Identification The Causal Relationship Between Related Gene, Metabolite And Osteoporosis

Osteoporosis is a common disease characterized by low bone mineral density(BMD),micro-architectural deterioration of bone tissue and an increased susceptibility to fractures.BMD has been used extensively for the clinical diagnosis of osteoporosis and prediction of osteoporotic fractures.Association studies can identify genetic variants,genes and metabolites associated with traits.However,association study can only suggest the correlation between genetic variation,genes or metabolites and traits,and the specific pathogenesis of diseases still needs to be clarified in the research of gene or metabolite function.Inferring the causal direction between correlated phenotypes is a pervasive issue in biology that simple regression and association analysis cannot answer.Causal inference can be used to identify causal relationship between phenotypes.We used causal inference method to identify genes or metabolites which have a causal effect on BMD.We used the summary data-based Mendelian randomization method(SMR)to identify genes which have a causal effect on BMD.The method used the most significant SNP as an instrumental variable to identify the causal relationship between gene expression and BMD.Weighted gene coexpression network analysis(WGCNA)was used to explore functional relationships for the identified novel genes with known putative osteoporosis genes.Further,we assessed functions of the identified genes through in vitro cellular study.Observational studies have demonstrated a controversial association between leptin and bone mineral density(BMD),and functional studies of the relationship between leptin and BMD still largely vary by different studies.We conducted a two-sample Mendelian randomization study to identify whether genetically lowered leptin levels were associated with BMD.Since only few independent SNPs were identified by GWAS to be associated with metabolites in blood,this makes the common Mendelian randomization methods less effective in identifying the causal relationship between metabolites and traits.And we need to verify whether the instrumental variables used are valid for each metabolite.Therefore,this method is not suitable for screening metabolites which is causally associated with traits.Here,we extended a causal inference method to make it more applicable for the general situations.We extended Pickrell's method by replacing the putative causal SNPs with the lead SNPs(the set of the most significant SNPs in each independent locus)and applied our extended method to identify metabolites which had a causal effect on femoral neck bone mineral density(FN-BMD).By applying the SMR method,we identified two potentially causal genes(ASB16-AS1 and SYN2)associated with BMD.ASB16-AS1 coexpressed with several known putative osteoporosis risk genes.In vitro cellular study showed that over-expressed ASB16-AS1 increased the expression of osteoblastogenesis related genes(BMP2 and ALPL),indicating its functional significance.The previous study showed that SYN2 encodes one of synapsins and the release of glutamate via synapsin can directly promote osteoblast differentiation.We found that circulating leptin levels may causally decrease lumbar spine BMD(p value = 0.016).The estimated effect sizes of leptin on forearm BMD and FN-BMD were not significant,and leptin did not increase or decrease total body BMD.Simulations suggested that when the same number of genetic variants wass used,our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al.But in practice,our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs.By applying our extended method to summary statistics from GWAS for blood metabolites and FN-BMD,we successfully identified ten blood metabolites(nonadecanoate,1-stearoylglycerophosphoinositol,1-palmitoleoylglycerophosphocholine,leucylleucine,aspartylphenylalanine,ascorbate and 4 unknown metabolites)that may causally influence FN-BMD.In this study,we identify to genes(ASB16-AS1 and SYN2)which may cause the variation of BMD.ASB16-AS1 may play an important role in osteoblast proliferation and differentiation,at least by interacting with known genes in these processes.Circulating leptin level causally decreased lumbar spine BMD.The effects of leptin on BMD at different skeletal sites may be differentially associated with various components of bone.We extend a casual inference method which was more suitable to infer the causal relationship between metabolites and traits,and identify a number of potential causal metabolites for FN-BMD,which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.