epigenomics has helped to reveal several surprising large - scale functional relationships among the genes themselves and the surrounding nongenic dna , previously hinted at by the beta - globin cluster . one is the generality of large ( 10 's to 100 's of kb ) genomic regions regulating gene expression . while the beta - globin gene cluster had been studied for decades3 , linking progressive chromatin changes to globin gene switching during development4 , the generality and size of multigene chromatin domains only emerged with large - scale epigenomic mapping . as increasing numbers of imprinted genes were found , it was discovered that they were organized in gene clusters , often with common regulatory elements such as ctcf binding sites5 . with the advent of genome - scale mapping of histone modifications , many large regions of heterochromatin modifications were found , such as specific modifications associated with the inactive x chromosome6 . moreover , large autosomal regions of heterochromatin modification across hox gene clusters were found to be more highly conserved across species than the underlying dna sequence , while not being a simple reflection of exonic boundaries7 . thus , epigenomicstudies revealed that the scope of genome that is apparently functional was at least an order of magnitude greater than that suspected from the sequence alone . epigenomics provided the functional anatomy of the genome that vesalius gave gross anatomy a half millennium ago . another surprising large - scale genomic relationship is frequent intra - chromosomal and inter - chromosomal interactions mediated by chromatin proteins . these were discovered through chromatin capture methods , described in detail elsewhere in this issue 8 , designed to preserve chromatin - mediated interactions over long distances . dna loop structures , mediated by chromatin , surprisingly common and highly dynamic , were found to be associated with function . for example , multiple interleukin genes in the 200 kb mouse th2 cytokine locus , when transcriptionally active , are folded into numerous loops , anchored by satb at their bases9 . remarkably , trans - interactions between chromosomes involve some of the same sequences that epigenetically regulate imprinted gene domains , for example the h19 differentially methylated region , and may act through transvection to regulate genes in trans10 . a recent example of large - scale genomic organization mediated by chromatin is the link between long rnas , heterochromatin modification and gene activity . at the cold spring harbor genome biology meeting in 2005 , tom gingeras asked for a wager on the number of genes that will ultimately be agreed upon , arguing that the nearly 50% of the genome that may be untranslated rna will be proved functional11 . for example , antisense rnas appears to establish heterochromatin in mammalian genes , independent of dicer and the posttranslational mirna machinery12 . these regions may be > 100 kb12 , affect multiple genes , and involve argonaut family proteins13 . an exciting recent discovery is the role of long intergenic noncoding rnas ( lincrnas ) in establishing heterochromatin . for example , hotair is a lincrna that retargets prc2 over hox domains with profound changes in gene expression relevant to cancer progression14 . finally , large organized chromatin k(lysine ) modifications ( or locks ) have been shown to organize the genome into very large blocks ( 100 's to 1000 's of kb ) , some of which are differentiation - specific in their location and extent and correspond to lamin - associated domains ( lads)1517 . these very large regions may provide a dynamic mechanism for functional organization of the genome and are altered in cancer15 . additional clues that many such large - scale epigenetic networks profoundly influence cellular development and genome function come from large - scale mapping studies . for example , ctcf , which mediates h19 imprinting described earlier , appears to play a general role in defining functional gene region boundaries18 . similarly polycomb target genes , thought to be involved in stable gene silencing , may alternate between functionally active and silent states over large gene regions19 . that such networks have a general role in organizing the genome functionally is suggested by the identification of chromosome territories with closely approximated gene - rich regions20 . just as epigenomics provides a functional anatomy of the normal genome , genome - scale studies of epigenetic disease are revealing a pandora s box of epigenetic pathology . just as cancer was the vanguard for gene - specific disease epigenetics21 , genome - scale epigenetic studies of disease have also focused first on cancer , and these studies have revealed much more genetic pathology than was suggested by candidate gene approaches . for example , methylation changes can affect large genomic regions in colorectal cancer22 , and widespread methylation changes are even more striking outside of the usually examined cpg islands , i.e. in shores and gene bodies23 . similarly , it came as a surprise to most when widespread alterations in histone acetylation and methylation were found ubiquitously in cancer24 . stem cells , a hoped for therapeutic target for many diseases , have shown promiscuous methylation differences from somatic cells on a genome - scale , surprisingly involving non - cpg sites25 . remarkably , the sites of differential methylation largely overlap , with strong statistical significance , among physiological states , such as normal vs. cancer , stem cells vs. differentiated cells , and tissues derived from differing germ layers26 . thus , the language of epigenomic organization appears to be common for normal development and for disease , just as the language of anatomy is common for normal and abnormal physiology . the increasingly appreciated importance of large - scale epigenetic control in regulating gene function has had a profound influence on how disease - based genomic studies are being organized . while published genome - scale studies represent only about 2% of cancer epigenetics , the rate of increase over the last 5 years of cancer epigenomic studies is more than double that of conventional gene - based analyses ( fig . the same relative increase in genome - scale studies also appears to be true for the nascent field of non - cancer human disease epigenetics , such as cardiovascular , immunological and neuropsychiatric disease 27 , 28.these differences are of course driven in part by the availability of new technology , but also by the growing realization that variation in both dna methylation and chromatin are widespread across the genome , and may be organized into large genomic domains . but another important factor driving such  disease epigenomics  is the relatively limited yield to date of conventional snp - based genetic analysis in explaining most common human disease . as widely described in both scientific29 , 30 and lay publications31 , the gap between original expectation of genetic analysis and attributable risk of disease is much greater than anticipated a decadeago . how is epigenomics transforming the search for genetic causes of common human disease ? many have suggested that one contributing factor may be the importance of environmentally driven epigenetic variation in disease risk , particularly as a surrogate for mutational change3234 ( table 1 ) . but we should also consider another dimension to this epigenetic argument for common disease that has received comparatively less attention . since the actual  genome anatomy  target is likely much larger than we realized , perhaps involving half or more of the genome , and since the understanding of the normal function of this genome anatomy requires epigenomics , perhaps much of what appears to be negative genetic data could become meaningful in an epigenomic context ( table 1 ) . for example , most gwas studies identify not genes , but nearby regions or intergenic deserts . yet these same regions frequently harbor differentially methylated regions ( dmrs ) that discriminate tissue types , or distinguish cancer from normal cells . they are also the canonical regions for long intergenic noncoding rnas ( lincs ) that help establish chromatin structure and normal gene function . another way in which disease - associated dna sequence variants might affect disease risk is through their linkage to dna sequences regulating dna methylation or chromatin modification or binding factors . an intriguing additional possibility we have proposed is that dna sequence variants might themselves affect the stochastic or environmentally influenced variance in the epigenome . according to this model , complex species would have an evolutionary advantageto include alleles for increased epigenetic variation per se , i.e. genetic alleles that increase epigenetic variance but not the mean38 . this would be like an evolutionary  hedging one 's bet ,  and confer an advantage for genes in pathways for which the environment changes epochally , e.g. the abundance of food and water . examining inbred mice from the same litter and living in the same cage , we identified hundreds of  variably methylated regions ,  or vmrs , that were highly enriched for key genes in development and embryonic pattern formation . thus development itself , which epigenetics regulates , likely includes a great deal of stochasticism at the epigenetic level . genetic variants that increase this developmental plasticity at specific targets may confer an evolutionary advantage but might be deleterious to some individuals after a recent epochal change in the environment , such as the recent western diet38 . finally , we are only beginning to understand the role of locks and lads in functional genome organization , and their assessment in disease will require robust genome - scale approaches to native chromatin measurement , and availability of clinical specimens permitting such analyses ( table 1 ) . what potential areas for future technology development will fuel growth in this area ? of course , all roads lead to sequencing , including bisulfite genome - scale sequencing for dna methylation , just as in non - epigenetic genome science . the rollout of inexpensive , comprehensive , high throughput single molecule sequencing has been slower than promised , and second generation sequencing is still impractical for large scale epidemiological studies involving thousands of patients , except for capture - based methods such as padlock probes 40 . the conundrum in such studies is that while they offer enormous advantages in throughput , single base resolution , and allele - specific data , they will not reveal regions of differential methylation where we do not already know to look , which may be vast as epigenomics is applied to an ever increasing number of disease states . at the same time , high throughput sequencing is relatively cheap now for examining chromatin modifications , but that is true for modifications representing a relatively small fraction of the genome purified by chromatin immunoprecipitation , for example . for large regional changes such as locks , an important advance will come from reagents that are cheap and amenable to processing by typical university core laboratories , such as illumina and other arrays . for example , a soon to be released methylation chip will provide ~450,000 targets , including all cpg islands and shores , as well as dnase hypersensitive sites and other regions identified and curated for this purpose by a consortium of laboratories organized by tom hudson . while this reagent may not be next or even this year s most comprehensive approach , last year s is nt bad and such cooperative approaches open epigenomic research to any general laboratory , a very exciting development . other exciting technological initiatives include epigenomic analysis of microdissected samples or even single cells , and enrichment of small chromosomal fragments for biochemical analysis of chromatin 41 . samples in ongoing and future large - scale cohorts must be preserved to allow dna methylation and chromatin analysis . but retrospectively , a great deal can be added to existing cohort studies , since dna methylation is stable over decades . much of the existing genetic data might be made clearer by adding epigenomic analysis to those studies . new cohort sampling should include standard sources , such as lymphocytes , but also , as much as possible , target tissues affected by the disease . additionally , we need to develop new statistical and epidemiological tools for disease epigenomics , and for its synthesis with conventional genetic analysis . for example , unlike snps , epigenetic variation is inherently quantitative , and thus does not lend itself to simple allele designation , e.g. quantitative levels of dna methylation or polycomb complex members . the quantitative nature of epigenome variation can help explain complex traits with a smaller number of contributing loci , since they do not necessarily require as many of the additive signals originally proposed by r.a . fisher42 . such an approach is being applied , for example , to the analysis of quantitative traits associated with vmrs39 . but i do n't think vesalius would have been intimidated , and i know victor would have been delighted .
standfirst headerepigenomics provides the functional context of genome sequence , analogous to the functional anatomy of the human body provided by vesalius a half millennium ago . 
 much of what appear to be inconclusive genetic data for common disease could therefore become meaningful in an epigenomic context .