We have reported an independent genome-wide association scan of ADHD. None of the SNPs achieved genome-wide significance (p<5.0e-08) in either the sample reported here or in a meta-analysis or our results with other samples (see ADHD GWAS Consortium, this issue). Given the extent to which ADHD is genetic, it is highly likely that within the set of SNPs with P-value < 10−3 there are true associations for which we do not yet have sufficient power to unequivocally detect.
Although no finding achieved genome-wide significance, several of our top findings deserve further comment. The PRKG1 gene regulates neuronal migration, signal transduction, dendrite development, long term potentiation and forebrain development 35-38
. Thus, it is a reasonable candidate for a gene that might lead to brain abnormalities and ADHD.
One of our top findings was in the CDH13 gene (p=2.28E-05). CDH13 was implicated by a GWAS of 343 ADHD adults and 250 controls 22
and in the IMAGE GWAS of ADHD symptom counts 24, 25
. This gene lies under a linkage peak implicated in a meta-analysis of ADHD linkage studies 15
and has been implicated in substance use disorders 39
, which co-occur with ADHD 40, 41
Column seven of helps with the interpretation of our findings in the context of other ADHD GWAS studies. This column gives the p-value from a meta-analysis (see ADHD GWAS Consortium, this issue) that comprises the data in this paper and data from three other consortia: IMAGE 23
, PUWMa (See Mick et al., this issue) and CHOP (unpublished data). The Table shows that most SNPs show a dramatically higher p-value on the combined sample. No SNP increases in significance and a few retain significance levels less than .0001 . This does not create confidence in the idea that many of our top 100 SNPs are true associations.
Our negative results indicating the existence of very small genetic effects when individual variants are considered alone is not surprising. GWAS findings are now emerging for other psychiatric disorders. There have been replicated copy number variation associations for schizophrenia 42, 43
and for autism 44-46
, a genome-wide significant association for bipolar disorder 47
, and a significant association from a schizophrenia-bipolar dataset 48
. These early GWAS results suggest that, due to the many statistical comparisons required to scan the genome, large samples are needed to detect some genes and extremely large samples are needed to detect many.
For example, the successful bipolar disorder GWAS, which detected two loci at genome-wide levels of significance, required 4387 cases and 6209 controls 47
. Studies of this size and larger implicated several genes for diabetes 49
but a pooled sample of 60,000 subjects was required to definitively implicate a large set of genes 50
. For Type II Diabetes and Crohn's disease mapping of one or a small number of disease-associated variants was successful in studies with similar sample sizes to the present study, but the vast majority of findings have emerged with the incorporation of multiple scans involving sample sizes many times larger than the one presented here 50, 51
and in most cases consisted of genetic loci conferring odds ratio in the regions of 1.1 – 1.4. The statistical requirement for large sample size for GWAS studies should not be interpreted as meaning that the effects of individual genetic variants are very much smaller than the effects of individual environmental variants. In fact, the latter are small as well 52
The general expectation from GWAS of complex disorders is for multiple genes of very small effect 53
. Backward power calculations on some of the initial true results from these diseases indicate that many of the identified candidates were extremely unlikely to be detected from the initial study 53
. Thus, these initial studies were either fortunate or many such effects (potentially one hundred or more) with a similar effect size must exist. In this study we have not been fortunate, insofar as we did not identify a variant above genome-wide significance, which we define as 5×10-8 54, 55
. Concerning the existing candidate genes for ADHD, the genome-wide association data do not provide genome-wide significant support for any of the previously postulated candidates. That is not to say that these genes should be rejected from consideration, but rather that the effect sizes for each of these variants must be small if they are real effects, which is consistent with the meta-analyses of candidate gene studies 10, 11, 16
We have considered the pathophysiological and clinical implications of genetic effects so small that they cannot be detected with our current sample size. Such small effects can arise for several potential reasons. First it may be correct that genetic risks for ADHD are due to numerous small additive effects of common risk variants. However, it is also possible that multiple rare variants of small to moderately large effect size could account for these findings 56
. Alternative explanations include sample heterogeneity, the possible interaction of genetic variants either within or between genes; and their interaction with environmental risk factors. Although the heritability of ADHD is high, this does not give an indication of the underlying genetic architecture, although it does imply that genetic influences are important for the etiology of the disorder. Recent modeling of complex behavioral and biological traits in the mouse suggests that as heritability increases the number of genetic variants involved increases, though effect sizes of individual variants remain small 57
. For ADHD, our expectation is that novel genes for ADHD will be identified from GWAS once sufficient whole genome association data has been accumulated from the analysis of 5,000 – 10,000 cases.
Given the expense of GWAS, it is reasonable to ask if genes of very small effect are worth discovering. Theoretical considerations suggest that the smallness of a gene effect should not be confused with the potential importance of its discovery. For example, should we someday discover a rare variant or a common variant of small effect that implicates a new biological pathway in ADHD, that pathway could then be searched for biological targets that might yield treatments which are more efficacious than standard therapies for the disorder. The discovery of such a variant would also focus research on the implicated gene and pathway, which could lead the discovery of similar variants.
The need to search for DNA variants that lead to ADHD cannot be understood without placing the disorder in the context of current knowledge. ADHD is a common disorder affecting up to 10% of children 1
. In the majority of cases, the disorder persists into adulthood 58
and is associated with serious impairments including traffic accidents 58
, increased health care utilization 58
, substance abuse 58
, unemployment 58
, divorce 58
, and risk behaviors for acquired immunodeficiency syndrome (AIDS) 59
. About 25 percent of ADHD patients do not respond well to currently available therapies 60, 61
. Moreover, the currently preferred treatment for ADHD is stimulant medication. Although medications for ADHD are effective in controlling symptoms for many patients, they do not “cure” the disorder. Even those receiving treatment are at risk for adverse outcomes 62
. Currently available treatments improve outcome, but leave patients with much residual disability and do not markedly improve the executive dysfunction seen in many ADHD patients. These treatments also have adverse effects, including delays in growth 63
The outcome of the present study may have been influenced by a number of limitations. Most notably is the issue of power. We had 80% power for an odds ratio of 1.65 assuming a multiplicative model and a 10% minor allele frequency. Thus, we did not have sufficient power to detect smaller effects or the same effect at lower allele frequencies. Much larger samples or meta-analyses of the current samples will provide a stronger strategy for advancing knowledge regarding the molecular genetics of ADHD along with paradigms designed to search for rare genetic variants. Another limitation of the current study was the differences in genotyping platforms used to analyze case and control samples. This design limitation lead to the exclusion of numerous SNPs through additional QC steps prior to imputation. While it is possible that this might lead to artificial inflation of the test statistics it is unlikely that this had much influence on the outcome of the study given the relatively low genomic control value and the lack of significant results.
Imputation analysis, while extremely useful at generating estimates of association evidence at genetic loci that have not been typed is not perfect. The uncertainty inherent in these analyses reduces the effective sample size, thus limiting power. Also, imputation, like genome-wide association, has limited capacity for the analysis of rarer variation. These analyses used population-based controls not screened for ADHD. Although this will have reduced power somewhat, given the prevalence of the disorder, we do not expect that this had much impact on the results. Finally, ADHD very likely is genetically heterogeneous, such that many different genetic architectures give rise to similar clinical presentations. This includes the possibility that rare variants account for part of the disorder's heritability. Such genetic heterogeneity and complexity reduces power to detect significant association.
In summary, although the current analyses have not identified any convincing results the sample is a useful addition to the present literature and has made a valuable contribution to the current meta-analysis of ADHD GWAS studies (see ADHD GWAS Consortium, this issue), which combines data from four ADHD GWAS studies.