In the second stage, we attempted to meta-analyze the findings from both populations, to increase statistical power and to assess the consistency of evidence in two ethnicities using weighted Z-transformed test as implemented in the R. A weighted Z-transformed test was chosen because it has been suggested that when the number of tests are small, the weighted Z-transformed test performs better than other combination probability methods, such as Fisher’s test and generalized binomial test [5, 6]. Gene-based genome-wide significant level and suggestive level

Among 17,640 genes included in the analysis, 14,605 overlapped with either 5′ and/or 3′ genes with the average overlapping size per gene size (overlapping size with other gene/gene size) 0.62. We therefore selleck screening library arbitrarily defined the gene-based genome-wide significant level as 0.05/(3,035 × 1 + 14,605 × 0.38) = 5.8 × 10−6, while the suggestive level was 1/(3,035 × 1 + 14,605 × 0.38) = 1.2 × 10−4. Identification of enriched physiological role in genes associated with BMD The top 35 genes were imported into the Ingenuity Pathways Analysis (IPA) Software (Ingenuity Systems, Redwood City, CA, USA) to see more obtain networks for further analyses

and to determine whether their physiological role was enriched. These top 35 genes were chosen because 35 was the limited number of genes/molecules required to form a functional regulatory gene network in the later gene network inference analysis. The enriched physiological roles were ranked by the p values

of the Fisher’s Exact Test that indicated the probability of the input gene (from the gene-based GWAS) being associated with genes in the physiological roles by chance. Gene network inference ID-8 via knowledge-based data mining We next analyzed biological selleck kinase inhibitor interactions among top hits using the IPA tool. The gene annotations from the top hits with suggestive p value were entered into the IPA analysis tool to construct the biological networks of the clustered genes. Networks are generated from the gene set by maximizing the specific connectivity of the input genes, which represents their interconnectedness with each other relative to other molecules to which they are connected in Ingenuity’s Knowledge Database. Networks were limited to 35 molecules each to keep them to a functional size. The p value of probability for the genes forming a network was calculated using the right-tailed Fisher’s Exact Test based on the hypergeometric distribution. Results Genomic control of SNP data before gene-based GWAS In single SNP GWAS of spine and hip BMD in southern Chinese, an inflation factor of 1 was observed for both sites. An inflation factor of 1.22 and 1.18 for spine and hip BMD was observed in the p value distribution from the dCG GWAS data.