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A major fraction of loci identified by genome-wide association studies (GWASs) mediate alternative splicing, but mechanistic interpretation is hindered by the technical limitations of short-read RNA sequencing (RNA-seq), which cannot directly link splicing events to full-length protein isoforms. Long-read RNA-seq represents a powerful tool to characterize transcript isoforms, and recently, infer protein isoform existence. Here, we present an approach that integrates information from GWASs, splicing quantitative trait loci (sQTLs), and PacBio long-read RNA-seq in a disease-relevant model to infer the effects of sQTLs on the ultimate protein isoform products they encode. We demonstrate the utility of our approach using bone mineral density (BMD) GWAS data. We identified 1,863 sQTLs from the Genotype-Tissue Expression (GTEx) project in 732 protein-coding genes that colocalized with BMD associations (H4PP ≥ 0.75). We generated PacBio Iso-Seq data (N = ∼22 million full-length reads) on human osteoblasts, identifying 68,326 protein-coding isoforms, of which 17,375 (25%) were unannotated. By casting the sQTLs onto protein isoforms, we connected 809 sQTLs to 2,029 protein isoforms from 441 genes expressed in osteoblasts. Overall, we found that 74 sQTLs influenced isoforms likely impacted by nonsense-mediated decay and 190 that potentially resulted in the expression of unannotated protein isoforms. Finally, we functionally validated colocalizing sQTLs in TPM2, in which siRNA-mediated knockdown in osteoblasts showed two TPM2 isoforms with opposing effects on mineralization but exhibited no effect upon knockdown of the entire gene. Our approach should be to generalize across diverse clinical traits and to provide insights into protein isoform activities modulated by GWAS loci., Competing Interests: Declaration of interests The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

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.

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.

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., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Osteoporosis is a genetic disease characterized by progressive reductions in bone mineral density (BMD) leading to an increased risk of fracture. Over the last decade, genome-wide association studies (GWASs) have identified over 1000 associations for BMD. However, as a phenotype BMD is challenging as bone is a multicellular tissue affected by both local and systemic physiology. Here, we focused on a single component of BMD, osteoblast-mediated bone formation in mice, and identified associations influencing osteoblast activity on mouse Chromosomes (Chrs) 1, 4, and 17. The locus on Chr. 4 was in an intergenic region between Wnt4 and Zbtb40, homologous to a locus for BMD in humans. We tested both Wnt4 and Zbtb40 for a role in osteoblast activity and BMD. Knockdown of Zbtb40, but not Wnt4, in osteoblasts drastically reduced mineralization. Additionally, loss-of-function mouse models for both genes exhibited reduced BMD. Our results highlight that investigating the genetic basis of in vitro osteoblast mineralization can be used to identify genes impacting bone formation and BMD., Competing Interests: The authors have declared that no competing interests exist.

Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. Genome-wide association studies (GWAS) for BMD have identified dozens of associations; yet, the genes responsible for most associations remain elusive. Here, we used a bone co-expression network to predict causal genes at BMD GWAS loci based on the premise that genes underlying a disease are often functionally related and functionally related genes are often co-expressed. By mapping genes implicated by BMD GWAS onto a bone co-expression network, we predicted and inferred the function of causal genes for 30 of 64 GWAS loci. We experimentally confirmed that two of the genes predicted to be causal, SPTBN1 and MARK3, are potentially responsible for the effects of GWAS loci on chromosomes 2p16.2 and 14q32.32, respectively. This approach provides a roadmap for the dissection of additional BMD GWAS associations. Furthermore, it should be applicable to GWAS data for a wide range of diseases., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Patient bone mineral density (BMD) predicts the likelihood of osteoporotic fracture. While substantial progress has been made toward elucidating the genetic determinants of BMD, our understanding of the factors involved remains incomplete. Here, using a systems genetics approach in the mouse, we predicted that bicaudal C homolog 1 (Bicc1), which encodes an RNA-binding protein, is responsible for a BMD quantitative trait locus (QTL) located on murine chromosome 10. Consistent with this prediction, mice heterozygous for a null allele of Bicc1 had low BMD. We used a coexpression network-based approach to determine how Bicc1 influences BMD. Based on this analysis, we inferred that Bicc1 was involved in osteoblast differentiation and that polycystic kidney disease 2 (Pkd2) was a downstream target of Bicc1. Knock down of Bicc1 and Pkd2 impaired osteoblastogenesis, and Bicc1 deficiency-dependent osteoblast defects were rescued by Pkd2 overexpression. Last, in 2 human BMD genome-wide association (GWAS) meta-analyses, we identified SNPs in BICC1 and PKD2 that were associated with BMD. These results, in both mice and humans, identify Bicc1 as a genetic determinant of osteoblastogenesis and BMD and suggest that it does so by regulating Pkd2 transcript levels.

The objective of our study was to identify genetic factors that regulate bone mineral density (BMD) in mice using well defined recombinant inbred strains. For this purpose we chose the BXD recombinant inbred (RI) strains derived from progeny of the C57BL/6J (B6) and DBA/2J (D2) progenitor strains. We sampled both male and female mice (∼4 each) of 46 strains at 3 months-of-age, measured their BMD, and conducted QTL mapping. The data were analyzed to identify candidates genes contained within the most significant quantitative trait locus (QTL). Evaluation of candidate genes included functional assessment, single nucleotide polymorphism (SNP) genotyping and direct sequencing. We established that there was a QTL for BMD in males on chromosome 15 that has the impact larger than QTLs on all other chromosomes. The QTL on chromosome 15 was narrowed to a genomic region between 38 Mbp and 52 Mbp. By examining transcripts within this region, we found an important candidate gene: trichorhinophalangeal syndrome, type I (Trps1). SNP analysis identified a nonsynonymous SNP (rs32398060) in Trps1 that co-segregated with bone mineral density. Analysis of association between this SNP within TRPS1 and BMD in a human population confirmed its significance.

Osteoporosis, characterized by low levels of bone mineral density (BMD), is a prevalent medical condition in humans. We investigated its genetic and environmental basis by searching for quantitative trait loci (QTLs) affecting six skeletal (including three BMD) traits in a G10 advanced intercross population produced from crosses of mice from the inbred strain C57BL/6J with mice from a strain selected for high voluntary wheel running. The mice in this population were fed either a high-fat or a matched control diet throughout the study, allowing us to test for QTL by diet interactions for the skeletal traits. Our genome scan uncovered a number of QTLs, the great majority of which were different from QTLs previously found for these same traits in an earlier (G4) generation of the same intercross. Further, the confidence intervals for the skeletal trait QTLs were reduced from an average of 18.5 Mb in the G4 population to an equivalent of about 9 Mb in the G10 population. We uncovered a total of 50 QTLs representing 32 separate genomic sites affecting these traits, with a distal region on chromosome 1 harboring several QTLs with large effects on the BMD traits. One QTL was located on chromosome 5 at 4.0 Mb with a confidence interval spanning from 4.0 to 4.6 Mb. Only three protein coding genes reside in this interval, and one of these, Cyp51, is an attractive candidate as others have shown that developing Cyp51 knockout embryos exhibit shortened and bowed limbs and synotosis of the femur and tibia. Several QTLs showed significant interactions with sex, although only two QTLs interacted with diet, both affecting only mice fed the high-fat diet., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Genome-wide association studies (GWAS) have emerged as the method of choice for identifying common variants affecting complex disease. In a GWAS, particular attention is placed, for obvious reasons, on single-nucleotide polymorphisms (SNPs) that exceed stringent genome-wide significance thresholds. However, it is expected that many SNPs with only nominal evidence of association (e.g., P < 0.05) truly influence disease. Efforts to extract additional biological information from entire GWAS datasets have primarily focused on pathway-enrichment analyses. However, these methods suffer from a number of limitations and typically fail to lead to testable hypotheses. To evaluate alternative approaches, we performed a systems-level analysis of GWAS data using weighted gene coexpression network analysis. A weighted gene coexpression network was generated for 1918 genes harboring SNPs that displayed nominal evidence of association (P ≤ 0.05) from a GWAS of bone mineral density (BMD) using microarray data on circulating monocytes isolated from individuals with extremely low or high BMD. Thirteen distinct gene modules were identified, each comprising coexpressed and highly interconnected GWAS genes. Through the characterization of module content and topology, we illustrate how network analysis can be used to discover disease-associated subnetworks and characterize novel interactions for genes with a known role in the regulation of BMD. In addition, we provide evidence that network metrics can be used as a prioritizing tool when selecting genes and SNPs for replication studies. Our results highlight the advantages of using systems-level strategies to add value to and inform GWAS.

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits., Competing Interests: The authors have declared that no competing interests exist.

Bone mineral density (BMD) is influenced by a complex network of gene interactions; therefore, elucidating the relationships between genes and how those genes, in turn, influence BMD is critical for developing a comprehensive understanding of osteoporosis. To investigate the role of transcriptional networks in the regulation of BMD, we performed a weighted gene coexpression network analysis (WGCNA) using microarray expression data on monocytes from young individuals with low or high BMD. WGCNA groups genes into modules based on patterns of gene coexpression. and our analysis identified 11 gene modules. We observed that the overall expression of one module (referred to as module 9) was significantly higher in the low-BMD group (p = .03). Module 9 was highly enriched for genes belonging to the immune system-related gene ontology (GO) category "response to virus" (p = 7.6 × 10(-11)). Using publically available genome-wide association study data, we independently validated the importance of module 9 by demonstrating that highly connected module 9 hubs were more likely, relative to less highly connected genes, to be genetically associated with BMD. This study highlights the advantages of systems-level analyses to uncover coexpression modules associated with bone mass and suggests that particular monocyte expression patterns may mediate differences in BMD., (© 2010 American Society for Bone and Mineral Research.)

Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J x C3H/HeJ (BXH) F(2) mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F(2) mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.