Table_2_Integrating Genome-Wide Association Studies With Pathway Analysis and Gene Expression Analysis Highlights Novel Osteoarthritis Risk Pathways and Genes.xls

Osteoarthritis (OA) is the most common degenerative joint disorder worldwide. To identify more genetic signals, genome-wide association study (GWAS) has been widely used and elucidated some OA susceptibility genes. However, these susceptibility genes could only explain only a small part of heritability of OA. It is suggested that the identification of disease-related pathways may contribute to understand the genomic etiology of OA. Here, we integrated the GWAS into pathway analysis to identify novel OA risk pathways. In this study, we first selected 187 independent genetic variants identified by GWAS (P < 1.00E−05) and found that most of these genetic variants are noncoding mutations. We then conducted an expression quantitative trait loci analysis and found that 165 of these 187 genetic variants could significantly regulate the expression of nearby genes. Third, we identified OA susceptibility genes corresponding to these genetic variants, conducted a pathway analysis, and identified novel OA-related KEGG pathways, GO biological processes, GO molecular functions, and GO cellular components. In KEGG database, transforming growth factor β signaling pathway is the most significant signal (P = 5.98E−05) and is the only pathway after the BH multiple-test adjustment with false discovery rate (FDR) = 0.02. In GO database, we identified 24 statistically significant GO biological processes, one statistically significant GO molecular function, and five statistically significant GO cellular components (FDR < 0.05). These signals are related with chondrocyte differentiation and development, which are all known biological pathways associated with OA. Finally, we conducted an OA case–control gene expression analysis to evaluate the differential expression of these OA risk genes. Using an OA case–control gene expression analysis, we showed that 44 risk genes were suggestively differentially expressed in OA cases compared with controls (P < 0.05). Three genes, WWP2, COG5, and MAPT, were statistically differentially expressed in OA cases compared with controls (P < 0.05/122 = 4.10E−04). Hence, our findings may contribute to understanding the genomic etiology of OA.

骨关节炎（Osteoarthritis, OA）是全球范围内最常见的退行性关节疾病。为发掘更多遗传信号，全基因组关联研究（Genome-Wide Association Study, GWAS）已被广泛应用，并阐明了部分骨关节炎易感基因。然而，这些易感基因仅能解释骨关节炎遗传力的极小一部分。有研究指出，识别疾病相关通路或有助于理解骨关节炎的基因组病因。本研究将全基因组关联研究数据整合至通路分析中，以鉴定新型骨关节炎风险通路。
本研究首先筛选了187个经全基因组关联研究鉴定的独立遗传变异（P < 1.00×10^−5），并发现其中绝大多数为非编码突变。随后开展了表达数量性状基因座（expression quantitative trait loci, eQTL）分析，结果显示187个遗传变异中有165个可显著调控邻近基因的表达。第三，本研究鉴定了对应这些遗传变异的骨关节炎易感基因，开展了通路分析，并识别出与骨关节炎相关的新型京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路、基因本体（Gene Ontology, GO）生物过程、GO分子功能及GO细胞组分。
在KEGG数据库中，转化生长因子β（transforming growth factor β, TGF-β）信号通路是最显著的信号（P = 5.98×10^−5），且是经BH多重检验校正后假发现率（false discovery rate, FDR）=0.02的唯一通路。在GO数据库中，本研究共鉴定出24个具有统计学意义的GO生物过程、1个具有统计学意义的GO分子功能以及5个具有统计学意义的GO细胞组分（FDR < 0.05）。这些信号与软骨细胞分化及发育相关，而这些均为已被证实与骨关节炎相关的生物学通路。
最后，本研究开展了骨关节炎病例-对照基因表达分析，以评估这些骨关节炎风险基因的差异表达情况。通过骨关节炎病例-对照基因表达分析，结果显示相较于对照组，44个风险基因在骨关节炎病例组中呈现提示性差异表达（P < 0.05）。其中，WWP2、COG5及MAPT这3个基因在病例组与对照组间呈现出统计学意义的差异表达（P < 0.05/122 = 4.10×10^−4）。因此，本研究结果或有助于加深对骨关节炎基因组病因的理解。