Proteomics study the potential targets for Rifampicin-resistant spinal tuberculosis.

Introduction: The escalating global surge in Rifampicin-resistant strains poses a formidable challenge to the worldwide campaign against tuberculosis (TB), particularly in developing countries. The frequent reports of suboptimal treatment outcomes, complications, and the absence of definitive treatment guidelines for Rifampicin-resistant spinal TB (DSTB) contribute significantly to the obstacles in its effective management. Consequently, there is an urgent need for innovative and efficacious drugs to address Rifampicin-resistant spinal tuberculosis, minimizing the duration of therapy sessions. This study aims to investigate potential targets for DSTB through comprehensive proteomic and pharmaco-transcriptomic analyses. Methods: Mass spectrometry-based proteomics analysis was employed to validate potential DSTB-related targets. PPI analysis confirmed by Immunohistochemistry (IHC) and Western blot analysis. Results: The proteomics analysis revealed 373 differentially expressed proteins (DEPs), with 137 upregulated and 236 downregulated proteins. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses delved into the DSTB-related pathways associated with these DEPs. In the context of network pharmacology analysis, five key targets-human leukocyte antigen A chain (HLAA), human leukocyte antigen C chain (HLA-C), HLA Class II Histocompatibility Antigen, DRB1 Beta Chain (HLA-DRB1), metalloproteinase 9 (MMP9), and Phospholipase C-like 1 (PLCL1)-were identified as pivotal players in pathways such as "Antigen processing and presentation" and "Phagosome," which are crucially enriched in DSTB. Moreover, pharmaco-transcriptomic analysis can confirm that 58 drug compounds can regulate the expression of the key targets. Discussion: This research confirms the presence of protein alterations during the Rifampicin-resistant process in DSTB patients, offering novel insights into the molecular mechanisms underpinning DSTB. The findings suggest a promising avenue for the development of targeted drugs to enhance the management of Rifampicin-resistant spinal tuberculosis.

Inferring Causal Relationships Between Metabolites and Polycystic Ovary Syndrome Using Summary Statistics from Genome­Wide Association Studies.

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. Previous studies have suggested that metabolites may play a pivotal mediating role in the progression of phenotypic variations. Although several metabolites had been identified as potential markers for PCOS, the relationship between blood metabolites and PCOS was not comprehensively explored. Previously, Pickrell et al. designed a robust approach to infer evidence of a causal relationship between different phenotypes using independently putative causal SNPs. Our previous paper extended this approach to make it more suitable for cases where only a few independently putative causal SNPs were identified to be significantly associated with the phenotypes (i.e., metabolites). When the most significant SNPs in each independent locus (the independent lead SNPs) with p-values of < 1 × 10-5 were used, 3 metabolites (2-tetradecenoyl carnitine, threitol, 1-docosahexaenoylglycerophosphocholine) causally influencing PCOS and 2 metabolites (asparagine and phenyllactate) influenced by PCOS were identified, (relative likelihood r < 0.01). Under a less stringent threshold of r < 0.05, 7 metabolites (trans-4-hydroxyproline, glutaroyl carnitine, stachydrine, undecanoate, 7-Hoca, N-acetylalanine and 2-hydroxyisobutyrate) were identified. Taken together, this study can provide novel insights into the pathophysiological mechanisms underlying PCOS; whether these metabolites can serve as biomarkers to predict PCOS in clinical practice warrants further investigations.

Gut microbiota impacts bone via Bacteroides vulgatus-valeric acid-related pathways.

Although the gut microbiota has been reported to influence osteoporosis risk, the individual species involved, and underlying mechanisms, remain largely unknown. We performed integrative analyses in a Chinese cohort of peri-/post-menopausal women with metagenomics/targeted metabolomics/whole-genome sequencing to identify novel microbiome-related biomarkers for bone health. Bacteroides vulgatus was found to be negatively associated with bone mineral density (BMD), which was validated in US white people. Serum valeric acid (VA), a microbiota derived metabolite, was positively associated with BMD and causally downregulated by B. vulgatus. Ovariectomized mice fed B. vulgatus demonstrated increased bone resorption and poorer bone micro-structure, while those fed VA demonstrated reduced bone resorption and better bone micro-structure. VA suppressed RELA protein production (pro-inflammatory), and enhanced IL10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells and enhanced maturation of osteoblasts in vitro. The findings suggest that B. vulgatus and VA may represent promising targets for osteoporosis prevention/treatment.

Functional Enrichment Analysis Identifying Regulatory Information Associated with Human Fracture.

Dozens of loci associated with fracture have been identified by genome-wide association studies (GWASs). However, most of these variants are located in the noncoding regions including introns, long terminal repeats, and intergenic regions. Although combining regulation information helps to identify the causal SNPs and interpret the involvement of these variants in the etiology of human fracture, regulation information which was truly associated with fracture was unknown. A novel functional enrichment method GARFIELD (GWAS Analysis of Regulatory of Functional Information Enrichment with LD correction) was applied to identify fracture-associated regulation information, including transcript factor binding sites, expression quantitative trait loci (eQTLs), chromatin states, enhancer, promoter, dyadic, super enhancer and Epigenome marks. Fracture SNPs were significantly enriched in exon (Bonferroni correction, p value < 7.14 × 10-3) at two GWAS p value thresholds through GARFIELD. High level of fold-enrichment was observed in super enhancer of monocyte and the enhancer of chondrocyte (Bonferroni correction, p value < 4.45 × 10-3). eQTLs of 44 tissues/cells and 10 transcription factors (TFs) were identified to be associated with human fracture. These results provide new insight into the etiology of human fracture, which might increase the identification of the causal SNPs through the fine-mapping study combined with functional annotation, as well as polygenic risk score.

Putative Candidate Drug Targets for Sarcopenia-Related Traits Identified Through Mendelian Randomization Analysis of the Blood Proteome.

Purpose: The increasing prevalence of sarcopenia remains an ongoing challenge to health care systems worldwide. The lack of treatments encouraged the discovery of human proteomes to find potential therapeutic targets. As one of the major components of the human proteome, plasma proteins are functionally connected with various organs of the body to regulate biological processes and mediate overall homeostasis, which makes it crucial in various complex processes such as aging and chronic diseases. By performing a systematic causal analysis of the plasma proteome, we attempt to reveal the etiological mechanism and discover drug targets for sarcopenia. Methods: By using data from four genome-wide association studies for blood proteins and the UK Biobank data for sarcopenia-related traits, we applied two-sample Mendelian randomization (MR) analysis to evaluate 310 plasma proteins as possible causal mediators of sarcopenia-related traits: appendicular lean mass (ALM) and handgrip strength (right and left). Then we performed a two-sample bidirectional Mendelian randomization analysis for the identified putatively causal proteins to assess potential reverse causality that the trait values may influence protein levels. Finally, we performed phenome-wide MR analysis of the identified putatively causal proteins for 784 diseases to test the possible side effects of these proteins on other diseases. Results: Five plasma proteins were identified as putatively causal mediators of sarcopenia-related traits. Specifically, leukocyte immunoglobulin-like receptor subfamily B member 2 (LILRB2), asporin (ASPN), and contactin-2 (CNTN2) had potential causal effects on appendicular lean mass, and ecto-ADP-ribosyltransferase 4 (ART4) and superoxide dismutase 2 (SOD2) had putative causal effects on the handgrip strength, respectively. None of the five putatively causal proteins had a reverse causality relationship with sarcopenia-related traits, and no side effects on other diseases were identified. Conclusion: We identified five plasma proteins that may serve as putatively potential novel drug targets for sarcopenia. Our study attested to the value of two-sample MR analysis in identifying and prioritizing putatively potential therapeutic targets for complex diseases.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans.

BACKGROUND: Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels. SUBJECTS AND METHODS: Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans. RESULTS: By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases. CONCLUSIONS: The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Identification of PDXDC1 as a novel pleiotropic susceptibility locus shared between lumbar spine bone mineral density and birth weight.

An increasing number of epidemiological studies have suggested that birth weight (BW) may be a determinant of bone health later in life, although the underlying genetic mechanism remains unclear. Here, we applied a pleiotropic conditional false discovery rate (cFDR) approach to the genome-wide association study (GWAS) summary statistics for lumbar spine bone mineral density (LS BMD) and BW, aiming to identify novel susceptibility variants shared between these two traits. We detected 5 novel potential pleiotropic loci which are located at or near 7 different genes (NTAN1, PDXDC1, CACNA1G, JAG1, FAT1P1, CCDC170, ESR1), among which PDXDC1 and FAT1P1 have not previously been linked to these phenotypes. To partially validate the findings, we demonstrated that the expression of PDXDC1 was dramatically reduced in ovariectomized (OVX) mice in comparison with sham-operated (SHAM) mice in both the growth plate and trabecula bone. Furthermore, immunohistochemistry assay with serial sections showed that both osteoclasts and osteoblasts express PDXDC1, supporting its potential role in bone metabolism. In conclusion, our study provides insights into some shared genetic mechanisms for BMD and BW as well as a novel potential therapeutic target for the prevention of OP in the early stages of the disease development. KEY MESSAGES : We investigated pleiotropy-informed enrichment between LS BMD and BW. We identified genetic variants related to both LS BMD and BW by utilizing a cFDR approach. PDXDC1 is a novel pleiotropic gene which may be related to both LS BMD and BW. Elevated expression of PDXDC1 is related to higher BMD and lower ratio n-6/n-3 PUFA indicating a bone protective effect of PDXDC1.

NaN(SiMe3)2/CsTFA Copromoted Aminobenzylation/Cyclization of 2-Isocyanobenzaldehydes with Toluene Derivatives or Benzyl Compounds: One-Pot Access to Dihydroquinazolines and Quinazolines.

A NaN(SiMe3)2/CsTFA copromoted aminobenzylation/cyclization reaction of 2-isocyanobenzaldehydes with toluene derivatives or benzyl compounds has been developed. The reaction works with a broad range of toluene derivatives and benzyl compounds, and provides a simple and efficient strategy for the synthesis of 4-benzyl-substitued dihydroquinazoline and quinazoline derivatives from easily available acyclic starting materials in a single step. Further applications, including synthesis of quinazoline, dihydroindolo[1,2-c]quinazoline, and dihydro-8H-isoquinolino[2,3-c]quinazoline, demonstrated the tremendous potential of the tandem reaction.

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.

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.

Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics.

Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug-disease pharmacological effect between drug-target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

Integrated metagenome and metabolome analyses of blood pressure studies in early postmenopausal Chinese women.

OBJECTIVE: We carried out sensitivity analyses on gut microbiota metagenomic sequencing, untargeted metabolome, targeted metabolome for short-chain fatty acids (SCFAs) and human 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. METHODS: Clusters of co-abundant gut bacterial species and serum untargeted metabolites were identified by weighted gene co-expression network analysis (WGCNA). Partial least square analysis and joint analysis were performed to detect BP-associated omics-variables. Partial Pearson correlation was conducted to identify the interactions of microbe--host for host BP variation. Mendelian randomization analysis and causal inference test were used to examine causal relationships among gut microbiota, metabolites and BP variation. RESULTS: In the present study, 651 bacterial species and 296 metabolites were binned into 53 and 26 co-abundance clusters by WGCNA, respectively. Then, we totally identified four gut bacterial species, one host metabolites and two SCFAs that were significantly associated with both SBP and DBP. Moreover, we found that gut microbiota would play important roles in host metabolic activity. Finally, our results revealed that increased Bacteroides fragilis could elevate BP via decreased caproic acid, and phenylacetylglutamine mediated the causal relationships of both B. fragilis and Clostridium sp.CAG.226 on DBP variation. CONCLUSION: Multi-omics datasets integration has the potential to capture complementary effect and their interactions for BP variation, revealed the potential pathogenesis of BP variation and may be useful for studying other complex diseases/traits.

Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.

MOTIVATION: Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction. RESULTS: By incorporating the Hi-C data, we propose a novel and powerful deep learning model of artificial intelligence to classify interacting and non-interacting fragment pairs and predict the functional effects of sequence alteration of single nucleotide on chromatin interaction and thus on gene expression. The changes in chromatin interaction probability between the reference sequence and the altered sequence reflect the degree of functional impact for the variant. The model was effective and efficient with the classification of interacting and non-interacting fragment pairs. The predicted causal SNPs that had a larger impact on chromatin interaction were more likely to be identified by GWAS and eQTL analyses. We demonstrate that an integrative approach combining artificial intelligence-deep learning with high throughput experimental evidence of chromatin interaction leads to prioritizing the functional variants in disease- and phenotype-related loci and thus will greatly expedite uncover of the biological mechanism underlying the association identified in genomic studies. AVAILABILITY AND IMPLEMENTATION: Source code used in data preparing and model training is available at the GitHub website (https://github.com/biocai/DeepHiC). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying Pleiotropic SNPs Associated With Femoral Neck and Heel Bone Mineral Density.

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 < 9.8E-6), 137 (cFDR < 8.9E-4) and 124 SNPs associated with eBMD, FNK-BMD, and both eBMD and FNK-BMD, respectively, with 37 genomic regions where there was a SNP that influences both eBMD and FNK-BMD. Most genomic regions only contained putative causal SNPs associated with eBMD and 3 regions contained two distinct putative causal SNPs influenced both traits, respectively. We demonstrated a causal effect of FNK-BMD/eBMD on fracture. CONCLUSION: Most of SNPs or putative causal SNPs associated with FNK-BMD were also associated with eBMD. However, most of SNPs or putative causal SNPs associated with eBMD were not associated with FNK-BMD. The novel variants we identified may help to account for the additional proportion of variance of each trait and advance our understanding of the genetic mechanisms underlying osteoporotic fracture.

Network-based Transcriptome-wide Expression Study for Postmenopausal Osteoporosis.

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.

Osteoporosis- and obesity-risk interrelationships: an epigenetic analysis of GWAS-derived SNPs at the developmental gene TBX15.

A major challenge in translating findings from genome-wide association studies (GWAS) to biological mechanisms is pinpointing functional variants because only a very small percentage of variants associated with a given trait actually impact the trait. We used an extensive epigenetics, transcriptomics, and genetics analysis of the TBX15/WARS2 neighbourhood to prioritize this region's best-candidate causal variants for the genetic risk of osteoporosis (estimated bone density, eBMD) and obesity (waist-hip ratio or waist circumference adjusted for body mass index). TBX15 encodes a transcription factor that is important in bone development and adipose biology. Manual curation of 692 GWAS-derived variants gave eight strong candidates for causal SNPs that modulate TBX15 transcription in subcutaneous adipose tissue (SAT) or osteoblasts, which highly and specifically express this gene. None of these SNPs were prioritized by Bayesian fine-mapping. The eight regulatory causal SNPs were in enhancer or promoter chromatin seen preferentially in SAT or osteoblasts at TBX15 intron-1 or upstream. They overlap strongly predicted, allele-specific transcription factor binding sites. Our analysis suggests that these SNPs act independently of two missense SNPs in TBX15. Remarkably, five of the regulatory SNPs were associated with eBMD and obesity and had the same trait-increasing allele for both. We found that WARS2 obesity-related SNPs can be ascribed to high linkage disequilibrium with TBX15 intron-1 SNPs. Our findings from GWAS index, proxy, and imputed SNPs suggest that a few SNPs, including three in a 0.7-kb cluster, act as causal regulatory variants to fine-tune TBX15 expression and, thereby, affect both obesity and osteoporosis risk.

Zn(OAc)2-catalyzed tandem cyclization of isocyanides, α-diazoketones, and anhydrides: a general route to polysubstituted maleimides.

A novel Zn(OAc)2-catalyzed three-component tandem cyclization reaction of isocyanides, α-diazoketones and anhydrides has been developed. The reaction demonstrates the wide scope of substrates and provides a novel and efficient strategy for the synthesis of polysubstituted maleimides from readily available substrates in a single step.

Examining the causal role of leptin in bone mineral density: A Mendelian randomization study.

Leptin, a small polypeptide hormone secreted by the adipocytes, controls body weight and gonadal function by binding to a special receptor located in the hypothalamus. Observational studies have demonstrated a controversial association between leptin and bone mineral density (BMD), and functional studies of the relationship between leptin and BMD still largely vary by different studies. Using SNPs strongly associated with leptin levels in 52,140 individuals, we conducted a two-sample Mendelian randomization study to identify whether genetically lowered leptin levels were associated with BMD by using an inverse-variance weighted method, a weighted median method, MR-Egger and Robust Adjusted Profile Score. We found that circulating leptin levels may causally decrease lumbar spine BMD (effect size = -0.45, 95% CI: -0.82, -0.083; p value = 0.016). The association estimates of circulating leptin levels on femoral neck, forearm and total body BMD were not significant. Our study suggests that genetically predicted higher circulating leptin was associated with lower LS-BMD.

Spinal cord injury induced Neuregulin 1 signaling changes in mouse prefrontal cortex and hippocampus.

Accumulated evidence has recently demonstrated that spinal cord injury (SCI) can lead to chronic damage in a wide range of brain regions. Neuregulin 1 (Nrg1) signaling has been broadly recognized as an important mechanism contributing to neural differentiation and regeneration. We here studied the effect of SCI on Nrg1 signaling in prefrontal cortex (PFC) and hippocampus (HIP) in a mouse model. As was indicated by the increased levels of GFAP and Iba-1, our results demonstrated that SCI significantly induced activation of astrocytes and microglial cells in both PFC and HIP. In addition, both western blot and morphological assay demonstrated that Nrg1 was altered in both regions at 8 weeks post SCI, which was accompanied with decreased phosphorylation levels of its cognitive receptors Neu and ErbB4. Our combined results indicated that SCI can influence Nrg1 signaling, which may contribute to the worsening of pathophysiological changes in major brain regions during SCI. These results also suggested that exogenous Nrg1 treatment may have a therapeutic role in counteracting SCI-induced brain damage.

Integration of summary data from GWAS and eQTL studies identified novel causal BMD genes with functional predictions.

PURPOSE: Osteoporosis is a common global health problem characterized by low bone mineral density (BMD) and increased risk of fracture. Genome-wide association studies (GWAS) have identified >100 genetic loci associated with BMD. However, the functional genes responsible for most associations remain largely unknown. We conducted an innovative summary statistic data-based Mendelian randomization (SMR) analysis to identify novel causal genes associated with BMD and explored their potential functional significance. METHODS: After quality control of the largest GWAS meta-analysis data of BMD and the largest expression quantitative trait loci (eQTL) meta-analysis data from peripheral blood samples, 5967 genes were tested using the SMR method. Another eQTL data was used to verify the results. Next we performed a fine-mapping association analysis to investigate the functional SNP in the identified loci. Weighted gene co-expression network analysis (WGCNA) was used to explore functional relationships for the identified novel genes with known putative osteoporosis genes. Further, we assessed functions of the identified genes through in vitro cellular study or previous functional studies. RESULTS: We identified two potentially causal genes (ASB16-AS1 and SYN2) associated with BMD. SYN2 was a novel osteoporosis candidate gene and ASB16-AS1 locus was known to be associated with BMD but was not the nearest gene to the top GWAS SNP. Fine-mapping association analysis showed that rs184478 and rs795000 was predicted to be possible causal SNPs in ASB16-AS1 and SYN2, respectively. ASB16-AS1 co-expressed with several known putative osteoporosis risk genes. In vitro cellular study showed that over-expressed ASB16-AS1 increased the expression of osteoblastogenesis related genes (BMP2 and ALPL), indicating its functional significance. CONCLUSION: Our findings support that ASB16-AS1 and SYN2 may represent two novel functional genes underlying BMD variation. The findings provide a basis for further functional mechanistic studies.

Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies.

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. 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 using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is 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 ten blood metabolites that may causally influence FN-BMD. 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.