genome - wide association ( gwa ) studies are now well established for discovering hypothesis - free genetic loci whose variations are associated with a phenotype [ 1 - 4 ] . downstream analysis of the study result in terms of gene sets is gaining popularity these days , as it facilitates biological interpretation of a gwa result . the korea association resource ( kare ) project has collected epidemiological and genotype data from the regional cohorts in ansung and ansan , korea . a number of gwa studies based on this kare data have been published so far [ 6 - 8 ] . we pursued re - analyses of these studies in terms of gene sets in a systematic way to warrant cross - comparisons of the traits . we mimicked the analysis conditions of the original published gwa works as much as possible , based on the de facto standard program plink . for the gene set analysis , we used gsa - snp software , an efficient java - based application that accepts a list of single nucleotide polymorphisms ( snps ) and their association p - values and outputs the gene set p - values after multiple testing correction . we applied this procedure to 49 baseline quantitative traits that were of epidemiological , anthropometric , or blood chemistry parameters . as expected , those traits that are known to share common biological mechanisms and to overlap in their population incidences indeed showed similar profiles of the significantly associated gene sets . to our surprise , some of the traits that were phenotypically uncorrelated in our study population and were seemingly unrelated to each other showed similar profiles of the significantly associated gene sets . our results may demonstrate a useful strategy of discovering pleiotropy , which refers to a phenomenon of common pathways involved in distinct phenotypes . briefly , the genotype data were measured for a total of 10,038 residents in both ansung and ansan provinces , korea , using affymetrix genome - wide human snp array 5.0 chips ( affymetrix inc . , after quality control , 352k snp genotypes for 8,842 samples were used in the subsequent gwa analyses . the epidemiological trait data for these individuals were also received from the kare project . among a total of 49 quantitative traits gwa analyses of these 49 quantitative traits were performed by linear regression under the additive genetic model , as implemented in plink . we performed gsa using the gene ontology ( go ) databases , where only gene sets having 10 - 200 members ( 2,476 biological process go terms ) were used . we applied the z - statistic method , as implemented in gsa - snp , with the default options . briefly , those snps residing inside or within 20 kb of the boundary of each gene were compiled , and the second best p - value was assigned to the gene . see kwon et al .   for the rationale of using the second best p - value instead of the best p - value . the gene score was defined as the -log of the p - value assigned to the gene . the p - values for each gene set were computed under the assumption of a normal distribution of the z - statistic , followed by multiple testing correction using the false discovery rate method . when the member genes of a gene set overlapped in their genomic loci or were located in tandem within a short block of strong linkage disequilibrium , the p - values assigned to them might have been highly correlated . in such cases , the r package gosemsim was developed to compute semantic similarity among go terms , sets of go terms , gene products , and gene clusters . this package contains functions to estimate the semantic similarity of go terms based on resnik 's , lin 's , jiang and conrath 's , rel 's , and wang 's methods . here this method determines the semantic similarity of two go terms based on both the locations of these terms in the go graph and their relationships with their ancestor terms . the similarity index between the two go terms was between 0 ( the least similar ) and 1 ( identical ) . first , the highest value against all members of the other set was calculated for a given go term in one of the sets . then , all of these values were collected and averaged to provide the similarity between the two sets . the significance of the similarity index was inferred , based on the distribution of the indices from 10 random samplings . briefly , the genotype data were measured for a total of 10,038 residents in both ansung and ansan provinces , korea , using affymetrix genome - wide human snp array 5.0 chips ( affymetrix inc . , after quality control , 352k snp genotypes for 8,842 samples were used in the subsequent gwa analyses . the epidemiological trait data for these individuals were also received from the kare project . among a total of 49 quantitative traits gwa analyses of these 49 quantitative traits were performed by linear regression under the additive genetic model , as implemented in plink . we performed gsa using the gene ontology ( go ) databases , where only gene sets having 10 - 200 members ( 2,476 biological process go terms ) were used . we applied the z - statistic method , as implemented in gsa - snp , with the default options . briefly , those snps residing inside or within 20 kb of the boundary of each gene were compiled , and the second best p - value was assigned to the gene . see kwon et al .   for the rationale of using the second best p - value instead of the best p - value . the gene score was defined as the -log of the p - value assigned to the gene . the p - values for each gene set were computed under the assumption of a normal distribution of the z - statistic , followed by multiple testing correction using the false discovery rate method . when the member genes of a gene set overlapped in their genomic loci or were located in tandem within a short block of strong linkage disequilibrium , the p - values assigned to them might have been highly correlated . in such cases , the r package gosemsim was developed to compute semantic similarity among go terms , sets of go terms , gene products , and gene clusters . this package contains functions to estimate the semantic similarity of go terms based on resnik 's , lin 's , jiang and conrath 's , rel 's , and wang 's methods . here , we used wang 's method for our analysis . this method determines the semantic similarity of two go terms based on both the locations of these terms in the go graph and their relationships with their ancestor terms . the similarity index between the two go terms was between 0 ( the least similar ) and 1 ( identical ) . first , the highest value against all members of the other set was calculated for a given go term in one of the sets . then , all of these values were collected and averaged to provide the similarity between the two sets . the significance of the similarity index was inferred , based on the distribution of the indices from 10 random samplings . parallel and systematic gwa studies of a number of traits using a single genetic epidemiology dataset give a unique opportunity to explore common biological pathways regulating multiple traits that do not display phenotypic correlations . in this report , we focused on the 49 baseline quantitative trait data that were demographic , anthropometric , or blood chemistry parameters . we surveyed the literature for the published gwa studies , based on the kare data , and extracted the information on the analysis conditions , such as exclusion of samples and covariates of association regression . for the gwa analyses of all 49 traits , we included area , age , and sex as covariates , and for some of the traits , additional covariates were added , based on the literature information ( table 1 ) . for example , the directions pointed by waist and body mass index ( bmi ) were similar in the pca plot ( fig . 1 ) ; using bmi as one of the covariates for the analysis of waist . on the contrary gwa analyses for all 49 traits were performed using linear regression , as implemented in plink , and the resulting snp p - values were fed into gsa - snp for gene set analysis . for each of the 49 traits , we identified biological process go terms that showed p < 0.05 after multiple testing correction ( the lists are available upon request ) . as go terms are hierarchically arranged , verbatim matches are not desirable for the comparison of a pair of gene sets . instead , one should take into consideration the number of nodes in the go tree that separate a pair of terms . for this , we used so - called semantic similarity , as implemented in the r package gosemsim . for each pair of traits , we calculated the semantic similarity of the gene sets and plotted it against the correlation coefficient between the trait values ( fig . the pairs showing semantic similarity greater than 0.8 corresponded to traits that had high correlations in the trait values . we selected 46 trait pairs that showed a semantic similarity in gene sets greater than an arbitrary cutoff of 0.75 and depicted the interaction network using cytoscape ( fig . 3 ) . the traits related to blood lipid levels , such as total cholesterol , high density lipoprotein cholesterol , low density lipoprotein cholesterol , and triglycerides ( tgs ) , formed a small network by themselves . as cholesterol and tgs are components of lipoproteins , it is not surprising to observe such a network . both hip and waist - hip ratio are connected to suprailiac skinfold ; they are all related to body fat . the most striking feature is a large network involving 20 traits . in this network , the traits related to blood glucose levels , such as glu0 , glu60 , glu120 , hba1c , and homa , form a subnetwork . in addition , the traits related to either blood pressure or liver damage also formed respective subnetworks , as expected from their high phenotypic correlations . interestingly , the ph level was connected to 7 other traits , none of which showed high phenotypic correlations with it . we examined the gene sets that were commonly shared between these traits ( table 2 ) . among them , 11 were related to neuron development and function . we surveyed the literature for putative involvement of neuronal or nerve systems in the regulation of those 8 traits ( table 3 ) . although it is presumptive , this may imply that these traits are regulated partly by common pathways that involve the neuronal system . in conclusion
gene set analysis is a powerful tool for interpreting a genome - wide association study result and is gaining popularity these days . 
 comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits . based on the previously published single nucleotide polymorphism ( snp ) genotype data on 8,842 individuals enrolled in the korea association resource project 
 , we performed a series of systematic genome - wide association analyses for 49 quantitative traits of basic epidemiological , anthropometric , or blood chemistry parameters . 
 each analysis result was subjected to subsequent gene set analyses based on gene ontology ( go ) terms using gene set analysis software , gsa - snp , identifying a set of go terms significantly associated to each trait ( pcorr < 0.05 ) . 
 pairwise comparison of the traits in terms of the semantic similarity in their go sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways . for example , the ph level was related to 7 other traits that showed low phenotypic correlations with it . 
 a literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems .