Integrating GWAS and Co-expression Network Data Identifies Bone Mineral Density Genes SPTBN1 and MARK3 and an Osteoblast Functional Module

Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations

Genome-wide association studies (GWASs) have identified many sources of genetic variation associated with bone mineral density (BMD), a clinical predictor of fracture risk and osteoporosis. Aside from the identification of causal genes, other difficult challenges to informing GWAS include characterizing the roles of predicted causal genes in disease and providing additional functional context, such as the cell type predictions or biological pathways in which causal genes operate. Leveraging single-cell transcriptomics (scRNA-seq) can assist in informing BMD GWAS by linking disease-associated variants to genes and providing a cell type context for which these causal genes drive disease. Here, we use large-scale scRNA-seq data from bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) from Diversity Outbred (DO) mice to generate cell type-specific networks and contextualize BMD GWAS-implicated genes. Using trajectories inferred from the scRNA-seq data, we identify networks enriched with genes that exhibit the most dynamic changes in expression across trajectories. We discover 21 network driver genes, which are likely to be causal for human BMD GWAS associations that colocalize with expression/splicing quantitative trait loci (eQTL/sQTL). These driver genes, including Fgfrl1 and Tpx2, along with their associated networks, are predicted to be novel regulators of BMD via their roles in the differentiation of mesenchymal lineage cells. In this work, we showcase the use of single-cell transcriptomics from mouse bone-relevant cells to inform human BMD GWAS and prioritize genetic targets with potential causal roles in the development of osteoporosis.

Inferring causal cell types of human diseases and risk variants from candidate regulatory elements

The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.

TEAD1, MYO7A and NDUFC2 are novel functional genes associated with glucose metabolism in BXD recombinant inbred population

AIM

The liver is an important metabolic organ that governs glucolipid metabolism, and its dysfunction may cause non-alcoholic fatty liver disease, type 2 diabetes mellitus, dyslipidaemia, etc. We aimed to systematic investigate the key factors related to hepatic glucose metabolism, which may be beneficial for understanding the underlying pathogenic mechanisms for obesity and diabetes mellitus.

MATERIALS AND METHODS

Oral glucose tolerance test (OGTT) phenotypes and liver transcriptomes of BXD mice under chow and high-fat diet conditions were collected from GeneNetwork. QTL mapping was conducted to pinpoint genomic regions associated with glucose homeostasis. Candidate genes were further nominated using a multi-criteria approach and validated to confirm their functional relevance in vitro.

RESULTS

Our results demonstrated that plasma glucose levels in OGTT were significantly affected by both diet and genetic background, with six genetic regulating loci were mapped on chromosomes 1, 4, and 7. Moreover, TEAD1, MYO7A and NDUFC2 were identified as the candidate genes. Functionally, siRNA-mediated TEAD1, MYO7A and NDUFC2 knockdown significantly decreased the glucose uptake and inhibited the transcription of genes related to insulin and glucose metabolism pathways.

CONCLUSIONS

Our study contributes novel insights to the understanding of hepatic glucose metabolism, demonstrating the impact of TEAD1, MYO7A and NDUFC2 on mitochondrial function in the liver and their regulatory role in maintaining in glucose homeostasis.

Identification of bone mineral density associated genes with shared genetic architectures across multiple tissues: Functional insights for EPDR1, PKDCC, and SPTBN1

Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, which are highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were in the intronic and intergenic regions for the threshold at P≤5×10-8 and P≤5×10-100, respectively. EPDR1 was highly expressed in multiple tissues, including muscles, adrenal glands, blood vessels, and the thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.

Interaction between MARK3 (rs11623869), PLCB4 (rs6086746) and GEMIN2 (rs2277458) variants with bone mineral density and serum 25-hidroxivitamin D levels in Mexican Mestizo women

Introduction

Understanding the genetic factors contributing to variations in bone mineral density (BMD) and vitamin D could provide valuable insights into the pathogenesis of osteoporosis. This study aimed to evaluate the association of single nucleotide variants in MARK3 (rs11623869), PLCB4 (rs6086746), and GEMIN2 (rs2277458) with BMD in Mexican women.

Methods

The gene-gene interaction was evaluated in these variants in serum 25(OH)D levels and BMD. A genetic risk score (GRS) was created on the basis of the three genetic variants. Genotyping was performed using predesigned TaqMan assays.

Results

A significant association was found between the rs6086746-A variant and BMD at the total hip, femoral neck, and lumbar spine, in women aged 45 years or older. However, no association was observed between the variants rs11623869 and rs2277458. The rs11623869 × rs2277458 interaction was associated with total hip (p=0.002) and femoral neck BMD (p=0.013). Similarly, for vitamin D levels, we observed an interaction between the variants rs6086746 × rs2277458 (p=0.021). GRS revealed a significant association with total hip BMD (p trend=0.003) and femoral neck BMD (p trend=0.006), as well as increased vitamin D levels (p trend=0.0003). These findings provide evidence of the individual and joint effect of the MARK3, PLCB4, and GEMIN2 variants on BMD and serum vitamin D levels in Mexican women.

Discussion

This knowledge could help to elucidate the interaction mechanism between BMD-related genetic variants and 25OHD, contributing to the determination of the pathogenesis of osteoporosis and its potential implications during early interventions.

GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density

Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblast 1.19 cells (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from cross-cell type stratified LD-score regression involving 98 cell types grouped into 15 tissues. 24 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues that warrant attention in future screens.

Tmem263 deletion disrupts the GH/IGF-1 axis and causes dwarfism and impairs skeletal acquisition

Genome-wide association studies (GWAS) have identified a large number of candidate genes believed to affect longitudinal bone growth and bone mass. One of these candidate genes, TMEM263, encodes a poorly characterized plasma membrane protein. Single nucleotide polymorphisms in TMEM263 are associated with bone mineral density in humans and mutations are associated with dwarfism in chicken and severe skeletal dysplasia in at least one human fetus. Whether this genotype-phenotype relationship is causal, however, remains unclear. Here, we determine whether and how TMEM263 is required for postnatal growth. Deletion of the Tmem263 gene in mice causes severe postnatal growth failure, proportional dwarfism, and impaired skeletal acquisition. Mice lacking Tmem263 show no differences in body weight within the first 2 weeks of postnatal life. However, by P21 there is a dramatic growth deficit due to a disrupted growth hormone (GH)/insulin-like growth factor 1 (IGF-1) axis, which is critical for longitudinal bone growth. Tmem263-null mice have low circulating IGF-1 levels and pronounced reductions in bone mass and growth plate length. The low serum IGF-1 in Tmem263-null mice is associated with reduced hepatic GH receptor (GHR) expression and GH-induced JAK2/STAT5 signaling. A deficit in GH signaling dramatically alters GH-regulated genes and feminizes the liver transcriptome of Tmem263-null male mice, with their expression profile resembling wild-type female, hypophysectomized male, and Stat5b-null male mice. Collectively, our data validates the causal role for Tmem263 in regulating postnatal growth and raises the possibility that rare mutations or variants of TMEM263 may potentially cause GH insensitivity and impair linear growth.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci

BACKGROUND

As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.

RESULTS

We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.

CONCLUSIONS

RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

Genetic and Gene Expression Resources for Osteoporosis and Bone Biology Research

PURPOSE OF REVIEW

The integration of data from multiple genomic assays from humans and non-human model organisms is an effective approach to identify genes involved in skeletal fragility and fracture risk due to osteoporosis and other conditions. This review summarizes genome-wide genetic variation and gene expression data resources relevant to the discovery of genes contributing to skeletal fragility and fracture risk.

RECENT FINDINGS

Genome-wide association studies (GWAS) of osteoporosis-related traits are summarized, in addition to gene expression in bone tissues in humans and non-human organisms, with a focus on rodent models related to skeletal fragility and fracture risk. Gene discovery approaches using these genomic data resources are described. We also describe the Musculoskeletal Knowledge Portal (MSKKP) that integrates much of the available genomic data relevant to fracture risk. The available genomic resources provide a wealth of knowledge and can be analyzed to identify genes related to fracture risk. Genomic resources that would fill particular scientific gaps are discussed.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Tmem263 deletion disrupts the GH/IGF-1 axis and causes dwarfism and impairs skeletal acquisition

Genome-wide association studies (GWAS) have identified a large number of candidate genes believed to affect longitudinal bone growth and bone mass. One of these candidate genes, TMEM263, encodes a poorly characterized plasma membrane protein. Single nucleotide polymorphisms in TMEM263 are associated with bone mineral density in humans and mutations are associated with dwarfism in chicken and severe skeletal dysplasia in at least one human fetus. Whether this genotype-phenotype relationship is causal, however, remains unclear. Here, we determine whether and how TMEM263 is required for postnatal growth. Deletion of the Tmem263 gene in mice causes severe postnatal growth failure, proportional dwarfism, and impaired skeletal acquisition. Mice lacking Tmem263 show no differences in body weight within the first two weeks of postnatal life. However, by P21 there is a dramatic growth deficit due to a disrupted GH/IGF-1 axis, which is critical for longitudinal bone growth. Tmem263-null mice have low circulating IGF-1 levels and pronounced reductions in bone mass and growth plate length. The low serum IGF-1 in Tmem263-null mice is associated with reduced hepatic GH receptor (GHR) expression and GH-induced JAK2/STAT5 signaling. A deficit in GH signaling dramatically alters GH-regulated genes and feminizes the liver transcriptome of Tmem263-null male mice, with their expression profile resembling a wild-type female, hypophysectomized male, and Stat5b-null male mice. Collectively, our data validates the causal role for Tmem263 in regulating postnatal growth and raises the possibility that rare mutations or variants of TMEM263 may potentially cause GH insensitivity and impair linear growth.

Single-Cell Transcriptomics of Bone Marrow Stromal Cells in Diversity Outbred Mice: A Model for Population-Level scRNA-Seq Studies

Genome-wide association studies (GWASs) have advanced our understanding of the genetics of osteoporosis; however, the challenge has been converting associations to causal genes. Studies have utilized transcriptomics data to link disease-associated variants to genes, but few population transcriptomics data sets have been generated on bone at the single-cell level. To address this challenge, we profiled the transcriptomes of bone marrow-derived stromal cells (BMSCs) cultured under osteogenic conditions from five diversity outbred (DO) mice using single-cell RNA-seq (scRNA-seq). The goal of the study was to determine if BMSCs could serve as a model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells from large populations of mice to inform genetic studies. By enriching for mesenchymal lineage cells in vitro, coupled with pooling of multiple samples and downstream genotype deconvolution, we demonstrate the scalability of this model for population-level studies. We demonstrate that dissociation of BMSCs from a heavily mineralized matrix had little effect on viability or their transcriptomic signatures. Furthermore, we show that BMSCs cultured under osteogenic conditions are diverse and consist of cells with characteristics of mesenchymal progenitors, marrow adipogenic lineage precursors (MALPs), osteoblasts, osteocyte-like cells, and immune cells. Importantly, all cells were similar from a transcriptomic perspective to cells isolated in vivo. We employed scRNA-seq analytical tools to confirm the biological identity of profiled cell types. SCENIC was used to reconstruct gene regulatory networks (GRNs), and we observed that cell types show GRNs expected of osteogenic and pre-adipogenic lineage cells. Further, CELLECT analysis showed that osteoblasts, osteocyte-like cells, and MALPs captured a significant component of bone mineral density (BMD) heritability. Together, these data suggest that BMSCs cultured under osteogenic conditions coupled with scRNA-seq can be used as a scalable and biologically informative model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells in large populations. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Anaphase-Promoting Complex Subunit 1 Associates with Bone Mineral Density in Human Osteoporotic Bone

Genome-wide association studies (GWAS) are one of the most common approaches to identify genetic loci that are associated with bone mineral density (BMD). Such novel genetic loci represent new potential targets for the prevention and treatment of fragility fractures. GWAS have identified hundreds of associations with BMD; however, only a few have been functionally evaluated. A locus significantly associated with femoral neck BMD at the genome-wide level is intronic SNP rs17040773 located in the intronic region of the anaphase-promoting complex subunit 1 (ANAPC1) gene (p = 1.5 × 10-9). Here, we functionally evaluate the role of ANAPC1 in bone remodelling by examining the expression of ANAPC1 in human bone and muscle tissues and during the osteogenic differentiation of human primary mesenchymal stem cells (MSCs). The expression of ANAPC1 was significantly decreased 2.3-fold in bone tissues and 6.2-fold in muscle tissue from osteoporotic patients as compared to the osteoarthritic and control tissues. Next, we show that the expression of ANAPC1 changes during the osteogenic differentiation process of human MSCs. Moreover, the silencing of ANAPC1 in human osteosarcoma (HOS) cells reduced RUNX2 expression, suggesting that ANAPC1 affects osteogenic differentiation through RUNX2. Altogether, our results indicate that ANAPC1 plays a role in bone physiology and in the development of osteoporosis.

A study of the influence of genetic variance and sex on the density and thickness of the calvarial bone in collaborative cross mice

BACKGROUND

Bone microarchitecture is affected by multiple genes, each having a small effect on the external appearance. It is thus challenging to characterize the genes and their specific effect on bone thickness and porosity. The purpose of this study was to assess the heritability and the genetic variation effect, as well as the sex effect on the calvarial bone thickness (Ca.Th) and calvarial porosity (%PoV) using the Collaborative Cross (CC) mouse population.

METHODS

In the study we examined the parietal bones of 56 mice from 9 lines of CC mice. Morphometric parameters were evaluated using microcomputed tomography (μCT) and included Ca.Th and %PoV. We then evaluated heritability, genetic versus environmental variance and the sex effect for these parameters.

RESULTS

Our morphometric analysis showed that Ca.Th and %PoV are both significantly different among the CC lines with a broad sense heritability of 0.78 and 0.90, respectively. The sex effect within the lines was significant in line IL111 and showed higher values of Ca.Th and %PoV in females compared to males. In line IL19 there was a borderline sex effect in Ca.Th in which males showed higher values than females.

CONCLUSIONS

These results stress the complexity of sex and genotype interactions controlling Ca.Th and %PoV, as the skeletal sexual dimorphism was dependent on the genetic background. This study also shows that the CC population is a powerful tool for establishing the genetic effect on these traits.

Genome-wide association study for carcass weight in pasture-finished beef cattle in Hawai'i

Introduction: Genome-wide association studies (GWAS) have identified genetic markers for cattle production and reproduction traits. Several publications have reported Single Nucleotide Polymorphisms (SNPs) for carcass-related traits in cattle, but these studies were rarely conducted in pasture-finished beef cattle. Hawai'i, however, has a diverse climate, and 100% of its beef cattle are pasture-fed. Methods: Blood samples were collected from 400 cattle raised in Hawai'i islands at the commercial harvest facility. Genomic DNA was isolated, and 352 high-quality samples were genotyped using the Neogen GGP Bovine 100 K BeadChip. SNPs that did not meet the quality control standards were removed using PLINK 1.9, and 85 k high-quality SNPs from 351 cattle were used for association mapping with carcass weight using GAPIT (Version 3.0) in R 4.2. Four models were used for the GWAS analysis: General Linear Model (GLM), the Mixed Linear Model (MLM), the Fixed and Random Model Circulating Probability Unification (FarmCPU), the Bayesian-Information and Linkage-Disequilibrium Iteratively Nested Keyway (BLINK). Results and Discussion: Our results indicated that the two multi-locus models, FarmCPU and BLINK, outperformed single-locus models, GLM and MLM, in beef herds in this study. Specifically, five significant SNPs were identified using FarmCPU, while BLINK and GLM each identified the other three. Also, three of these eleven SNPs ("BTA-40510-no-rs", "BovineHD1400006853", and "BovineHD2100020346") were shared by multiple models. The significant SNPs were mapped to genes such as EIF5, RGS20, TCEA1, LYPLA1, and MRPL15, which were previously reported to be associated with carcass-related traits, growth, and feed intake in several tropical cattle breeds. This confirms that the genes identified in this study could be candidate genes for carcass weight in pasture-fed beef cattle and can be selected for further breeding programs to improve the carcass yield and productivity of pasture-finished beef cattle in Hawai'i and beyond.

Identification of MKNK1 and TOP3A as ovarian endometriosis risk-associated genes using integrative genomic analyses and functional experiments

The risk of endometriosis (EM), which is a common complex gynaecological disease, is related to genetic predisposition. However, it is unclear how genetic variants confer the risk of EM. Here, via Sherlock integrative analysis, we combined large-scale genome-wide association studies (GWAS) summary statistics on EM (N = 245,494) with a blood-based eQTL dataset (N = 1490) to identify EM risk-related genes. For validation, we leveraged two independent eQTL datasets (N = 769) for integration with the GWAS data. Thus, we prioritised 14 genes, including GIMAP4, TOP3A, and NMNAT3, which showed significant association with susceptibility to EM. We also utilised two independent methods, Multi-marker Analysis of GenoMic Annotation and S-PrediXcan, to further validate the EM risk-associated genes. Moreover, protein-protein interaction network analysis showed the 14 genes were functionally connected. Functional enrichment analyses further demonstrated that these genes were significantly enriched in metabolic and immune-related pathways. Differential gene expression analysis showed that in peripheral blood samples from patients with ovarian EM, TOP3A, MKNK1, SIPA1L2, and NUCB1 were significantly upregulated, while HOXB2, GIMAP5, and MGMT were significantly downregulated compared with their expression levels in samples from the controls. Immunohistochemistry further confirmed the increased expression levels of MKNK1 and TOP3A in the ectopic and eutopic endometrium compared to normal endometrium, while HOBX2 was downregulated in the endometrium of women with ovarian EM. Finally, in ex vivo functional experiments, MKNK1 knockdown inhibited ectopic endometrial stromal cells (EESCs) migration and invasion. TOP3A knockdown inhibited EESCs proliferation, migration, and invasion, while promoting their apoptosis. Convergent lines of evidence suggested that MKNK1 and TOP3A are novel EM risk-related genes.

MARK3 kinase: Regulation and physiologic roles

Microtubule affinity-regulating kinase 3 (MARK3), a member of the MARK family, regulates several essential pathways, including the cell cycle, ciliated cell differentiation, and osteoclast differentiation. It is important to understand the control of their activities as MARK3 contains an N-terminal serine/threonine kinase domain, ubiquitin-associated domain, and C-terminal kinase-associated domain, which perform multiple regulatory functions. These functions include post-translational modification (e.g., phosphorylation) and interaction with scaffolding and other proteins. Differences in the amino acid sequence and domain position result in different three-dimensional protein structures and affect the function of MARK3, which distinguish it from the other MARK family members. Recent data indicate a potential role of MARK3 in several pathological conditions, including congenital blepharophimosis syndrome, osteoporosis, and tumorigenesis. The present review focuses on the physiological and pathological role of MARK3, its regulation, and recent developments in the small molecule inhibitors of the MARK3 signalling cascade.

Network analysis of genome-wide association studies for drug target prioritisation

Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.

Targeted Resequencing of Otosclerosis Patients from Different Populations Replicates Results from a Previous Genome-Wide Association Study

Otosclerosis is one of the most common causes of hearing loss in young adults. It has a prevalence of 0.3-0.4% in the European population. Clinical symptoms usually occur between the second and fifth decade of life. Different studies have been performed to unravel the genetic architecture of the disease. Recently, a genome-wide association study (GWAS) identified 15 novel risk loci and replicated the regions of three previously reported candidate genes. In this study, seven candidate genes from the GWAS were resequenced using single molecule molecular inversion probes (smMIPs). smMIPs were used to capture the exonic regions and the 3' and 5' untranslated regions (UTR). Discovered variants were tested for association with the disease using single variant and gene-based association analysis. The single variant results showed that 13 significant variants were associated with otosclerosis. Associated variants were found in five of the seven genes studied here, including AHSG, LINC01482, MARK3, SUPT3H and RELN. Conversely, burden testing did not show a major role of rare variants in the disease. In conclusion, this study was able to replicate five out of seven candidate genes reported in the previous GWAS. This association is likely mainly driven by common variants.

A genetic correlation scan identifies blood proteins associated with bone mineral density

Background

Osteoporosis is a common metabolic bone disease that is characterized by low bone mass. However, limited efforts have been made to explore the functional relevance of the blood proteome to bone mineral density across different life stages.

Methods

Using genome-wide association study summary data of the blood proteome and two independent studies of bone mineral density, we conducted a genetic correlation scan of bone mineral density and the blood proteome. Linkage disequilibrium score regression analysis was conducted to assess genetic correlations between each of the 3283 plasma proteins and bone mineral density.

Results

Linkage disequilibrium score regression identified 18 plasma proteins showing genetic correlation signals with bone mineral density in the TB-BMD cohort, such as MYOM2 (coefficient = 0.3755, P value = 0.0328) among subjects aged 0 ~ 15, POSTN (coefficient = − 0.5694, P value = 0.0192) among subjects aged 30 ~ 45 and PARK7 (coefficient = − 0.3613, P value = 0.0052) among subjects aged over 60.

Conclusions

Our results identified multiple plasma proteins associated with bone mineral density and provided novel clues for revealing the functional relevance of plasma proteins to bone mineral density.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05453-z.

Transcriptome-wide association study and eQTL colocalization identify potentially causal genes responsible for human bone mineral density GWAS associations

Genome-wide association studies (GWASs) for bone mineral density (BMD) in humans have identified over 1100 associations to date. However, identifying causal genes implicated by such studies has been challenging. Recent advances in the development of transcriptome reference datasets and computational approaches such as transcriptome-wide association studies (TWASs) and expression quantitative trait loci (eQTL) colocalization have proven to be informative in identifying putatively causal genes underlying GWAS associations. Here, we used TWAS/eQTL colocalization in conjunction with transcriptomic data from the Genotype-Tissue Expression (GTEx) project to identify potentially causal genes for the largest BMD GWAS performed to date. Using this approach, we identified 512 genes as significant using both TWAS and eQTL colocalization. This set of genes was enriched for regulators of BMD and members of bone relevant biological processes. To investigate the significance of our findings, we selected PPP6R3, the gene with the strongest support from our analysis which was not previously implicated in the regulation of BMD, for further investigation. We observed that Ppp6r3 deletion in mice decreased BMD. In this work, we provide an updated resource of putatively causal BMD genes and demonstrate that PPP6R3 is a putatively causal BMD GWAS gene. These data increase our understanding of the genetics of BMD and provide further evidence for the utility of combined TWAS/colocalization approaches in untangling the genetics of complex traits.

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

Multidisciplinary management of osteoporotic vertebral fractures. An overview

Vertebral fractures represent the most frequent complication associated with osteoporosis. Patients harboring a vertebral fracture complain physical impairment including low back pain and spine balance alteration, i.e., kyphosis, leading to subsequent systemic complication, with an increase in morbidity and mortality risk. Different strategies are available in the management of osteoporotic vertebral fractures: medical therapy acts as a prevention strategy while surgical vertebral augmentation procedures, when correctly indicated, aim to reduce pain and to restore the physiological vertebral height. Considering the growing prevalence and incidence of this condition and its socio-economic burden, prevention, diagnosis and treatment of osteoporotic vertebral fractures are of utmost importance. Our aim is to review the current strategies for the management of osteoporotic vertebral fractures providing an integrated multidisciplinary endocrinological, radiological and neurosurgical point of view.

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease.

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genome-scale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.

•

Disease-associated gene modules are conserved across multiple NAFLD cohorts

•

The central genes in disease-associated modules are key enzymes in cholesterol synthesis

•

YY1 and KLF13 are potential key transcriptional regulators of NAFLD development

A computational approach for identification of core modules from a co-expression network and GWAS data

This protocol describes the application of the "omnigenic" model of the genetic architecture of complex traits to identify novel "core" genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with "core-like" characteristics. For complete details on the use and execution of this protocol, please refer to Sabik et al. (2020).

Chondrocyte protein co-synthesis network analysis links ECM mechanosensing to metabolic adaptation in osteoarthritis

BACKGROUND

Knee osteoarthritis (OA) is one of the most common structural OA disorders globally. Incomplete understanding of the fundamental biological aspects of osteoarthritis underlies the current lack of effective treatment or disease modifying drugs.

RESEARCH DESIGN AND METHODS

We implemented a systems approach by making use of the statistical network concepts in Weighted Gene Co-expression Analysis to reconstruct the organization of the core proteome network in chondrocytes obtained from OA patients and healthy individuals. Protein modules reflect groups of tightly co-ordinated changes in protein abundance across healthy and OA chondrocytes.

RESULTS

The unbiased systems analysis identified extracellular matrix (ECM) mechanosensing and glycolysis as two modules that are most highly correlated with ΟΑ. The ECM module was enriched in the OA genetic risk factors tenascin-C (TNC) and collagen 11A1 (COL11A1), as well as in cartilage oligomeric matrix protein (COMP), a biomarker associated with cartilage integrity. Mapping proteins that are unique to OA or healthy chondrocytes onto the core interactome, which connects microenvironment sensing and regulation of glycolysis, identified differences in metabolic and anti-inflammatory adaptation.

CONCLUSION

The interconnection between cartilage ECM remodeling and metabolism is indicative of the dynamic chondrocyte states and their significance in osteoarthritis.

Using "-omics" Data to Inform Genome-wide Association Studies (GWASs) in the Osteoporosis Field

PURPOSE OF REVIEW

Osteoporosis constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 loci influencing bone mineral density (BMD); however, few of the causal genes have been identified. Here, we review approaches that use "-omics" data and genetic- and systems genetics-based analytical strategies to facilitate causal gene discovery.

RECENT FINDINGS

The bone field is beginning to adopt approaches that are commonplace in other disease disciplines. The slower progress has been due in part to the lack of large-scale "omics" data on bone and bone cells. This is however changing, and approaches such as eQTL colocalization, transcriptome-wide association studies (TWASs), network, and integrative approaches are beginning to provide significant insight into the genes responsible for BMD GWAS associations. The use of "-omics" data to inform BMD GWASs has increased in recent years, leading to the identification of novel regulators of BMD in humans. The ultimate goal will be to use this information to develop more effective therapies to treat and ultimately prevent osteoporosis.

TMEM263: a novel candidate gene implicated in human autosomal recessive severe lethal skeletal dysplasia

INTRODUCTION

Skeletal dysplasia is a common, clinically and genetically heterogeneous disorder in the human population. An increasing number of different genes are being identified causing this disorder. We used whole exome sequencing (WES) for detection of skeletal dysplasia causing mutation in a fetus affected to severe lethal skeletal dysplasia.

PATIENT

Fetus was assessed by ultrasonography in second trimester of pregnancy. He suffers from severe rhizomelic dysplasia and also pathologic shortening of ribs. WES was applied to finding of causal mutation. Furthermore, bioinformatics analysis was performed to predict mutation impact.

RESULTS

Whole exome sequencing (WES) identified a homozygous frameshift mutation in the TMEM263 gene in a fetus with severe lethal skeletal dysplasia. Mutations of this gene have been previously identified in dwarf chickens, but this is the first report of involvement of this gene in human skeletal dysplasia. This gene plays a key role in the growth hormone signaling pathway.

CONCLUSION

TMEM263 can be considered as a new gene responsible for skeletal dysplasia. Given the complications observed in the affected fetus, the mutation of this gene appears to produce much more intense complications than that found in chickens and is likely to play a more important role in bone development in human.

Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength

Genome-wide association studies (GWASs) for osteoporotic traits have identified over 1000 associations; however, their impact has been limited by the difficulties of causal gene identification and a strict focus on bone mineral density (BMD). Here, we use Diversity Outbred (DO) mice to directly address these limitations by performing a systems genetics analysis of 55 complex skeletal phenotypes. We apply a network approach to cortical bone RNA-seq data to discover 66 genes likely to be causal for human BMD GWAS associations, including the genes SERTAD4 and GLT8D2. We also perform GWAS in the DO for a wide-range of bone traits and identify Qsox1 as a gene influencing cortical bone accrual and bone strength. In this work, we advance our understanding of the genetics of osteoporosis and highlight the ability of the mouse to inform human genetics.

Osteoporosis GWAS faces two challenges, causal gene discovery and a lack of phenotypic diversity. Here, the authors use the Diversity Outbred mouse population to inform human GWAS using networks and map genetic loci for 55 bone traits, identifying new potential bone strength genes.

EyeDiseases: an integrated resource for dedicating to genetic variants, gene expression and epigenetic factors of human eye diseases

Eye diseases are remarkably common and encompass a large and diverse range of morbidities that affect different components of the visual system and visual function. With advances in omics technology of eye disorders, genome-scale datasets have been rapidly accumulated in genetics and epigenetics field. However, the efficient collection and comprehensive analysis of different kinds of omics data are lacking. Herein, we developed EyeDiseases (https://eyediseases.bio-data.cn/), the first database for multi-omics data integration and interpretation of human eyes diseases. It contains 1344 disease-associated genes with genetic variation, 1774 transcription files of bulk cell expression and single-cell RNA-seq, 105 epigenomics data across 185 kinds of human eye diseases. Using EyeDiseases, we investigated SARS-CoV-2 potential tropism in eye infection and found that the SARS-CoV-2 entry factors, ACE2 and TMPRSS2 are highly correlated with cornea and keratoconus, suggest that ocular surface cells are susceptible to infection by SARS-CoV-2. Additionally, integrating analysis of Age-related macular degeneration (AMD) GWAS loci and co-expression data revealed 9 associated genes involved in HIF-1 signaling pathway and voltage-gate potassium channel complex. The EyeDiseases provides a valuable resource for accelerating the discovery and validation of candidate loci and genes contributed to the molecular diagnosis and therapeutic vulnerabilities with various eyes diseases.

Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data

The "omnigenic" model of the genetic architecture of complex traits proposed two categories of causal genes: core and peripheral. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. Using a cell-type- and time-point-specific gene co-expression network for mineralizing osteoblasts, we identify a co-expression module enriched for genes implicated by bone mineral density (BMD) genome-wide association studies (GWASs), correlated with in vitro osteoblast mineralization and associated with skeletal phenotypes in human monogenic disease and mouse knockouts. Four genes from this module (B4GALNT3, CADM1, DOCK9, and GPR133) are located within the BMD GWAS loci with colocalizing expression quantitative trait loci (eQTL) and exhibit altered BMD in mouse knockouts, suggesting that they are causal genetic drivers of BMD in humans. Our network-based approach identifies a "core" module for BMD and provides a resource for expanding our understanding of the genetics of bone mass.

Genomic variants within chromosome 14q32.32 regulate bone mass through MARK3 signaling in osteoblasts

Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. GWAS have identified hundreds of loci influencing BMD, but few have been functionally analyzed. In this study, we show that SNPs within a BMD locus on chromosome 14q32.32 alter splicing and expression of PAR-1a/microtubule affinity regulating kinase 3 (MARK3), a conserved serine/threonine kinase known to regulate bioenergetics, cell division, and polarity. Mice lacking Mark3 either globally or selectively in osteoblasts have increased bone mass at maturity. RNA profiling from Mark3-deficient osteoblasts suggested changes in the expression of components of the Notch signaling pathway. Mark3-deficient osteoblasts exhibited greater matrix mineralization compared with controls that was accompanied by reduced Jag1/Hes1 expression and diminished downstream JNK signaling. Overexpression of Jag1 in Mark3-deficient osteoblasts both in vitro and in vivo normalized mineralization capacity and bone mass, respectively. Together, these findings reveal a mechanism whereby genetically regulated alterations in Mark3 expression perturb cell signaling in osteoblasts to influence bone mass.

Cross-species data integration to prioritize causal genes in lipid metabolism

PURPOSE OF REVIEW

More than one hundred loci have been identified from human genome-wide association studies (GWAS) for blood lipids. Despite the success of GWAS in identifying loci, subsequent prioritization of causal genes related to these loci remains a challenge. To address this challenge, recent work suggests that candidate causal genes within loci can be prioritized through cross-species integration using genome-wide data from the mouse.

RECENT FINDINGS

Mouse model systems provide unparalleled access to primary tissues, like the liver, that are not readily available for human studies. Given the key role the liver plays in controlling blood lipid levels and the wealth of liver genome-wide transcript and protein data available in the mouse, these data can be leveraged. Using coexpression network analysis approaches with mouse genome-wide data, coupled with cross-species analysis of human lipid GWAS, causal genes within lipid loci can be prioritized. Prioritization through both mouse and human along with biochemical validation provide a systematic and valuable method to discover lipid metabolism genes.

SUMMARY

The prioritization of causal lipid genes within GWAS loci is a challenging process requiring a multidisciplinary approach. Integration of data types across species, such as the mouse, can aid in causal gene prioritization.

Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets

In recent decades, many genome-wide association studies on insomnia have reported numerous genes harboring multiple risk variants. Nevertheless, the molecular functions of these risk variants conveying risk to insomnia are still ill-studied. In the present study, we integrated GWAS summary statistics (N=386,533) with two independent brain expression quantitative trait loci (eQTL) datasets (N=329) to determine whether expression-associated SNPs convey risk to insomnia. Furthermore, we applied numerous bioinformatics analyses to highlight promising genes associated with insomnia risk. By using Sherlock integrative analysis, we detected 449 significant insomnia-associated genes in the discovery stage. These identified genes were significantly overrepresented in six biological pathways including Huntington’s disease (P=5.58 × 10⁻⁵), Alzheimer’s disease (P=5.58 × 10⁻⁵), Parkinson’s disease (P=6.34 × 10⁻⁵), spliceosome (P=1.17 × 10⁻⁴), oxidative phosphorylation (P=1.09 × 10⁻⁴), and wnt signaling pathways (P=2.07 × 10⁻⁴). Further, five of these identified genes were replicated in an independent brain eQTL dataset. Through a PPI network analysis, we found that there existed highly functional interactions among these five identified genes. Three genes of LDHA (P=0.044), DALRD3 (P=5.0 × 10⁻⁵), and HEBP2 (P=0.032) showed significantly lower expression level in brain tissues of insomnic patients than that in controls. In addition, the expression levels of these five genes showed prominently dynamic changes across different time points between behavioral states of sleep and sleep deprivation in mice brain cortex. Together, the evidence of the present study strongly suggested that these five identified genes may represent candidate genes and contributed risk to the etiology of insomnia.

Integration of summary data from GWAS and eQTL studies identified novel risk genes for coronary artery disease

Several genetic loci have been reported to be significantly associated with coronary artery disease (CAD) by multiple genome-wide association studies (GWAS). Nevertheless, the biological and functional effects of these genetic variants on CAD remain largely equivocal. In the current study, we performed an integrative genomics analysis by integrating large-scale GWAS data (N = 459,534) and 2 independent expression quantitative trait loci (eQTL) datasets (N = 1890) to determine whether CAD-associated risk single nucleotide polymorphisms (SNPs) exert regulatory effects on gene expression. By using Sherlock Bayesian, MAGMA gene-based, multidimensional scaling (MDS), functional enrichment, and in silico permutation analyses for independent technical and biological replications, we highlighted 4 susceptible genes (CHCHD1, TUBG1, LY6G6C, and MRPS17) associated with CAD risk. Based on the protein-protein interaction (PPI) network analysis, these 4 genes were found to interact with each other. We detected a remarkably altered co-expression pattern among these 4 genes between CAD patients and controls. In addition, 3 genes of CHCHD1 (P = .0013), TUBG1 (P = .004), and LY6G6C (P = .038) showed significantly different expressions between CAD patients and controls. Together, we provide evidence to support that these identified genes such as CHCHD1 and TUBG1 are indicative factors of CAD.

βII spectrin (SPTBN1): biological function and clinical potential in cancer and other diseases

βII spectrin, the most common isoform of non-erythrocyte spectrin, is a cytoskeleton protein present in all nucleated cells. Interestingly, βII spectrin is essential for the development of various organs such as nerve, epithelium, inner ear, liver and heart. The functions of βII spectrin include not only establishing and maintaining the cell structure but also regulating a variety of cellular functions, such as cell apoptosis, cell adhesion, cell spreading and cell cycle regulation. Notably, βII spectrin dysfunction is associated with embryonic lethality and the DNA damage response. More recently, the detection of altered βII spectrin expression in tumors indicated that βII spectrin might be involved in the development and progression of cancer. Its mutations and disorders could result in developmental disabilities and various diseases. The versatile roles of βII spectrin in disease have been examined in an increasing number of studies; nonetheless, the exact mechanisms of βII spectrin are still poorly understood. Thus, we summarize the structural features and biological roles of βII spectrin and discuss its molecular mechanisms and functions in development, homeostasis, regeneration and differentiation. This review highlight the potential effects of βII spectrin dysfunction in cancer and other diseases, outstanding questions for the future investigation of therapeutic targets. The investigation of the regulatory mechanism of βII spectrin signal inactivation and recovery may bring hope for future therapy of related diseases.

Recent Advances of Osterix Transcription Factor in Osteoblast Differentiation and Bone Formation

With increasing life expectations, more and more patients suffer from fractures either induced by intensive sports or other bone-related diseases. The balance between osteoblast-mediated bone formation and osteoclast-mediated bone resorption is the basis for maintaining bone health. Osterix (Osx) has long been known to be an essential transcription factor for the osteoblast differentiation and bone mineralization. Emerging evidence suggests that Osx not only plays an important role in intramembranous bone formation, but also affects endochondral ossification by participating in the terminal cartilage differentiation. Given its essentiality in skeletal development and bone formation, Osx has become a new research hotspot in recent years. In this review, we focus on the progress of Osx's function and its regulation in osteoblast differentiation and bone mass. And the potential role of Osx in developing new therapeutic strategies for osteolytic diseases was discussed.

Discover novel disease-associated genes based on regulatory networks of long-range chromatin interactions

Identifying genes and non-coding genetic variants that are genetically associated with complex diseases and the underlying mechanisms is one of the most important questions in functional genomics. Due to the limited statistical power and the lack of mechanistic modeling, traditional genome-wide association studies (GWAS) is restricted to fully address this question. Based on multi-omics data integration, cell-type specific regulatory networks can be built to improve GWAS analysis. In this study, we developed a new computational infrastructure, APRIL, to incorporate 3D chromatin interactions into regulatory network construction, which can extend the networks to include long-range cis-regulatory links between non-coding GWAS SNPs and target genes. Combinatorial transcription factors that co-regulate groups of genes are also inferred to further expand the networks with trans-regulation. A suite of machine learning predictions and statistical tests are incorporated in APRIL to predict novel disease-associated genes based on the expanded regulatory networks. Important features of non-coding regulatory elements and genetic variants are prioritized in network-based predictions, providing systems-level insights on the mechanisms of transcriptional dysregulation associated with complex diseases.

Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

Background

Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown.

Methods

In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma.

Results

In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10^− 6), type I diabetes mellitus (Corrected P = 7.09 × 10^− 5), and asthma (Corrected P = 1.72 × 10^− 3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (P_eQTL = 2.98 × 10^− 8 and P_GWAS = 3.40 × 10^− 8), rs11265180 (P_eQTL = 6.0 × 10^− 6 and P_GWAS = 1.99 × 10^− 3), and rs1867087 (P_eQTL = 1.0 × 10^− 4 and P_GWAS = 1.84 × 10^− 5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P = 1.55 × 10^− 6).

Conclusions

Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

Is hull cleaning wastewater a potential source of developmental toxicity on coastal non-target organisms?

Chemical contaminants can be discharged by vessel hull cleaning processes, such as scraping, jet spraying, and painting, all of which produce readily transportable contaminants into the marine environment, where they are referred to as 'hotspots' of contamination in coastal areas. However, many countries have not yet established effective evaluation methods for disposal of waste mixtures or management guidelines for areas of hull cleaning. To define the toxic effects of wastewater from vessel hull cleaning in dry docks on resident non-target organisms, we investigated the chemical concentrations and developmental toxicity on embryonic flounder, which is an organism sensitive to chemical contamination. In this study, the dominant inorganic metal discharged was zinc when cleaning Ship A (300 tons) and copper for Ship B (5,000 tons). The wastewater from high-pressure water blasting (WHPB) of Ship A (300 tons) and Ship B (5,000 tons) produced a largely overlapping suite of developmental malformations including pericardial edema, spinal curvature, and tail fin defects. Forty-eight hours after exposure, the frequency percentage of malformation began to increase in embryos exposed to a 500-fold dilution of WHPB from Ships A and B. We performed transcriptome sequencing to characterize the toxicological developmental effects of WHPB exposure at the molecular level. The results of the analysis revealed significantly altered expression of genes associated with muscle cell differentiation, actin-mediated cell contraction, and nervous system development (cutoff P < 0.01) in embryonic flounder exposed to high-pressure cleaning effluent from Ship A. Genes associated with chromatin remodeling, cell cycling, and insulin receptor signaling pathways were significantly altered in embryonic flounder exposed to WHPB of Ship B (cutoff P < 0.01). These findings provide a greater understanding of the developmental toxicity and potential effects of WHPB effluent on coastal embryonic fish. Furthermore, our results could inform WHPB effluent management practices to reduce impacts on non-target coastal organisms.

Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BACKGROUND

Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.

METHODS

In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.

RESULTS

Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group.

CONCLUSIONS

We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

D2 dopamine receptor gene (DRD2) Taq1A (rs1800497) affects bone density

Schizophrenia patients are susceptible to lower bone mineral density (BMD). However, studies exploring the genetic effects are lacking. Genes that affect the activity of antipsychotics may be associated with BMD, particularly in patients receiving long-term antipsychotic treatment. We aimed to explore the relationship between the dopamine receptor D₂ (DRD2) gene Taq1A (rs1800497) polymorphism and BMD in chronic schizophrenia patients. We recruited schizophrenia patients (n = 47) and healthy controls (n = 39) from a medical center in Taiwan and collected data that may affect BMD. Patients’ BMD was measured by dual-energy X-ray absorptiometer (DEXA). DRD2 rs1800497 was genotyped through polymerase chain reaction–Restriction Fragment Length Polymorphism (PCR–RFLP). Among all participants, subjects with DRD2 rs1800497(T;T) allele had lower DEXA T score and DEXA Z score compared to those with rs1800497(C;T) and rs1800497(C;C) alleles (p = 0.008, 0.003, respectively). In schizophrenia patients, subjects with rs1800497(T;T) allele also had lower DEXA Z score compared to the other two alleles (p = 0.045). Our findings suggest that individuals with the DRD2 rs1800497(T;T) had lower BMD than those with the rs1800497(C;T) and rs1800497(C;C) genotypes. Therefore, genes should be considered as one of the risk factors of lower BMD.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named ‘core genes’, while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer’s Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets—consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer–where we identify 109 candidate core genes.

A recent theory suggests that only a small number of genes underpin the biology of a disease, these genes are called ‘core genes’, and for most diseases, these core genes remain unknown. The suggested methods for finding them requires complex and expensive experiments. We reasoned that if we merge currently available datasets in smart ways, we may be able to uncover these ‘core genes’. Our method finds “hub” proteins by merging lists of genes previously linked with disease to information on how proteins interact with each other. We found that many of these hub proteins have central roles in disease, such as insulin for both A1C measurement and Type 2 Diabetes, BRCA1 in Breast cancer, and Amyloid Precursor Protein in Alzheimer’s Disease. We think these ‘hub’ proteins are candidate ‘core genes’, and offer our method as a way to find ‘core genes’ by utilizing publicly available reference datasets.

Analysis of the genes controlling three quantitative traits in three diverse plant species reveals the molecular basis of quantitative traits

Most traits of agricultural importance are quantitative traits controlled by numerous genes. However, it remains unclear about the molecular mechanisms underpinning quantitative traits. Here, we report the molecular characteristics of the genes controlling three quantitative traits randomly selected from three diverse plant species, including ginsenoside biosynthesis in ginseng (Panax ginseng C.A. Meyer), fiber length in cotton (Gossypium hirsutum L. and G. barbadense L.) and grain yield in maize (Zea mays L.). We found that a vast majority of the genes controlling a quantitative trait were significantly more likely spliced into multiple transcripts while they expressed. Nevertheless, only one to four, but not all, of the transcripts spliced from each of the genes were significantly correlated with the phenotype of the trait. The genes controlling a quantitative trait were multiple times more likely to form a co-expression network than other genes expressed in an organ. The network varied substantially among genotypes of a species and was associated with their phenotypes. These findings indicate that the genes controlling a quantitative trait are more likely pleiotropic and functionally correlated, thus providing new insights into the molecular basis underpinning quantitative traits and knowledge necessary to develop technologies for efficient manipulation of quantitative traits.

A trans-eQTL network regulates osteoclast multinucleation and bone mass

Functional characterisation of cell-type-specific regulatory networks is key to establish a causal link between genetic variation and phenotype. The osteoclast offers a unique model for interrogating the contribution of co-regulated genes to in vivo phenotype as its multinucleation and resorption activities determine quantifiable skeletal traits. Here we took advantage of a trans-regulated gene network (MMnet, macrophage multinucleation network) which we found to be significantly enriched for GWAS variants associated with bone-related phenotypes. We found that the network hub gene Bcat1 and seven other co-regulated MMnet genes out of 13, regulate bone function. Specifically, global (Pik3cb^-/-, Atp8b2^+/-, Igsf8^-/-, Eml1^-/-, Appl2^-/-, Deptor^-/-) and myeloid-specific Slc40a1 knockout mice displayed abnormal bone phenotypes. We report opposing effects of MMnet genes on bone mass in mice and osteoclast multinucleation/resorption in humans with strong correlation between the two. These results identify MMnet as a functionally conserved network that regulates osteoclast multinucleation and bone mass.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways

Supplemental Digital Content is available in the text.

Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease.