Scandinavian Journal of Psychology, 2014, 55, 255–262

DOI: 10.1111/sjop.12112

Genetic architecture of cognitive traits STEPHANIE LE HELLARD1,2 and VIDAR M STEEN1,2 1

The K.G. Jebsen Center for Psychosis Research and the Norwegian Centre for Mental Disorders Research (NORMENT CoE), Department of Clinical Science, University of Bergen, Norway 2 Dr. E. Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway

Le Hellard, S. & Steen, V. M. (2014). Genetic architecture of cognitive traits. Scandinavian Journal of Psychology 55, 255–262. The last decade has seen the development of large-scale genetics studies which have advanced our understanding of the genetic architecture of many complex heritable traits. In this review, we examine what progress has been made in understanding the genetics of cognitive traits. We cover the whole spectrum of distribution in cognitive abilities, from studies that have identified single genes implicated in intellectual disabilities, through studies investigating the missing and hidden heritability of cognitive abilities in the general population, and finally to studies looking at “high intelligence” samples. Key words: Genetics, cognition, intelligence, genetic architecture, variants. Stephanie Le Hellard, Department of Clinical Science, University of Bergen, Lab. building, Jonas Liesvei 87, N-5021 Bergen, Norway. E-mail: [email protected]

INTRODUCTION

COGNITIVE ABILITIES: NORMAL DISTRIBUTION IN THE POPULATION AND THE EXTREMES

Human genetics and especially clinical genetics are strongly imprinted by a Mendelian view of heritability. In classical Mendelian genetics, the phenotype under study is usually a simple, qualitative trait caused by variants in a single gene. The Austrian-Hungarian monk Gregor Mendel pioneered the principles of heritability, with the characterization of the transmission of simple traits (color and shape) in peas. After the discovery of DNA and its transmission through successive generations, the extension of these principles to molecular genetics led to development of new tools for mapping and identification of genes that were implicated in heritable diseases. In the last decade, the development of high-performance techniques for large-scale genetic mapping, including the possibility of sequencing patients’ exomes (i.e., complete coding sequence) or whole genomes, the number of genes implicated in monogenic traits (i.e., diseases caused by mutations in a single gene), has increased dramatically (Ku, Vasiliou & Cooper, 2012; Veltman & Brunner, 2012). However, such simple Mendelian genetics applies to only a small fraction of traits and more specifically to genetic traits that are most relevant to a clinical setting rather than to a populationbased setting. The vast majority of heritable traits, such as height and cognitive functions, and also several common diseases, like schizophrenia and many autoimmune disorders, follow a more complex pattern of inheritance (Plomin, Haworth & Davis, 2009). Indeed, the genetics of biometric traits seem to imply that these phenotypes are caused by the accumulation of many genes of small effect (that individually follow the principles of mendelian transmission) and possibly their interaction, although rare variants of larger effect may also play a role. It has also become apparent that common disorders follow the rules of quantitative genetics (Plomin et al., 2009), with numerous genetic variants implicated rather than a “common disease – common variant” model (Reich & Lander, 2001). In this review, we will describe some of the latest developments and findings from genetic studies of complex traits that are relevant for understanding the genetic architecture of cognitive traits.

Cognitive traits measured with psychometric tools show a normal distribution in the general population. Some studies have been designed to look at the extremes of the distribution, based on the hypothesis that the genetic effects in the extremes of the distribution may be stronger and thus genetic studies would have more power to detect these genetic factors.

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EXTREMELY LOW INTELLIGENCE – INTELLECTUAL DISABILITY The role of heritability A small percentage of the population display severe deficits in their cognitive performance, with IQ scores below 70. Such deficits are often combined with markedly reduced skills related to activities of daily living, communication and social functioning, and the subjects in question may be unable to take care of themselves and live independently. The term intellectual disability (ID) is now usually preferred over mental retardation to describe such cases. They are further subdivided into syndromic and non-syndromic conditions. In syndromic intellectual disability, other medical or behavioral signs and symptoms sum up to complex phenotypes and recognizable syndromes, whereas in non-syndromic intellectual disability, cases display severe cognitive deficits with no additional abnormalities. Depending on the etiology, the phenotype may be present from early life or show a more delayed onset. The underlying causes of ID are numerous, including a substantial fraction of idiopathic cases. Some examples of environmental factors are alcohol exposure or maternal infections during pregnancy, birth trauma, malnutrition and childhood infections. The role of heritability is also substantial (Ropers, 2010). At this extreme of cognitive impairment, the genetic factors are often well-defined pathogenic variants, that range from full chromosomal aneuploidies, for example, Down syndrome (Patterson & Costa, 2005), to large chromosomal defects with portions of chromosomes being deleted or duplicated, that is, copy number variants (CNVs), to single gene disorders that can

256 S. Le Hellard and V. M. Steen be identified as single nucleotide variants (SNVs) in specific genes. These genetic defects can be transmitted with X-linked, autosomal dominant or autosomal recessive inheritance, but many also appear as de novo mutations found only in the affected children (Ropers, 2010). Whereas the penetrance of monogenic forms of ID is often high, other types of ID-related genetic factors (e.g., copy number variations (CNVs)) may show markedly reduced penetrance and also pleiotropy (see below). The most common inherited type of intellectual disability is trisomy 21 (Down syndrome; Patterson & Costa, 2005). For the X-linked IDs, which represent 10–12% of IDs, more than 90 genes have been implicated. With the development of powerful molecular genetic tools in recent decades, many additional genomic factors have now been identified for other forms of ID. CNVs of various sizes have been found in many cases. However, since these CNVs are usually large, they often implicate many genes and it is thus difficult to pinpoint the genes that are most important for the various aspects of the phenotype. Some of these CNVs can be seen across several disorders; for instance a CNV at chromosome 16p12.1 has been observed in cases with ID, or autism, or epilepsy or schizophrenia (Girirajan, Rosenfeld, Cooper et al., 2010). Most certainly, further identification and molecular characterization of these CNVs across disorders will help us to understand key genetic events in brain development and related disorders (Cooper, Coe, Girirajan et al., 2011). Other recent studies have identified mutations in specific genes implicated in ID, either in consanguineous families with cases with ID (Najmabadi, Hu, Garshasbi et al., 2011), or in specific syndromes with ID. Among the Mendelian cases, hundreds of different genes have been proven as causative factors (Santen, Aten, Sun et al., 2012; Tsurusaki, Okamoto, Ohashi et al., 2012). Those genes are implicated in several biological pathways, but notably two major pathways seem to emerge from these studies, which will probably

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also be highly relevant to further studies of the genetics of cognitive abilities in the general population. The first of these pathways implicates groups of genes that are involved in epigenetic mechanisms that modify genomic DNA (Ronan, Wu & Crabtree, 2013; Santen et al., 2012; Tsurusaki et al., 2012). For instance these mechanisms can methylate specific DNA motifs, leading to the silencing (no expression) of a neighboring gene. Such processes are implicated in the genomic response to the environment and also to the specific on/off switching of genes during development. The other major pathway that has been disclosed through the identification of mutations in several cases of ID includes genes implicated in synaptic transmission (Pavlowsky, Chelly & Billuart, 2012; Verpelli, Montani, Vicidomini, Heise & Sala, 2013). Interestingly, genetic defects in synaptic transmission have also been linked to several cases of autism. Although intellectual disabilities lie at the limit of the spectrum of cognitive dysfunction and are often associated with serious structural abnormalities and/or dysfunctions of the brain, they do help us understand the extreme end of the genetic architecture of cognitive functions (see Fig. 1). However, it is clear that they are separated from normal variation in cognitive skills, and it remains to be investigated how (if at all) the genetic variations implicated in ID relate to genetic effects in the normal distributions of cognitive abilities.

COGNITIVE ABILITIES IN THE GENERAL POPULATION: HERITABILITY, MISSING HERITABILITY AND HIDDEN HERITABILITY Population-based heritability Population- and epidemiological studies have established that genetic factors contribute to a large extent to the inter-individual

Fig. 1. The spectrum of genetic variation in cognitive abilities. This figure is an adaptation of a figure now largely used in reviews of the genetic architecture of complex traits (Maher, Reimers, Riley & Kendler, 2010; Manolio et al., 2009; McCarthy et al., 2008; Sullivan, Daly & O’Donovan, 2012). It describes the spectrum of genetic variants implicated in complex traits in terms of their frequency in the general population (very rare to common) by their effect on the trait (weak to strong). Mendelian or single genetic traits, such as many forms of intellectual disability, are typically due to genetic variants very rare in the population but which have a strong effect, while GWAS typically capture variants of very small effect that are rather common in the general population. Common genetic variants of moderate effect have not been identified as phenotype–modifying factors in cognitive abilities, but the ApoE*E4 variant has been implicated in Alzheimer disease. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

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variability in cognitive abilities (Deary, 2012). From a genetic point of view, the traits that have been the most studied in large samples are the general g factor and the Intelligence Quotient (IQ). The general g factor is derived from a hierarchical view of cognitive abilities, where different functions tested by different tests are organized in cognitive domains, such as attention, memory, and speed of processing, and the high correlation between these different domains is denoted as the g factor (Deary, Penke & Johnson, 2010). The g factor can be fragmented into the fluid factor, or fluid intelligence, which reflects the capacity to think logically and solve problems in novel situations; and crystallized intelligence, which reflects the ability to use acquired knowledge. In twin studies, the abilities of monozygotic twins, who share 100% of their genome, are compared with dizygotic twins, who share 50%, to estimate how much of the inter-individual variability in abilities is explained by genetic factors or environmental factors (Plomin & Spinath, 2004). The heritability is defined as the proportion of the trait variability that can be explained by genetic factors (Visscher, Hill & Wray, 2008). These twin studies have estimated that the heritability of the g factor is around 0.5–0.6, and it increases with age (Briley & Tucker-Drob, 2013; Deary, Johnson & Houlihan, 2009; Haworth, Wright, Luciano et al., 2010). The heritability of specific domains such as memory is around 0.4 for verbal cognitive ability and 0.39 for language ability (Plomin, Haworth, Meaburn, Price & Davis, 2013).

The heritability explained by common genetic variants in genome-wide association studies (GWAS) With the development of molecular genetics tools that allow for the low-cost typing of genetic variants (i.e., genotyping) across the whole genome, very large genetic studies – so-called genome-wide association studies (GWAS) – have been conducted on many complex traits to screen for genetic variants responsible for this heritability. During a GWAS, single nucleotide polymorphisms (SNPs) that cover the whole genome are genotyped using high-density arrays to obtain genetic information from a large sample of individuals. The genetic variations are then tested for association or correlation with the phenotype of interest. The hypothesis that justified starting these large-scale studies was that common traits present in the general population should be associated with common genetic variants, that is, present in more than 5% of the population (Reich & Lander, 2001). Geneticists were especially interested in common disorders, such as autoimmune disorders, diabetes and psychiatric disorders, which were hypothesized to be due largely to genetic variants that are rather frequent in the general population. However, after the first wave of GWAS it became clear that the number of common variants associated with the different traits was much lower than anticipated (Maher, 2008). More recently, following improvements in GWAS power, more and more loci were identified and confirmed. With samples that now include hundreds of thousands of individuals, more than 160 loci implicated in variability of body height have been identified (Yang, Benyamin, McEvoy et al., 2010), and even such a complex phenotype as school attainment has recently been subject to GWAS, which identified and confirmed the association between three genetic variants and © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Genetic architecture of cognitive traits 257 the duration of schooling (Rietveld, Medland, Derringer et al., 2013). Almost all the genetic variants that have been identified with these GWAS have very small effects (i.e., odds ratios usually in the range of 1.0–1.2). For instance, in the school attainment study that was carried out on 120,000 individuals, the effect of the strongest variant would correspond to a difference of one month in education between the individuals with the “education increasing” allele as compared to the individuals with the alternative allele (Rietveld et al., 2013). For childhood intelligence, a GWAS on the biggest samples reported so far (>17,000 individuals in total) failed to identify genetic variants that could be confirmed (Benyamin, Pourcain, Davis et al., 2013). Still, although no single SNP achieved genome-wide significance, the gene FNBP1L showed evidence of association, namely, when the effect of all the SNPs in the locus was aggregated. In another recent study of the g factor by the COGENT consortium on more than 5,000 adult individuals collected from nine different cohorts, no single SNP reached genome-wide significance (Lencz, Knowles, Davies et al., 2013). One could be worried or surprised that with such big samples there has not been greater success in identifying phenotyperelated genetic variants. However, if we compare these results with those from the Psychiatric Genomics Consortium (PGC) on schizophrenia, it was not until the Consortium reached 20,000 cases that they started identifying and validating specific variants (Ripke, Sanders, Kendler et al., 2011). In the recent reports at the World Congress of Psychiatric Genetics, the PGC-schizophrenia sample has now reached around 100,000 subjects and has been used to identify about 100 loci. This shows that genetic variants will be identified even for highly complex traits if the samples are large enough and proves the point of “Don’t give up on GWAS” made by Sullivan, Daly & O’Donovan (2012), Sullivan (2012) and Visscher, Goddard, Derks & Wray (2012). Also for the school attainment phenotype, which is more heterogeneous than IQ and g factor, since environmental factors play an even greater role (see Fig. 2), several genetic variants were identified with a sample of >120,000. So it is most likely that the collection of bigger samples for g factor or IQ will lead to the identification of specific genetic variants.

The missing and hidden heritability For some complex traits, variants identified by GWAS can account for more than 10% of the heritability (see Fig. 2). However, for other traits, including cognitive abilities, the amount of accumulated variability that is explained by GWAS-identified loci is actually zero even with very large samples. The observation that in many cases the level of explained variability is quite low compared to the estimated heritability has led to the paradigm of the “missing heritability”, that is, the heritability of the trait that cannot be explained by the variants identified in the GWAS (Maher, 2008). It is especially notable in traits that are highly heritable but most likely also very complex, such as IQ, where even samples of 17,000 individuals have failed to identify and confirm a single genetic variant (Benyamin et al., 2013). One of the major problems with GWAS is that because of the high number of tests, the results are subjected to a very stringent

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Non-genetic factors Missing heritability Total heritability captured by GWAS (hidden heritability) Heritability explained by variants identified by GWAS Fig. 2. Estimated, hidden and missing heritability in complex traits. Bar charts showing the architecture of several complex human traits. The genetic contribution is divided into the following separate parts: the known heritability (made up of specific genes or variants that have been identified by GWAS); the hidden heritability (made up of variants that have been captured by GWAS but not specifically identified); and the missing heritability (variants that have not been identified by the currently available approaches). References to the relevant publications for the different samples are: childhood IQ (Benyamin et al., 2013), schizophrenia (Lee, DeCandia, Ripke et al., 2012; Ripke et al., 2011) and the latest communication at the World Congress of Psychiatric Genetics from the PGC–schizophrenia consortium (https://pgc.unc.edu/Results.php#Schz), school attainment (Rietveld et al., 2013), height (Yang et al., 2010); Crohn’s disease (Jostins, Ripke, Weersma et al., 2012)

multiple testing significance threshold, namely the so-called genome-wide significance level of 5 9 10-8. Thus SNPs of small effect do not pass this stringent cutoff. However, new analysis tools have been developed that account for the total amount of genetic variability genotyped during the GWAS. So instead of looking only at the few genetic variants that reach genome-wide significance, these studies compare the genome-wide genetic inter-individual variability and correlate it with the phenotypic inter-individual variability (Yang et al., 2010). This approach has shown that the genetic variability captured by common variants genotyped during GWAS can actually explain a large proportion © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

of the heritability. For instance for cognitive traits in the general population, Davies, Tenesa, Payton et al. (2011) have shown that the heritability explained by GWAS is 0.4 for crystallized intelligence and 0.51 for fluid intelligence; thus common variants could explain about two thirds of the heritability. Using the same tools, Plomin et al. (2013) also found in their twin sample that the heritability explained by common variants was more than two thirds for general cognitive ability and for language ability. So common variants altogether contribute a large proportion of the heritability of cognitive traits, but they have such small effects and are likely to be so numerous that they do not pass the genomewide significance threshold and are thus “hiding” in the GWAS (see Fig. 2, medium shading for the hidden heritability).

How to capture the hidden heritability: the polygenic model of intelligence Since it is apparent from these studies that a large proportion of the heritability is indeed present in the GWAS data, but is not captured by p-value threshold-based studies, several methods have been developed to better disclose the genetic factors that make up the hidden heritability. Polygenic methods consider the effect of groups of variants together. These methods can be divided into two types: SNPbased methods that are implemented to look at overlap between phenotypes and prediction, and gene-based methods, which are used to look at genetic pathways. SNP-based polygenic methods have examined genetic variants that are shared between populations to try and predict the trait in another sample (i.e., prediction applications) and have also been used to look for possible genetic overlap between traits (i.e., pleiotropy). In a meta-analysis on samples with schizophrenia where no SNP was found to be associated at the genome-wide level, Purcell, Wray, Stone et al. (2009) showed that by using genome-wide effects from an association study for schizophrenia, they could accurately predict not only other samples with schizophrenia but also samples with bipolar disorder, thus demonstrating the genetic overlap between schizophrenia and bipolar disorder. Davies et al. (2011) used a discovery sample to calculate genomewide polygenic scores for crystallized and fluid intelligence that were tested in an independent sample and which predicted the phenotype in this other sample, even if the variability explained was small. Using a bivariate model for whole-genome covariability of two cognitive phenotypes, Trzaskowski. Davis, DeFries, Yang, Visscher and Plomin (2013) demonstrated that in their twin sample the genetic overlap (pleiotropy) between the g factor and the measure of different cognitive abilities (language, mathematics, reading) was between 66–80%. Finally, SNP-based polygenic models have also been used to explore the genetic overlap between g factor and schizophrenia. McIntosh, Gow, Luciano et al. (2013) showed that the genetic variants associated with schizophrenia had a significant effect on the g factor, while the COGENT consortium showed that the converse was also true, that is, the genetic variants implicated in the g factor have a significant effect on the risk of presenting schizophrenia (Lencz et al., 2013). Like SNP-based models, gene-based polygenic models address questions such as genetic overlap between traits, but also search for more biologically relevant solutions. In gene-based methods,

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the SNPs identified in the GWAS are mapped (or “binned”) to the genes that they are located in or near, then a gene-based score is calculated for each gene, taking into account the number of SNPs in the bin. Using such methods, we looked at the effect of genes associated with different cognitive domains in schizophrenia and bipolar disorder. We showed that the genes associated with cognitive inhibition showed significant enrichment for association with schizophrenia, and the genes associated with learning showed significant enrichment for association with bipolar disorder (Fernandes, Christoforou, Giddaluru et al., 2013). The other use of gene-based polygenic models is to test for the effect of groups of genes in specific traits. For instance Hill, Davies, van de Lagemaat et al. (2014) tested the enrichment of genes associated with the structure and function of different parts of the synapse and found that the genes implicated in the NMDA receptor signalling complex (NRSC)/membrane-associated guanylate kinase associated signalling complex (MASC) component of the synapse were significantly enriched for association with the g factor, using a gene-set based enrichment analysis. Similarly, Ruano, Abecasis, Glaser et al. (2010), looking at a vertical deconstruction of the synapse, found that the g factor was most strongly associated with a group of genes encoding synaptic G-proteins. Using a similar method we also found that genes that are especially expressed in the temporal cortex showed enrichment of association with Matrix Reasoning abilities (Ersland, Christoforou, Stansberg et al., 2012). Gene-based studies can also be implemented to find which genetic pathways are the most significant in GWAS of specific traits. Biological pathways are established by using different molecular and cellular genetics tools that annotate the genes for their function in the cell or in a tissue, for example, genes implicated in axon guidance or genes implicated in neurodevelopment. There are several databases that can be searched for specific pathways. We screened these pathways to find which biological process or functions were the most significantly associated with the crystallized and fluid aspects of general cognitive abilities. We found that at the pathway level we could distinguish these two domains, with fluid intelligence being more associated with functions related to the amount and quantity of neurons in the brain, while crystallized intelligence was associated with pathways for long-term synaptic depression (Christoforou, A., Espeseth, T., Davies G., Fernandes, C.P.D., Giddaluru, S., Mattheisen, M., Tenesa, A., Harris, S.E., Liewald, D.C., Payton, A., Ollier, W., Horan, M., Pendleton, N., Haggarty, P., Djurovic, S., Herms, S., Hoffman, P., Cichon, S., Starr, J.M., Lundervold, A.J., Reinvang, I., Steen, V.M., Deary, I.J. and Le Hellard S submitted). Geneand pathway-based methods can thus be used to analyse the genetic variability captured by common variants.

Where is the missing heritability? On average, the total heritability that is captured by common variants during GWAS explains about two thirds of the heritability estimated from population or twin studies. Assuming that these estimates are roughly correct, about 30% of the heritability is still not explained by common variants. Looking at studies of other complex traits gives us some idea of what additional genetic factors could explain this “missing heritability” (see © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

Genetic architecture of cognitive traits 259 Fig. 2). Many reviews have already addressed the problem of the missing heritability in detail (Maher, 2008; Manolio, Collins, Cox et al., 2009). Here we give an overview of possible explanations that are particularly relevant for cognitive abilities. As mentioned above about Mendelian forms of intellectual disabilities, rare variants have recently been characterized that are observed in only a few individuals and affect either larger regions of the genome (CNVs) or single nucleotides (SNVs). These variants have also been observed in some psychiatric disorders, mostly in autism and schizophrenia. However, every single person in the population carries a lot of rare variants of potential functional significance, so it is difficult to accumulate enough evidence to definitely designate a rare variant as causative in a certain trait. In quantitative traits, it will probably be even more difficult to quantify the contribution of rare, “private” variants to that person’s cognitive abilities. Until now, very few studies have looked at the effect of CNVs on cognitive abilities (MacLeod, Davies, Payton et al., 2012; McRae, Wright, Hansell, Montgomery & Martin, 2013). Since these variants are rare by definition, we will need to collect very large samples of individuals to have the power to test for the effect of such CNVs on cognitive traits. Another possible explanation for the missing heritability is that the cumulative effect of single variants could be more than the additive effect of every single variant. This means that the variants could have a multiplicative or interactive effect rather than additive, which is also known as epistasis. Until now, very few studies have looked at genome-wide interactions in cognitive traits, since these studies require very large computing power and are also heavily handicapped by even higher correction for multiple testing. However, such tools are now being developed and should be tested in complex traits such as cognitive abilities. On a related matter, it is possible that some of the unexplained heritability could be found in the interaction between genetic and environmental factors. Some genetic effects may become apparent only when the effect of environmental factors is taken into consideration. Using twin studies it is possible to distinguish the influence on cognitive abilities of shared versus unshared environment in twins. From these studies it seems that the unshared environment plays a major role in cognitive traits and it would thus be interesting to account for this in genetic studies. Two major problems with such studies are first that environmental information needs to be collected, which is not the case for many samples, and second that the sample size required for such studies to capture an effect of similar amplitude to a simple genetic effect is about four times larger than for a simple genetic GWAS (Thomas, 2010). Some studies looking at gene by environment interaction did find an effect of such interaction in traits related to personality or psychiatric disorders. One of the main interactions studied is the interaction between genetic variants in the serotonin transporter and stress or bullying on outcomes such as depression or anxiety. In cognitive abilities such studies have been very limited until now, so they need to be interpreted with great caution since to date none of them has been validated in large samples and they suffer from a strong publication bias (Duncan & Keller, 2011). Fortunately, much progress is being made in our understanding of the “living genome” thanks to projects such as the

260 S. Le Hellard and V. M. Steen “Epigenomics roadmap” (http://www.roadmapepigenomics.org/) or the ENCODE project (Bernstein, Birney, Dunham, Green, Gunter & Snyder, 2012), which are decoding the genome and more specifically the elements in the genome that are important for genetic regulation and regulation by the environment. Therefore it is likely that in the next few years we will have a more comprehensive map of the features of the genome that are modifiable by the environment. This map will be highly relevant to the study of gene 9 environment interactions.

STUDIES OF HIGH INTELLIGENCE Since cognitive abilities are normally distributed, some studies have looked at the other extreme of the distribution, that is, the individuals that show higher abilities than the general population. Davis, Butcher, Docherty et al. (2010) performed a GWAS where a pool of DNA containing equal amounts of DNA from several individuals with high IQ was compared with a similar pool of DNA from individuals with low IQ. The markers that showed some differences between the two pools were then examined at the single marker level and genotyped in single DNAs. No marker reached genome-wide significance, and none has been validated since. Currently some of the biggest efforts to identify genes for high IQ are ongoing in collaborative efforts between the Beijing Genomics Institute and the Genetics of High Cognitive Abilities consortium, based in the Behavioral Genetics Department at King’s College London. Together these groups aim to perform whole-genome sequencing on more than 5,000 individuals with high cognitive abilities, with the goal of identifying both common and rare variants. In the years to come we will see if this approach is more successful at identifying genetic variants associated with cognitive abilities than approaches that have looked at the normal distribution in the general population (Hayden, 2013a). Similarly, the entrepreneur Jonathan Rothberg and physicist Mark Tegmark, based at MIT in Cambridge, MA, have initiated “Project Einstein” which aims to sequence the genome of 400 mathematicians and theoretical physicists (Hayden, 2013b). Considering the high complexity of cognitive abilities as we have demonstrated in the present review and summarized in Fig. 1, it is highly questionable whether these studies will have enough power to detect any effect in those rather small samples from the perspective of complex traits genetics. Still, the study by Rietveld et al. (2013) proved an interesting point: even though the phenotype that they looked at is more heterogeneous than high IQ, school attainment is a phenotype that overall is easy to obtain from big cohorts. Thus the trade between high phenotypic characterization and sample size was in their favor in this case. Samples with high quality phenotypic characterization are much more difficult to obtain and thus usually rather small in terms of what is needed for genetic analyses. Large samples can probably only be obtained for broader phenotypes such as IQ or g factor where several samples can be aggregated like in the COGENT consortium (Lencz et al., 2013). However, the phenotype then becomes more heterogeneous, so it is likely that large samples will be required before true and validated effects can be detected. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

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SOME ETHICAL CONSIDERATIONS The study of intelligence genetics has been recently labeled as one of the most controversial and sensitive areas of research. Declarations such as this by Hsu in Nature: “I’m 100% sure that a technology will eventually exist for people to evaluate their embryos or zygotes for quantitative traits, like height or intelligence. I don’t see anything wrong with that.” (Hayden, 2013a, p.27) certainly trigger loud and justified ethical debates. The publication from Rietveld et al. (2013) identifying genetic variants associated with higher school attainment also raised some worries as to how information about the genetics of intelligence and cognitive traits could be used. However, as we have emphasized throughout this review, the genetic architecture of cognitive abilities is extremely complex, with genes explaining at most 50% of a person’s cognitive abilities, and environmental factors accounting for the other 50%. So it is most likely that performing IQ tests and following the environment and the familial history of an individual will be a more reliable way to estimate a person’s abilities than genetic tests. However, combining genetic information with other tests will probably help to identify people at higher risks for cognitive dysfunctions ranging from learning difficulties and cognitive impairment to neurodegenerative and psychiatric diseases. Moreover, the identification of genetic factors implicated in normal brain function will be crucial to understand the role of these genetic factors in brain dysfunction (see other articles in this special issue on ApoE*E4 and CHRNA4). “People can take science and assume it is far more determinative than it is and by making that assumption, make choices that we will regret as a society” says Nikita Faharani, a philosopher and lawyer at Duke University (Hayden, 2013a, p.26). It is evident that the genetics of intelligence or cognitive abilities are areas of research that are still sensitive because of the shadow of eugenics and the fear of an unethical drive to create a genetically engineered society. As scientists, we need to be aware of these issues and ensure that we clearly explain the limitations and implications of our work to society. However, these research areas should not be taboo since their predictive potential is not much greater than other more phenotype-driven approaches. On the other hand, they have enormous potential to improve our basic knowledge of the factors that underlie cognitive function and intelligence, and thus to understand the basis of many forms of cognitive impairment. We thank Isabel Hanson Scientific Writing for editing the text and assisting with preparation of the Figures. SLH was supported by a grant from the Bergen Research Foundation (BFS), and the work has also been supported in part by grants from the Research Council of Norway (to NORMENT CoE), the K.G. Jebsen Foundation, Helse Vest RHF and Dr E. Martens Fund. We thank the Centre for Advanced Study (CAS) at the Academy of Science and Letters in Oslo for hosting a research project on “Cognition in aging - contributions of cognitive neuroscience and cognitive neurogenetics” during 2011–2012.

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