was confirmed by immunofluorescence staining. Trajectory inference analysis suggested that the undetermined cluster, together with the preosteoblasts, are involved in the regulation of osteoblastogenesis and also give rise to mature osteoblasts. Investigation of the biological processes and signaling pathways enriched in each subpopulation revealed that in addition to bone formation, preosteoblasts and undetermined osteoblasts may also regulate both angiogenesis and hemopoiesis. Finally, we demonstrated that there are systematic differences between the transcriptional profiles of human osteoblastsin vivoand mouse osteoblasts bothin vivoandin vitro, highlighting the necessity for studying bone physiological processes in humans rather than solely relying on mouse models. Our findings provide novel insights into the cellular heterogeneity and potential biological functions of human primary osteoblasts at the single-cell level, which is an important and necessary step to further dissect the biological roles of osteoblasts in bone metabolism under various (patho-) physiological conditions.

Background Alzheimer's disease (AD) is a progressive brain disorder characterized by cognitive skills deterioration that affects many elderly individuals. The identified genetic loci for AD failed to explain the large variability in AD and very few causal factors have been identified so far. Results mvMR showed that increasing years of schooling (OR=0.674, 95%CI: 0.571-0.796, P=3.337E-06) and genetically elevated HDL cholesterol (OR ranging from 0.697 to 0.830, P=6.940E-10) were inversely associated with AD risk, genetically predicted total cholesterol (OR=1.300, 1.196 to 1.412; P=6.223E-10) and LDL cholesterol (OR=1.193, 1.097 to 1.296, P=3.564E-05) were associated with increasing AD risk. Genetically predicted FG was suggestively associated with increased AD risk. Furthermore, MR-BMA analysis also confirmed FG and years of schooling as two of the top five causal risk factors for AD. Conclusions Our findings might provide us novel insights for treatment and intervention into the causal risk factors for AD or AD-related complex diseases. Methods By using extension methods of Mendelian randomization (MR)--multivariable MR (mvMR) and MR based on Bayesian model averaging (MR-BMA), we intend to estimate the potential causal relationship between nine risk factors and AD outcome and try to prioritize the most causal risk factors for AD.

Bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stromal cells, which have a critical role in the maintenance of skeletal tissues such as bone, cartilage, and the fat found in bone marrow. In addition to providing microenvironmental support for hematopoietic processes, BM-MSCs can differentiate into various mesodermal lineages including osteoblast/osteocyte, chondrocyte, and adipocyte cells that are crucial for bone metabolism. While BM-MSCs have high cell-to-cell heterogeneity in gene expression, the cell subtypes that contribute to this heterogeneity in vivo in humans have not been characterized. To investigate the transcriptional diversity of BM-MSCs, we applied single-cell RNA sequencing (scRNA-seq) on freshly isolated CD271+ BM-derived mononuclear cells (BM-MNCs) from two human subjects. We successfully identified LEPRhiCD45low BM-MSCs within the CD271+ BM-MNC population, and further codified the BM-MSCs into distinct subpopulations corresponding to the osteogenic, chondrogenic, and adipogenic differentiation trajectories, as well as terminal-stage quiescent cells. Biological functional annotations of transcriptomes suggest that osteoblast precursors may induce angiogenesis coupled with osteogenesis, and chondrocyte precursors may have the potential to differentiate into myocytes. We discovered transcripts for several cluster of differentiation (CD) markers that were highly expressed (e.g., CD167b, CD91, CD130 and CD118) or absent (e.g., CD74, CD217, CD148 and CD68) in BM-MSCs and could be novel markers for human BM-MSC purification. This study is the first systematic in vivo dissection of human BM-MSCs cell subtypes at the single-cell resolution, revealing insight into the extent of their cellular heterogeneity and bone homeostasis.

Bone mineral density (BMD) and lean body mass (LBM) not only have a considerable heritability each, but also are genetically correlated. However, common genetic determinants shared by both traits are largely unknown. In the present study, we performed a bivariate genome-wide association study (GWAS) meta-analysis of hip BMD and trunk lean mass (TLM) in 11,335 subjects from 6 samples, and performed replication in estimated heel BMD and TLM in 215,234 UK Biobank (UKB) participants. We identified 2 loci that nearly attained the genome-wide significance (GWS, p < 5.0 × 10(−8)) level in the discovery GWAS meta-analysis and that were successfully replicated in the UKB sample: 11p15.2 (lead SNP rs12800228, discovery p = 2.88 × 10(−7), replication p = 1.95 × 10(−4)) and 18q21.32 (rs489693, discovery p = 1.67 × 10(−7), replication p = 1.17 × 10(−3)). The above 2 pleiotropic loci may play a pleiotropic role for hip BMD and TLM development. So our findings provide useful insights that further enhance our understanding of genetic interplay between BMD and LBM.

Although gut microbiota influences osteoporosis risk, the individual species involved, and underlying mechanisms, are unknown. We performed integrative analyses in a Chinese cohort with metagenomics/targeted metabolomics/whole-genome sequencing. Bacteroides vulgatus was found negatively associated with bone mineral density (BMD), this association was validated in US Caucasians. Serum valeric acid was positively associated with BMD, and B.vulgatus causally downregulated it. Ovariectomized mice fed B.vulgatus had decreased bone formation and increased bone resorption, lower BMD and poorer bone micro-structure. Valeric acid suppressed NF-κB p65 protein production (pro-inflammatory), and enhanced IL-10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells, and enhanced maturation of osteoblasts in vitro. B.vulgatus and valeric acid represent promising targets for osteoporosis prevention/treatment.

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally performed for single-trait/disease and failed to assess the pleiotropic role of the identified variants. To identify novel functional loci and the pleiotropic relationship between CAD and T2D, the targeted cFDR analysis on CpG-SNPs was performed by integrating two independent large and multi-centered GWASs with summary statistics of T2D (26,676 cases and 132,532 controls) and CAD (60,801 cases and 123,504 controls). Applying the cFDR significance threshold of 0.05, we observed a pleiotropic enrichment between T2D and CAD by incorporating pleiotropic effects into a conditional analysis framework. We identified 79 novel CpG-SNPs for T2D, 61 novel CpG-SNPs for CAD, and 18 novel pleiotropic loci for both traits. Among these novel CpG-SNPs, 33 of them were annotated as methylation quantitative trait locus (meQTL) in whole blood, and ten of them showed expression QTL (eQTL), meQTL, and metabolic QTL (metaQTL) effects simultaneously. To the best of our knowledge, we performed the first targeted cFDR analysis on CpG-SNPs, and our findings provided novel insights into the shared biological mechanisms and overlapped genetic heritability between T2D and CAD.

Zona pellucida (ZP), which is composed of at most four extracellular glycoproteins (ZP1, ZP2, ZP3, and ZP4) in mammals, shelters the oocytes and is vital in female fertility. Several studies have identified the indispensable roles of ZP1–3 in maintaining normal female fertility. However, the understanding of ZP4 is still very poor because only one study on ZP4-associated infertility performed in rabbits has been reported up to date. Here we investigated the function of mammalian Zp4 by creating a knockout (KO) rat strain (Zp4−/− rat) using CRISPR–Cas9-mediated DNA-editing method. The influence of Zp4 KO on ZP morphology and some pivotal processes of reproduction, including oogenesis, ovulation, fertilization, and pup production, were studied using periodic acid–Schiff’s staining, superovulation, in vitro fertilization, and natural mating. The ZP morphology in Zp4−/− rats was normal, and none of these pivotal processes was affected. This study renewed the knowledge of mammalian Zp4 by suggesting that Zp4 was completely dispensable for female fertility.

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10−6) and 1,044 pleotropic genes (P < 4.34 × 10−6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.

Osteoporosis is mainly characterized by low bone mineral density (BMD) and is an increasingly serious public health concern. DNA methylation is a major epigenetic mechanism that may contribute to the variation in BMD and may mediate the effects of genetic and environmental factors of osteoporosis. In this study, we performed an epigenome-wide DNA methylation analysis in peripheral blood monocytes of 118 Caucasian women with extreme BMD values. Further, we developed and implemented a novel analytical framework that integrates Mendelian randomization with genetic fine mapping and colocalization to evaluate the causal relationships between DNA methylation and BMD phenotype. We identified 2,188 differentially methylated CpGs (DMCs) between the low and high BMD groups and distinguished 30 DMCs that may mediate the genetic effects on BMD. The causal relationship was further confirmed by eliminating the possibility of horizontal pleiotropy, linkage effect and reverse causality. The fine-mapping analysis determined 25 causal variants that are most likely to affect the methylation levels at these mediator DMCs. The majority of the causal methylation quantitative loci and DMCs reside within cell type-specific histone mark peaks, enhancers, promoters, promoter flanking regions and CTCF binding sites, supporting the regulatory potentials of these loci. The established causal pathways from genetic variant to BMD phenotype mediated by DNA methylation provide a gene list to aid in designing future functional studies and lead to a better understanding of the genetic and epigenetic mechanisms underlying the variation of BMD.

Osteoporosis is a highly prevalent chronic aging-related disease that frequently is only detected after fracture. We hypothesized that aminobutyric acids could serve as biomarkers for osteoporosis. We developed a quick, accurate, and sensitive screening method for aminobutyric acid isomers and enantiomers yielding correlations with bone mineral density (BMD) and osteoporotic fracture. In serum, γ-aminobutyric acid (GABA) and (R)-3-aminoisobutyric acid (D-BAIBA) have positive associations with physical activity in young lean women. D-BAIBA positively associated with hip BMD in older individuals without osteoporosis/osteopenia. Lower levels of GABA were observed in 60–80 year old women with osteoporotic fractures. Single nucleotide polymorphisms in seven genes related to these metabolites associated with BMD and osteoporosis. In peripheral blood monocytes, dihydropyrimidine dehydrogenase, an enzyme essential to D-BAIBA generation, exhibited positive association with physical activity and hip BMD. Along with their signaling roles, BAIBA and GABA might serve as biomarkers for diagnosis and treatments of osteoporosis., Wang et al. develop an LC/MS based screening method to separate and quantify aminobutyric acids isoforms. Applying it to osteoporosis clinical studies, their method yields important correlations with bone mineral density and osteoporotic fracture and highlight the role of γ-aminobutyric acid and β-aminoisobutyric acid as biomarkers for osteoporosis.

Background: Mounting evidence suggested associations between gut microbiota (GM), serum metabolites and blood pressure (BP). To date, the impact of GM and serum metabolites on BP variation was not well elucidated, particularly in postmenopausal women. Epidemiologic studies showed a higher prevalence of hypertension in postmenopausal women than in premenopausal women. Therefore, significant gut bacterial species and serum metabolites associated with BP in early postmenopausal women may serve as biomarkers for early diagnosis, prevention, intervention and treatment of hypertension in postmenopausal older women. Methods: In the present study, we carried out analyses on metagenomic, serum untargeted and targeted metabolomics and whole genome sequencing from 402 early postmenopausal Chinese women to search for early omics-biomarkers and gain novel insights into the potential mechanisms of BP regulation in postmenopausal women. Findings: We totally identified four gut bacterial species were significantly associated with both systolic BP and diastolic BP. Systematic integrative multi-omics analysis indicated that increased Bacteroides fragilis could elevate BP via decreased caproic acid, and phenylacetylglutamine mediated the causal relationships of both Bacteroides fragilis and Clostridium sp.CAG.226 on diastolic BP variation in early postmenopausal Chinese women. Interpretation: The application of systematic integrative multi-omics analysis discovered a list of candidate omics-biomarkers for future BP related biological experiments, which would be considered as potential biomarkers for early diagnosis, prevention, intervention and treatment of hypertension in older postmenopausal women, and detected the causal effects among GM, metabolic activity and BP variation. Funding Statement: Hong-Wen Deng was partially supported by grants from the National Institutes of Health [U19AG05537301, R01AR069055, P20GM109036, R01MH104680, R01AG061917, U54MD007595]. Hong-Mei Xiao was partially supported by the National Key R&D Program of China (2017YFC1001100 and 2016YFC1201805). Declaration of Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Ethics Approval Statement: All the participants had signed informed consent form before they were recruited, and this study was approved by the Third Affiliated Hospital of Southern Medical University (Guangzhou City, China) institutional review board.

Backgrounds To explore the genetic correlation between schizophrenia (SCZ) and osteoporosis (OP). Design, setting, participants, measurements We conducted a trans-ethnic two-stage genetic correlation analysis of OP and SCZ, totally invoking 2286 Caucasia subjects in discovery stage and 4124 Chinese subjects in replication stage. The bone mineral density (BMD) and bone area values of ulna & radius, hip and spine were measured using Hologic 4500W dual energy X-ray absorptiometry machine. SCZ was diagnosed according to DSM-IV criteria. For the genome-wide association study (GWAS) of Caucasian OP, Chinese OP and Chinese SCZ, SNP genotyping was performed using Affymetrix SNP 6.0 array. For the GWAS of Caucasian SCZ, SNP genotyping was conducted using the Affymetrix 5.0 array, Affymetrix 6.0 array and Illumina 550 K array. Polygenetic risk scoring (PRS) analysis was conducted by PRSice software. Also, Linkage disequilibrium score regression (LD Score regression) analysis was performed to evaluate the genetic correlation between OP and SCZ. Multi-trait analysis of GWAS (MTAG) was performed to detect novel candidate genes for osteoporosis and SCZ. Results In the Caucasia discovery samples, significant genetic correlations were observed for ulna & radius BMD vs. SCZ (P value = 0.010), ulna & radius area vs. SCZ (P value = 0.031). In the Chinese replication samples, we observed significant correlation for ulna & radius area vs. SCZ (P value = 0.019). In addition, LD Score regression also identified significant genetic correlations between SCZ and bone phenotypes in Caucasian and Chinese sample respectively. MTAG analysis identified several novel candidate genes, such as CTNNA2 (MTAG P value = 2.24 × 10−6) for SCZ and FADS2 (MTAG P value = 2.66 × 10−7) for osteoporosis. Conclusions Our study results support the overlapped genetic basis for osteoporosis and SCZ, and provide novel clues for elucidating the biological mechanism of increased osteoporosis risk in SCZ patients.

Objective The relationships between early growth parameters and bone mineral density (BMD) remain elusive now. In this study, we performed a large scale polygenic risk score (PRS) analysis to evaluate the potential impact of early growth parameters on the variations of BMD. Methods We used 2286 Caucasian subjects as cohort 1 and 3404 Framingham Heart Study (FHS) subjects as cohort 2 in this study. BMD at ulna & radius, hip and spine were measured using dual energy X-ray absorptiometry. BMD values were adjusted for age, sex, height and weight as covariates. Genome-wide single-nucleotide polymorphism (SNP) genotyping of the 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The GWAS datasets of early growth parameters were driven from the Early Growth Genetics Consortium, including birth weight (BW), birth head circumference (BHC), childhood body mass index (CBMI), pubertal height growth related indexes and tanner stage. Polygenic Risk Score (PRSice) and linkage disequilibrium (LD) score regression analysis were conducted to assess the genetic correlation between early growth parameters and BMD. Results We detected significant genetic correlations in cohort 1, such as total spine BMD vs. CBMI (p value = 1.51 × 10−4, rg = 0.4525), right ulna and radius BMD vs. CBMI (p value = 1.51 × 10−4, rg = 0.4399) and total body BMD vs. tanner stage (p value = 7.00 × 10−4, rg = −0.0721). For cohort 2, significant correlations were observed for total spine BMD vs. height change standard deviation score (SDS) between 8 years and adult (denoted as PGF + PGM) (p value = 3.97 × 10−4, rg = −0.1425), femoral neck BMD vs. the timing of peak height velocity by looking at the height change SDS between age 14 years and adult (denoted as PTF + PTM) (p value = 7.04 × 10−4, rg = −0.2185), and total spine BMD vs. PTF + PTM (p value = 6.86 × 10−4, rg = −0.2180). Conclusion Our study results suggest that some early growth parameters could affect the variations of BMD.

DNA methylation plays an important role in the development of various cancers mainly through the regulation on gene expression. Hence, the study on the relation between DNA methylation and gene expression is of particular interest to understand cancers. Recently, an increasing number of datasets are available from multiple cancers, which makes it possible to study both the similarity and difference of genomic alterations across multiple tumor types. However, most of the existing pan-cancer analysis methods perform simple aggregations, which may overlook the heterogeneity of the interactions. In this paper, we propose a novel method to jointly detect complex associations between DNA methylation and gene expression levels from multiple cancers. The main idea is to apply joint sparse canonical correlation analysis to detect a small set of methylated sites, which are associated with another set of genes either shared across cancers or specific to a particular group (group-specific) of cancers. These methylated sites and genes form a complex module with strong multivariate correlations. We further introduce a joint sparse precision matrix estimation method to identify driver methylation-gene pairs in the module. These pairs are characterised by significant partial correlations, which may imply high functional impacts and contribute to complementary information to the main step. We apply our method to The Cancer Genome Atlas(TCGA) datasets with 1166 samples from four cancers. The results reveal significant shared and group-specific interactions between DNA methylation and gene expression levels. To promote reproducible research, the Matlab code is available at https://sites.google.com/site/jianfang86/jointTCGA.

Aiming to uncover a shared genetic basis of abdominal obesity and osteoporosis, we performed a bivariate GWAS meta-analysis of femoral neck BMD (FNK-BMD) and trunk fat mass adjusted by trunk lean mass (TFM(adj)) in 11,496 subjects from 6 samples, followed by in silico replication in the large-scale UK Biobank (UKB) cohort. A series of functional investigations were conducted on the identified variants. Bivariate GWAS meta-analysis identified two novel pleiotropic loci 12q15 (lead SNP rs73134637, p = 3.45 × 10(–7)) and 10p14 (lead SNP rs2892347, p = 2.63 × 10(–7)) that were suggestively associated and that were replicated in the analyses of related traits in the UKB sample (osteoporosis p = 0.06 and 0.02, BMI p = 0.03 and 4.61 × 10(–3), N up to 499,520). Cis-eQTL analysis demonstrated that allele C at rs73134637 was positively associated with IFNG expression in whole blood (N = 369, p = 0.04), and allele A at rs11254759 (10p14, p = 9.49 × 10(–7)) was negatively associated with PRKCQ expression in visceral adipose tissue (N = 313, p = 0.04) and in lymphocytes (N = 117, p = 0.03). As a proof-of-principle experiment, the function of rs11254759, which is 235 kb 5′-upstream from PRKCQ gene, was investigated by the dual-luciferase reporter assay, which clearly showed that the haplotype carrying rs11254759 regulated PRKCQ expression by upregulating PRKCQ promoter activity (p = 4.60 × 10(–7)) in an allelic specific manner. Mouse model analysis showed that heterozygous PRKCQ deficient mice presented decreased fat mass compared to wild-type control mice (p = 3.30 × 10(–3)). Mendelian randomization analysis demonstrated that both FNK-BMD and TFM(adj) were causally associated with fracture risk (p = 1.26 × 10(–23) and 1.18 × 10(–11)). Our findings may provide useful insights into the genetic association between osteoporosis and abdominal obesity.

Adiposity has been associated with the risk of coronary artery disease (CAD) in observational studies, but their association may differ according to specific characteristics of studies. In Mendelian randomization (MR) analyses, genetic variants are used as instrumental variables (IVs) of exposures to examine causal effects to overcome confounding factors and reverse causation. We performed MR analyses for adiposity (n = 322,154) on risk of CAD (60,801 cases and 123,504 controls) based on the currently largest genome-wide association studies. The estimated associations between adiposity traits and CAD were calculated by an inverse-variance weighted method with and without excluding the IVs, which are associated with the well-known risk factors of CAD. Genetic variants are identified to be associated with the well-known risk factors of CAD by a cross-phenotype meta-analysis method. Our results furnished strong evidence for a causal role of adiposity in risk of CAD, with the odds ratios (ORs) for CAD being 1.53 (95% CI 1.36-1.72) for body mass index (BMI), 1.48 (1.20-1.96) for waist-hip ratio (WHR), and 1.34 (1.07-1.59) for WHR adjusted for BMI (WHRadjBMI), respectively. After excluding mediators-associated IVs from the MR analyses, the corresponding ORs were 1.46 (1.28-1.67) for BMI, 1.39 (1.01-1.93) for WHR, and 1.38 (1.04-1.84) for WHRadjBMI, respectively. Furthermore, our results suggested that central adiposity and general adiposity might pose a similar risk for CAD. In summary, our data supported that genetically driven adiposity traits imposed the risk of CAD independent of blood pressure, dyslipidaemia, glycaemic traits, and type 2 diabetes.

BACKGROUND: Genome-wide association studies have identified many susceptibility loci for obesity. However, missing heritability problem is still challenging and ignorance of genetic interactions is believed to be an important cause. Current methods for detecting interactions usually do not consider regulatory elements in non-coding regions. Interaction analyses within chromatin regulatory circuitry may identify new susceptibility loci. METHODS: We developed a pipeline named interaction analyses within chromatin regulatory circuitry (IACRC), to identify genetic interactions impacting body mass index (BMI). Potential interacting SNP pairs were obtained based on Hi-C datasets, PreSTIGE (Predicting Specific Tissue Interactions of Genes and Enhancers) algorithm, and super enhancer regions. SNP × SNP analyses were next performed in three GWAS datasets, including 2286 unrelated Caucasians from Kansas City, 3062 healthy Caucasians from the Gene Environment Association Studies initiative, and 3164 Hispanic subjects from the Women’s Health Initiative. RESULTS: A total of 16,643,227 SNP × SNP analyses were performed. Meta-analyses showed that two SNP pairs, rs6808450–rs9813534 (combined P = 2.39 × 10(−9)) and rs6808450–rs3773306 (combined P = 2.89 × 10(−9)) were associated with BMI after multiple testing corrections. Single-SNP analyses did not detect significant association signals for these three SNPs. In obesity relevant cells, rs6808450 is located in intergenic enhancers, while rs9813534 and rs3773306 are located in the region of strong transcription regions of CAND2 and RPL32, respectively. The expression of CAND2 was significantly downregulated after the differentiation of human Simpson–Golabi–Behmel syndrome (SGBS) preadipocyte cells (P = 0.0241). Functional validation in the International Mouse Phenotyping Consortium database showed that CAND2 was associated with increased lean body mass and decreased total body fat amount. CONCLUSIONS: Detecting epistasis within chromatin regulatory circuitry identified CAND2 as a novel obesity susceptibility gene. We hope IACRC could facilitate the interaction analyses for complex diseases and offer new insights into solving the missing heritability problem.

Recent studies suggested that long noncoding RNAs (lncRNAs) were widely transcribed in the genome, but their potential roles in the genetic complexity of human disorders required further exploration. The purpose of the present study was to explore genetic polymorphisms of lncRNAs associated with bone mineral density (BMD) and its potential value. Based on the lncRNASNP database, 55,906 lncSNPs were selected to conduct a genome-wide association study meta-analysis among 11,140 individuals of seven independent studies for BMDs at femoral neck (FN), lumbar spine, and total hip (HIP). Promising results were replicated in Genetic Factors for Osteoporosis Consortium (GEFOS Sequencing, n = 32,965). We found two lncRNA loci that were significantly associated with BMD. MEF2C antisense RNA 1 (MEF2C-AS1) located at 5q14.3 was significantly associated with FN-BMD after Bonferroni correction, and the strongest association signal was detected at rs6894139 (P = 3.03 × 10-9 ). LOC100506136 rs6465531 located at 7q21.3 showed significant association with HIP-BMD (P = 7.43 × 10-7 ). MEF2C-AS1 rs6894139 was replicated in GEFOS Sequencing with P-value of 1.43 × 10-23 . Our results illustrated the important role of polymorphisms in lncRNAs in determining variations of BMD and provided justification and evidence for subsequent functional studies.

CONTEXT: Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive. OBJECTIVE: To explore the relationship between blood metabolites and osteoporosis. DESIGN AND METHODS: We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate. RESULTS: We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (P(discovery) < 0.0001; P(replication) = 0.0361), inosine (P(discovery) = 0.0018; P(replication) = 0.0256), theophylline (P(discovery) = 0.0048; P(replication) = 0.0433, gamma-glutamyl methionine (P(discovery) = 0.0047; P(replication) = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; P(discovery) = 0.0018; P(replication) = 0.0390), and X-12127 (P(discovery) = 0.0002; P(replication) = 0.0249). CONCLUSIONS: Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis.

OBJECTIVE: Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell’s method to make it more applicable for the general situations. METHOD: We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method by using both simulation and empirical data. RESULTS: Simulations suggested that when the same number of genetic variants are used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs on the other hand would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified 10 blood metabolites that may causally influence FN-BMD. CONCLUSION: We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.

Motivation Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g. the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. Results We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. Availability and implementation The source code is available at https://github.com/xu1912/EPSLASSO. Supplementary information Supplementary data are available at Bioinformatics online.

Osteoporosis is a prevalent bone metabolic disease and peripheral blood monocytes represent a major systemic cell type for bone metabolism. To identify the key dysfunctional pathways in osteoporosis, we performed pathway analyses on microarray data of monocytes from subjects with extremely high/low hip bone mineral density. We first performed a traditional pathway analysis for which different pathways were treated as independent. However, genes overlap among pathways will lead to “crosstalk” phenomenon, which may lead to false positive/negative results. Therefore, we applied correction techniques including a novel approach that considers the correlation among genes to adjust the crosstalk effects in the analysis. In traditional analysis, 10 pathways were found to be significantly associated with BMD variation. After correction for crosstalk effects, three of them remained significant. Moreover, the MAPK signaling pathway, which has been shown to be important for osteoclastogenesis, became significant only after the correction for crosstalk effects. We also identified a new module mainly consisting of genes present in mitochondria to be significant. In summary, we describe a novel method to correct the crosstalk effect in pathway analysis and found five key independent pathways involved in BMD regulation, which may provide a better understanding of biological functional networks in osteoporosis.

In recent years, a comprehensive study of complex disease with multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to explore complementary information of disease, analysis of multi-view data with complex interactions is challenging for a deeper and holistic understanding of biological systems. In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets (GKMAHCE). This generalized semi-parametric (a mixed-effect linear model) approach includes the marginal and joint Hadamard product of features from different views of data. The proposed kernel machine approach considers multi-view data as predictor variables to allow a more thorough and comprehensive modeling of a complex trait. We applied GKMAHCE approach to both synthesized datasets and real multi-view datasets from adolescent brain development and osteoporosis study. Our experiments demonstrate that the proposed method can effectively identify higher-order composite effects and suggest that corresponding features (genes, region of interests, and chemical taxonomies) function in a concerted effort. We show that the proposed method is more generalizable than existing ones. To promote reproducible research, the source code of the proposed method is available at.

Prostate Specific Antigen (PSA) level in the serum is one of the most widely used markers in monitoring prostate cancer (PCa) progression, treatment response, and disease relapse. Although significant efforts have been taken to analyze various socioeconomic and cultural factors that contribute to the racial disparities in PCa, limited research has been performed to quantitatively understand how and to what extent molecular alterations may impact differential PSA levels present at varied tumor status between African–American and European–American men. Moreover, missing values among patients add another layer of difficulty in precisely inferring their outcomes. In light of these issues, we propose a data-driven, deep learning-based imputation and inference framework (DIIF). DIIF seamlessly encapsulates two modules: an imputation module driven by a regularized deep autoencoder for imputing critical missing information and an inference module in which two deep variational autoencoders are coupled with a graphical inference model to quantify the personalized and race-specific causal effects. Large-scale empirical studies on the independent sub-cohorts of The Cancer Genome Atlas (TCGA) PCa patients demonstrate the effectiveness of DIIF. We further found that somatic mutations in TP53, ATM, PTEN, FOXA1, and PIK3CA are statistically significant genomic factors that may explain the racial disparities in different PCa features characterized by PSA.

Context Genome-wide association studies (GWASs) have been successful in identifying loci associated with osteoporosis and obesity. However, the findings explain only a small fraction of the total genetic variance. Objective The aim of this study was to identify novel pleiotropic genes important in osteoporosis and obesity. Design and setting A pleiotropic conditional false discovery rate method was applied to three independent GWAS summary statistics of femoral neck bone mineral density, body mass index, and waist-to-hip ratio. Next, differential expression analysis was performed for the potentially pleiotropic genes, and weighted genes coexpression network analysis (WGCNA) was conducted to identify functional connections between the suggested pleiotropic genes and known osteoporosis/obesity genes using transcriptomic expression data sets in osteoporosis/obesity-related cells. Results We identified seven potentially pleiotropic loci-rs3759579 (MARK3), rs2178950 (TRPS1), rs1473 (PUM1), rs9825174 (XXYLT1), rs2047937 (ZNF423), rs17277372 (DNM3), and rs335170 (PRDM6)-associated with osteoporosis and obesity. Of these loci, the PUM1 gene was differentially expressed in osteoporosis-related cells (B lymphocytes) and obesity-related cells (adipocytes). WGCNA showed that PUM1 positively interacted with several known osteoporosis genes (AKAP11, JAG1, and SPTBN1). ZNF423 was the highly connected intramodular hub gene and interconnected with 21 known osteoporosis-related genes, including JAG1, EN1, and FAM3C. Conclusions Our study identified seven potentially pleiotropic genes associated with osteoporosis and obesity. The findings may provide new insights into a potential genetic determination and codetermination mechanism of osteoporosis and obesity.

Bone mineral density (BMD) is a complex trait with high missing heritability. Numerous evidences have shown that BMD variation has a relationship with coronary artery disease (CAD). This relationship may come from a common genetic basis called pleiotropy. By leveraging the pleiotropy with CAD, we may be able to improve the detection power of genetic variants associated with BMD. Using a recently developed conditional false discovery rate (cFDR) method, we jointly analyzed summary statistics from two large independent genome wide association studies (GWAS) of lumbar spine (LS) BMD and CAD. Strong pleiotropic enrichment and 7 pleiotropic SNPs were found for the two traits. We identified 41 SNPs for LS BMD (cFDR

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.

In this study, label-free-based quantitative subcellular proteomics integrated with network analysis highlighted several candidate genes including P4HB, ITGB1, CD36, and ACTN1 that may be involved in osteoporosis. All of them are predicted as significant membrane proteins with high confidence and enriched in bone-related biological process. The results were further verified in transcriptomic and genomic levels. Osteoporosis is a metabolic bone disease mainly characterized by low bone mineral density (BMD). As the precursors of osteoclasts, peripheral blood monocytes (PBMs) are supported to be important candidates for identifying genes related to osteoporosis. We performed subcellular proteomics study to identify significant membrane proteins that involved in osteoporosis. To investigate the association between monocytes, membrane proteins, and osteoporosis, we performed label-free quantitative subcellular proteomics in 59 male subjects with discordant BMD levels, with 30 high vs. 29 low BMD subjects. Subsequently, we performed integrated gene enrichment analysis, functional annotation, and pathway and network analysis based on multiple bioinformatics tools. A total of 1070 membrane proteins were identified and quantified. By comparing the proteins’ expression level, we found 36 proteins that were differentially expressed between high and low BMD groups. Protein localization prediction supported the notion that the differentially expressed proteins, P4HB (p = 0.0021), CD36 (p = 0.0104), ACTN1 (p = 0.0381), and ITGB1 (p = 0.0385), are significant membrane proteins. Functional annotation and pathway and network analysis highlighted that P4HB, ITGB1, CD36, and ACTN1 are enriched in osteoporosis-related pathways and terms including “ECM-receptor interaction,” “calcium ion binding,” “leukocyte transendothelial migration,” and “reduction of cytosolic calcium levels.” Results from transcriptomic and genomic levels provided additional supporting evidences. Our study strongly supports the significance of the genes P4HB, ITGB1, CD36, and ACTN1 to the etiology of osteoporosis risk.

The human gut microbiome can modulate metabolic health and affect insulin resistance, and it may play an important role in the etiology of gestational diabetes mellitus (GDM). Here, we compared the gut microbial composition of 43 GDM patients and 81 healthy pregnant women via whole-metagenome shotgun sequencing of their fecal samples, collected at 21–29 weeks, to explore associations between GDM and the composition of microbial taxonomic units and functional genes. A metagenome-wide association study identified 154 837 genes, which clustered into 129 metagenome linkage groups (MLGs) for species description, with significant relative abundance differences between the 2 cohorts. Parabacteroides distasonis, Klebsiella variicola, etc., were enriched in GDM patients, whereas Methanobrevibacter smithii, Alistipes spp., Bifidobacterium spp., and Eubacterium spp. were enriched in controls. The ratios of the gross abundances of GDM-enriched MLGs to control-enriched MLGs were positively correlated with blood glucose levels. A random forest model shows that fecal MLGs have excellent discriminatory power to predict GDM status. Our study discovered novel relationships between the gut microbiome and GDM status and suggests that changes in microbial composition may potentially be used to identify individuals at risk for GDM.

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.

Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.

DNA methylation is an important epigenetic modification that contributes to the lineage commitment and specific functions of different cell types. In this study, we compared ENCODE-generated genome-wide DNA methylation profiles of human osteoblast with 21 other types of human cells in order to identify osteoblast-specific methylation events. For most of the cell strains, data from two isogenic replicates were included, resulting in a total of 51 DNA methylation datasets. We identified 852 significant osteoblast-specific differentially methylated CpGs (DMCs) and 295 significant differentially methylated regions (DMRs). Significant DMCs/DMRs were not enriched in CpG islands (CGIs) and promoters, but more strongly enriched in CGI shores/shelves and in gene body and intergenic regions. The genes associated with significant DMRs were highly enriched in biological processes related to transcriptional regulation and critical for regulating bone metabolism and skeletal development under physiologic and pathologic conditions. By integrating the DMR data with the extensive gene expression and chromatin epigenomics data, we observed complex, context-dependent relationships between DNA methylation, chromatin states, and gene expression, suggesting diverse DNA methylation-mediated regulatory mechanisms. Our results also highlighted a number of novel osteoblast-relevant genes. For example, the integrated evidences from DMR analysis, histone modification and RNA-seq data strongly support that there is a novel isoform of neurexin-2 (NRXN2) gene specifically expressed in osteoblast. NRXN2 was known to function as a cell adhesion molecule in the vertebrate nervous system, but its functional role in bone is completely unknown and thus worth further investigation. In summary, we reported a comprehensive analysis of osteoblast-specific DNA methylation profiles and revealed novel insights into the epigenetic basis of osteoblast differentiation and activity., Highlights • Identify and characterize osteoblast-specific methylation patterns across the genome • Osteoblastic-specific methylation enriched in regulatory regions beyond the promoters • Diverse, context-dependent DNA methylation-mediated regulatory mechanisms • Revealed novel insights into the epigenetic basis of osteoblast differentiation

Although genomewide association studies (GWASs) have been able to successfully identify dozens of genetic loci associated with bone mineral density (BMD) and osteoporosis-related traits, very few of these loci have been confirmed to be causal. This is because in a given genetic region there may exist many trait-associated SNPs that are highly correlated. Although this correlation is useful for discovering novel associations, the high degree of linkage disequilibrium that persists throughout the genome presents a major challenge to discern which among these correlated variants has a direct effect on the trait. In this study we apply a recently developed Bayesian fine-mapping method, PAINTOR, to determine the SNPs that have the highest probability of causality for femoral neck (FNK) BMD and lumbar spine (LS) BMD. The advantage of this method is that it allows for the incorporation of information about GWAS summary statistics, linkage disequilibrium, and functional annotations to calculate a posterior probability of causality for SNPs across all loci of interest. We present a list of the top 10 candidate SNPs for each BMD trait to be followed up in future functional validation experiments. The SNPs rs2566752 (WLS) and rs436792 (ZNF621 and CTNNB1) are particularly noteworthy because they have more than 90% probability to be causal for both FNK and LS BMD. Using this statistical fine-mapping approach we expect to gain a better understanding of the genetic determinants contributing to BMD at multiple skeletal sites. © 2017 American Society for Bone and Mineral Research.