in the search for susceptibility genes for common complex diseases , we are faced with enormous challenges . the past decade 's paradigm of focusing a study on just one or a few candidate genes limits our ability to identify novel genetic effects associated with disease . in addition , many susceptibility genes can show effects that are partially or solely dependent on interactions with other genes and/or the environment . genome - wide association studies ( gwas ) have been proposed as a solution to these problems ; however , the analysis of whole - genome data is problematic because we must separate the one or few true but modest signals from the extensive background noise . moreover , with gwas data alone , the ability to elucidate gene - environment interactions is limited . gwas researchers must embrace the abundant clinical and environmental data available to complement the rich genotypic data , with the ultimate goal of revealing the genetic and environmental factors that are important for disease risk . so far , gwas have taken a simplistic , ' one snp at a time ' analysis approach . recent technological advances enable the genotyping of hundreds of thousands of human single - nucleotide polymorphisms ( snps ) on thousands of samples . we are hindered in exploiting these laboratory advances because strategies for analyzing the data have not kept pace with technological progress . even with these challenges , successful reports of gwas have emerged in the literature [ 1 - 6 ] . in fact , the national human genome research institute ( nhgri ) keeps an updated gwas catalog on their website , which lists over 273 published gwas so far . unfortunately , as expected , only the strongest associations can be detected using these traditional approaches , and there are many more genes still to be found . the majority of these studies analyzed one snp at a time , meaning that they have barely scratched the surface of interesting information within these datasets . ultimately , supplementary data , replication datasets , or multiple analytical approaches must be used to filter the results down to a manageable number of the ' most likely ' genes . in their recent report in human molecular genetics , baranzini et al .   developed and applied a pathway- and network - based analysis to exploit interesting association signals in the snps that fell between the thresholds of p = 0.05 and p = 10 in the original single - snp association analysis . it is methods such as this that are likely to allow us to better characterize and exploit gwas signals in the so - called ' gray region ' between genome - wide significance ( p = 10 ) and the typical p = 0.05 . in the field , genome - wide significance has been determined to be at p = 10because that is the bonferroni correction for p = 0.05 for 1 million tests . because some of the replicating , positive results in gwas fell below the level of genome - wide significance ( that is , had p - values over 10 ) , baranzini and colleagues   propose a protein interaction and network - based analysis ( pinbpa ) for the study of a multiple sclerosis ( ms ) dataset . this approach is similar to those in microarray studies in which gene ontologies are used for analysis . the idea of using prior knowledge for gwas has been used successfully in studies of diseases such as parkinson 's disease , age - related macular degeneration , bipolar disorder , rheumatoid arthritis , and crohn 's disease [ 12 - 14 ] . the first step of pinbpa is to compute a gene - wise p - value by choosing the lowest p - value of all snps mapping to a given gene . any markers that do not map to genes or unannotated genes are eliminated from this analysis . next , using a plug - in for the cytoscape   software , searches are conducted to extract potentially meaningful sub - networks associated with the phenotype of interest . finally , a test is performed to determine the extent to which significant network modules could be obtained by chance . baranzini and colleagues   applied pinbpa to two ms datasets : ( i ) the international multiple sclerosis genetics consortium ( imsgc ) gwas , consisting of 334,923 snps passing quality control from the affymetrix human mapping 500 k array in 931 family trios , and ( ii ) the genemsa study , with 551,642 snps passing quality control from the illumina humanhap550 bead chip in 978 cases and 883 controls . after single - locus analysis using logistic regression , 78 and 87 snps had a p - value of less than 1  10 in the imsgc and genemsa datasets , respectively . using pinbpa analysis , 346 significant modules were identified on the basis of their aggregate degree of association with ms . because of the nature of the algorithm , many modules overlap extensively ; thus , the modules with the highest scores were selected . module i included several human leukocyte antigen ( hla ) genes , including the known risk factor for ms hla - drb1 . hla - drb1 and hla - dra are in high linkage disequilibrium and some snps in hla - dra serve as proxies for hla - drb1 with high sensitivity . module ii includes an extensive pattern of immunity - related genes , including several hla genes : cd4 , cd82 , itgb2 , il2ra , and cd58 . finally , modules iii and iv suggest a neural component , including genes expressed in neurons and glia , such as nck2 , epha3 , epha4 ( module iii ) and glutamate receptor genes ( module iv ) and many more . the results of this study   provide insights into the role of several immunological pathways , including cell adhesion , signaling , and communication , and , more importantly , neural pathways in ms . in particular , signals for axon guidance and synaptic potentiation were over - represented in ms . this is very exciting , as it is one of the first reports demonstrating genetic associations in a neural pathway contributing to the susceptibility of ms . because the pathophysiology of ms suggests that neural pathways are likely to have a role baranzini et al .   demonstrate the utility of protein interaction network information in the analysis of ms data . several gwas [ 12 - 14,18,19 ] have proposed the use of prior knowledge in the form of pathway databases , such as the kyoto encyclopedia of genes and genomes ( kegg ) and biocarta , or gene ontology databases . .   suggest that the network - based approach not only reduces the number of relevant interactions found but also increases the likelihood that proteins that interact are part of the same biological pathway . the biofilter combines six sources of disease - independent information ( information that is not related to the phenotype of interest ) from the public domain : kegg , reactome , gene ontology , database of interacting proteins ( dip ) , protein families database ( pfam ) , and netpath . it also includes disease - dependent information in the nature of previous linkage regions , association studies , and microarray expression results . all these sources are combined specifically to prioritize the search for gene - gene interactions in gwas data . pathway - based approaches are continuing to emerge in the literature as a more comprehensive approach to the analysis of gwas data . this trend is likely to continue as we learn more about the optimal strategies for incorporating prior knowledge into analyses . in fact , as we move to using next - generation sequencing data , such approaches may also expand into the next - generation sequencing arena : looking for rare variants in a particular pathway that are present in a higher proportion of disease cases than healthy controls . as more biological knowledge and genomic data become publicly available and more easily accessible , we will continue to see methodological developments exploit this information to better dissect the genetic architecture of common , complex disease . gwas : genome - wide association studies ; imsgc : international multiple sclerosis genetics consortium ; kegg : kyoto encyclopedia of genes and genomes ; ms : multiple sclerosis ; pinbpa : protein interaction and network - based analysis ; snp : single - nucleotide polymorphism . mdr is currently analyzing the imsgc gwas data , along with a subset of the genemsa data , in a biofilter analysis to explore gene - gene interactions in ms .
the efforts of the human genome project are beginning to provide important findings for human health . 
 technological advances in the laboratory , particularly in characterizing human genomic variation , have created new approaches for studying the human genome - genome - wide association studies ( gwas ) . 
 however , current statistical and computational strategies are taking only partial advantage of this wealth of information . in the quest for susceptibility genes for complex diseases in gwas data 
 , several different analytic strategies are being pursued . in a recent report , 
 baranzini and colleagues used a pathway- and network - based analysis to explore potentially interesting single locus association signals in a gwas of multiple sclerosis . 
 this and other pathway - based approaches are likely to continue to emerge in the gwas literature , as they provide a powerful strategy to detect important modest single - locus effects and gene - gene interaction effects .