a lot is being asked of the genetic analysis of major depression ( md ) : to find the biological underpinnings of one of the commonest psychiatric illnesses and one of the world s leading causes of morbidity . while lifetime prevalence estimates vary , from 3% in japan to 16.9% in the u.s . , in all countries the disorder is common , with a frequency typically varying from 8% to 12% ( demyttenaere et  al . , 2004 ; kessler et  al . , , md has the greatest impact of all biomedical diseases on disability ; in europe , it is the third leading cause of disability ( alonso et  al . ustn et  al . , 2004 ) . despite its prevalence and md s enormous burden on our health care systems ( scott et  al . , 2003 ) , our treatments there is even dispute about the value of medication ( khin et  al . , 2011 ; , 2008 ; turner et  al . , 2008 ; vhringer and ghaemi , 2011 ) and psychological therapies ( cuijpers et  al . , 2008 , 2010 , 2011 ) . genetic analysis , by identifying risk variants and thereby increasing our understanding of how md arises , could lead to improved prevention and the development of new and more effective therapies . although genetic analysis has identified risk loci for many other common medical diseases ( hindorff et  al . , 2009 ) , success has yet to visit md . in this review , we consider what has so far been learnt , consider reasons for the difficulties encountered , and propose how these might be overcome . we start by reviewing evidence from the genetic epidemiology literature relevant to the genetic basis of md . we then consider what genome - wide association studies ( gwass ) have told us . the gwas results are particularly important for interpreting the large , forbidding literature on candidate gene studies , which we review next . in addition , gwas findings inform us about the extent to which rare but more highly penetrant genetic variants might contribute to liability to md . we finally examine whether there exist forms of md that might be more genetically homogeneous and consider how these might be identified . studies showing that md aggregates within families date back to the early decades of the 20 century ( reviewed in tsuang and faraone , 1990 ) . meta - analysis of the highest - quality family studies produced an estimated odds ratio for increased risk for md in first - degree relatives of md probands of 2.84 ( sullivan et  al . , 2000 ) . surprisingly , no high - quality adoption study of md has been performed , so our evidence of the role of genetic factors in its etiology comes solely from twin studies . while the first of these also date to early in the 20 century , only six high - quality studies were identified in the review completed in 2000 ( sullivan et  al . , 2000 ) . meta - analysis estimated heritability for md to be 37% ( 95% confidence intervals 3142 ) . there was no evidence from these studies that shared environmental factors contributed meaningfully to the familial aggregation for md . one particularly large - sample twin study of md estimated the heritability of md at 38% ( kendler et  al . , 2006 ) . epidemiological studies of md have consistently shown a higher prevalence rate for women ( weissman et  al . therefore , twin researchers have been interested in asking whether the heritability of md differs across sexes and , more interestingly , whether the same genetic factors impact on risk for md in men and women . the two major studies that have addressed this question found reassuringly similar answers ( kendler et  al . md was appreciably more heritable in women than in men ( 40% versus 30% and 42 versus 29% , respectively ) and clear evidence was found for sex - specific genetic effects with genetic correlations estimated at + 0.55 and + 0.63 . a substantial proportion of genetic risk factors for md appeared to be shared in men and women . however , these results also predict that when the individual genetic variants that impact on risk for md are definitively characterized , an appreciable proportion of them will be relatively sex specific in their effect . gwass are typically carried out in two stages : a discovery phase , in which the entire genome is screened , and a replication phase , in which a subset of snps are tested in an independent cohort . some studies report the replication and discovery results separately ; others combine the p values of all studies ( including the discovery sample ) in a meta - analysis . a simple summary of table 1 is that nothing significant has been found and indeed many of the papers and reviews of this field make that point ( e.g. , cohen - woods et  al . , 2013 ) . however , one paper claims a genome - wide significant association : a marker within a gene desert on chromosome 12 ( kohli et  al . , 2011 ) . we need to consider not only whether this finding is likely to be true , but also whether the negative findings are meaningful . in short , how do we assess false - positive and false - negative rates in table 1 ? gwas interrogates common variation in the genome , usually variants with frequencies greater than 5% , and typically requires a genome - wide significance threshold of 5   10 ( peer et  al . , 2008 ) ( this threshold depends on a number of factors , including the number of variants tested , also listed in table 1 ) . for the diallelic snps that are genotyped on gwas arrays , allele frequencies are usually reported as the frequency of the least common allele ( which will always be < 0.5 ) . genotypes from dense sets of snps are partially , and locally , correlated ( sachidanandam et  al . , 2001 ) . the pattern of correlation is nonrandom , since recombination does not occur uniformly across the genome but is localized to hotspots ( mcvean et  al . , 2004 ) , giving rise to blocks of linkage disequilibrium . the extent of linkage disequilibrium ( that is to say the degree of correlation between markers ) is one determinant of the ability of a set of markers on a genotyping array to detect genetic signal . an important consequence is that genotyping only a subset of loci captures most of the common variation in the genome . conversely , if a causative variant is not correlated with any markers on a genotyping array , it can not be detected . the degree to which genotyping arrays capture genomic information is partly population specific , because population history affects the extent of linkage disequilibrium . thus , linkage disequilibrium tends to increase the further away a population is from africa ( conrad et  al . , 2006 ) , consistent with the hypothesis that humans migrated out of africa , experiencing severe population bottlenecks on the way to colonizing the rest of the world ( degiorgio et  al . standard gwas approaches do not work so well in african populations ( teo et  al . one explanation for the failure of gwas applied to md might be that the causative variants , or markers sufficiently close to them , have not been genotyped on the available arrays . in fact , due to the blocks of linkage disequilibrium , in non - african populations gwas is remarkably effective at detecting a large fraction of common variants of reasonable effect size ( odds ratios greater than 1.2 ) that contribute to complex traits , even though a very small fraction of the total amount of sequence variation segregating in a population is actually genotyped . to illustrate this , figure  1 shows the results of simulations that compare gwas carried out using an affymetrix 500k genotyping array , with the results from using all the variants in hapmap ( frazer et  al . , 2007 ) . even this relatively sparse array ( current platforms interrogate millions of variants ) has power of 82% ( for a sample size of 9,000 ) to detect a locus with an odds ratio of 1.2 , compared to 88% with the complete set of snps ( 9,240 is the largest discovery sample size used in gwas of md [ ripke et  al . , 2013b ] ) . in other words , differences in coverage between chips do not translate into big differences in power . furthermore , imputation ( howie et  al . , 2009 ) using the very high density of variants available from the 1000 genomes project ( abecasis et  al . , 2010 ) , has further extended the scope of genotyping arrays to interrogate millions of ungenotyped variants . in short , failure of gwas to detect common variants ( maf > 5% ) conferring risk to md is unlikely to be due to insufficient information about these variants from genotyping arrays . the most likely explanation for the failure of gwas for md is that studies have been underpowered to detect the causative loci ( wray et  al . , 2012 ) . while gwas coverage of common variants is good , gwas requires large sample size in order to obtain adequate power to detect variants of small effect ( odds ratios less than 1.2 ) . in the following sections , we treat with common variants and the power of gwas ( and candidate gene studies ) to find them figure  1 demonstrates the nonlinear relationship between sample size and effect size for common variants . to detect loci with an odds ratio of 1.1 or less , sample sizes in the tens of thousands will be required ( note that this depends on the prevalence of the disease ; in the following discussions , we assume that md has a prevalence of 10% ) . table 1 shows that the largest gwas for md used 9,240 cases and 9,519 controls ( ripke et  al . , 2013b ) . figure  1 shows that such a sample has 90% power to detect loci with an odds ratio of 1.2 ; it will detect effects of this magnitude or greater at more than 93% of all known common variants . note that the one positive finding reported in table 1 is an outlier : no other gwas detected the signal ( kohli et  al . , 2011 ) . the study used a discovery sample of 353 cases and 366 controls to detect , at genome - wide significance , an association between md and a marker next to the slc6a15 gene ( kohli et  al . , 2011 ) . without further replication , the status of this finding is dubious and is likely to be a false positive . while table 1 only includes gwass of md , there are also a number of studies of phenotypes that are genetically related to md , such as the personality trait of neuroticism ( kendler et  al . , 1993 ; shifman et  al . , 2008 ) or depressive symptoms ( foley et  al . , 2001 ; hek et  al . , 2013 ) . the largest is a study of depressive symptoms in 34,549 individuals that reports one , unreplicated , p value of 4.78   10 . overall , we can conclude that no study has robustly identified a locus that exceeds genome - wide significance for md or genetically related traits . we can also conclude that gwas results have set some constraints on the effect sizes likely to operate at common variants contributing to susceptibility to md . candidate gene studies of md have generated many publications but few robust findings . at the time of writing ( 2013 ) , searching for articles dealing with genetic association and md returned more than 1,500 hits . almost 200 genes have been subject to testing , many by multiple groups ( bosker et  al . , 2011 ; lpez - len et  al . , the difficulty , common in this area of research , is that few groups agree with each other . resolution of conflicting results is usually attempted through meta - analysis and table 2 summarizes data for 26 genes analyzed by meta - analysis , of which seven yield a significant ( p  < 0.05 ) result : 5http / slc6a4 , apoe , drd4 , gnb3 , htr1a , mthfr , and slc6a3 . we can use the results from table 1 to interpret the results presented in table 2 . first , we note that the mean effect size ( expressed as an odds ratio ) across the studies that report a significant effect is 1.35 . second , all of the variants tested , whether significant or not , are common ; none have an maf less than 10% , and the mean is 38% ( column headed maf in table 2 ) . this means that the results of gwas are relevant ( recall that gwas interrogates common variants ) . virtually all of the candidate variants should be detectable by the published gwas , particularly if imputation is used to obtain data from markers not present on the arrays ( howie et  al . the fact that the candidate variants do not occur in table 1 suggests that the results in table 2 are false positives ( recall that the largest published gwas has greater than 80% power to detect an odds ratio greater than 1.2 ) . most gwass include a section reporting the analysis of variants in candidate genes , and by providing a much larger sample size than almost any of the meta - analyses listed in table 2 , their findings are likely to be more robust than the meta - analyses . boomsma and colleagues tested 92 snps in 57 candidate genes in a gwas sample of 1,738 cases and 1,802 controls ( bosker et  al . 2006 ] and in npy [ heilig et  al . , 2004 ] ) scored p values less than 0.05 , where four would have been expected by chance . the finding is therefore compatible with no effect at any locus tested . at the gene level ( testing for enrichment of significant snps ) , 2003 ) and the norepinephrine transport ( net ) ( inoue et  al . , 2004 ) , again compatible with chance expectations . wray and colleagues tested 180 candidate genes , and after correcting for the number of tests carried out , found that no candidate gene was significantly associated ( wray et  al . , 2012 ) . table 2 shows the power of each meta - analysis , using the effect size estimated from each meta - analysis ( purcell et  al . , 2003 ) and assuming a disease prevalence of 10% . the mean odds ratio estimated over all candidate gene meta - analyses is 1.15 , requiring a sample size of greater than 3,000 cases . only six meta - analyses use sample sizes in excess of 3,000 , and just two of these six reported a significant finding . note that for those studies reporting a significant result , the mean power was only 60% . in summary , the data from table 2 are consistent with a lack of significant findings in any candidate gene meta - analysis . moreover , the meta - analyses discussed here represent less than a quarter of all the genes tested in the literature ( and a smaller fraction of the variants ) . with lower sample sizes than reported in the meta - analyses , however , lack of evidence does not mean an effect can be excluded ; the negative findings are also compatible with a lack of power to detect an effect . in fact , as we discuss below , estimates of the likely number of genetic variants contributing to md risk run into the thousands . given that about 18,000 genes are expressed in the brain ( lein et  al . , 2007 ) , it would not be surprising if some of the candidates in table 2 are true risk variants , but nowhere near the effect size currently considered plausible . this raises the question , so far unanswered , at what point can we say a candidate has been excluded . nevertheless the conclusion is straightforward : candidate gene studies provide little convincing support for the involvement of any candidate gene in md . this point should be born in mind by all those wishing to use association data to support a particular explanation of the biological causes of depression . neuroscientists sometimes claim that genetic results can be interpreted as evidence in favor of their particular theory ( duman et  al . , 1997 ; holsboer , 2000 ; luscher et  al . , 2011 ; samuels and hen , 2011 ) gwas data can be used to constrain further the likely genetic architecture of md , by using marker results that do not reach genome - wide significance . this is important because it might be that the genetic architecture of md consists primarily of rare but relatively large effect loci . for example , it could be that there are many susceptibility alleles with frequencies much less than 5% and odds ratios greater than 3 . . however , gwas results make that extremely unlikely , as can be appreciated from the following argument . suppose that the genetic architecture of md consists of many small - effect loci , smaller than can be detected at genome - wide significance by currently available samples . for example , suppose the odds ratio for these risk variants are 1.05 and suppose the variants have a frequency of 50% ( alleles with a higher frequency are easier to detect , so this is a conservative assumption ) . power to detect a single variant of this effect size at this frequency in a sample size of 10,000 cases and 10,000 controls is less than 0.001% , at a p value of 1   10 and disease prevalence of 10% ( purcell et  al . , 2003 ) . but there is a 67% chance that such a variant will have a p  value less than 0.5 . this means that if all snps are ranked by their p values , then p values less than 0.5 will be enriched with snps that contribute to disease susceptibility . in other words , if there are small - effect variants contributing to md , then the distribution of snp p values will depart from null expectations . this method is referred to as polygenic scoring and has been used to investigate the polygenic nature of complex traits . a second class of method uses the snp data to estimate genetic similarity and thereby assess heritability . . regions of the genome contributing to disease susceptibility will be enriched among those with the same disease . the degree of sharing of common variants will reflect the heritability of the trait , at least that portion due to such common variation . thus , by assessing the amount of sharing by descent between individuals with the disease , it is possible to estimate the heritability from snps ( hence sometimes called snp heritability ) . there are currently two implementations of this idea ( so et  al . , 2011 ; yang et  al . , 2011 ) . two papers report snp heritabilities for md ranging from 21% ( lee et  al . , 2013 ) to 30% ( lubke et  al . , 2012 ) . the discrepancy between snp- and family - based heritability estimates ( of about 38% ) is in part attributable to the fact that causal variants are not in linkage disequilibrium with genotyped markers ( yang et  al . , 2010a ) ; this means that the snp - based heritability is a lower bound on that arising from common variants . even though the snp heritabilities have wide confidence intervals ( from 15%50% ) , they provide a critically important constraint on our understanding of the genetics of md : they indicate that common variants of small effect ( with odds ratio less than 1.2 , and probably much less ) make a large contribution to the genetic susceptibility to the disease , accounting for more than 50% of the heritability . indeed , the snp heritability is consistent with the view that the genetic basis of md consists of many thousands of independently acting loci , each of very small effect , that contribute to disease susceptibility . before we consider some alternative possibilities , we pursue what this conclusion means for genetic studies of md . complex traits show clear differences in the number of samples required to obtain a significant finding . figure  2 shows results for two diseases ( cancer and crohn s disease ) and two quantitative traits ( height and weight ) ( park et  al . , 2010 ) . which genetic architecture is most similar to that of md ? if we could answer this question , we would be in a good position to estimate the sample sizes needed to detect genetic loci , thus informing our interpretation of existing data , and the design of future experiments . wray and visscher asked this question about the genetic architecture of schizophrenia ( wray and visscher , 2010 ) . their answer involved finding a phenotype with a genetic architecture predicted to be similar to schizophrenia and for which many genetic loci have been found . they suggested , from similar heritability estimates , risks to relatives , and the disease prevalence , that the genetic architecture of schizophrenia resembles that of height . in order to compare genetic analysis of height with schizophrenia , they assume that genetic liability to schizophrenia is quantitative and that the dichotomous nature of schizophrenia arises because the number of predisposing alleles in some individuals exceeds a certain threshold . for example , an individual with predisposing alleles at 100 loci or more might present with schizophrenia , while someone with fewer such alleles would show no symptoms . by considering that disease prevalence represents the fraction of individuals whose genetic susceptibility exceeds this threshold , and that schizophrenia has otherwise the same genetic architecture as height , it is possible to apply what we know from height gwas data to estimate sample sizes needed to detect schizophrenia risk loci ( yang et  al . , 2010b ) . in order to compare the power to detect a locus affecting a disease in a case - control study with the power to detect a locus affecting a quantitative trait ( assuming that both have the same genetic architecture and heritability ) , visscher and colleagues show that only the disease prevalence and proportion of cases and controls need be known ( yang et  al . , 2010b ) . this means that we can estimate sample sizes for a gwas of md by comparing it with a quantitative trait that has a similar genetic architecture and for which loci have been found . but weight ( or more properly body mass index ) might be an appropriate model : many loci have been mapped ( berndt et  al . , 2013 ; it has a heritability similar to md ( a  recent estimate based on 20,000 sibling pairs gave 40% [ hemani et  al . , 2013 ] , though this is lower than a large meta - analysis of twin data [ nan et  al . , 2012 ] ) . in figure  3 , we show results under the assumption that md has a similar genetic architecture to weight ( red dotted line ) or to height ( black continuous line ) ( yang et  al . , 2010b ) . we estimated the number of samples needed for an md gwas to have 80% power to detect at least one locus , for different disease prevalences . if md has a genetic architecture similar to weight ( red dotted line ) , then , for a disease prevalence of 10% ( typical of most surveys of md ) , a sample size of more than 50,000 cases will be needed to detect at least one genome - wide significant hit . for example , while power to detect a variant that explains 0.08% of the variance on liability to md will be 4% , in a sample size of 10,000 cases and 10,000 controls , power in schizophrenia ( prevalence 1% ) is approximately 50% for the same sample size . the effect of disease prevalence ( shown on the vertical axis ) is not linearly related to sample size . in order to find genes with a smaller sample size , we need to collect a sample that has a lower prevalence . if md is truly a quantitative phenotype , then the extremes of the distribution will represent a less prevalent form of disease . we could take disease that is so severe that it has a prevalence of 0.5% or lower , so that fewer than 20,000 cases would provide 80% power to detect at least one locus . alternatively , we could identify rare subtypes of depression that are less prevalent and we hope represent a more homogenous condition than md broadly defined . ideally , such subtypes would have a different genetic architecture , veering more toward that of height than of weight , so that much smaller samples are needed . we start however with a review of the genetics literature to determine whether there might be rare but relatively large - effect loci that gwass have been unable to detect . the data we have summarized so far are compatible with the hypothesis that the genetic basis of md arises from the joint effect of very many loci of small effect , with odds ratios of much less than 1.2 . however , it is also compatible with the existence of larger effect loci , under two alternative ( but not incompatible ) hypotheses ; first , some of the heritability of md is explained by rare relatively large - effect loci ; second , larger effect sizes would be observed if more homogeneous heritable phenotypic groupings could be identified . we consider in this section whether rare , large - effect variants might exist and return to the issues of phenotypic homogeneity later . it is sometimes forgotten that linkage studies provide information about rare , relatively penetrant susceptibility loci . family - based designs are typically not well powered to detect the small effects found in gwass . where two siblings have the same disease , departure from this 50% sharing indicates regions that harbor risk variants ; but since the sd for sharing is large ( approximately 3.7% ) , large sample sizes are required to detect a significant departure . family designs can however detect one form of genetic variation that is hidden from gwass : the joint effect of independent , rare , mutations in the same gene ( recall that gwass are effective for common variants ) . in a linkage study , the effects of independent mutations will combine together , since the unit of analysis in linkage ( the average distance between recombinations in the human genome in a single meiosis ) is a much larger genomic region than is the case for association analyses . in cases in which linkage asserts that there is an effect but association fails  to detect one , then one explanation is allelic heterogeneity : multiple effects exist in the gene but on different haplotypes . results are reported as a logarithm of the odds ( lod ) score , rather than a p value . the majority of the studies reported in table 3 used an affected sibling design ( in which two siblings have md ) . in this design , an lod score of 2.2 is suggestive evidence for linkage ( expected to occur once by chance in a genome scan ) , an lod score greater than 3.6 represents significant linkage ( expected to occur by chance with a probability of 5% ) , and an lod score of 5.4 is highly significant ( probability of chance occurrence is less than 0.1% ) ( lander and kruglyak , 1995 ) . the outlier here is the zubenko study ( zubenko et  al . , 2003 ) , which reports more loci at higher levels of significance than all the others . three groups report data in multiple publications , usually because they acquired additional data ( utah families [ abkevich et  al . , 2003 ; camp et  al . , 2005 ] , dent [ breen et  al . , 2011 ; mcguffin et  al . , 2005 ] , and genred [ holmans et  al . , 2004 , 2007 ; levinson et  al . , 2007 ] ) . the additional samples collected by the genred consortium failed to confirm the 15q linkage reported in their initial paper ( holmans et  al . , the authors considered that the first finding might be a false positive , that the second finding might be a false negative , or that both findings were true , the difference being attributable to variation in the clinical features of the families ( holmans et  al . , 2007 ) . third , there are overlaps in the locations identified by linkage results ( table 3 ) . the confidence intervals for the position of loci found by linkage studies are notoriously broad ( roberts et  al . , 1999 ) , so that overlaps between localizations often occur by chance . however , if we restrict analysis to a window of just 5 mb , then five regions are repeatedly found : chromosome 11 , 7580 mb ( breen et  al . , 2011 ; 2003 ) , chromosome 15 , 3742 mb ( zubenko et  al . , 2003 ; camp et  al . , 2005 ) , chromosome 15 , 8792 mb ( breen et  al . , 2011 ; holmans et  al . , 2004 , 2007 2008 ) , and chromosome 2 , 6468 mb ( middeldorp et  al . , 2008 ; schol - gelok et  al . , this is partly , but not entirely , due to the large number of loci found in one study ( zubenko et  al . , 2003 ) , a study that has attracted criticism ( e.g. , unusually low simulation - based lod score thresholds reported for analyses without covariates [ levinson , 2006 ] ) , so we can not come to any firm conclusions , but this result suggests that some of the signal may be true . four groups report differences in linkage results when the analysis incorporates sex as a covariate . as predicted by the twin results summarized earlier , some loci appear to be sex specific ( abkevich et  al . , 2003 ; camp et  al . , 2005 ; holmans et  al . , 2007 one interpretation of the linkage studies is that rare but relatively penetrant variants might contribute to the genetic risk . nevertheless , it is also possible that the linkage findings could be explained as false positives or the overinterpretation of nonsignificant results . in this respect , it is useful to consider the  results of a study of weight in 20,240 siblings ( from 9,570 nuclear families ) showing that a highly polygenic genetic architecture ( such as that underlying md ) can falsely indicate the presence of large - effect loci in a linkage analysis ( hemani et  al . , 2013 ) . there is some limited evidence from other sources that mendelian - acting mutations give rise to md . attempts to fit morbid risk data to single major locus models have all been inconclusive ( gershon et  al . , 1976 ; goldin et  al . , 1983 ; price et  al . , 1985 ) , as have been attempts to find markers that cosegregate with md in a mendelian inheritance pattern ( ashby and crowe , 1978 ; weitkamp et  al . , 1980 ; wilson et  al . , a review of the online catalog of mendelian disorders ( omim ) identified four single gene disorders in which md is present as a clinical feature ( table 4 ) . in addition ( and not reported in the table ) , there are well - known relationships between md and familial cushing syndrome and parkinson disease . the examples in table 4 are rare , such as perry syndrome , for which eight families are known worldwide , and typically present with additional phenotypes that would not lead them to be classified among the majority of cases of md . table 4 contains one example of a mendelian mutation that underlies pure md : wolfram syndrome , a rare autosomal recessive disorder due to a mutation on the short arm of chromosome 4 ( polymeropoulos et  al . , 1994 ) . the disease itself ( in homozygotes ) is characterized by a broad spectrum of psychiatric and neurological disorders , but heterozygote carriers show a purer md phenotype : in one report , out of 11 individuals carrying a wolfram mutation , eight were hospitalized for major md , significantly more than the three relatives expected if there were no association between psychiatric hospitalizations and mutations at this locus ( swift and swift , 2005 ) . the authors argue that  if the population frequency of wolframin mutations that predispose carriers to psychiatric illness is about 1% , with an odds ratio of 7.1 , wolframin mutation carriers would be estimated to be about 7% of patients hospitalized for md overall , we can not rule out the possibility that rare large - effect risk alleles exist , but we also can not extend much hope for their discovery . it is possible that risk alleles with odds ratios between 3 and 4 , occurring at low frequencies ( less than 5% ) , make a contribution to md , but their discovery will require either a new generation of genotyping arrays , interrogating rare variants , or the deployment of population - scale sequencing . the second hypothesis to explore is the idea that larger - effect loci might be detected if md were to be analyzed differently . for example , consider the possibility that md is not one but two disorders that can not be differentiated on a clinical basis alone . suppose that 50 variants contribute to disease through one pathway ( leading to one subtype of md ) and 50 to a second pathway ( leading to the second subtype ) . since variation in the first pathway is irrelevant to disease susceptibility in the second subtype , the genetic effect of loci acting on one pathway is reduced by half , and power is similarly reduced . this point is not merely important in helping design genetic studies , it is critically important for their interpretation . without knowledge of the existence of two unrelated mechanisms , it would be difficult , perhaps impossible , to interpret the results of the study . we would be left guessing whether the 100 variants represented one , two , or more mechanistic pathways . do subforms of genetically homogeneous md exist ? a large literature addresses this issue , not all of it readily summarized ; here we tackle two questions that are key to understanding how genetic effects operate in md : first , how separate is md from other disorders ? ? two disorders that most frequently overlap diagnostically with depressive illness are anxiety and bipolar disorder . the prevailing view is that md is highly comorbid with anxiety : about 60% of individuals with md report a lifetime history of one or more anxiety disorders ( alonso et  al . , 2004a ; angst , 1993 ; blazer et  al . , 1994 ; hunt et  al . , 2004 ; kessler et  al . , 1996 , 2003 ; , 1996 ; mineka et  al . , 1998 ; pini et  al . the most closely related condition , symptomatically , is generalized anxiety disorder ( gad ) . longitudinal studies indicate that while gad precedes the occurrence of md in about one - third of cases , conversely in about a third of cases , md precedes gad ( moffitt et  al . , 2007 ) . while there is general agreement in the literature for comorbidity between anxiety and md , bipolar disorder and md a distinction between unipolar ( md only ) and bipolar ( episodes of md and mania ) can be drawn on the basis that bipolar disorder s onset age is on average 15 years younger than unipolar , recurs more frequently , is associated with different personality types ( md is associated with neuroticism and bipolar with sensation seeking or extraversion ) ( perris , 1966b ) , and has an increased risk of bipolar illness in relatives ( gershon et  al . , 1982 ; lieb et  al . , 2002 ; weissman et  al . , 1984 ) . genetics provides a way of testing the diagnostic uniqueness or otherwise of md by determining the degree of genetic correlation between diseases . do the same genetic loci that increase susceptibility to md also increase susceptibility to other disorders ? two quantitative reviews ( meta - analyses ) agree that there is a high genetic correlation between anxiety and md ( cerd et  al . , 2010 ; middeldorp et  al . , of 16 twin studies that report genetic covariation between anxiety and md , all found that the genetic correlation between gad and md is not significantly different from unity . demirkan and colleagues have recently confirmed the genetic correlation between md and anxiety using snp data to generate genetic risk scores ( demirkan et  al . , 2011 ) . thus , for anxiety , the comorbidity can be attributed , in part , to a common genetic basis . at a genetic level , gad and , genetic data have been employed to support a separation of unipolar from bipolar affective illnesses : relatives of those with bipolar are more likely to develop bipolar , and conversely relatives of unipolar probands more likely to develop unipolar illness ( md , in other words ) ( perris , 1966a ) . with few exceptions , subsequent studies have confirmed this observation : bipolar illness aggregates in the families of bipolar probands much more than in families of unipolar probands ( weissman et  al . , 1984 ) . however , it is also true that in comparison to the general population , relatives of both bipolar and unipolar probands have increased risks of both forms of affective disorder ( gershon et  al . , 1982 ; lieb et  al . , 2002 ; weissman et  al . , 1984 ) . the risk for bipolar disorder in relatives of md probands is only modestly increased , approximately 2-fold across studies ( on a relative risk scale ) ( tsuang and faraone , 1990 ) . conversely , there is about a 3-fold increase in risk of developing unipolar depression for a first - degree relative with bipolar disorder . note that the base rates of unipolar and bipolar illnesses are very different : about 1% for bipolar as against 10% for unipolar . altogether , a third to over a half of the affectively ill family members of bipolar patients manifest depressive illness ( weissman et  al . , 1984 ) . gershon argued from a study of 1,254 relatives of probands and controls that different affective disorders represent  thresholds on a continuum of underlying multifactorial vulnerability if true , then bipolar disorder would be a more severe form of unipolar depression . genetic correlation data to test this hypothesis are limited : one twin study of 67 pairs of twins with bipolar and 177 with unipolar depression yielded a genetic correlation of 0.65 between the two  disorders . however , the data were not consistent with the threshold model , namely that bipolar is a more severe subform of unipolar ( mcguffin et  al . , 2003 ) . a larger study of 486 twin pairs with affective illness provided some support for the threshold model , but the number of bipolar probands was small , so power to discriminate models was low ( kendler et  al . , 1995b ) . , 2011 ; yang et  al . , 2011 ) , there are now estimates of the genetic correlations between md and bipolar disorder ( lee et  al . , 2013 ) . the genetic correlation with bipolar disorder was 0.47 ( se 0.06 ) , compatible with the twin - study genetic correlation of 0.64 ( mcguffin et  al . , 2003 ) . this finding suggests an overlap between unipolar and bipolar illnesses in which some loci contribute to both conditions . consistent with this , genetic analysis of loci that act across disorders has been used to implicate calcium - channel signaling in the etiology of affective disorders ( cross - disorder group of the psychiatric genomics consortium , 2013 ) . however , before concluding that molecular genetic analysis trumps the phenotypic separation of unipolar from bipolar , two points should be born in mind . gwas results show that the majority of heritability can be assigned to many loci of small effects . how many that might be depends on the unknown contribution of rarer variants of large effect , but we can provide a rough estimate by assuming that depression is a quantitative trait , in which md is one extreme ( following the same reasoning for the power estimates for a successful md gwas [ yang et  al . , 2010b ] ) . from the distribution of effect sizes of other quantitative traits , we can estimate the number of loci required to explain the heritability of md . assuming an exponential distribution ( goldstein , 2009 ) , about 2,500 loci are required to explain half the heritability . this estimate is conservative , since the distribution of variants more closely follows a weibull distribution than an exponential ( park et  al . , 2010 ) . in short , the number of variants required to explain md heritability implies that about one in five genes expressed in the brain are likely to be involved . if thousands of variants confer susceptibility to md , then this could explain a genetic correlation with other psychiatric disorders . we have no reason to expect the genetic architecture of anxiety , bp , or schizophrenia to be very different from md : they are all likely to involve many loci of small effect , and they are all , at some level , brain disorders . indeed , ripke and colleagues estimate that 8,300 independent snps contribute to the genetic basis of schizophrenia , accounting for 50% of the variance in liability to schizophrenia ( ripke et  al . , 2013a ) . with 18,000 genes expressed in the brain ( lein et  al . , 2007 ) , and each disorder influenced by variants in thousands of genes , genetic correlation may be inevitable . the second point to note about the correlation between md and other disorders concerns for example , no one has been able to identify features that distinguish with high accuracy episodes of md in unipolar cases from episodes of md in cases with bipolar illness . furthermore , there is evidence that md and bp share more characteristics than is sometimes appreciated : several authors have claimed that a large number of patients diagnosed with unipolar disorder have features of bipolar illness ( angst et  al . , 2010 , 2011 ; when symptoms of subthreshold mania are sought ( elevated mood , irritable mood , or increased activity ) , a large proportion of unipolar cases are found to qualify : up to half of all cases with unipolar illness ( angst et  al . , 2010 , 2011 ; zimmermann et  al . , 2009 ) . however , subthreshold diagnoses depend critically on the quality of the assessments and the exact interpretation of what constitutes subclinical mania ( it is easy to confuse a state of hypomania with elation from  normal  causes like falling in love , or getting a grant funded in grim times , or hyperactivity from the agitation that occurs in some depressive subtypes ) . we can conclude that genetic and phenotypic classifications concur in identifying considerable overlap between anxiety and md , with mixed support for a distinction between md and bipolar disorder . the genetic data point to genetic overlap , but this may  be , to some extent , a consequence of the polygenicity of complex traits . we turn next to the question of whether there exists a pure md , rarer and harder to distinguish from bipolar than currently acknowledged , which has at least partly distinct genetic roots . or more generally , we ask , are there genetically homogenous subtypes of md ? those unfamiliar with the literature debating the division of md into subtypes may be surprised not only at the diversity of the proposed classificatory systems employed ( e.g. , dimensional , hierarchical , or categorical ) but also at the vehemence with which each position has been defended , or more usually attacked ( eysenck , 1970 ; parker , 2000 ) . the importance of this acrimonious debate is the extent to which genetic research strategies might resolve it and potentially guide interpretation of the underlying pathogenic mechanisms . it is more heritable in women and the genetic correlation between the sexes is approximately + 0.60 . to put this in perspective , the figure is comparable to the genetic correlations estimated between bipolar disorder and md from twin studies ( 0.64 ; mcguffin et  al . , 2003 ) and snp heritability ( 0.47 ; lee et  al . , 2013 ) . how can md be one condition , when the degree of genetic correlation between the sexes is of the same magnitude as that between two supposedly separate disorders ? currently , md is diagnosed when depressed mood , or a loss of interest or pleasure in daily activities , is present for more than 2  weeks , and five or more out of nine symptoms ( including low mood and loss of interest ) occur nearly every day . do these nine dsm symptomatic criteria for md reflect a single underlying genetic factor ? surprisingly , only one study has addressed this question ( kendler et  al . , 2013 ) . the best - fitting model to explain md concordance in 7,500 adult twin pairs required three genetic factors , reflecting the psychomotor / cognitive , mood , and neurovegetative features of md . as might have been predicted from a set of criteria chosen on the basis of clinical judgment rather than psychometric properties or validation from biological features , the nine dsm symptomatic criteria for md do not appear to represent a single underlying genetic factor . if we look in families , do we find that related individuals share similar phenotypic features ? for example , some subjects report an atypical pattern of increased sleep and appetite ( rather than the opposite ) ; does this represent a heritable feature that might identify a genetically homogenous subtype ? while some studies examining the inheritance of clinical features find that they do not  breed true ( weissman et  al . , 1986 , 2006 ) , latent class analysis of 14 symptoms of depression , assessed in 1,029 female twin pairs , revealed that members of a twin pair concordant for depression were significantly more likely than expected to share features of the latent class - derived syndrome ( kendler et  al . , 1996 ) . this raises a third question , as to which , if any , features characterize an inherited form of md . an old distinction between  endogenous  and  reactive  md ( gillespie , 1929 ) is based upon the presumed occurrence of depressive episodes that were independent of precipitating events , compared to episodes that were an exaggerated reaction to life events . is it possible that the endogenous form of md is more genetically determined than others ? the short answer to that question is no ; in fact , contrary to the hypothesis that subjects whose md appears to be devoid of precipitating events should have increased genetic predisposition ( indexed by greater family history ) , the opposite is true : those reporting more stressful life events are more likely to have a family history ( kendler and karkowski - shuman , 1997 ) . however , this finding does indicate , as the large literature on familial md confirms ( reviewed in rutter et  al . , 1999 ; sullivan et  al . , 2000 ) , that clinical differences exist between those with and those without a family history of md . distinguishing features are relatively nonspecific : those with a family history of md have more clinically severe illness , tend to present at an earlier age , and suffer higher rates of recurrence ( kendler et  al . , 1994 , 1999 ; lieb et  al . , 2002 ; weissman et  al . , indicate that genetic risk factors for md not only alter average risk but also impact on sensitivity to the depressogenic effects of environmental adversities , particularly various forms of childhood maltreatment and recent stressful life events . the finding of increased genetic susceptibility to environmental stressors , or in short a gene by environment interaction , suggests the possibility of subdividing md on the basis of environmental effects : theoretically genetic effects will be more homogeneous , relatively larger , and easier to detect in populations with clearly defined exposures . while twin studies have shown that aggregate genetic risk factors for md interact with stressful events , in recent years the field has been preoccupied with one of the many possible ways in which this effect might be explained at the molecular level . the dispute is whether or not the serotonin transporter 5-httlpr variant is involved in a gene by environment interaction . the original study was carried out on a longitudinal cohort in new zealand , and empirical literature dealing with whether that finding is robust , and replicable , is unclear and considerably polarized ( caspi et  al . , 2010 ; kaufman et  al . , 2010 ; mcguffin et  al . , two meta - analyses found no evidence for an interaction ( munaf et  al . 2009 ) , while one meta - analysis concluded that there was an effect ( karg et  al . , 2011 ) . the authors of the positive gxe meta - analysis take the view that the effect of gxe is broad :  rather than focus on a specific class of studies , we sought to perform a meta - analysis on the entire body of work assessing the relationship between 5-httlpr , stress , and md the one study that came closest to replicating the original design , a longitudinal study of a birth cohort in new zealand , failed to replicate the first report ( fergusson et  al . , 2011 ) . all that we can reasonably conclude is that current attempts to subdivide md on the basis of interactions with environmental effects using candidate genes are unlikely to yield quick insights into the origins of the disease . genetic analysis of md was recently recognized to be among the greatest challenges facing health researchers ( collins et  al . , 2011 ) . for some complex traits , including schizophrenia ( ripke et  al . , 2013a ) , there are now a number of verified genetic loci that contribute to disease susceptibility ; in some cases , their discovery has implicated disease mechanisms , casting light on known , suspected , or indeed novel biological processes that explain why some people fall ill ( teslovich et  al . , 2010 ; van der harst et  al . , despite convincing evidence for a genetic contribution to disease susceptibility , there has been a dearth of substantive molecular genetic findings . the failure of gwas analysis of more than 9,000 cases of md  ( ripke et  al . , 2013b ) to find robust evidence for loci that exceed genome - wide significance is compatible with a paradigm in which the majority of the genetic variance is due to the joint effect of multiple loci of small effect . twin studies and snp - based heritability tests of the samples used for genome - wide association discount the possibility that there are no genetic effects to be found , leaving two nonmutually exclusive possibilities : either the effects are smaller than expected and/or the disorder is heterogeneous : different diseases might manifest with similar symptoms ( incorrectly identified as the same illness ) , or there may be many different pathways to the same outcome ( different environmental precipitants trigger md in different ways , according to the genetic susceptibility of the individual ) . we have reviewed evidence that indicates that md is heterogeneous . this is clearly seen in the difference between sexes : genetics sees a greater difference between md in men and md in women than physicians recognize between anxiety and md . however , while there is considerable agreement in the literature that md has heterogeneous causes , there is much less agreement about its homogeneity as a clinical disease ( parker , 2000 ) . attempts to subdivide md on the basis of inheritance have so far yielded only limited fruit : relatively nonspecific features , recurrence , and earlier onset indicate greater genetic predisposition . the picture is consistent with a fairly undifferentiated phenotype emerging as the final common outcome of diverse processes , a process called equifinality in the development literature . the list of possible pathways is large : in addition to long - running favorites such as abnormalities of monoamine metabolism ( including postreceptor components of the downstream camp signaling pathway [ duman et  al . , 1997 ] ) and impaired corticosteroid receptor signaling ( holsboer , 2000 ) , more recent hypotheses include the involvement of neurotrophins ( samuels and hen , 2011 ) , fibroblast growth factors ( both ligands and receptors ) ( turner et  al . , 2012 ) , gabaergic deficits ( luscher et  al . , 2011 ) , and epigenetic changes , specifically alterations in methylation and acetylation profiles at the promoters of glucocorticoid receptors and brain - derived neurotrophic factor ( mcgowan et  al . , 2009 ) genetics does not support the primacy of one theory over another ; indeed as our review of the candidate gene literature indicates , genetics does not support any of the biological theories put forward to date . first , there is no reason to suppose that undifferentiated md is intractable to gwas , but success will require very large sample sizes ( figure  3 ) . however , interpreting the results of such a study is likely to be challenging . we have seen that md is highly comorbid with anxiety , and etiologically heterogeneous , at both genetic and environmental levels . without information on comorbidity , known risk factors , and clinical phenotypes , . some will be sex specific , some will act only in  situations of environmental stress , and others will predispose to anxiety . genetic studies will need to include an extensive amount of phenotypic information if we are to make sense of hard - won mapping results . second , our review indicates that we should not abandon attempts to concentrate on subtypes of md . so far , studies using recurrent and early - onset md have been no more successful than those that examine undifferentiated md , but this may be due to lack of power . if we consider md as part of a quantitative trait ( representing liability to depression ) , then using a sample of more extreme cases would be equivalent to analyzing a rare disease ( as figure  3 demonstrates ) . even a small improvement in genetic tractability could result in a large saving in the number of samples that need to be analyzed ( reducing from 50,000 to 20,000 , for example ) . the problem is that we do not know for sure how to determine the scale on which severity is measured : is it the number of episodes of md , the length of episodes , the number of symptoms , or some other feature or combination of features ? furthermore , the severity scale needs to differentiate cases with higher genetic risk , not those cases resulting largely from environmental adversities . alternatively , subdividing md on the basis of one or more clinical features ( e.g. , typical versus atypical vegetative features , standard versus postpartum onset ) , sensitivity to environmental stress , or sex , might identify a rarer , or at least a more genetically homogenous , subtype . at present , deciding which features to investigate is likely to be an ad hoc enterprise . without knowing beforehand which to use , studies will need to be comprehensive , collecting as broad a range as possible of clinical features and known or putative risk factors . forty years ago , a perceptive review of depressive disorders in science ( akiskal and mckinney , 1973 ) argued that a psychoanalytic model of md as object loss ( a proximal cause of md ) could be conceptualized as loss of reinforcement , or loss of control over reinforcement , then subject to experimental investigation in animal models , and integrated with anatomical , biochemical , and pharmacological data as a process occurring in the diencephalic centers of reward . in this view , md is a final common pathway , a decrease in the functional capacity of the reward system . since then , md has begun to appear as a relatively thin covering serving to unite a plethora of independently acting mechanisms . genetic analyses can identify risk variants , both rare and common , and in so doing cast much needed illumination on the biology of the commonest psychiatric disorder . the difficulties of sample size and clinical differentiation are daunting but unavoidable if we are to take advantage of the promise that genetics makes .
major depression is the commonest psychiatric disorder and in the u.s . has the greatest impact of all biomedical diseases on disability . 
 here we review evidence of the genetic contribution to disease susceptibility and the current state of molecular approaches . 
 genome - wide association and linkage results provide constraints on the allele frequencies and effect sizes of susceptibility loci , which we use to interpret the voluminous candidate gene literature . 
 we consider evidence for the genetic heterogeneity of the disorder and the likelihood that subtypes exist that represent more genetically homogenous conditions than have hitherto been analyzed .