One method to remedy this is to perform more genotyping with dens

One method to remedy this is to perform more genotyping with denser SNP; another method is to perform gene network inference to identify genes that are connected with other BMD genes. Using the gene network inference approach, several bone-related

hub genes or complexes have been identified, such as ERK1/2 [33, 34], P38 MAPK [35, 36] (Fig. 1a), prostaglandin E2 [37], and TNF [38] (Fig. 1b). Overlaying the gene network with known canonical signaling pathways revealed that aryl hydrocarbon receptor signaling; role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis; and role of macrophages, fibroblasts, and endothelial cells in rheumatoid arthritis (7 genes out of 35 genes in each signaling pathway) this website were the predominant themes of the spine BMD gene network (Supplementary Table 3a), whereas acute phase response signaling (8 genes out of 35 genes) was the predominant theme of the hip BMD gene network (Supplementary Table 3b). Interestingly, acute phase response was one of the underlying mechanisms of action of bisphosphonate in the selleck inhibitor treatment of osteoporosis [39]. Our findings suggest that hip BMD genes F2, MBL2,

and HMOX1 may be the genes involved in bisphosphonate treatment and may be used to monitor treatment response. There are a number of limitations in the current gene-based GWAS. First, click here our definition of gene-based GWAS significance level may not be accurate. The most accurate way would be to use simulation; however, this would require extremely heavy computations, as the number of SNPs included in each study and the number of independent genes will vary from study to study. The C1GALT1 LD structure also varies in different ethnicities. Nonetheless, our gene-based GWAS significant level 5.8 × 10−6 was not much different to the conservative Bonferroni-corrected GWAS significance level of 2.8 × 10−6 (=0.05/17,640, assuming each gene is

independent to each other). Second, our definition of the gene locus (±50 kb 5′ upstream and 3′ downstream of the coding region) might strongly affect the test statistics and hence the gene-based p value. Noting that large boundaries lead to a longer overlapping region with the neighbor genes, hence some markers are included in multiple genes. Thus, we justified how long the boundaries should be included by averaging the distance between the top intergenic SNPs identified in the recent meta-analysis of GWAS to the nearest coding genes [1]. Notably, the highly significant SNP may also inflate the test statistics in a number of nearby genes, which poses interpretation difficulty. Thirdly, although a gene-based approach can identify genes with multiple causal SNPs with small effect size, it cannot identify genes with only one very significant SNP, while other SNPs in the gene do not show any significant p value.

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