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Background DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer (PCa). However, it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores (PRSs). Here, we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation, and other genomic information using an integrative method. Methods Using data from the PRACTICAL consortium, we derived multiple sets of genetic scores, including those based on available single‐nucleotide polymorphisms through widely used methods of pruning and thresholding, LDpred, LDpred‐funt, AnnoPred, and EBPRS, as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy. In the tuning step, using the UK Biobank data (1458 prevalent cases and 1467 controls), we selected PRSs with the best performance. Using an independent set of data from the UK Biobank, we developed an integrative PRS combining information from individual scores. Furthermore, in the testing step, we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls. Results Our constructed PRS had improved performance (C statistics: 76.1%) over PRSs constructed by individual benchmark methods (from 69.6% to 74.7%). Furthermore, our new PRS had much higher risk assessment power than family history. The overall net reclassification improvement was 69.0% by adding PRS to the baseline model compared with 12.5% by adding family history. Conclusions We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa. Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes., An integrative score incorporating genetically predicted gene expression and DNA methylation and other genomic and non‐genomic information advanced our understanding of using genomic information to stratify subjects for prostate cancer.

BackgroundThe TNF, LTA, and TNFRSF1B genes have been implicated in various traits related to menarche and menopause.AimTo analyse the TNF, LTA, and TNFRSF1B genes for their association with ages at menarche (AM) and natural menopause (ANM).Subjects and methodsThe study sample consisted of 314 unrelated females of European ancestry. Twenty SNPs located in and near the genes were analysed using various statistical methods. In addition, the functional significance of the loci associated with AM and ANM was analysed in silico.ResultsLocus rs2229094 of the LTA gene was associated with AM according to the additive (β = -0.295, Pperm = 0.016) and recessive (β = -0.940, Pperm = 0.016) genetic models. Haplotype GG rs1148459-rs590368 of the TNFRSF1B gene was associated with AM (β = 0.307, Pperm = 0.023). Haplotype GCA rs2844484-rs2229094-rs1799964 was associated with ANM after adjustment for covariates (β = -1.020, Pperm = 0.035). All studied loci were associated with ANM after adjustment for breastfeeding (raw P < 0.05). In addition, eight most significant models of interlocus interactions were associated with AM and five with ANM.ConclusionThe results of the present study suggest that the TNF, LTA, and TNFRSF1B genes are associated with AM and ANM.

Genome-wide association studies (GWASs) have identified hundreds of genetic loci for osteoporosis (OP) and rheumatoid arthritis (RA), individually, however, a large proportion of the total trait heritability remains unexplained. Previous studies demonstrated that these two diseases may share some common genetic determination and risk factors, but they were generally focused on individual trait and failed to identify the common variants that play key functional roles in the etiology of these two diseases. Here, we performed a conditional false discovery rate (cFDR) analysis to identify novel pleiotropic variants shared between them by integrating two independent GWASs with summary statistics for total body bone mineral density (TB-BMD, a major risk factor for osteoporosis) (n = 66,628) and RA (n = 58,284). A fine-mapping approach was also applied to identify the most probable causal variants with biological effects on both TB-BMD and RA. As a result, we found 47 independent pleiotropic SNPs shared between TB-BMD and RA, and 40 of them were validated in heel ultrasound estimated BMD (eBMD), femoral neck BMD (FN-BMD) or lumbar spine (LS-BMD). We detected one SNP (rs13299616) was novel and not identified by previous BMD or RA-related studies. Combined with fine-mapping and GWAS-eQTL colocalization analyses, our results suggested that locus 1p13.2 (including PTPN22, MAGI3, PHTF1, and RSBN1) was an important region to regulate TB-BMD and RA simultaneously. These findings provide new insights into the shared biological mechanisms and functional genetic determinants between OP and RA, and novel potential targets for treatment development.

The zona pellucida (ZP) plays vital roles in reproductive processes including oogenesis, fertilization, and preimplantation development. Both human and rat ZP consist of four glycoproteins, called ZP1, ZP2, ZP3, and ZP4. Our previous research reported a novel Zp1 mutation in cases of human infertility, associated with an abnormal phenotype involving the absence of the ZP. Here, we developed a homologous rat strain to investigate the pathogenic effect. The ovaries of homozygous (Zp1MT/MT) females possessed both growing and fully grown oocytes; the oocytes completely lacked a ZP, but ZP1 was detectable inside the cytoplasm. Only 1–2 eggs were recovered from oviducts of superovulated Zp1MT/MT females, while an average of 21 eggs were recovered from superovulated Zp1WT/WT per female. The eggs of Zp1MT/MT females were not surrounded by a ZP and lost their fertilization capacity in vitro. Zp1MT/MT females mated with wild-type males failed to become pregnant. Studies in 293T cells showed that mutant Zp1 resulted in a truncated ZP1 protein, which might be intracellularly sequestered and interacted with wild-type ZP3 or ZP4. Our results suggest that the Zp1 point mutation led to infertility and loss of the ZP in oocytes in rats.Summary sentenceZp1 mutation can lead to congenital deficiencies and ZP loss, which leads to human infertility. The interaction between truncated ZP1 and ZP3 or ZP4 is gained, which affects their normal transport and secretion, suggesting that normal ZP1 is crucial for the structure of the ZP and for fertility.

SUMMARY: This study estimates causality of physical activity (PA) on bone mineral density (BMD) by conducting multivariable Mendelian randomization (MR). The findings suggest that habitual vigorous PA increases lumbar spine BMD, and higher overall acceleration average would improve forearm BMD. The results could promote PA intervention targeting individuals with optimized type. INTRODUCTION: Evidence from epidemiologic studies showed type, frequency, and duration of PA influenced BMD. However, these observational studies may be confounded by many factors, resulting in spurious associations. We aimed to conduct multivariable MR to estimate the causal effect of self-reported and device-measured PA on osteoporosis. METHODS: Three self-reported and two device-measured PA-related traits were selected as exposures. Outcomes were BMD at different skeletal sites: femoral neck BMD (FN BMD), lumbar spine BMD (LS BMD), and forearm BMD (FA BMD). Exposure datasets were obtained from UK Biobank with total 377,234 subjects. Outcome datasets were obtained from GEFOS consortium with 53,236 subjects. Standard MR analysis and multivariable MR were conducted to assess the total and direct causal effect of PA on BMD. RESULTS: For self-reported PA, inverse-normalized moderate-to-vigorous had a direct causal effect on FN BMD independently (β = − 1.116 (95% confidence interval, 95%CI: − 2.210, − 0.023), P = 0.045); vigorous PA showed a direct effect (β = 3.592 (95%CI: 0.310, 6.874), P = 0.032) on LS BMD independently. While overall acceleration average and fraction of accelerations both had a direct causal effect on FA BMD independently. CONCLUSIONS: Habitual vigorous PA could increase LS BMD. Individuals with higher overall acceleration average would have a higher FA BMD.

SUMMARY: Both sarcopenia and low bone mineral density (BMD) have become public health concerns. We found that presarcopenic and/or sarcopenic individuals were more likely to have lower BMD. And this relationship has race and sex-specific discrepancy. PURPOSE: The purpose of the study was to investigate the racial and gender differences in the relationship between sarcopenia and BMD among older adults. METHODS: Totally, 5476 subjects (mean age = 65.7 ± 6.4) of non-Hispanic White (n = 3297), non-Hispanic Black (n = 1265), and non-Hispanic Asian (n = 914) were analyzed. Sarcopenia was defined according to the revised European consensus on definition and diagnosis of sarcopenia (EWGSOP2). General linear model and multivariable linear regression model were used to examine the relationship between sarcopenia and regional/whole body BMD stratified by race and sex. Adjustments were conducted for physiological, behavioral, and disease factors. RESULTS: Comparing with normal older participants, presarcopenic and sarcopenic elderly were more likely to have lower BMD. Although the difference was not statistically significant in a few sub-groups, among the three racial groups, the strongest association between sarcopenia and BMD was found in non-Hispanic Black people, followed by non-Hispanic White people and non-Hispanic Asian people. In addition, significant differences of BMD across sarcopenia stages were found in more sub-groups in women than in men after adjusting for covariates. CONCLUSIONS: In this older cohort, sarcopenia is significantly related to low regional/whole-body BMD, and these associations vary by race and sex. Consideration in race and sex is warranted when developing strategies to maintain or minimize BMD loss.

Both bone mineral density (BMD) and lean body mass (LBM) are important physiological measures with strong genetic determination. Besides, BMD and LBM might have common genetic factors. Aiming to identify pleiotropic genomic loci underlying BMD and LBM, we performed bivariate genome-wide association study meta-analyses of femoral neck bone mineral density and LBM at arms and legs, and replicated in the large-scale UK Biobank cohort sample. Combining the results from discovery meta-analysis and replication sample, we identified three genomic loci at the genome-wide significance level (p < 5.0 × 10(−8)): 2p23.2 (lead SNP rs4477866, discovery p = 3.47 × 10(−8), replication p = 1.03 × 10(−4)), 16q12.2 (rs1421085, discovery p = 2.04 × 10(−9), replication p = 6.47 × 10(−14)) and 18q21.32 (rs11152213, discovery p = 3.47 × 10(−8), replication p = 6.69 × 10(−6)). Our findings not only provide useful insights into lean mass and bone mass development, but also enhance our understanding of the potential genetic correlation between BMD and LBM.

Objective:To assess the prevalence and to identify the associated factors of malnutrition among elderly Chinese with physical functional dependency.Design:Face-to-face interviews using standardised questionnaires were conducted to collect demographic information, health-related issues and psychosocial status. Physical function was measured by the Barthel Index (BI), and nutrition status was assessed by the Mini Nutritional Assessment–Short Form. Multivariate binary logistic regression was used to assess associated factors of malnutrition.Setting:China.Participants:A total of 2323 participants (aged ≥ 60 years) with physical functional dependency in five provinces in China were enrolled using a multistage cluster sampling scheme.Results:The prevalence of malnutrition was 17·9 % (95 % CI 16·3, 19·4). Multivariable binary logistic regression revealed the independent risk factors of poor nutrition status were being female, older age, lower educational status, poor hearing, poor physical functional status, lack of hobbies, low religious participation, poor social support, lack of social participation and changes in social participation. The study found that the most significant independent risk factor for malnutrition was complete physical functional dependence (OR 4·46, 95 % CI 2·92, 6·82).Conclusions:The findings of the study confirm that malnutrition and the risk of malnutrition are prevalent in Chinese older adults with physical functional dependency. In addition to demographic and physical health-related factors, psychosocial factors, which are often overlooked, are independently associated with nutrition status in Chinese older adults with physical functional dependency. A holistic approach should be adopted to screen for malnutrition and develop health promotion interventions in this vulnerable population.

SUMMARY: Aiming to identify pleiotropic genomic loci for bone mineral density and bone size, we performed a bivariate GWAS in five discovery samples and replicated in two large-scale samples. We identified 2 novel loci at 2q37.1 and 6q26. Our findings provide insight into common genetic architecture underlying both traits. INTRODUCTION: Bone mineral density (BMD) and bone size (BS) are two important factors that contribute to the development of osteoporosis and osteoporotic fracture. Both BMD and BS are highly heritable and they are genetically correlated. In this study, we aim to identify pleiotropic loci associated with BMD and BS. METHODS: We conducted a bivariate genome-wide association (GWA) analysis of hip BMD and hip BS in 6180 participants from 5 samples, followed by in silico replication in the UK Biobank study of BMD (N = 426,824) and the deCODE study of BS (N = 28,954), respectively. RESULTS: SNPs from 2 genomic loci were significant at the genome-wide significance (GWS) level (p lt; 5 × 10(−8)) in the discovery samples and were successfully replicated in the replication samples (2q37.1, lead SNP rs7575512, discovery p = 1.49 × 10(−10), replication p = 0.05; 6q26, lead SNP rs1040724, discovery p = 1.95 × 10(−8), replication p = 0.03). Functional annotations suggested functional relevance of the identified variants to bone development. CONCLUSION: Our findings provide insight into the common genetic architecture underlying BMD and BS, and enhance our understanding of the potential mechanism of osteoporosis fracture.

Whole body lean mass (WBLM) is a heritable trait predicting sarcopenia. To identify genomic locus underlying WBLM, we performed a genome-wide association study of fat-adjusted WBLM in the Framingham Heart Study (FHS, N = 6,004), and replicated in the Kansas City Osteoporosis Study (KCOS, N = 2,207). We identified a novel locus 3p27.1 that was associated with WBLM (lead SNP rs3732593 P = 7.19 × 10−8) in the discovery FHS sample, and the lead SNP was successfully replicated in the KCOS sample (one-sided P = 0.04). Bioinformatics analysis found that this SNP and its adjacent SNPs had the function of regulating enhancer activity in skeletal muscle myoblasts cells, further confirming the regulation of WBLM by this locus. Our finding provides new insight into the genetics of WBLM and enhance our understanding of sarcopenia.

Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in ‘systems genetics and genomics’. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans. In this Review, the authors highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis, which is a highly heritable condition. They also summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis.

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.

BACKGROUNDAlcohol use disorder (AUD) is a leading cause of death and disability worldwide. Genome-wide association studies (GWAS) have identified ∼30 AUD risk genes in European populations, but many fewer in East Asians.METHODSWe conducted GWAS and genome-wide meta-analysis of AUD in 13,551 subjects with East Asian ancestry, using published summary data and newly genotyped data from four cohorts: 1) electronic health record (EHR)-diagnosed AUD in the Million Veteran Program (MVP)sample; 2) DSM-IV diagnosed alcohol dependence (AD) in a Han Chinese-GSA (array) cohort;3) AD in a Han Chinese-Cyto (array) cohort; and 4) two AD datasets in a Thai cohort. The MVP and Thai samples included newly genotyped subjects from ongoing recruitment. In total, 2,254 cases and 11,297 controls were analyzed. An AUD polygenic risk score was analyzed in an independent sample with 4,464 East Asians (Kaiser Permanente data from dbGaP). Phenotypes from survey data and ICD-9-CM diagnoses were tested for association with the AUD PRS.RESULTSTwo risk loci were detected: the well-known functional variant rs1229984 in ADH1B and rs3782886 in BRAP (near the ALDH2 gene locus) are the lead variants. AUD PRS was significantly associated with days per week of alcohol consumption (beta = 0.43, se = 0.067, p = 2.47×10−10) and nominally associated with pack years of smoking (beta = 0.09, se = 0.05, p = 4.52×10−2) and ever vs. never smoking (beta = 0.06, se = 0.02, p = 1.14×10−2).CONCLUSIONSThis is the largest GWAS of AUD in East Asians to date. Building on previous findings, we were able to analyze pleiotropy, but did not identify any new risk regions, underscoring the importance of recruiting additional East Asian subjects for alcohol GWAS.

Objective To examine the contribution of skeletal mass index (SMI) as a mediator in the relationship between menarcheal age and hip/spine bone mineral density (BMD) in premenopausal women by race/ethnicity. Methods The data of 4,329 participants (age ≥ 18; mean age=35.7 ± 9.5) of Whites (n = 2,543), African Americans (n = 1,236), and Asians (n = 550) enrolled from October 2011 to January 2019 from the Louisiana Osteoporosis Study were analyzed. After adjustment for physiological and behavioral factors, multivariable linear regression analyses were conducted to evaluate each component of the proposed mediation models, and mediation was verified by the bootstrapping resampling approach. Results Premenopausal women with early menarcheal age tended to have higher SMI and BMD than women with normal menarcheal age among all races/ethnicities included. Women with late menarcheal age were, however, more likely to have a lower SMI than their counterparts with normal menarcheal age (r = -0.212, 95% CI = [-0.321 to -0.103] for White women; r = -0.181, 95% CI = [-0.410 to -0.008] for African-American women; r = -0.174, 95% CI = [-0.343 to -0.006] for Asian women). Similar results were found for both spine and hip BMD. SMI fully mediated the difference in BMD due to different menarcheal ages among Whites, African Americans, and Asian women with early menarcheal age; however, no mediating effects were observed for Asian women with late menarcheal age. Conclusions SMI, as a full mediator, affected the relationship between menarcheal age and BMD among premenopausal women, and the mediating effects varied by race/ethnicity. To prevent or slow down the loss of hip/spine BMD and the development of osteoporosis, measures aiming at minimizing the risk for muscle mass loss should be recommended, especially for White and African-American women with late menarcheal age.

Bone mineral density (BMD) and whole-body lean mass (WBLM) are two important phenotypes of osteoporosis and sarcopenia. Previous studies have shown that BMD and lean mass were phenotypically and genetically correlated. To identify the novel common genetic factors shared between BMD and WBLM, we performed the conditional false discovery rate (cFDR) analysis using summary data of the genome-wide association study of femoral neck BMD (n = 53,236) and WBLM (n = 38,292) from the Genetic Factors for Osteoporosis Consortium (GEFOS). We identified eight pleiotropic Single Nucleotide Polymorphism (SNPs) (PLCL1 rs11684176 and rs2880389, JAZF1 rs198, ADAMTSL3 rs10906982, RFTN2/MARS2 rs7340470, SH3GL3 rs1896797, ST7L rs10776755, ANKRD44/SF3B1 rs11888760) significantly associated with femoral neck BMD and WBLM (ccFDR < 0.05). Bayesian fine-mapping analysis showed that rs11888760, rs198, and rs1896797 were the possible functional variants in the ANKRD44/SF3B1, JAZF1i, and SH3GL3 loci, respectively. Functional annotation suggested that rs11888760 was likely to comprise a DNA regulatory element and linked to the expression of RFTN2 and PLCL1. PLCL1 showed differential expression in laryngeal posterior cricoarytenoid muscle between rats of 6 months and 30 months of age. Our findings, together with PLCL1's potential functional relevance to bone and skeletal muscle function, suggested that rs11888760 was the possible pleiotropic functional variants appearing to coregulate both bone and muscle metabolism through regulating the expression of PLCL1. The findings enhanced our knowledge of genetic associations between BMD and lean mass and provide a rationale for subsequent functional studies of the implicated genes in the pathophysiology of diseases, such as osteoporosis and sarcopenia.

Background Although genome-wide association studies (GWAS) have been extensively applied in identifying SNP associated with metabolic diseases, the SNPs identified by this prevailing univariate approach only explain a small percentage of the genetic variance of traits. The extensive previous studies have repeatedly shown type2 diabetes (T2D), obesity and coronary artery disease (CAD) have common genetic mechanisms and the overlapping pathophysiological pathways. Methods The genetic pleiotropy-informed metaCCA method was applied on summary statistics data from three independent meta-GWAS summary statistics to identify shared variants and pleiotropic effect between T2D, obesity and CAD. Furthermore, to refine all genes, we performed gene-based association analyses for these three diseases respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes. Results After metaCCA analysis, 833 SNPs reached the Bonferroni corrected threshold (p Conclusions The metaCCA method identified novel variants associated with T2D, obesity and CAD by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of metabolic diseases based on the pleiotropic genes and common mechanisms identified.

Obesity has result in increased prevalence of type 2 diabetes (T2D) in children. The genetic mechanisms underlying their relationship, however, are not fully understood. Here, we aim to identify novel SNPs associated with T2D and childhood obesity (CO), especially their pleiotropic loci. We integrated the summary statistics for two independent GWASs of T2D (n = 149,821) and childhood body mass index (CBMI) (n = 35,668) using the pleiotropy-informed conditional false discovery rate (cFDR) method. By leveraging the information of different levels of association for CBMI, we observed a strong enrichment of genetic variants associated with T2D. We identified 139 T2D-associated SNPs with 125 novel ones (cFDR

Background: Recent studies have shown that epigenetic alterations, especially the hypermethylated promoters of tumor suppressor genes (TSGs), contribute to prostate cancer progression and metastasis. This article proposes a novel algorithm to identify epigenetically silenced TSGs (epi-TSGs) for prostate cancer. Methods: Our method is based on the perception that the promoter CpG island(s) of a typical epi-TSG has a stratified methylation profile over tumor samples. In other words, we assume that the methylation profile resembles the combination of a binary distribution of a driver mutation and a continuous distribution representing measurement noise and intratumor heterogeneity. Results: Applying the proposed algorithm and an existing method to The Cancer Genome Atlas prostate cancer data, we identify 57 candidate epi-TSGs. Over one third of these epi-TSGs have been reported to carry potential tumor suppression functions. The negative correlations between the expression levels and methylation levels of these genes are validated on external independent datasets. We further find that the expression profiling of these genes is a robust predictive signature for Gleason scores, with the AUC statistic ranging from 0.75 to 0.79. The identified signature also shows prediction strength for tumor progression stages, biochemical recurrences, and metastasis events. Conclusions: We propose a novel method for pinpointing candidate epi-TSGs in prostate cancer. The expression profiling of the identified epi-TSGs demonstrates significant prediction strength for tumor progression. Impact: The proposed epi-TSGs identification method can be adapted to other cancer types beyond prostate cancer. The identified clinically significant epi-TSGs would shed light on the carcinogenesis of prostate adenocarcinomas.

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.

Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Motivation Transcriptome-wide association studies (TWAS) have successfully facilitated the discovery of novel genetic risk loci for many complex traits, including late-onset Alzheimer’s disease (AD). However, most existing TWAS methods rely only on gene expression and ignore epigenetic modification (i.e. DNA methylation) and functional regulatory information (i.e. enhancer-promoter interactions), both of which contribute significantly to the genetic basis of AD. Results We develop a novel gene-level association testing method that integrates genetically regulated DNA methylation and enhancer–target gene pairs with genome-wide association study (GWAS) summary results. Through simulations, we show that our approach, referred to as the CMO (cross methylome omnibus) test, yielded well controlled type I error rates and achieved much higher statistical power than competing methods under a wide range of scenarios. Furthermore, compared with TWAS, CMO identified an average of 124% more associations when analyzing several brain imaging-related GWAS results. By analyzing to date the largest AD GWAS of 71 880 cases and 383 378 controls, CMO identified six novel loci for AD, which have been ignored by competing methods. Availabilityand implementation The data used in this work were obtained from the following publicly available datasets: IGAP1, GWAX, UK Biobank, a 2019 meta-analyzed AD GWAS results and a imaging-derived phenotype GWAS results. The data resources are summarized in Supplementary Table S7. We used the publicly available software and tools for competing methods. All codes used to generate results that are reported in this manuscript and software for our newly proposed method CMO are available at https://github.com/ChongWuLab/CMO. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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.

Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice., Machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffers from a significant bottleneck of requiring massive amounts of labeled data. Here, the authors propose a semi-supervised model based on the mean teacher architecture that provides pathological predictions at both patch- and patient-levels.

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.

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.

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.

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.

Osteoporosis and obesity are two severe complex diseases threatening public health worldwide. Both diseases are under strong genetic determinants as well as genetically correlated. Aiming to identify pleiotropic genes underlying obesity and osteoporosis, we performed a bivariate genome-wide association (GWA) meta-analysis of hip bone mineral density (BMD) and total body fat mass (TBFM) in 12,981 participants from seven samples, and followed by in silico replication in the UK biobank (UKB) cohort sample (N = 217,822). Combining the results from discovery meta-analysis and replication sample, we identified one novel locus, 17q21.31 (lead SNP rs12150327, NC_000017.11:g.44956910G > A, discovery bivariate P = 4.83 × 10(−9), replication P = 5.75 × 10(−5)) at the genome-wide significance level (ɑ = 5.0 × 10(−8)), which may have pleiotropic effects to both hip BMD and TBFM. Functional annotations highlighted several candidate genes, including KIF18B, C1QL1, and PRPF19 that may exert pleiotropic effects to the development of both body mass and bone mass. Our findings can improve our understanding of the etiology of osteoporosis and obesity, as well as shed light on potential new therapies.

BackgroundLow lean body mass is the most important predictor of sarcopenia with strong genetic background. The aim of this study was to uncover genetic factors underlying lean mass development.Materials and methodsWe performed a genome-wide association study (GWAS) of fat-adjusted leg lean mass in the Framingham Heart Study (FHS,N = 6587), and replicated in the Women’s Health Initiative–African American sub-sample (WHI-AA,N = 847) and the Kansas City Osteoporosis Study (KCOS,N = 2219). We also cross-validated significant variants in the publicly available body mass index (BMI) summary results (N~ 700,000). We then performed a series of functional investigations on the identified variants.ResultsFour correlated SNPs at 6p21.1 were identified at the genome-wide significance (GWS,α = 5.0 × 10−8) level in the discovery FHS sample (rs551145, rs524533, rs571770, and rs545970,p = 3.40–9.77 × 10−9), and were successfully replicated in both the WHI-AA and the KCOS samples (one-sidedp = 1.61 × 10−3–0.04). They were further cross-validated by the large-scale BMI summary results (p = 7.0–9.8 × 10−3). Cis-eQTL analyses associated these SNPs with theNFKBIEgene expression. Electrophoresis mobility shift assay (EMSA) in mouse C2C12 myoblast cells implied that rs524533 and rs571770 were bound to an unknown transcription factor in an allelic specific manner, while rs551145 and rs545970 did not. Dual-luciferase reporter assay revealed that both rs524533 and rs571770 downregulated luciferase expression by repressing promoter activity. Moreover, the regulation pattern was allelic specific, strengthening the evidence towards their differential regulatory effects.ConclusionsThrough a large-scale GWAS followed by a series of functional investigations, we identified 2 correlated functional variants at 6p21.1 associated with leg lean mass. Our findings not only enhanced our understanding of molecular basis of lean mass development but also provided useful candidate genes for further functional studies.

Zona pellucida (ZP) plays a vital role in reproductive processes including oogenesis, fertilization and preimplantation development of embryo. The ZP of humans is composed of four glycoproteins (ZP1-ZP4), same as rats ZP. Our previous research reported a first case of human infertility due toZP1mutation, but the mechanism was unclear. Here we developed a genome editingin vivorat model and a co-transfectedin vitrocell model to investigate the pathogenic effect. In rat homozygous for the homologous mutation, ZP were absent in all of collected eggs. Further the growing and fully grown oocytes in the mutant ovaries completely lack a ZP but with detectable intracellular ZP1 protein. After mating with male rats, none of the mutant female rats got pregnant. Moreover, the co-transfected cell experiments and the ovarian experiments showed that the truncated ZP1 sequestered intracellularly ZP3 and ZP4 to impede their release outside, resulting in an intracellular accumulation of ZP1, ZP3 and ZP4, leading to absence of ZP in mutant oocytes. Our results clearly establish the causal role ofZP1mutation on ZP defects and female infertility.Summary statementRat model mirrored completely the phenotypes observed in humans, infertility and abnormal eggs that lack a zona pellucida, through the negative effects ofZP1mutation.

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.

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.

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.

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

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.

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.

Low bone mineral density (BMD) and osteoporosis have become a public health problem. We found that non-Hispanic white, black, and Asian adults with extremely low education and personal income are more likely to have lower BMD. This relationship is gender-specific. These findings are valuable to guide bone health interventions. The evidence is limited regarding the relationship between socioeconomic status (SES) and bone mineral density (BMD) for minority populations in the USA, as well as the relationship between SES and BMD for men. This study explored and examined the relationship between SES and BMD by race/ethnicity and gender. Data (n = 6568) from the Louisiana Osteoporosis Study (LOS) was examined, including data for non-Hispanic whites (n = 4153), non-Hispanic blacks (n = 1907), and non-Hispanic Asians (n = 508). General linear models were used to estimate the relationship of SES and BMD (total hip and lumbar spine) stratified by race/ethnicity and gender. Adjustments were made for physiological and behavioral factors. After adjusting for covariates, men with education levels below high school graduate experienced relatively low hip BMD than their counterparts with college or graduate education (p

OBJECTIVE Body fat mass (BFM) is more homogeneous and accurate than body total mass in measuring obesity but has rarely been studied. Aiming to uncover the genetic basis of fat-induced obesity, a genome-wide association meta-analysis of BFM, after adjustment by body lean mass, was performed in the European population. METHODS Three samples of European ancestry were included in the meta-analysis: the Framingham Heart Study (N = 6,004), the Kansas City osteoporosis study (N = 2,207), and the Omaha osteoporosis study (N = 968). RESULTS At the genome-wide significance level (α = 5.0×10-8 ), a cluster of 10 single-nucleotide polymorphisms (SNPs) at chromosomal region 20p11 that were associated with BFM (lead SNP rs2069126, P = 1.82×10-9 , closest gene SLC24A3) was identified in 9,179 subjects. One of the top SNPs, rs6046308 (P = 3.74×10-8 ), was found to be nominally significant for body fat percentage in another independent study (P = 0.03, N = 75,888) and was reported to transregulate the expression of the MPZ gene at 1q23.3 (unadjusted P = 9.78×10-6 , N = 1,490). Differential gene expression analysis demonstrated that SLC24A3 and CFAP61 at the identified locus were differentially expressed in tissues of people with versus without obesity (P = 3.40×10-5 and 8.72×10-4 , N = 126 and 70), implying their potential role in fat development. CONCLUSIONS These results may provide new insights into the biological mechanism that underlies fat-induced obesity pathology.

Recent study observed defective autophagy in chondrocytes with Kashin-Beck Disease (KBD). To clarify the potential role of autophagy-related ATG4C gene in the development of KBD, we conducted an integrative analysis of genetic association, messenger ribonucleic acid (mRNA) and protein expression of ATG4C in KBD patients.1026 subjects (559 KBD patients and 467 healthy cases) were enrolled in discovery association study. Four single nucleotide polymorphisms (SNPs) of ATG4C gene (rs11208030, rs4409690, rs12097658 and rs6587988) were genotyped by Sequenom MassARRAY platform. Association analysis was conducted by PLINK software. The significant SNPs of ATG4C were replicated using an independent sample of 899 subjects (including 90 KBD patients and 809 healthy controls). Ungenotyped SNPs in ATG4C gene were imputed by IMPUTE 2.0. Knee cartilage specimens were collected from five KBD patients and five healthy subjects. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were performed to compare the mRNA and protein expression levels of ATG4C between KBD cartilage and control cartilage.We observed significant association between KBD and rs11208030 (P value = 0.003), rs4409690 (P value = 0.004), rs12097658 (P value = 0.003) and rs6587988 (P value = 0.003) in both discovery and replication samples. The mRNA expression level of ATG4C (ratio = 0.168, P value = 0.007) in KBD chondrocytes was significantly lower than that in normal chondrocytes. Western blot (P value 0.001) further confirmed the reduced expression of ATG4C protein in both KBD cartilage and chondrocytes.Our results strongly suggest that ATG4C was a novel autophagy-related susceptibility gene of KBD.

Background Clinical and epidemiological findings point to an association between type 2 diabetes (T2D) and low birth weight (BW). However, the nature underlying their relationship is largely unknown. Here, we aim to identify novel single-nucleotide polymorphisms (SNPs) in T2D, BW and their pleiotropic loci. Methods We applied a pleiotropy-informed conditional false discovery rate (cFDR) method to two independent GWAS summary statistics of T2D (n = 149,821) and BW (n = 26,836). Results Conditional Q-Q plot shows a strong enrichment of genetic variants in T2D conditioned on different levels of association with BW. We identified 133 SNPs with a significance threshold of cFDR