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Abstract：
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.
摘要：
全基因组关联研究（GWAS）已经确定了许多与骨密度（BMD）相关的遗传变异来源，骨折风险和骨质疏松症的临床预测指标。除了因果基因的鉴定，告知GWAS的其他困难挑战包括表征预测的致病基因在疾病中的作用，并提供额外的功能背景，例如因果基因运作的细胞类型预测或生物学途径。利用单细胞转录组学（scRNA-seq）可以通过将疾病相关变体与基因联系起来并提供这些致病基因驱动疾病的细胞类型背景来帮助告知BMDGWAS。这里,我们使用来自在成骨条件下培养的来自多样性远交(DO)小鼠的骨髓来源基质细胞(BMSC-OBs)的大规模scRNA-seq数据来生成细胞类型特异性网络,并将BMDGWAS相关基因进行情境化。使用从scRNA-seq数据推断的轨迹，我们确定了富含基因的网络，这些基因在轨迹上表现出最动态的表达变化。我们发现了21个网络驱动基因，这可能是与表达/剪接数量性状基因座（eQTL/sQTL）共定位的人类BMDGWAS关联的原因。这些驱动基因，包括Fgfrl1和Tpx2，以及它们相关的网络，通过它们在间充质谱系细胞分化中的作用，被预测为BMD的新型调节因子。在这项工作中,我们展示了使用来自小鼠骨骼相关细胞的单细胞转录组学来告知人类BMDGWAS,并优先考虑在骨质疏松症发展中具有潜在因果作用的遗传靶标.
