In summary, we have looked for genetic associations with 10 cognitive phenotypes and their first principal component in a large sample size using a genome-wide SNP panel thought to represent the majority of common genetic variation in non-African populations (60
). We were not able to identify any common SNPs that associated with any of the cognitive tests after correction for all of the tested SNPs.
We also examined, for the first time, the role of common CNVs in these cognitive phenotypes, using a set of CNV-tagging SNPs reported by McCarroll et al
). Again, we could not find any associations that remained significant after correction for multiple testing. However, in this case, we cannot claim to have performed a comprehensive analysis, since many known common CNVs cannot be identified with the genotyping platforms used in this study.
Although our study is a negative one in the sense that there is no polymorphism that can be clearly connected to cognitive performance, there is an intriguing suggestion in our data of a role for polymorphisms near previously studied candidate genes. We emphasize that this observation does not provide a replication of any previous findings, because the variants showing the strongest association in our study are not the same variants previously associated, violating now accepted standards of replication (46
). On the other hand, the apparent enrichment of low P
-values in the candidate genes does warrant consideration. One possible explanation is that there are multiple causal variants in these genes creating variable signals of association. We must view these observations as highly tentative, however, since it is difficult to assign formal statistics to patterns in Q–Q plots.
Because several genomic regions have recently been associated with neurological and psychiatric disorders such as epilepsy, autism, schizophrenia and mental retardation, we also focused specifically on a set of these regions to see whether there was evidence for association with healthy cognition. Perhaps surprisingly, these regions were, in general, rather less associated with our PC1 cognitive score than would be expected under the null hypothesis. The NRXN1
gene, however, did show an excess of low P
-values, and the lowest P
-value withstood correction for all the SNPs tested at that locus. This suggests one of two possibilities: either the strongly associated SNPs themselves (all intronic) or a genetic variant they are in LD with directly affect the cognitive scores or that there are multiple rare genetic variants in this region that by chance occur more frequently with one allele of a common variant than the other. Given the multiple recent reports of rare deletions in NRXN1
in schizophrenia and autism (6
), this locus seems to warrant further investigation.
In order to assess power, we first note that since we are dealing with a quantitative trait, the power is a function of both the inter-genotype differences and the allele frequency, and these parameters are confounded into the proportion of population variation that the polymorphism is responsible for. A further caveat is that the variant must be sufficiently common to be well represented either directly or indirectly on the gene chips (that is, they have a minor allele frequency of at least ~5%). We assume that such variants are nearly perfectly tagged and estimate power by simply considering the proportion of variation for the trait in the total population that a given SNP is responsible for. We can then evaluate the probability that a test statistic will reach a given significance threshold for such an SNP. Thus, for the tests assessed in ~750 people, we had 95% power to detect a common variant that explained 5% or more of the variation in the cognitive trait, 84% power to detect a variant accounting for 4 and 58% power to detect a variant accounting for 3%. For the larger sample sizes (SRM and VRM), we had close to 100% power to detect a variant accounting for 4% or more of the variance, 95% power for a variant explaining 3, and 67% power to detect a variant accounting for 2% or more of the variance. Although it is possible that we could have failed to detect some genetic variants in this study with strong effect sizes, especially those that are not well tagged, it is highly improbable that we would have failed to detect multiple such associations. We can therefore conclusively rule out the possibility that the high heritability of human memory and related cognitive phenotypes is fully accounted for by a small number of SNPs with very strong effects.
There are a number of factors that may serve to reduce the power of this study design to detect associations with certain types of genetic variant. Since the majority of this cohort had some form of higher education and were aged below 40, we have reduced ability to detect associations that are specific to older or less well-educated populations. It is also possible that EIGENSTRAT has not completely corrected for population stratification and this may reduce the power to find truly associated variants that differ strongly in frequency between the two main race/ethnicities in the study (Asian and European).
We also note that there are many types of learning and memory that we have not assessed in this study, including delayed or remote memory—which could theoretically be under much simpler genetic control. We cannot generalize these findings to other cognitive phenotypes. However, we have examined multiple cognitive domains in this study, and, in a separate study, have also failed to find genome-wide association with traditional neuropsychological test measures (Cirulli et al., unpublished data). This evidence, together with the similar findings emerging from multiple neuropsychiatric traits, suggest that neurocognition, in health and disease, is not going to be a simple phenotype to genetically decipher.
There are a number of possible explanations for the lack of positive findings in this study. It is possible that the traits being measured are too noisy to gauge a reliable phenotype. The phenotypes used in this study, as for other genetic studies of cognition, were collected during a single test session, and many non-genetic variables are known to affect cognitive performance from day-to-day, including fatigue, hunger, motivation, affective distress, illness. However, we performed heritability assessments on the data gathered in this study by comparing dizygotic and monozygotic twin pairs and found that most of the measures had a very substantial heritable component by this measure. Some of the heritabilities (especially IED and PRM) may be over-inflated here due to a low correlation in dizygotic twins, however, the heritability of memory as measured by CANTAB tests is supported elsewhere using other methods (23
). This suggests that these measures are suitable phenotypes for a genetic study. It is possible that methodological weaknesses inherent in the monozygotic–dizygotic twin comparisons have led to systematic overestimation of the heritabilities of cognitive traits. For instance, monozygotic twins, due to their very similar appearances, may be treated more similarly than dizygotic twins in educational environments. Other types of heritability study, e.g. comparison of adopted children to biological parents (66
) or of twins reared apart (24
) give estimates of heritability of ~0.3–0.5 for cognitive tests similar to those used here. However, no measure of heritability is without flaws and it is possible that the genetic component of cognition has been systematically overestimated.
Three further explanations for these findings remain consistent with a role for common variation in normal cognition: first, those CNVs, or other types of common genetic variant (e.g. microsatellites) that were not well represented by tagging SNPs in this study, could account for the heritability of these traits. However, due to the very large SNP panel used, and the degree to which these SNPs represent the total amount of common variation in the genome (56
), it is unlikely that most of the genetic contribution to cognition happens to be both common and unrepresented.
Secondly, it could be that common genetic variation underlies cognitive traits but that the variants interact with each other to such an extent that they do not produce detectable main effects in these sample sizes. Tackling this possibility is going to be difficult, given the number of possibilities for even simple two-way interactions. One is faced with a high risk of either type I error (if one fails to adequately correct for multiple testing) or type II error (since the interaction effects would have to be huge to achieve formal significance in consideration of the number of tests). With currently assembled datasets, it seems implausible to perform non-targeted screens for interactive genetic variants with effects on neurocognition. Interactive effects between predetermined candidate variants could be assessed but presently there are no good candidate polymorphisms that consistently replicate between different datasets. Thirdly, the genetic variation could be completely attributed to hundreds of common variants each with a tiny effect size, however there are some reasons for doubting that is the case for cognitive disorders and arguably for normal cognitive function (6
As has been suggested for other neuropsychiatric traits (67
), it is possible that epigenetic modifications affect cognition in healthy subjects. This kind of genetic contribution would be undetectable with these methods and remains to be explored.
Perhaps the most likely explanation for these findings is that the genetic variation underlying neurocognitive traits is too rare to be detectable with current genotyping platforms. There is strong recent evidence for this in other neurocognitive traits such as schizophrenia, autism and epilepsy, in which large, rare CNVs have been associated with disorders that have not shown obvious associations with common variation in genome-wide screens (6
). This hypothesis has considerable implications for the future study of cognitive traits.
Whether the majority of associated variants will be only marginally or substantially less common than those we have examined in genome-wide association studies we do not yet know. If the variants are only marginally below the detection threshold for current genome-wide association studies (minor allele frequency ~3–5%), then the new catalogue of variants down to a frequency of 1% generated by the 1000 genomes project (73
) will provide a useful set of variants for further study of cognitive phenotypes. However, very large sample sizes would be needed, particularly given the measurement error inherent in cognitive testing.
If, on the other hand, the most important variants are less than 1%, this framework will not work, and complete genomic resequencing at a high coverage will be necessary to detect memory-influencing genetic variants. However, it is not obvious how to apply this methodology to a trait such as normal variation in cognition. For traits that clearly have extreme phenotypes, discovery of very rare associated genetic variants may be possible by searching for enrichment of causal variants in subjects with the extreme phenotypes, when compared with controls. However, the relevant extreme phenotypes for cognition are not obvious. Consideration might be given to looking for association with neurophysiological phenotypes, such as ion-channel activity in cultured neurons, or synaptic vesicle size (74
); or to detailed phenotyping of cognitive tasks that may more directly reflect such specific activity, such as visual attention (75
). Another possibility would be to use subjects with exceptional memory (76
) or very high IQ. Alternatively, if the endophenotype hypothesis is correct, the same genes that control cognition in healthy subjects are those that have gone awry in schizophrenia. If this is the case, a promising direction would be to search for rare genetic variants that are enriched in schizophrenia patients (e.g. NRXN1
) and then look at the effects of rare variants in those same genes in healthy people.
Finally, one clear and surprising finding that has emerged from the recent associations of rare CNVs with neuropsychiatric disease is that the same rare variants are associated with multiple neuropsychiatric conditions (such as autism, mental retardation and schizophrenia), rather than being confined to particular disease classifications, and are also present in apparently unaffected people (6
). Since all these neuropsychiatric conditions can be associated with some form of cognitive impairment, there is some possibility that these rare genetic variants are acting indirectly to contribute to these disorders by causing cognitive changes. Differences in the ultimate phenotype (which, if any, disease state) may then be dependent on interaction with other genetic or environmental influences. If this is the case, detailed cognitive assessments of patients and unaffected relatives carrying such variants may reveal specific cognitive deficits associated with the rare variants.
In conclusion, our findings indicate that cognitive endophenotypes will not be the simple solution to the problem of complexity in schizophrenia genetics, and that, like schizophrenia itself, the heritability of cognitive traits cannot be accounted for by common variants with strong effect. We suggest that these findings may be attributed to a stronger role for rare variation in cognition than previously expected and suggest some possible future directions for cognitive genomics.