genome - wide association ( gwa ) analysis has provided the first effective strategy to allow a systematic dissection of the genetic basis of common , complex , multifactorial traits .
although for many of these we remain some distance from a complete enumeration of causal mechanisms , there have already been substantial advances in understanding of disease - the role of autophagy in inflammatory bowel disease   and cell adhesion in autism , for instance . however , for most common traits the proportion of the overall phenotypic variance explained remains small , limiting the extent to which prediction of individual disease risk is possible .
there is growing speculation about the mechanisms that might account for the substantial proportion of trait heritability that remains to be characterized .
this speculation has repercussions well beyond recondite theoretical discussion about the genetic architecture of complex traits .
with advances in technology ( particularly next - generation sequencing ) and growing enthusiasm for funding large - scale gene discovery efforts , hypotheses about the nature of this so - called ' genetic dark matter '   have a direct bearing on research strategies .
recently , this debate has seemed increasingly polarized between those who feel a continued search for common susceptibility variants is of limited value , because all that remains to be found are variants of vanishingly small effect , and those who feel that , pending reductions in costs that will allow high - quality , whole - genome sequence data to be generated in adequately powered sample sizes , there is virtue in persisting with an approach of proven worth .
there is good reason to assume that this ' dark matter ' is neither an illusion created by inflated estimates of heritability nor the consequence of marked non - additivity of effects .
if so , then the sum total of genetic variance should largely be explicable in terms of the main effects of all the risk alleles of various types ( single nucleotide polymorphisms , indels , copy number variants ( cnvs ) and inversions ) , allele frequencies ( rare , low - frequency and common ) and effect sizes .
so far , the only parts of this ' space ' explored systematically are those occupied by rare , penetrant alleles ( principally through linkage analysis of monogenic phenotypes ) and common , mostly low - effect alleles ( accessible through gwa analysis ) .
as we seek to make sensible decisions about the direction of future discovery efforts - in terms of the characteristics of the variants we are seeking and the technologies we should use to find them - we need to understand what the exploration of the ' known ' genetic landscape can tell us about the parts that remain largely uncharted .
one long - standing view is that complex trait susceptibility is predominantly a matter of common variants .
common variants collectively account for most individual variation in dna sequence , and the same might be expected to be the case for phenotypic variation .
if true , the results of gwa studies so far indicate that most of the as - yet - undiscovered variants must ( in europeans at least ) have very small effects , because the high coverage and large sample sizes used will have left few , if any , large common - variant effects undiscovered .
evidence ( for example , from large - scale meta - analyses ) is , for many traits , consistent with the notion of a long ' polygenic tail ' of small effects , but it remains unclear how much of overall heritability can be explained under this model .
the idea that complex - trait susceptibility involves a very large number of variants of modest effect has led some to suggest that the value of all such discoveries is diminished , on the basis that one learns little about the biology of disease if too many genes are implicated . however , for many phenotypes , the overall salience of the loci of greatest effect emerging from gwa studies ( the pathways implicated and the relationships to monogenic forms of the same traits ) argues forcefully against such a nihilistic interpretation .
the contrasting viewpoint holds that common - trait susceptibility derives mostly from the action of rare or low - frequency variants .
although such variants account for less individual sequence variation than common variants , there may be a disproportionate effect on disease susceptibility .
the more recent origin of low - frequency variants may allow alleles with more dramatic phenotypic effects to be represented in the population . also , large - effect alleles may cause phenotypic disturbances that are not as easily buffered by compensatory changes during development as are well tolerated , small - effect , common - variant alleles .
recent evidence that large , rare cnvs are associated with behavioral and psychiatric disease phenotypes   supports this view .
some argue that such a rare variant architecture is precisely what one would expect for diseases causing low reproductive fitness , though this rationalization fails to explain the high yield of common - variant signals reported for other diseases , such as type 1 diabetes , that were , until recently , fatal during early life .
it has even been suggested that many of the common - variant associations discovered by recent gwa studies may turn out to be due to the concerted action of multiple low - frequency and rare causal variants .
the nod2 ( card15 ) signal for crohn 's disease indicates that this is certainly possible . for many diseases ,
however , evidence that common - variant associations are consistent across multiple ethnic groups   represents a strong counter to such a model : one would expect the linkage disequilibrium patterns around recent rare and low - frequency causal variants to result in far more inter - ethnic heterogeneity than is actually observed .
although both extreme positions have merit , the likelihood is that , for most diseases , the architecture of predisposition features causal variants that have a wide range of allele frequencies and effect sizes . for most complex traits
, the absence of compelling signals from linkage studies conducted in families segregating multifactorial diseases imposes an upper bound to feasible effect sizes ; even so , it is easy to show that a limited number of low - frequency susceptibility alleles of medium effect could go a long way to explaining missing heritability .
for example , the effect of a low - frequency variant with a population minor allele frequency of 1% and a per - allele odds ratio of 3 , when measured in terms of sibling relative risk ( a commonly used measure of familial aggregation ) , exceeds that of the largest common - variant effect known for type 2 diabetes ( around tcf7l2 ) .
twenty such variants across the genome would account for most of the unexplained heritability for this condition .
such a constellation of variants could provide a respectable tool for individual disease prediction , and the variants discovered would ( because of their relatively large effect size ) be valuable resources for detailed molecular and physiological study
. the extent to which variants with these characteristics are segregating in the population remains unknown , but this is an area in which the combination of next - generation sequencing technologies and large - scale association analysis provides a powerful stimulus to discovery .
early results of this approach ( such as the identification of low - frequency variants within the ifih1 gene that have a marked effect on type 1 diabetes susceptibility ) are encouraging .
ultimately , we can expect large - scale , high - depth , genome - wide sequencing to enable the systematic exploration of the entire allele - frequency , effect - size space and provide empirical resolution of many of these issues .
however , there remain serious financial , logistical and analytical barriers to the implementation of this technology , and the number of such experiments that could be supported by the major funders is , for the time being , limited .
all this means that , for the next few years , the power of next - generation sequencing will need to be used carefully if a profusion of underpowered discovery efforts is to be avoided .
efforts targeted to specific genomic regions ( around particular candidate genes or pathways or exons across the genome , for example ) are attractive because high coverage of the selected areas in large sample sizes can be generated at reasonable cost .
whole - genome sequencing will , for now , be restricted to low - pass coverage across respectable sample sizes , or high - depth coverage in smaller , highly selected , phenotypically extreme sample sets .
the genomic distribution of disease - effect loci will have a major impact on the success of these alternative approaches ( figure 1 ) .
if the low - frequency and rare variants influencing a given trait are disproportionately located in the same loci as the common variants that have been found to date , then targeted follow - up of regions revealed by gwa studies will be a powerful approach , and extending the range and scope of gwa analysis ( to other ethnic groups , for example ) should be a particularly efficient strategy . if , on the other hand , the ' dark matter ' variants have little positional ( or biological ) overlap with those already known , then genome - wide resequencing is likely to be the only practical way to find them .
the evidence so far ( overlap between monogenic and multifactorial loci ; growing numbers of loci with multiple independent association signals ; extensive pleiotropy , and so on ) provides some support for the former view .
effort in tracking down common susceptibility variants , as well as being valuable in its own right , should therefore guide researchers towards other types of causal variants .
two possible versions of the state of nature are presented ( see text ) . in one ( ' even ' ) , causal variants differing in terms of allele frequency ( color scale ) and effect size ( height of bar ) are distributed randomly across the genome : the location of common - variant ( red / orange ) associations of modest effect provides no guide to the location of lower - frequency variants ( yellow / green ) , some of which have quite large effects . in the other ( ' lumpy ' ) , causal variants congregate around certain genomic positions ( ' genes ' ) : gwa studies that reveal the location of the common - variant associations will also reveal the positions of lower - frequency variants , and the proportion of disease biology explained by the loci discovered through gwa studies will be far greater than the proportion of variance explained would suggest .
with only limited empirical data to guide future locus - discovery efforts , extrapolation from the modest proportion of genetic variance so far explained is fraught with danger .
the menu of possible research strategies is large , but each choice makes some implicit assumption about the characteristics of the variants being sought and the genomic architecture of the disease under consideration . given uncertainties over the true state of nature , it is difficult to say which approaches will be most productive .
this argues for open minds , a healthy disdain for orthodoxy , and careful exploration of the technological and methodological options . at the same time
, it is important that the next wave of large - scale discovery efforts is designed so as to test assumptions about trait architecture and technological performance so that lessons of generic value to the field can be learned .
i thank the many colleagues around the world who contributed to the discussions that informed this article .
 identification of common - variant associations for many common disorders has been highly effective , but the loci detected so far typically explain only a small proportion of the genetic predisposition to disease . 
 extending explained genetic variance is one of the major near - term goals of human genetic research . 
 next - generation sequencing technologies offer great promise , but optimal strategies for their deployment remain uncertain , not least because we lack a clear view of the characteristics of the variants being sought . here 
 , i discuss what can and can not be inferred about complex trait disease architecture from the information currently available and review the implications for future research strategies . 