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apPENDIX I
Book chapter Genes and Intelligence
Appendix I
Abstract
Intelligence is a highly heritable complex trait. The proportion of variance in
intelligence that is attributable to genetic factors ranges from 40% in young childhood
to 80% in late adulthood. In the past decade, considerable progress has been made
in molecular genetics technologies, which facilitates the detection of the actual
genetic factors that underlie the high heritability of intelligence. In this chapter we
provide an overview of the progress that has been made to date in identifying genetic
variants for intelligence using various methods from genetic linkage to genome wide
association studies. In addition we discuss a number of challenges to identify genetic
variants associated with intelligence, and provide suggestions for future directions.
This appendix is subbmited as: Thais S. Rizzi and Danielle Posthuma, Chapter 52: Genetics and Intelligence.
The Oxford Handbook of Cognitive Psychology, 2013. 864 pages Jan 2013, Not Yet Published ISBN13:
9780195376746
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Introduction
This chapter provides an overview of the current state of knowledge on the
genetic (and environmental) contribution to individual differences in intelligence.
Besides some general explanations on genetics, it will include a brief summary of the
literature on the heritability of intelligence, followed by an extensive discussion of
the findings of molecular genetic studies on intelligence.
Intelligence is one of the most studied complex behavioral traits and has been
a research focus for more than a century. The most accepted view among scientists
in the field of intelligence is that intelligence is a very general mental capability
that, among other things, involves the ability to reason, plan, solve problems, think
abstractly, comprehend complex ideas, learn quickly and learn from experience
(Gottfredson, 1997). Two general views on the nature of intelligence have been
formulated. The first encompasses the idea of a single general cognitive factor
referred to as “g” or ‘general cognitive ability’ (Spearman, 1904). The second view
distinguishes several different, unrelated cognitive abilities, (e.g. Thurstone, 1938).
The typical statistical model of intelligence takes into account the presence of “g”,
several different abilities, as well test-specific factors.
Psychometric intelligence tests usually consist of a number of component subtests
that taken together are used to infer a general IQ (Intelligence Quotient) score.
Intelligence tests such as the Wechsler Adult Intelligence Scale (WAIS (Wechsler,
1997)) and Wechsler Intelligence Scale for children (WISC (Wechsler, 1991) are
theoretically based on Thurstone’s factor analytic theory of intelligence (Thurstone,
1947) and provide an index of both general IQ and primary abilities such as verbal
comprehension, working memory, perceptual speed, and perceptual organisation.
In a population, large inter-individual differences in IQ test scores can be observed.
The distribution of IQ scores in the general population follows a Gaussian normal
distribution. Ninety-five percent of the population have an IQ score between 75 and
130 and sixty-eight percent of the general population scores between 85 and 115
IQ scores (Reynolds et al., 1987, Gottfredson, 1997, Hermstein & Murray, 1994 ). In
the general population, 2.5% have an IQ under 75 and this is generally considered as
the threshold for “mental retardation”, a condition of limited mental ability, which
is characterized by difficulties in adapting to the demands of life (Curry et al., 1997;
Roeleveld et al., 1997; Croen et al., 2001). In the other extreme (above 140) are
the high intellectual quotient/giftedness representing approximately 0.25% of the
population (1 in 400) (Reynolds et al., 1987, Gottfredson, 1997 , Hermstein & Murray,
1994 ).
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Individuals from the same family tend to resemble each other more with respect
to their IQ test scores, than unrelated people. These differences between unrelated
persons and the resemblance between relatives can be due to genetic factors, shared
environmental factors, cultural transmission from one generation to the next, social
interactions between family members, or a combination of these mechanisms (see
Box 1).
Box 1 | Genetic Terminology
Allele = Allele is an alternative form of a gene or genetic region.
Biological pathway = Series of molecule actions in a cell that leads to a certain product.
Copy number variation (CNV) = Structural variation that results in abnormal number of copies,
deletions or insertions of one or more genetic regions of the DNA.
Cosegregation = The transmission of two or more genes (regions) on a chromosome to the same
daughter cell leading to the inheritance by the offspring of these genes together.
Crossing over = Exchange of genetic material between homologous chromosomes.
Cultural transmission = Cultural transmission is the process of passing on culturally relevant knowledge,
skills, attitudes, and values from one generation to the next.
Deoxyribonucleic acid (DNA) = Nucleic acid that contains the genetic information used in the
development and functioning of most of the organisms.
DNA methylation = Biochemical process that leads to inhibition of transcription initiation
Gene = Genomic region corresponding to a unit of inheritance, which is associated with regulatory
regions, transcribed regions (that code for a type of protein or for an RNA chain that has a function in
the organism), and or other functional sequence regions.
Genetic association = Statistical association between a genetic variant and a trait.
Genetic dominance = Non-additive relationship between two variant forms of a single gene, in which
one copy of an allele is sufficient to cause a disease.
Genetic marker = A DNA sequence with a known location on a chromosome that can be used to
identify individuals.
Genome = The complete hereditary information of an organism.
Genome wide association = Approach that involves rapidly scanning markers across the genomes of
many people to find genetic variants associated with a particular disease or complex trait.
Haplotype = Combination of alleles at different loci on the same ancestral chromosome.
Heritability = Proportion of variation of a trait within a population that is due to genetic variation.
Homologous chromosomes = Chromosome pairs with same length, centromere position and genes for
the same characteristics.
Linkage Analysis = Statistical method that tests for co-segregation of a chromosomal region and a trait
of interest.
Locus (Plural: loci) = Specific location of a gene or DNA sequence on a chromosome.
LOD score = The Log of the odds (LOD) of obtaining the observed data when two loci (or a trait and a
marker) are indeed linked, versus the likelihood of observing the same data purely by chance.
Mendel's second Law of Independent Assortment = Two different genes located on different
chromosomes or located relatively far from each other will assort independently and thus randomly.
Monogenic disease = Disorders caused by the inheritance of a single defective gene.
Penetrance = The rate of occurrence of a disease expressed in individuals with the detrimental allele.
Recombination = Exchanged of portions of DNA between homologous chromosomes.
Restriction Fragment Length Polymorphisms = Genetic variations at the site where a restriction enzyme
cuts a piece of DNA resulting in variation in the size of the resulting fragments.
Ribonucleic acid (RNA) = RNA is transcribed from DNA and it is central to protein synthesis.
Shared environmental factors = Factors in the environment that are shared between siblings and make
siblings resemble each other more on a trait.
Single-nucleotide polymorphism (SNP) = Genetic variation occurring when a single nucleotide (A, T, C,
or G) differs between individuals or paired chromosomes in the same individual.
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Heritability is the proportion of phenotypic variation in a population that is due
to genetic variation between individuals. Phenotypic variation among individuals
may be due to genetic or environmental factors. Heritability estimates are typically
based on twin and family studies, in which known genetic relatedness (e.g. parentalchild genetic relatedness is 0.5, because each child receives 50% of their genetic
material from each parent) is used to predict phenotypic resemblance. Due to the
rapid advances in genome technology, it has become possible to estimate a lower
bound of the heritability of a trait based on actual, measured genetic variation. This
method does not require genetically related individuals, but simply uses an overall
measure of the average number of alleles that are physically the same between two
unrelated individuals. If a trait is heritable, than the more alleles two individuals have
in common, the more they will resemble each other on the trait.
The first scientific study of the heritability of intelligence was published in
1869 by Sir Francis Galton, when he published his Hereditary Genius, in which he
described his observation that family members tend to be more alike for intellectual
capabilities than unrelated persons (Galton, 1869). Multiple twin and family studies
were conducted in the second half of the twentieth century which were summarized
in a landmark study by Bouchard and McGue who reviewed all 111 twin and family
correlations for IQ published before 1980. The mean correlation of IQ scores
between monozygotic twins was 0.86, between siblings, 0.47, between half-siblings,
0.31, and between cousins, 0.15 (Bouchard & McGue, 1981, 2003; Deary, Spinath, &
Bates, 2006; Vinkhuyzen, van der Sluis, & Posthuma, 2011). Twenty-five years later
heritability estimates for adult IQ based on twin studies are generally accepted to
range from 60-80% (e.g. (Deary et al., 2006)). Using measured genotypes in a sample
of 3,500 unrelated adults, Davies et al., (Davies et al., 2011) recently confirmed
that at least 40% to 50% of individual differences in intelligence could be traced to
additive genetic variation.
The estimated heritability of IQ tends to change across the lifespan: specifically it
increases from 25% in preschool children to 80% in early adolescence and adulthood
(Ando, Ono, & Wright, 2001; Bartels, Rietveld, Van Baal, & Boomsma, 2002; Plomin,
1999; Posthuma, Neale, Boomsma, & de Geus, 2001; Thurstone, 1947). In parallel,
shared environmental influences, i.e., environmental influences that are shared by
members from the same family and render them more alike, explain about half of
the variation in intelligence in young children (Bartels et al., 2002) while this effect is
decreased drastically once adolescence is reached (Posthuma, de Geus, & Boomsma,
2001).
These longitudinal studies suggest that genetic factors contribute differently to
variation in intelligence across the lifespan, especially during the transition from early
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to middle childhood and from middle childhood to late adolescence. One possible
explanation for this phenomenon is that the same genes might have different (larger)
effects in middle/late childhood as compared to early childhood (Plomin, 1986) due
to differential gene expression regulation across developmental stages (Harris et al.,
2009). The human biological clock switches genes on or off in a pre-programmed
way, affecting physical development. Many critical maturational processes take place
in the human brain during postnatal development; in particular during adolescence
where white matter expansion takes place (Bartzokis et al., 2001; Rice & Barone,
2000). A recent study showed that the expression pattern of approximately 2000
genes in the prefrontal cortex was significantly correlated with age (Harris et al.,
2009). For many of these genes, the most dramatic changes in expression occur in
early post-natal development (0–2 years), reaching a peak around adolescence (1525 years) and showing only subtle changes in expression thereafter (35-50 years)
(Harris et al., 2009). RNA expression profiling has revealed peak expression in late
adolescence for genes involved in energy metabolism, protein and lipid synthesis,
together with a decrease of expression of genes associated with signaling and
neuronal development such as axon guidance, morphogenesis and synaptogenesis
(Harris et al., 2009). Such differential gene expression across developmental stages
may explain differences in heritability estimates across the life span.
Alternative explanations for the increased heritability later in life may be (i) active
gene-environment correlation, where the environment becomes a function of a
person’s genetic make-up and is estimated as part of the heritability in the classic
twin design, or (ii) a decrease in environmental influences with age while genetic
influences remain the same - as the heritability estimate is the proportion of genetic
variance of the total variation this will also lead to an increase in the heritability
estimate with age.
In sum, in adulthood the main factor explaining inter-individual variation in
intelligence is of genetic nature (McGuffin, Riley, & Plomin, 2001; Plomin, 1990;
Plomin, Owen, & McGuffin, 1994). A logical next question is: can we identify the
genes, or genetic mechanisms, that cause this variation in intelligence? In the
past decade, considerable progress has been made in genotyping technologies,
which facilitates the detection of the actual genetic variants that underlie the high
heritability of intelligence. In the remainder of this chapter, we review the progress
that has been made to date in identifying genetic variation for intelligence. Progress
in gene finding for intelligence is highly dependent on genotyping technologies below we will therefore follow the revolution from genetic linkage studies to genome
wide association studies and their impact on finding genes for intelligence. For the
genetically less informed reader, Box 2 provides a very brief tutorial on gene finding.
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Box2 | A brief tutorial on gene finding
The human genome is stored on 23 chromosome pairs (total of 46) plus the small mitochondrial DNA as
shown in the figure below.
A chromosome is an organized structure of DNA and protein found in each cell. Each individual has two
strands of the DNA and the information within a particular gene or DNA region (locus/loci) is not always
the same between individuals: Different individuals may carry different copies of a locus. Since, different
copies of a gene give different instructions to the cell, genetic variation may lead to phenotypic variation
or diseases when a copy of an allele leads to misinstructions of the cell. Each unique form of a single
gene is called an allele. For example, two sequenced DNA fragments from two different individuals,
TTCGGATAA to TTCGAATAA, contain a difference in a single nucleotide (sequence letter). In this case we
say that there are two alleles of this DNA fragment: G and A.
Three nucleotides form a codon, which codes for an amino acid. Multiple amino acids form proteins.
Genetic mutations can create new alleles and sometimes can lead to diseases such as Microcephaly, where
mutations in a single gene ASPM (abnormal spindle-like microcephaly) cause a brain developmental
disorder, altering neuronal migration and cortical layering process (for review see Bond et al., 2002).
The combination of these variants is called a haplotype. Haplotypes that occur more often in a disease
group are high-risk haplotypes. For example, in a schizophrenia family based study it was found that a
certain combination of 8 allele calls in the DTNBP1 (dystrobrevin binding protein 1) gene were unique for
the disease group (Van den Oord et al., 2003).
Human individuals differ from one another by about one base pair per thousand. If these differences
occur within coding or regulatory regions, phenotypic variation in a trait may result. It is estimated
that humans have more than 5.9 million sites where genetic variation exists (Durbin et al., 2010). Not
all of these variants lead to disease or phenotypic differences, but some will. The molecular biology
field is advancing at an exponential rate since the late 1970s, specifically the automated genotyping
of thousands of single nucleotide polymorphisms (SNPs) and, more recently, copy number variations
(CNVs), have revolutionized our ability to describe variation at all levels of genetic organization: genes,
individuals, populations, species, and genera. The past decade is characterized by several milestones
in genetics, including the International HapMap project (http://hapmap.ncbi.nlm.nih.gov) aiming to
determine common patterns of DNA sequence variation in the human genome, the publication of the
first draft sequence of the human genome (Venter et al., 2001), and the recent start of the 1000 genome
project (http://www.1000genomes.org/), which aims to find most genetic variants present in the
genome. Major efforts in genome wide association studies (GWAS) have been undertaken since ±2005,
where millions of SNPs have been tested for association with diseases and quantitative traits. This has
led to the discovery of several genes for Type-II diabetes, Crohn’s disease, prostate cancer, inflammatory
bowel disease, body mass index and age related macular degeneration (Saxena et al., 2007; Rioux, 2007;
Gudmundsson et al., 2007; Dewan et al., 2006; Duerr et al., 2006; Frayling et al., 2007). For complex
traits, which are influenced by multiple genetic variants of small effect, GWAS studies have been less
successful (Altshuler & Daly, 2007; Maher, 2008). Large study samples (> 40,000 subjects) are needed
to detect the multiple variants involved in complex traits (Visscher, 2008). Lately new tools have been
developed to test for the combined effect of multiple genetic variants in the same gene or indifferent
genes from the same biological pathway, to increase statistical power and allow more biologically driven
hypothesis testing (e.g. Ruano et al., 2010; Torkamani, A., Topol, E. J., & Schork, N. J., 2008).
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Linkage studies (widely used 1980’s-1990s’)
Genetic linkage analysis is a statistical method that is used to identify loci that are
inherited together with the trait. Genetic linkage was first discovered by the British
geneticists William Bateson and Reginald Punnett (Bateson, 1906; Punnett, 1908)
shortly after Mendel’s laws were rediscovered in 1900.
Linkage can be explained by reference to Mendel’s second Law - the Law of
Independent Assortment. This law states that separate genes for separate traits are
passed independently from one another from parents to offspring, in other words
they are segregated independently. During gamete formation chromosomes assort
randomly, such that the segregation of alleles of one locus is independent of the
segregation of alleles of another locus. The law of independent assortment always
holds for regions that are located on different chromosomes, but not necessarily
for loci that are on the same chromosome. When two loci are located on the same
chromosome, the chance of a crossover (recombination of genetic material between
the paired chromosomes inherited from each of one’s parents) between them is a
function of the distance between the two regions. If two loci are located further apart,
the chance of crossovers between them increases, whereas if the loci are very close,
the chance of crossover is very small, causing the two loci to be inherited together
(cosegregate) and assortment of these loci is no longer independent. The basic idea
of linkage analysis is to compare resemblance between subjects at a genetic marker
with resemblance at a measured trait (e.g., intelligence). Relatives who resemble
each other more phenotypically will also resemble each other more genetically if
the trait under study is heritable. In 1980 Botstein et al described a new basis for
the construction of a genetic linkage map of the human genome, based on so called
restriction fragment length polymorphisms (RFLPs) (Botstein, White, Skolnick, &
Davis, 1980). Pedigrees in which inherited traits are known to be segregating can
be tested for co-segregation of an RFLP marker (and unmeasured genes that are
physically close to the marker and are co-inherited) with the trait. The causal variant
might be the genetic marker in study or in a region around this marker that is linked
to it. Evidence for linkage is usually expressed in terms of LOD scores, i.e., a log of
the odds for linkage. Figure 1 explains crossing-over, cosegregation and how linkage
is conducted in more detail.
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Figure 1. Linkage, segregation and crossing over
Note: This figure shows a pedigree with 10 individuals, identified with letters from A to J. Three individuals
(B, E, and J) are affected with a certain disease. Each bar represents one chromosome, and each individual
has 1 pair of a chromosome. The two founder individuals (A and B) have colored bars indicating that
the white bar was inherited from the father of A, the blue bar from the mother of A, the yellow bar was
inherited from the father of B and the red bar from the mother of B. On each chromosome, 4 markers are
genotyped. Each pair of chromosomes contains the same markers in the same order, but the sequences are
not identical. Numbers 1–16 represent the different alleles from each of the four markers. Each marker has
4 possible alleles. For example, the first marker has possible alleles coded 1, 5, 9 and 13, the second marker
has alleles 2, 6, 10, and 14 etcetera. Individual C and F are not genetically related to the two founders
and have grey chromosomes that have the same order of markers with the same possible alleles, but not
necessarily with the same combination of alleles. Individuals D and E (children from parents A and B) have
one chromosome (represented in white) that was inherited from the father. These individuals also have
a chromosome inherited from the mother, however a genetic recombination took place and the children
inherited a mixture of the two maternal grandparental chromosomes (combination of yellow and red),
this phenomenon is known as crossover. If we follow the disease in the pedigree we notice that affected
individuals always carry alleles 15 and 16 of the third and fourth marker. These alleles are flanking a region
that segregates with the trait. We can conclude from the fictitious family pedigree that the red region
(between 15 and 16) shared by all the affected individuals likely contains the gene or casual variant for the
disease. We then say that this region is linked to the trait.
Linkage studies were widely used in the 90’s and progressed together with
other molecular techniques (Barrett et al., 2007; Mathew, 1999; Williamson, 1988).
Linkage studies have led to the detection of genetic loci for monogenic diseases using
classical penetrance-based linkage analysis methods (Mathew, 1999; Williamson,
1988). This penetrance-based linkage method attempts to characterize the mode of
inheritance of a trait, by statistically examining the segregation pattern of the trait
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through genetic samples of related individuals (as explained previously in figure
1). Penetrance is a measurement of the proportion of individuals in a population
who carry a disease-causing allele and can express the disease phenotype, in other
words if a mutation in the gene responsible for a particular autosomal dominant
disorder has 80% penetrance, then 80% of those with the mutation will develop
the disease, while 20% will not, even though they carry the mutation. An example
of disease with different levels of penetrance is Huntington’s disease (HD). HD is a
neurodegenerative genetic disorder that affects muscle coordination and leads to
cognitive decline and dementia. HD is one of several trinucleotide repeat disorders,
which are caused by the length of a repeated DNA section of a gene exceeding a
normal range (Walker, 2007).
Linkage studies have also provided evidence of genetic contribution to intelligence.
The first whole genome linkage scan for intelligence was published in 2005
(Posthuma et al., 2005). The sample used in this study consisted of a Dutch sample
(159 sibling pairs) and an Australian sample (475 sibling pairs). Results indicated two
significant areas of linkage to general intelligence on the long arm of chromosome
2 (2q) and the short arm of chromosome 6 (6p), and several areas of suggestive
linkage (4p, 7q, 20p, 21p). These first genome wide linkages show convergence
with linkage in clinical disorders that are characterized by cognitive disabilities. For
instance, the chromosome 2 area has been implicated in linkage scans for autism,
dyslexia and schizophrenia (Addington et al., 2005; Buxbaum et al., 2001; Fagerheim
et al., 1999; IMGSAC, 2001; Kaminen et al., 2003; Ylisaukko-Oja et al., 2005), while
the chromosome 6 region is the main linkage area for reading ability and dyslexia
(Cardon et al., 1994; Fisher, Stein, & Monaco, 1999; Grigorenko et al., 1997).
Four linkage studies for IQ have been published since (Buyske et al., 2006; Dick
et al., 2006; Luciano et al., 2006; Wainwright et al., 2006). Two studies with a partly
overlapping sample confirmed the importance of the areas on chromosomes 2 and
6 for specific aspects of intelligence (Luciano et al., 2006) as well as for academic
achievement, which is highly correlated with IQ scores (Wainwright et al., 2006). The
study by Luciano and others (Luciano et al., 2006) additionally showed that both
word recognition and IQ were linked to chromosome 2, corroborating the notion
that the same genes influence different aspects of cognitive ability (Plomin & Kovas,
2005).
Dick and others (Dick et al., 2006) also confirmed linkage of intelligence to the
chromosome 6 area, and a second scan on the same dataset (Buyske et al., 2006)
showed strong evidence for linkage of specific cognitive abilities on chromosome 14,
which had shown suggestive linkage in the two previous linkage studies (Dick et al.,
2006; Luciano et al., 2006). Converging evidence from these whole genome linkage
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studies provides support for the involvement of 3 different chromosomal regions in
human intelligence: 2q24.1-31.1, 6p25-21.2, and 14q11.2-12.
The significant genomic regions that result from a linkage study tend to be
relatively large (10-20Mb), harboring many potential candidate genes. The obvious
next step after linkage is therefore to fine map these linkage regions using more DNA
markers and/or select genes from these regions for further investigation, using a
candidate gene approach.
Candidate gene approach (widely used 1990s-2000’s)
The candidate gene approach is a commonly used technique to identify genetic risk
factors for complex traits, such as intelligence. In this approach, one (or several)
genes are selectively tested for association with a disease or trait. The statistical
test for association involves a simple comparison of allele-frequencies across cases
and controls (dichotomous traits) or a linear regression of genotype on trait level
(quantitative traits). The candidate gene approach allows researchers to investigate
the validity of a “biological guess” about the genetic basis of a specific phenotype.
A candidate gene study can be performed in unrelated individuals and does not
necessarily rely on family data, which is often more difficult to collect. The selection
of suitable and promising candidate genes, however, requires prior knowledge, e.g.
from previous linkage findings or from putative underlying biological mechanisms
(or pathways) that might be relevant to intelligence, such as neurotransmission
and neurodevelopment. Candidate genes can be selected based on linkage study,
known biological processes, adaptive evolution, animal studies or severe cognitive
impairment. Below we present some examples of candidate gene-based studies for
intelligence.
Candidate gene based studies for intelligence
Genes selected based on linkage study
The five previous whole-genome linkage scans discussed above have pointed
to three genomic regions (2q24.1-31.1, 6p25-21.2, and 14q11.2-12) important
to human intelligence (Buyske et al., 2006; Dick et al., 2006; Luciano et al., 2006;
Posthuma et al., 2005; Wainwright et al., 2006), but did not identify the responsible
genes or variants.
Recently, Rizzi et al (Rizzi et al., 2011) conducted an in silico fine mapping study of
six different chromosomal regions including these three previously reported linkage
regions and three regions that showed suggestive evidence for linkage. For their
analysis, the publicly available data of 947 families participating in the International
Multi-Centre ADHD (attention deficit/hyperactivity disorder) Genetics (IMAGE) study
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was used. SNPs that proved significant in the IMAGE samples were subsequently
tested in four independent samples (4,357 subjects), one ascertained for ADHD, and
three population-based samples. Associations between intelligence and SNPs in the
ATXN1 and TRIM31 genes (6p25-21.2) and in three intergenic regions (14q11.2-12)
showed replicated association, but only in the samples ascertained for ADHD (Rizzi
et al., 2011). This finding suggests that genetic variants important for intelligence
in a psychiatric (ADHD) population may not necessary overlap with genetic variants
important for intelligence in a non-psychiatric population.
Candidate genes based on known biological processes
Candidate genes may also be selected based on their known role in biological
processes that are thought to be important for intelligence. For example,
neurotransmission is thought to play a crucial role in cognitive functioning. Genetic
variants in genes involved in neurotransmission may have immediate consequences
in multiple events in the signalling cascade (release and reuptake process), altering
neurotransmitter receptors located in the cell membrane. Examples of candidates
for neurotransmission are the catechol-O-methyltransferase (COMT), the Cholinergic
Muscarinic Receptor 2 (CHRM2) and the synaptosomal-associated protein, 25kDa
(SNAP25) genes.
COMT is one of the major mammalian enzymes involved in the metabolic
degradation of catecholamines (Chen et al., 2004; Gogos et al., 1998) and has
been consistently associated with specific cognitive abilities, such as memory and
executive functioning (e.g (Gosso et al., 2008a; Savitz, Solms, & Ramesar, 2006;
Small, Rosnick, Fratiglioni, & Backman, 2004)). Savitz et al (Savitz et al., 2006)
found that 20 out of 26 studies reported significant association between COMT
polymorphisms (Val108/158Met) and cognitive function. Given its broad range of
activity, several research groups also reported association of genetic variants in COMT
with phenotypes other than cognition, such as Obsessive-Compulsive Disorder,
Schizophrenia, and Anorexia Nervosa (Frisch et al., 2001; Karayiorgou et al., 1997;
Karayiorgou et al., 1999; S. G. Lee et al., 2005; Shifman et al., 2002). One specific
variant (rs4680) has been associated with various traits such as cognitive ability,
Schizophrenia, Alzheimer, and pain sensitivity (Gosso et al., 2008a; Nackley et al.,
2007; Shifman et al., 2002; Sweet et al., 2005). These findings suggest that this gene
is probably not the only one involved in these traits, and interaction between COMT
and other genes might influence variation in cognitive function. For instance, Gosso
et al (Gosso et al., 2008a) found a significant interaction effect between COMT and
DRD2 polymorphisms for working memory performance in two independent familybased Dutch samples. Similarly, Qian et al (2009) reported that the IQ of Chinese
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boys with ADHD can be predicted by an interaction between COMT and MAOA
(monoamine oxidase A), an isozyme that preferentially deanimates dopamine. These
findings suggest that a single polymorphism effect may not easily be detected if the
interaction with other genes in the dopaminergic pathway is not taken into account
and if the trait is not well-defined.
A further example of a neurotransmission-related candidate is the CHRM2
(cholinergic muscarinic receptor 2) gene. Variants of the CHRM2 gene were reported
to explain 1% to 2% of the variance in intelligence in three independent studies
(Comings et al., 2003; Dick et al., 2007; Gosso, et al., 2006). The CHRM2 gene is the
most widely replicated gene for IQ although a recent study could not confirm this
association (Lind, et al., 2009).
The SNAP25 (synaptosomal-associated protein, 25kDa) protein is a component
of the SNARE (Soluble N-ethylmaleimide-sensitive factor Attachment Protein
Receptors) complex and has also been associated with intelligence. Variance in a
single nucleotide polymorphism of the SNAP25 gene was suggested to account for
3.4% of the phenotypic variance in intelligence in two independent Dutch cohorts
(Gosso et al., 2008b; Gosso, de Geus et al., 2006)
Selection of candidate genes for cognitive ability is not limited to neurotransmission.
For example, developmental trajectories - cognitive maturation in early childhood
and cognitive decline in aging - show considerable variation across individuals as
well, with a significant portion of this variation again being due to genetic factors.
The insulin-like growth factor 2 receptor (IGF2R) and cathepsin D (CTSD) genes were
both found to be associated with intelligence and are suggested to play a role in the
optimal development of the human brain (Chorney et al., 1998; Payton et al., 2003)
Variants in the PRNP (prion protein (p27-30)) and KL (klotho) genes were found
to be associated with general intelligence and play a role in the trajectory of normal
cognitive aging (Deary et al., 2005; Kachiwala et al., 2005).
Similarly, neurotrophins, i.e., regulatory factors that mediate the differentiation,
proliferation and survival of cholinergic, dopaminergic and serotonergic neurons,
could be related to variation in cognitive ability as well. For instance, one of these
neurotrophins, brain-derived neurotrophic factor (BDNF), has been previously
associated with intelligence (Tsai, Hong, Yu, & Chen, 2004) and reasoning skills
(Harris et al., 2006).
There are several lines of evidence that suggest that the biosynthetic fatty acid
pathway may be imported for IQ. The amount of fatty acid in the blood has been
shown to influence cognitive development (for a review, see (Innis, 2007), and
recently higher fatty acid (DHA) blood level was related to better performance on
tests of nonverbal reasoning and mental flexibility, working memory, and vocabulary
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(P-value< 0.05) in individuals between 35 and 54 years of age (Muldoon et al., 2010).
The FADS3 gene was associated with IQ in the genome wide scan of Butcher et al
2008 (Butcher, Davis, Craig, & Plomin, 2008). Caspi et al., 2007 showed a dominant
effect of the C allele of the FADS2 (Fatty Acid Desaturase 2) gene in response to
breast feeding: breastfed children carrying the C allele showed more than 6-IQ-points
advantage relative to children not fed with breast milk (P-value<0.001), but this effect
was later refuted by Martin et al, 2011(Caspi et al., 2007; Martin et al., 2011). Genetic
variants in the biosynthetic fatty acid pathway might influence the amount of fatty
acids in blood and consequently cognitive functioning in early and later age, but the
field currently waits systematic testing of the involvement of candidate genes from
the biosynthetic fatty acid pathway in IQ.
Candidate genes based on animal studies
Candidate genes can also be selected based on results from animal studies, as has
been shown for memory performance, one of the aspects of cognition. Molecular
genetic studies of memory in animals began more than 25 years ago with Seymour
Benzer and coworkers (Dudai, Jan, Byers, Quinn, & Benzer, 1976), who identified
“dunce”, an X-linked mutant fly with defective associative learning. Later it was
confirmed that this mutation was in a cAMP-specific phosphodiesterase gene. In the
past few years, a series of molecular-genetic, biochemical, cellular and behavioral
studies in fruit flies and mice have confirmed that long-term memory formation
is dependent on CREB (cAMP response element binding protein), a constitutively
expressed regulatory transcription factor (Bourtchouladze et al., 2003; Guzowski
& McGaugh, 1997; Tully, 1997). The importance of CREB in memory likely also
extends to humans, where the mutation of CBP (CREB-binding protein) results in the
Rubinstein-Taybi Syndrome (Petrij et al., 1995) a condition characterized by mental
retardation.
It has also been shown that transgenic mice that over express the NMDA receptor
2B (NR2B) gene, a synaptic coincidence detector, exhibit superior ability in learning
and memory in various behavioral tasks (Tang et al., 1999). In humans, NR2B is down
regulated in Alzheimer patients (Hynd, Scott, & Dodd, 2004) and genetic variants
in the NR2B subunit gene (GRIN2B) promoter region were associated with sporadic
Alzheimer’s disease in the North Chinese population (H. Jiang & Jia, 2009). However
until now no direct association has been found between memory or IQ and NR2B in
humans.
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Candidate genes based on severe cognitive impairment
Selection of candidate genes for variation in cognitive ability in the normal population
may also be based on genes that have already been associated with severe cognitive
impairment such as mental retardation (Inlow & Restifo, 2004; Ramakers, 2002) and
Down Syndrome. Down syndrome is a medical condition caused by an extra copy
of chromosome 21, and is characterized by an overexpression of cystathionine beta
synthase (CBS) and a low level of homocysteine in the blood (Al-Gazali et al., 2001).
Barbauz and others have linked variants in the CBS gene to individual differences in
intelligence in the normal range (Barbaux, Plomin, & Whitehead, 2000).
The DTNBP1 (dystrobrevin binding protein 1) gene is associated with the clinical
expression of schizophrenia (Straub et al., 2002), as well as with Hermansky-Pudlak
syndrome type 7 (Li et al., 2003). Variants in the DTNBP1 gene have also been
associated with general cognitive ability (‘g’) in a clinical and a normal population,
including 213 patients with schizophrenia or schizo-affective disorder, and 126 healthy
volunteers. In this study, individuals carrying the risk haplotype had lower g-scores
compared to non-carriers (Burdick et al., 2006). More recently, an independent study
showed that schizophrenic patients carrying the Dysbindin risk haplotype showed
significantly lower spatial working memory performance compared to patients who
were non-risk carriers, with genotype explaining 12% of variance in performance
(Donohoe et al., 2007).
The APOE (apolipoprotein E) variant is the largest known genetic risk factor
for late-onset Alzheimer’s disease (AD). Carriers of 2 APOE isoform E4 alleles have
between 10 and 30 times increased risk of developing AD by 75 years of age, as
compared to those not carrying any E4 alleles (e.g ( Jiang et al., 2008; Strittmatter
& Roses, 1996)). The APOE gene has also been consistently associated with specific
cognitive abilities, such as memory and executive functioning (e.g (Deary et al., 2002;
Savitz et al., 2006; Small et al., 2004)).
Candidate genes based on adaptive evolution
Allelic variants that show continuous adaptive evolution in modern humans have
been hypothesized to pose good candidate genes for intelligence. Genetic variants
that were positively selected for during human evolution and have swept to high
frequency under strong positive selection have been suggested to be related to
increased intellectual abilities (Evans et al., 2005; Gilbert, Dobyns, & Lahn, 2005;
Posthuma et al., 2002; Thompson et al., 2001; Zhang, 2003). Several studies have
shown that genes controlling brain development are favored targets of natural
selection during human evolution (Gilbert et al., 2005; Zhang, 2003), and it has
been suggested that genes that regulate brain size during development are chief
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contributors in driving the evolutionary enlargement of the human brain (Gilbert
et al., 2005). Humans have an exceptionally large brain relative to their body size.
Compared to chimpanzees, the human brain weight is 250% greater while the body
is only 20% heavier (McHenry, 1994). During homo sapiens evolution the brain size
tripled over a period of approximately 2 million years, ending around 0.4 million years
ago (McHenry, 1994; Wood & Collard, 1999). This brain expansion is believed to be
important to the emergence of human language and other higher-order cognitive
functions that was archived along the human evolution (Deacon, 1992)
Several studies, using a variety of methods, provide lists of genes positively
selected during homo sapiens evolution (Dorus et al., 2004; Pollard et al., 2006;
Sabeti et al., 2006; Voight, Kudaravalli, Wen, & Pritchard, 2006; Zhang, 2003).
However, until now, none of these selected genes could be associated with variation
in intelligence. Poor replication of an initially promising association result is a
common concern in the molecular genetic study of complex brain functioning. This
problem is illustrated by studies with two allelic variants that have been reported to
show continuous adaptive evolution in modern humans, ASPM (Mekel-Bobrov et al.,
2005) and the microcephalin (MCPH1) genes (Evans et al., 2005). These genes are
known to be under positive selection and to be involved in human brain volume, and
are therefore expected to pose suitable candidate genes for IQ (see e.g. (Posthuma
et al., 2002; Thompson et al., 2001)).
To confirm ASPM and MCPH1 findings, Mekel-Bobrov and colleagues tested
association of these genes with IQ in a sample of 2393 subjects from three family
based samples (one Australian and two Dutch) as well as from the population based
Scottish Aberdeen (ABC1936) and Lothian (LBC1921) birth cohorts. If selective
pressure is linked to higher IQ, we would expect to see an association between the
allele that is positively selected for during evolution (i.e. the derived allele) with higher
IQ. A significant association was found for ASPM in four of the five samples, with the
non-significant result in the youngest (12 year olds) sample (Mekel-Bobrov et al.,
2007). However, although in two samples (Dutch adults and LBC1921) the beneficial
allele was the allelic variant under selective pressure (the derived allele), in the other
two samples it was the ancestral allele that was associated with higher IQ scores. For
MCPH1, a significant positive association was seen for the derived allele in the Dutch
12 year olds, but this effect was not replicated in any of the other samples (MekelBobrov et al., 2007). These results thus remain inconclusive and selective pressure of
both ASPM and MCPH1 cannot be explained by selective pressure on brain size or IQ
of modern human (Woods et al., 2006).
Candidate gene studies can be performed relatively quickly and inexpensively
and may allow identification of genes with relatively small effects. However, the
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candidate gene approach is limited by how much is already known about the biology
or genomic location of the trait being investigated. Without previous knowledge,
exploratory, hypothesis free methods like genome wide association analysis might
be more suitable.
Genome Wide Association analysis (widely used 2000s-present)
Genome wide association (GWA) analysis allows testing for association between the
trait of interest and ‘all’ genetic variants genotyped across the entire genome.
A GWA study is hypothesis-free in the sense that it involves scanning the entire
genome for genetic loci that might be associated to intelligence, rather than focusing
on small, predetermined, candidate areas or genes. Plomin and coworkers (Butcher
et al., 2008; Butcher et al., 2005; Plomin, 1999; Plomin et al., 2004) conducted
several whole genome association studies [although smaller scaled, i.e. using
10,000 markers] and showed significant association of a functional polymorphism in
ALDH5A1 (aldehyde dehydrogenase 5 family) on chromosome 6p with intelligence.
Recently, the same group identified three novel genes through a whole genome scan
using a DNA pooling approach for low and high IQ groups involved in intelligence;
DNAJC13 (DnaJ (Hsp40) homolog, subfamily C, member 13), FADS3 (fatty acid
desaturase 3) and TBC1D7 (TBC1 domain family, member 7) (Butcher, Davis, Craig, &
Plomin, 2008). The chromosome 6 linkage lies close to, but a bit further downstream
of, the association that was reported in the genome wide allelic association study by
the same group in 2005 (Butcher et al., 2005). Recently, Ruano et al (Ruano et al.,
2010), used the traditional GWA strategy in a study with 627 individuals ascertained
for ADHD and almost 0.5 million genetic markers, but did not find any region
significantly associated with intelligence.
GWA studies have identified a number of genetic polymorphisms that are involved
in common diseases (Hindorff et al., 2009). However, by only associating genotypes
with the analyzed phenotype, little can be inferred about the actual biological
mechanisms underlying variation in the trait. Moreover, the effect size of genetic
associations with trait variance is often small. Therefore, large samples (N> 50,000)
are needed to obtain sufficient statistical power for the identification of new causal
genetic variants. Several new approaches have emerged as an alternative strategy
for detecting genetic variants with small effect size, such as analysis of copy number
variation, pathway analysis, and functional gene group analysis.
Copy number variations (widely used 2005 - present)
Apart from searching for changes in the genetic code, recent studies have also
tested the effects of structural variation such as copy number variants (CNVs) in
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the DNA on complex traits. American geneticist Calvin Bridges discovered CNVs
in 1936, when he noticed that flies that inherit a duplicate copy of a gene called
Bar develop very small eyes (Bridges, 1936). More than two decades later, Jérome
Lejeune identified CNV as the cause of Down syndrome (extra copy of chromosome
21) (Lejeune, Turpin, & Gautier, 1959).
CNVs are alterations in the genome that results in the cell having an abnormal
number of copies of one or more sections of the DNA. CNVs correspond to relatively
large regions of the genome that have been deleted or amplified on certain regions
of the chromosomes (Conrad et al., 2009; Estivill & Armengol, 2007; Redon et al.,
2006; Saus et al., in press). The mechanisms by which inter-individual changes in
CNVs can cause variation in complex traits include an altered gene dosage (deletion
or duplication) affecting the gene expression levels (Buckland, 2003; McCarroll et al.,
2006; Nguyen, Webber, & Ponting, 2006; Repping et al., 2006), or effects on gene
structure or regulation (Stranger et al., 2007). These genomic rearrangements can
affect normal development or maintenance of the central nervous system (J. A. Lee
& Lupski, 2006) and are highly heritable (h2 close to 1.0) (Locke et al., 2006).
CNVs contribute substantially to disease pathogenesis (Conrad et al., 2009; Estivill
& Armengol, 2007), and particularly to neurocognitive disorders like intellectual
disability (ID) (Mefford et al., 2009). Mefford and colleagues (Mefford et al., 2009)
screened 69 susceptible rearrangement regions in a cohort of 1105 individuals with
unexplained ID. From all tested individuals, 5.4% had pathogenic or potentially
pathogenic variants including deletions at 15q11.2 and 16p11.2, and a duplication
at 16p13.11. Compared to reported CNVs in control cohorts, these deleted and
duplicated regions are significantly (P-value≤0.003) enriched in children with ID,
supporting the hypotheses that they are risk factors for neurocognitive illness
(Mefford et al., 2009) .
Recently, Moreno-De-Luca et al described a pathogenic CNV deletion on 17q12
that confers a very high risk for autism and schizophrenia and showed that one or
more of the 15 genes in the deleted interval are dosage sensitive and essential for
normal brain development and function (Moreno-De-Luca et al., 2010).
These results suggest that normal cognitive function might depend on structural
variation in the DNA and variance in intelligence might be associated with genomic
rearrangements. Future functional analyses need to be conducted to confirm these
findings.
Pathway and functional gene group analysis for intelligence (2010-present)
GWA is a successful method for identifying common genes of relatively large effect
(Frayling et al., 2007; Saxena et al., 2007) but have been less effective when rare
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variants of large effect or genes of small effect are of importance (Maher, 2008).
Most complex traits, such as IQ, are now thought to be influenced by a (large)
number of rare and/or low penetrance variations that possibly interact with
environmental factors (Davies et al., 2011; Vinkhuyzen et al., 2011). It is most likely
that combined effects of many loci, each making a small contribution to overall
disease susceptibility, reveal insights into the genetic basis of common traits
(Torkamani, Topol, & Schork, 2008). Such small effects are difficult to detect and
collective testing of genes involved in biological pathways has been suggested as
an alternative strategy for testing the collective effect of many genetic variants with
small effect size (Holmans, 2010; Lips et al., 2011; Ruano et al., 2010; Torkamani
et al., 2008). Pathways are defined as a set of proteins that participate in cascades
of intracellular reactions, whereas functional gene groups are defined as a set of
proteins that share a similar cellular function. Functional mutations presented in
the genetic pathways or functional gene groups might lead to changes in cellular
responses, gene expression or cell morphology. Collective testing of the combined
effect of these genetic variants involved in the same pathway has been shown to be
more powerful than testing single gene effects (Torkamani et al., 2008).
Recently, Ruano and colleagues (Ruano et al., 2010) reported that the combined
effect of the heterotrimeric guanine nucleotide-binding proteins (G proteins) genetic
variants could explain 3.3% of the variance in cognitive ability. In their novel approach,
900 genes known to be expressed in the synapse were selected and organized in 23
different functional groups, and from all tested groups only the G proteins group
showed association with IQ (P-value=0.00019). G proteins are signal transducers that
communicate signals from many hormones, neurotransmitters, chemokines, and
autocrine and paracrine factors. Alterations in the G protein pathway might affect
the properties of neuronal networks in the brain in such a manner that impaired
cognition and lower intelligence result (Hurowitz et al., 2000; Neves, Ram, & Iyengar,
2002).
Conclusion
The past decade has seen major efforts to elucidate the genetic variants underlying
the high heritability of intelligence. Figure 2 provides a timeline of crucial events in
gene finding for intelligence. The main conclusion that can be drawn is that nearly 150
years after Sir Francis Galton’s observation that family members tend to be alike on
intellectual capabilities, we can merely confirm that IQ is indeed heritable and that at
least 40-50% can be traced to additive genetic variation. The past century has yielded
a handful of putative genes, of which perhaps the most promising (i.e. CHRM2) was
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recently refuted. It has become clear that human intelligence is influence by multiple
genes of small effect and most likely these genes aggregate in biological pathways or
functional gene groups, of which the group of genes encoding G-proteins is the first
to have been detected (see Figure 2).
The discrepancy between the relative high trait heritability and so little success in
gene-finding has been observed for many other traits as well and has been called the
“missing heritability” (Maher, 2008).
Explaining the missing heritability of intelligence
One possible explanation of the “missing heritability”, concerns the limitations
of the applied research approaches (Maher, 2008). The approaches generally
employed are limited to the detection of common genetic variants with small effect,
or rare variants with high penetrance (Mendelian disease). However, variation in
quantitative traits like intelligence is probably due to low-frequency variants with
intermediate penetrance effects (Maher, 2008; Manolio et al., 2009; McCarthy et al.,
2008). Most study designs that are currently used are unfortunately not well suited
to expose these.
GWAs, for example, use just a small fraction of known variants in the genome and
do not take into account rare variants that might be present in the region. One possible
solution would be to use deep sequencing approaches, which allow exploration of the
genetic variation in the genome in a very dense way. At present, deep sequencing
studies are still very costly, but this technique will certainly gain in popularity when
the costs go down, which is expected to happen within the next couple of years.
GWA techniques do not necessarily include information on structural variants,
such as CNVs. These stretches of DNA of tens or hundreds of base pairs long, that
are deleted or duplicated between individuals, might, however, account for a
considerable part of the genetic variability from person to person, and could account
for some of those rare mutations with moderate penetrance that GWAs cannot
detect.
Another explanation of the missing heritability might be that the heritability
estimates of traits like intelligence are actually overestimated. The recent study by
Davies et al provides a heritability estimate based on measured genotypes and sets
the lower bound of the heritability of adult intelligence at 40-50% (Davies et al., 2011).
The reported higher heritability of IQ in adults (60-80%) is derived from classical twin
studies in which the phenotypic resemblance of monozygotic twins is compared
to the phenotypic resemblance of dizygotic twins. Stronger resemblance between
monozygotic twins is taken as evidence for the involvement of genetic factors.
198
1
Galton, 1869; 2 Bouchard and McGue, 2001; 3 International Human Genome Sequencing Consortium, 2001; 4 Venter et al., 2001; 5 Posthuma et al., 2005; 6 Butcher
et al., 2006; 7 Ruano et al., 2010; 8 Davies et al., 2011; 9 Botstein et al., 1980;10 Plomin et al 2001; 11 McGue et al., 2003; 12 Gosso et al., 2006; 13 Dick et al., 2007; 14 Lind
et al., 2009
Figure 2. Timeline of benchmark events for research into the genetics of human intelligence.
Appendix I
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These classical twin studies, however, rely on a number of assumptions that may
not always be correct. For example, genetic effects are assumed to be stable across
different environmental conditions. However, multiple studies have shown that this is
not always the case.
In the context of intelligence, for instance, Vinkhuizen and colleagues (Vinkhuyzen
et al., 2011) studied the heritability of intelligence across samples of subjects who
were exposed to different life events. The study showed that the heritability of
intelligence ranged from only 9% to above 90% across levels of positive, negative,
and neutral life events, suggesting that genetic and environmental factors influence
cognitive variation differently depending on the type of life events that subjects
were exposed to. These so-called gene-environment interaction (GxE) studies show
that population-based estimates of heritability, i.e., averaged across subgroups and
various environmental conditions, can be misleading as the heritability may be higher
in some subgroups, and lower in others. In addition, these studies suggest that the
success of genetic association studies might increase when such subgroups and the
effects of environmental conditions are taken into account.
Two other assumptions underlying the classical twin design concern random mating
(i.e., the phenotypes of the parents of the twins are assumed to be uncorrelated),
and the absence of cultural transmission (i.e., environmental factors related to the
trait of interest are not transmitted from the parental generation to the offspring).
If these assumptions are violated (i.e., assortative mating and cultural transmission
do in actuality affect the trait of interest) but the effects are not accommodated
explicitly in heritability studies of intelligence, the result may be that effects of genetic
dominance (i.e., non-additive genetic factors) are underestimated (Fulker, 1982; Keller
et al., 2009). Since gene-finding methods like GWAs generally assume the absence
of genetic dominance, heritability may go missing when non-additive genetic effects
that are actually present are not sufficiently considered. Previous studies indeed
suggest that these assumptions may not always hold for intelligence. Various studies
have, for example, shown that mates do select each other on the basis of similar
cognitive levels (Loehlin, 1978; Mascie-Taylor, 1989; Reynolds, Baker, & Pedersen,
2000). In a study using an extended family design (twins, and the siblings, children and
parents of these twins, N=1314), Vinkhuyzen et al (Vinkhuyzen, van der Sluis, Maes,
& Posthuma, (under revision)) showed that the pattern of phenotypic resemblance
between family members other than twins did not comply with high heritability due
to additive genetic effects. They reported the presence of genetic dominance effects
on intelligence besides effects of assortative mating and cultural transmission, and
suggest that heritability estimates based solely on twin samples may not always
provide a complete picture of the dynamics between genes and environment.
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Another genetic mechanism that is not accounted for in current gene-finding
studies, is epigenetic inheritance, in which changes in phenotype and RNA expression
are passed through from generation to generation without being accompanied by
changes in the underlying DNA sequence of the organism. That is, specific changes in
the environment or food intake of the first generations can affect the phenotypes of
following generations without altering the DNA sequence. As a result, study designs
that focus on variation in DNA sequence only will not be able to link the trait to
the implicated genetic loci. Generally, animal models, in which external factors can
be controlled and manipulated, are necessary to reveal epigenetic effects. A nice
example is the coat-colour methylation study by Cooney and colleagues (Cooney,
Dave, & Wolff, 2002), who reported that maternal dietary methyl supplements
increased DNA methylation of the agouti gene and methylation-dependent
epigenetic phenotypes in the offspring, resulting in coat colors ranging from yellow
to heavily mottled, depending of the combination of the nutrients (Cooney et al.,
2002; Waterland, 2003; Waterland & Jirtle, 2003) .
In addition to environmental factors like food intake, gene-gene interactions
(epistasis) can also influence RNA expression. That is, the expression of a certain
gene can be enhanced or reduced by (several) other gene variants and, as in the
previous case, the phenotype can be passed through generations even if the genetic
variants that caused the effect are not ((Lam, Youngren, & Nadeau, 2004)). Complex
genetic mechanisms like epigenetics and epistasis are practically impossible to track
using relatively simple techniques such as GWAs and candidate gene studies, which
usually focus on the effects of single genes (rather than studying their concerted
effects) and generally make use of information collected in a single generation only.
Although studies in which these complex genetic processes are implicated in
intelligence are as yet lacking, it is deemed very likely that such complex genetic
processes underlie variation in complex traits such as intelligence. The fact that these
complex genetic processes are not well accommodated in current behavioral genetic
studies could therefore well be one of the reasons that a large part of the heritability
of intelligence remains as yet unexplained.
Future directions
One of the primary findings of the wide range of GWAs studies conducted to date
is that single gene effects are usually (very) small, suggesting that most of the genes
interact epistatically or work concertedly within pathways. It is, therefore, possible
that the effect of one gene on the trait of interest can only be observed when the
effects of other genes are taken into account. Functional pathway approaches
based on cellular function might promote the identification of genes with small
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effect that together explain more variance in the phenotype than alone. Future
efforts in gene finding for intelligence will benefit from the availability of sequence
data and may also benefit from looking at the combined effect of multiple genes
as opposed to testing for single SNP effects on intelligence. In addition, unraveling
gene environment interaction processes seems of utmost importance. Ideally, future
studies aimed at revealing the genetic basis of intelligence should combine these two
lines of research by studying groups or networks of genes, including rare variants,
that share the same cellular function while controlling for, or accommodating, the
effects of environmental factors that induce variation in cognitive functioning.
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