Review
Special Issue: The Genetics of Cognition
The genetics of cognitive ability
and cognitive ageing in healthy
older people
Sarah E. Harris1,2 and Ian J. Deary2
1
Centre for Cognitive Ageing and Cognitive Epidemiology, Medical Genetics Section, University of Edinburgh, Edinburgh,
EH4 2XU, UK
2
Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square,
Edinburgh, EH8 9JZ, UK
Determining the genetic influences on cognitive ability
in old age and in cognitive ageing are important areas of
research in an increasingly ageing society. Heritability
studies indicate that genetic variants strongly influence
cognitive ability differences throughout the lifespan,
including in old age. To date, however, only the genes
encoding apolipoprotein E (APOE) and possibly catechol-O-methyl transferase (COMT), brain-derived neurotrophic factor (BDNF) and dystrobrevin binding protein 1
(DTNBP1) have repeatedly been associated in candidate
gene studies with cognitive decline or with cognitive
ability in older individuals. Genome-wide association
studies have identified further potential loci, but results
are tentative. Advances in exome and/or whole-genome
sequencing, transcriptomics, proteomics and methylomics hold significant promise for uncovering the genetic
underpinnings of cognitive ability and decline in old age.
Human cognitive ability and non-pathological cognitive
ageing
To speak of the ‘genetics of’ cognitive ability and cognitive
ageing refers to the possibility that genetic differences
cause some of the variation in these two phenotypes that
is manifest in humans. To proceed, there must be clarification of the difference between cognitive ability and cognitive ageing, and of the phenotypes to be studied within
each. Pathological states of cognitive decline (the dementias, other neurodegenerative conditions and prodromal
states, such as mild cognitive impairment) are important
research areas, and with their own research fields, including genetics. However, in the present overview, we focus on
genetic contributions to normal, non-pathological variation
in cognitive ageing. This is an important and large research area in its own right, in part because cognitive
ageing affects people’s quality of life, predicts dementia
and death, and is at this stage more likely to be open to
amelioration [1,2]. We present evidence from twin studies
that indicate that cognitive ability in old age is highly
heritable. We then describe both candidate gene and genome-wide association studies (GWAS; see Glossary) that
Corresponding author: Deary, I.J. ([email protected]).
388
have attempted, with limited success, to determine the
specific genetic variants that influence cognitive ability
and cognitive ageing.
Cognitive ability and cognitive ageing
Consider the scores obtained by a 75-year-old individual in
several mental tests; say, tests of memory, reasoning,
speed of processing and so forth. This is an age at which
the adult peak in such skills is typically well past [3,4].
Therefore, scores on any one test will be the combination of
three things: the previous peak level of the ability, how
much age-associated change there has been, and occasionspecific variance (including error variance). To separate
the phenotypes of level and change in cognitive abilities,
longitudinal studies are required in which people have
taken mental tests on more than one occasion. This allows
a cognitive trajectory to be computed for each subject. From
a sample of such subjects, it is then possible to describe and
find determinants of the differences in levels and rates of
change in cognitive abilities. These are different things,
probably with different determinants.
Glossary
Allele: one of two or more versions of a gene.
Candidate gene study: a study that investigates associations between genetic
variants in a gene suspected of influencing a trait of interest (by virtue of the
function or chromosomal location of the gene) and the trait.
Cognitive change: the change in cognitive score between occasions of
measurement. Note that it is common to see ‘cognitive ageing’ referred to when
only one occasion of measurement has taken place. This is a common error.
Cognitive level: cognitive score obtained on one occasion.
General intelligence (g): operationally, g is typically the first unrotated principal
component (or factor) from a battery of mental tests administered to a sample
of a population. It describes the near-universal finding that all mental tests tend
to correlate positively.
Genome-wide association study: a hypothesis-free study whereby up to 1
million SNPs, spread throughout the genome, are investigated to identify
genomic regions associated with a particular trait.
Intercept and slope: given a number of occasions of cognitive measurements,
a regression equation can be applied to the longitudinal data of each individual
to discover their starting level of cognitive ability (intercept) and trajectory of
cognitive change (slope). Therefore, one can seek the antecedents (including
genetics) and outcomes of the slope and trajectory. Note that the slope might
be linear, quadratic or even more complex.
Single nucleotide polymorphism (SNP): a position on a chromosome where
humans are known to differ with regard to which base pair occurs.
1364-6613/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2011.07.004 Trends in Cognitive Sciences, September 2011, Vol. 15, No. 9
Review
Trends in Cognitive Sciences September 2011, Vol. 15, No. 9
mental test capabilities and forms taxonomies of abilities
based on correlations among cognitive skills [5] (Box 1).
The second approach, called the cognitive or neuropsychological approach, relies more on lesion and brain imaging
studies, and tends to focus on mental capabilities within a
given domain of function [6]. In the present article, we
largely use the differential psychology approach, as this is
the approach almost exclusively employed in behaviour
genetic studies and the bulk of molecular genetic studies
seeking to explain individual differences in cognitive function and change in old age.
Cognitive phenotypes
How do we define cognitive phenotypes? If there were just,
say, three types of mental work that brains performed, and
if there was a perfectly valid test for each of them, this
would be simple. One would apply these three tests each
time one wished to assess an individual’s cognitive capabilities. This is not the case, however. There is no widely
accepted taxonomy of brain functions that is isomorphic
with mental tests. Therefore, what is available as phenotypes is a set of cognitive tests that are classified largely by
their contents. To an extent, the contents do show isomorphisms with brain activation. Indeed, there tend to be
two somewhat separate approaches to cognitive phenotypes, including how they are applied to ageing. The first
is called the differential psychology (or psychometric)
approach. It studies the multivariate associations among
Factors affecting age-associated cognitive decline
There are changes in population mean levels of cognitive
test scores as people age, but what we are interested in here
is the individual differences in these life-course changes and
Box 1. The differential psychology approach to cognitive ability
respect to ageing, an important distinction should be made between
relatively age-sensitive cognitive domains (e.g. memory, speed of
processing, spatial ability, and reasoning) and relatively age-resistant
domains (e.g. verbal and numerical abilities) [50].
Furthermore, one might ask about the stability of cognitive
differences, and whether age affects any or all of these three levels
of cognitive variation. From youth (20 years) to later middle age (55
years) [51], and even from childhood (11 years) to old age (79 years)
[52,53], there is high stability in people’s rank order of cognitive ability
differences. In a study of male twins followed from age 20 to 55,
genetic factors were responsible for the stability (and the same
genetic influences were found at both ages) and non-shared
environmental causes explained the changes [51]. With regard to
how ageing affects cognitive abilities, a study of over 1200 people
subjected to 12 mental tests for up to seven years found that 39% of
the effect of age was on general cognitive function, 33% was at the
level of domains (i.e. abstract reasoning, spatial visualization,
episodic memory and processing speed) and 28% was at the level
of the individual test [54]. Other longitudinal [55] and cross-sectional
studies [3] also find this distribution of ageing effects across general
and specific cognitive skills. To summarise: some ageing effects
appear to be general and affect all cognitive skills, and some are more
specific.
Put simply, the differential psychology approach asks: do individuals
differ in how generally smart they are, or do they tend to differ in their
specific cognitive capabilities? Both are true to some extent, and the
answer lies somewhere in between. This is based on the finding that
all mental tests show positive covariation, and that there are groups
of types of test that show higher correlations among themselves than
with other mental tests. Therefore, the sources of variance (i.e. the
thing that one is trying to explore genetic contributions to) are at least
on three levels [5] (Figure I). Individuals differ in how generally
mentally able they are; this is often called general intelligence, or g.
Approximately half of the population variation in cognitive skills is
captured by variation at this level. As long as there is a reasonable
number of varied mental tests applied, this g factor ranks people
almost identically even when it is based on different tests [47,48].
Individuals differ in how good they are in broad mental domains (e.g.
memory, processing speed, visual perception, auditory perception,
retrieval ability, etc.). They also differ in their specific cognitive skills.
Therefore, one can seek genetic and other contributions to variation at
each of these levels. The result is that additive genetic factors
contribute approximately 70% or more of the influence on g in
adulthood [7,47,49]. Other levels have high heritability too, but much
of this is because they are highly correlated with g and so the genetic
causes of g partly also cause these lower-level differences. With
[(Box_1)TD$FIG]
g
Fluid
intelligence
Test 1
Test 2
Test 3
Crystallised
intelligence
Test 1
Test 2
Test 3
Memory and
learning
Test 1
Test 2
Test 3
Visual
perception
Test 1
Test 2
Test 3
Auditory
perception
Test 1
Test 2
Test 3
Retrieval
ability
Test 1
Test 2
Test 3
Cognitive
speediness
Test 1
Test 2
Test 3
Processing
speed
Test 1
Test 2
Test 3
TRENDS in Cognitive Sciences
Figure I. Schematic indicating the hierarchical structure of cognitive abilities. g reflects general intelligence (based on data from [5]).
389
Review
what might cause them. Multiple factors have been proposed as possible determinants of individual differences in
non-pathological, age-associated cognitive change [1]. A
recent and thorough review examined over 100 observational studies, and many randomised controlled trials and
systematic reviews [2]. The studies included nutritional,
medical, socio-economic, behavioural, toxic and genetic factors. Firm evidence was lacking for most factors, but there
appeared to be relatively robust evidence for risk of increased cognitive decline from the apolipoprotein E (APOE)
e4 allele, smoking and some medical conditions. There was
also some evidence for the protectiveness of physical exercise and cognitive training.
With regard to studies of the genetic causes of differences in cognitive ageing, the simplest division of these is
into behavioural and molecular genetic studies. Behavioral
genetic studies typically examine twins and adoptees.
They have been informative about how environmental
and genetic causes affect mental test performance in old
age. Mostly, they employ quite complex statistical modelling methods, but all are based on the facts that: monozygotic twins have 100% genetic similarity whereas dizygotic
twins share, on average, 50%; and that adoptees share
environmental but not genetic causes with their rearing
family, and the reverse with their biological parents. Behavioral genetic studies have been used to explore whether
genetic and environmental causes operate at the level of
general intelligence (g) or specific cognitive domains, and
have also explored causes of cognitive change as well as
cognitive level within old age. It is important to repeat here
that an association between a genetic variant and cognitive
scores in old age is not the same as studying genetic
contributions to cognitive ageing. Any association based
on tests taken at a single time point might just represent
an association with the life-long, stable trait of intelligence;
to study ageing per se, it is important to show some
association with individual differences in change across
two or more occasions of measurement.
Heritability of cognitive ageing
The total variance (from genetic and environmental influences) in cognitive ability increases with age, possibly
owing to stochastic effects. Although it might drop a little
in advanced old age, there is also an increase in the
percentage of this total variance that has a genetic component [7]. The contribution of twin studies to cognitive level
and change in old age has been summarised extensively by
Lee et al. [8]. As is found in earlier periods of life, the
majority of the genetic variation is in general cognitive
ability. The major cognitive domains also show high heritability, largely because they derive much of their variation
from g. Memory in particular tends to have its own genetic
causes in addition to those that derive from general cognitive ability [9].
Much of the information about genetic causes of ability
differences in old age has come from various samples based
on the Swedish Twin Registry. For example, the Swedish
Adoption Twin Study of Ageing estimated the heritability
of g at approximately 80% in the mid-60 s, and showed a
high correlation between g loadings of tests and their
heritability [10]. Longitudinal study of this sample tested
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Trends in Cognitive Sciences September 2011, Vol. 15, No. 9
on multiple occasions over 13 years separated the genetic
and environmental causes of the level and slope of cognitive functions [11]. The heritability of cognitive level was
very high, with most of the rest being caused by unique
and non-shared environmental effects. Whereas g still
accounted for substantial genetic contributions to the cognitive domains, an interesting finding was that the heritability in fluid and/or spatial ability decreased somewhat
with age, whereas that of memory increased. The slope of
cognitive function with age had linear and quadratic
aspects. The causes of differences in the former were
almost entirely the influence of a unique environment,
whereas the quadratic aspect (accounting for a much
smaller proportion of variance than the linear effect)
had some genetic influence. Analyses of the genetic effects
on the four cognitive domains in the SATSA tests (verbal,
spatial, memory and speed) over a period of up to 16 years
found significant genetic influences on the intercepts, but
not on the slopes [12]. However, genetic influences did
affect cognitive slopes via intercept–slope correlations;
that is, there were genetic influences on cognitive level,
and individuals starting at different cognitive levels did
not decline at the same rates. These complex analyses
came to an important conclusion concerning genetic contributions to age-related cognitive changes: that genetic
variance in processing speed is largely responsible for the
variation that occurs at a later time point in other cognitive
factors. Therefore, researchers interested in determining
genetic factors that influence cognitive ageing should look
for those that influence processing speed.
The Swedish study with the oldest subjects (the OctoTwin Study) reported that the heritability of g was 62% at a
median age of 82 years, with the heritability of cognitive
domains being between 32% and 62% [13]. The non-genetic
variance was caused by non-shared environmental factors.
Further analysis of this sample found that the genetic
contributions to the cognitive domains of verbal ability,
spatial ability, speed of processing and memory were
largely those that caused differences in g [9].
Similar results and conclusions were derived from studies of the Longitudinal Study of Aging Danish Twins [14–
16]. It should be stressed that the cognitive test battery in
this study was small: the number of animals that could be
named in 1 min; forward and backward digit span; immediate and delayed recall of a 12-word list. Even so, at initial
assessment, when subjects had a mean age of 80 years and
were all over 75 years, the additive genetic contribution
to the cognitive composite score was 54%, with the remainder owing to non-shared environment, which is similar to
that from the comparable Swedish study [13]. Follow-up of
this sample to include four waves of cognitive testing
showed a heritability of 75% for cognitive level but almost
zero for cognitive change [15]. The heritability estimate
for slope using data from a subsequent wave was 18%, but
still non-significant: most of the contribution to change
appeared to come from a non-shared environment [16].
Note that this higher heritability estimate for level is
because level is a latent trait summarising four individual
waves of testing, each of which had heritabilities typically
of approximately 50%; the estimate was lower at a subsequent follow-up [16]. Note also that the total retest time
Review
was no more than 6 years, which does not allow much time
for any genetic or environmental factors to influence
cognitive change.
A US study of male twins between 69 and 80 years old
reported a heritability of 79% for a latent trait of executive
control [17]. The contributing tests to this trait (digit
symbol, stroop, trail making and verbal fluency) make it
likely to be a reasonable measure of g.
In summary, the Swedish Twins-based and other studies have demonstrated that heritability of g and the major
cognitive domains is still high in old age and that the
heritability of the latter is due substantially to influences
on the former. It is important to note that heritability
studies have been more revealing for cognitive level than
for cognitive change. Cognitive change tends to suffer from
a poor phenotype: being studied over too-short periods of
time with insufficient assessments [8]. Having established
high heritability of cognitive level in old age, and with the
jury still being out on genetic contributions to cognitive
change, it has been important to seek the specific genes
that contribute to cognitive differences in old age. Here, we
consider several approaches, including candidate gene
studies and GWAS. Several more recent promising
approaches are summarised in Box 2.
Candidate genes
Several approaches have been taken to identify candidate
genes to test for association with both cognitive ability in
older people and age-related cognitive decline. Generally
speaking, selected genes have previously been associated
with age-related diseases, traits and mechanisms. Many of
these genes have been implicated in cognitive ability and
cognitive ageing, but few findings have been replicated.
Selected examples of candidate genes, for which there are
multiple positive associations, are given below. We first
discuss candidate gene studies in the context of normal
cognitive function as well as in neurocognitive and psychiatric disease. We then examine findings from two particular approaches, imaging genetics and oxidative stress,
which might prove promising in the study of cognitive
Trends in Cognitive Sciences September 2011, Vol. 15, No. 9
ability and cognitive decline. A more detailed review of
candidate gene studies is provided in [18].
Normal cognitive function
As memory is particularly badly affected by ageing, genes
previously associated with memory phenotypes have been
widely studied with regard to cognitive decline. For example, brain-derived neurotrophic factor (BDNF) has been
implicated in hippocampal-dependent learning and memory in humans and non-human species and there is a
steady decline in BDNF expression associated with normal
ageing [19]. A single functional common polymorphism has
been identified in BDNF, a valine to methionine change at
amino acid 66. The methionine allele has been implicated
in abnormal hippocampal function, lower hippocampal
volume and reduced cognitive function. Some, but not all
studies have identified an association between BDNF and
cognitive function in elderly subjects [18].
Catechol-O-methyl transferase (COMT) degrades the
catecholamine neurotransmitters, dopamine, norepinephrine and epinephrine. Similar to BDNF, the gene contains
a single common functional polymorphism. This is a valine
to methionine substitution at position 158. The methionine
allele reduces the thermostability and activity of the enzyme, thus reducing dopamine and other neurotransmitter
degradation. COMT has been associated mainly with executive phenotypes in several studies of both younger and
elderly individuals [18]. However, a meta-analysis failed to
find strong evidence for an association between COMT and
cognition, finding only suggestive evidence that methionine homozygotes score slightly higher on cognitive ability
tests [20].
Neurocognitive disease
The genetic contribution to Alzheimer’s disease (AD) has
been widely studied. Genes associated with the rare earlyonset form of the disease include those encoding amyloid
precursor protein (APP), presenilin 1 (PS1) and presenilin
2 (PS2), but none has been definitively associated with
cognitive function or age-related cognitive decline in
Box 2. The future
Exome and/or whole-genome sequencing
GWAS are designed to detect, and have been successful at detecting,
common genetic variation associated with diseases and traits.
However, there is evidence to suggest that a substantial amount of
the variation in complex traits, such as cognition and cognitive
decline, is influenced by rare genetic variants, present in less than 1%
of the population [56]. These variants can only be detected by directly
sequencing the DNA of each individual in the study. Until recently,
this had been prohibitively expensive. However, technological
developments mean that exome (i.e. coding and regulatory region)
sequencing is now feasible on the large numbers of individuals
required to obtain the necessary power to detect associations
between rare variants and complex traits [57]. Over the next few
years, it is hoped that the cost of whole-genome sequencing will drop
to a level that will make it possible too. This will allow genetic variants
in all regions of the genome to be examined.
Transcriptomics and proteomics
Studying genetic variants in DNA does not directly inform one of their
function. DNA is first transcribed into RNA, which is then translated
into protein. The potential of genetic variants to alter RNA and protein
levels is of great interest. Where suitable tissue is available, it is now
possible to identify genome-wide gene expression (RNA) levels and
to correlate these directly with levels of cognitive ability and cognitive
decline [58]. The biggest obstacle to this approach is that the most
appropriate tissue (the brain) is inaccessible in living subjects.
Another approach is to look directly at the abundance of the
proteins under the control of multiple genetic variants by using
methods such as mass spectrometry. A recent pilot study, looking at
cognitive extremes in old age, concluded that this is a promising
technique that will help in the understanding of the biological
foundations of intelligence differences [59].
Methylomics
Epigenetic mechanisms refer to DNA modifications that do not alter
the sequence of the DNA. The best-known epigenetic mechanism is
DNA methylation. Methylation mainly occurs at CpG islands that tend
to be concentrated in gene promoters where transcription is blocked
in the presence of a methyl group. Methylation states are known to
change with age and be altered in certain age-related diseases [60].
Techniques are now available that allow the methylation status
of > 450 000 loci covering > 14 000 genes to be determined [61].
391
Review
[(Figure_2)TD$IG]
non-demented individuals [21]. By contrast, the e4 allele of
the APOE gene, associated with the more common lateonset AD (LOAD), was shown by a meta-analysis to be
associated also with non-impaired cognitive function, particularly in elderly subjects [22]. e4 carriers performed
significantly worse on measures of episodic memory, executive functioning, global cognitive functioning and perceptual speed. In a Scottish sample that was studied for
cognitive change from age 11 to age 79 years, and then
from age 79 to age 87, individuals with at least one e4 allele
declined more in intelligence and memory functions across
these two periods of the life course [23,24] (Figure 1). More
recently, a poly-t repeat in the neighbouring translocase of
outer mitochondrial membrane 40 (TOMM40) gene has
been linked to the age of onset of LOAD [25]. Recent GWAS
(Figure 2) identified several more genetic variants that
increase an individual’s risk of developing LOAD in the
genes bridging integrator 1 (BIN1), clusterin (CLU), complement component (3b/4b) receptor 1 (CR1) and phosphatidylinositol binding clathrin assembly protein (PICALM)
[26–28]. However, reports on whether these variants are
associated with cognitive function in non-demented elderly
subjects are mixed [21,29,30].
[(Figure_1)TD$IG]
Trends in Cognitive Sciences September 2011, Vol. 15, No. 9
Stage 1
Genotype up to 1 million SNPs
in several thousand individuals
Stage 2
Genotype top few hundred
SNPs in further individuals from
the same population
Stage 3
Genotype SNPs that are
significantly associated with trait
in first population, in an
independent population
TRENDS in Cognitive Sciences
Figure 2. Genome-wide association study design. The exact number of individuals
and single nucleotide polymorphisms (SNPs) typed at each stage will differ from
study to study and will depend on the availability of subjects, effect size of
individual SNPs and funds.
(a) 104
APOE e4-
IQ score
102
100
98
APOE e4+
96
94
Age 11
Age 79
Logical memory score
(b) 38
36
34
32
Key:
30
APOE e4+
28
APOE e4-
26
78
80
82
84
86
88
Age
TRENDS in Cognitive Sciences
Figure 1. Apolipoprotein E (APOE) e4 status and ageing of cognition in the Lothian
Birth Cohort of 1921: (a) general intelligence from age 11 years to age 79 years; (b)
verbal declarative memory from age 79 years to age 87 years. Based on data from
[23,24], respectively.
392
Psychiatric disease
Cognitive deficits are present in many psychiatric diseases,
including schizophrenia and bipolar disorder, and are also
found at a higher rate in unaffected family members than
in the general population [31]. Therefore, genes previously
associated with psychiatric disease are good candidates for
cognitive deficits in the absence of psychiatric disease.
The gene Disrupted in Schizophrenia 1 (DISC1) was
initially linked to psychiatric illness when it was identified
at the breakpoint of a balanced translocation segregating
within a large Scottish family suffering from multiple
forms of mental illness [32]. There is some evidence implicating variation in DISC1 in cognitive function, in patients,
unaffected family members and the general population.
Elderly Scottish women homozygous for the cysteine allele
of a non-synonymous single nucleotide polymorphism
(SNP) in exon 11 had significantly lower cognitive ability
scores than men, controlling for their childhood cognitive
ability. This study suggests that variation in DISC1 affects
cognitive ageing specifically in women [33]. However, the
findings were not replicated in a second, younger Scottish
cohort [34]. Distinct allelic haplotypes have been associated with psychotic and bipolar spectrum disorders along
with cognitive impairments in a Finnish bipolar disorder
family study [35].
Dystrobrevin binding protein 1 (DTNBP1), initially associated with schizophrenia, has been associated via metaanalysis with cognitive function in several cohorts of varying ages [36].
Imaging endophenotypes
Associations between genetic variants and brain structure
have been investigated, based on the assumption that individual differences in brain structure are good intermediate
Review
phenotypes (so-called ‘endophenotypes’) for cognitive ability. For example, variation in adrenergic, beta-2-, receptor,
surface (ADRB2), has been associated both with cognitive
ability and white matter integrity (controlling for childhood
ability) in a cohort of elderly Scots [37,38]. There was some
evidence that white matter integrity mediated the association between ADRB2 and cognitive ability in old age.
Mattay et al. [39] also reviewed genes identified by brain
imaging genetic studies and related to systemic disease or
cardiovascular function [angiotensin I converting enzyme
(peptidyl-dipeptidase A) 1 (ACE) and methylenetetrahydrofolate reductase (NAD(P)H), (MTHFR)], or inflammatory processes [interleukin 1, beta (IL1B); tumor necrosis
factor (TNF) and caspase 1, apoptosis-related cysteine
peptidase (interleukin 1, beta, convertase), (CASP1;
ICE)]. They concluded that neuroimaging techniques provide a promising way to identify genes associated with
cognitive ageing.
Oxidative stress
One mechanism that has been used to identify candidate
genes is oxidative stress [40]. Oxidative stress occurs
during cellular respiration when the defence mechanisms
of the cell fail to remove damaging by-products formed
during respiration. These so-called ‘free-radicals’ can damage DNA and protein. Oxidative stress is believed to be
responsible for many aspects of ageing, including cognitive
decline. Therefore, anti-oxidant defence genes are good
candidates for influencing cognitive decline. However, to
date, no genetic variants within such genes have been
definitively associated with either cognitive ability in old
age or cognitive decline [40].
Summary of candidate gene studies
Despite many studies being published, to date only APOE
and possibly COMT, BDNF and DTNBP1 have repeatedly
been associated with either cognitive ability in older people
or cognitive decline [18,34]. It should also be noted that
even when genetic variants are significantly associated
with cognitive phenotypes, effect sizes are typically very
small (1–2%).
Genome-wide association studies
As candidate gene studies have failed to identify genetic
variants that account for much of the variance in cognitive
ability or cognitive ageing, researchers have more recently
turned to a hypothesis-free study design. GWAS allow
multiple (up to 1 million) SNPs to be investigated in a
single study (Figure 2) and have been used to identify
genetic variation influencing many common diseases and
traits (e.g. http://www.genome.gov/gwastudies; http://
www.gwascentral.org).
A small-scale GWAS failed to identify common genetic
variants associated with cognitive ability [41,42], although
other GWAS have implicated WW and C2 domain containing 1 (WWC1; KIBRA); calmodulin binding transcription
activator 1 (CAMTA1) and sodium channel, voltage-gated,
type I, alpha subunit (SCN1A) with memory phenotypes
[43–45]. A recent GWAS, which included 3500 older individuals, identified no single genetic variant associated
with fluid and crystallised intelligence [46]. However, it
Trends in Cognitive Sciences September 2011, Vol. 15, No. 9
Box 3. Questions for future research
Given sufficiently good phenotypic data on age-related cognitive
changes, how heritable are these phenotypes?
What are the genetic variants that influence cognitive ability and
cognitive ageing and can they be identified by deep sequencing
individual genomes?
What are the gene expression differences at both the RNA and
protein level that influence cognitive ability and cognitive ageing?
Do epigenetic mechanisms influence cognitive ability and cognitive ageing?
concluded that approximately 50% of the variation in
cognitive ability in later life is accounted for by multiple
genetic variants in linkage disequilibrium (LD) with common SNPs, each having a very small effect.
Concluding remarks
In summary, despite behavioural genetic studies consistently showing that the heritability of cognitive ability in
old age is high, very few specific genetic variants that
influence cognitive ability and cognitive ageing have been
identified. To date, the majority of studies have focussed on
the identification of relatively common genetic variants
that influence these traits. With the introduction of largescale whole-genome sequencing, it is hoped that multiple
rare variants influencing cognitive ability and cognitive
ageing will be identified. By investigating gene expression
levels, researchers will be also able to identify the effects of
multiple genetic variants on gene function. Finally, technological advances now allow epigenetic mechanisms to be
investigated on a genome-wide scale. This might lead to
new insights into mechanisms that influence variation in
cognitive ability and cognitive function (Box 3).
Acknowledgements
The work was undertaken by The University of Edinburgh Centre for
Cognitive Ageing and Cognitive Epidemiology, part of the cross council
Lifelong Health and Wellbeing Initiative (G0700704/84698). Funding
from the Biotechnology and Biological Sciences Research Council
(BBSRC), Engineering and Physical Sciences Research Council
(EPSRC), Economic and Social Research Council (ESRC) and Medical
Research Council (MRC) is gratefully acknowledged.
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