Download Population genetics models of common diseases

Population genetics models of common diseases
Anna Di Rienzo
The number and frequency of susceptibility alleles for common
diseases are important factors to consider in the efficient
design of disease association studies. These quantities are the
results of the joint effects of mutation, genetic drift and
selection. Hence, population genetics models, informed by
empirical knowledge about patterns of disease variation, can
be used to make predictions about the allelic architecture of
common disease susceptibility and to gain an overall
understanding about the evolutionary origins of such diseases.
Equilibrium models and empirical studies suggest a role for
both rare and common variants. In addition, increasing
evidence points to changes in selective pressures on
susceptibility genes for common diseases; these findings are
likely to form the basis for further modeling studies.
Addresses
Department of Human Genetics, University of Chicago, Chicago,
IL 60637, USA
Corresponding author: Di Rienzo, Anna
([email protected])
Current Opinion in Genetics & Development 2006, 16:630–636
This review comes from a themed issue on
Genomes and evolution
Edited by Chris Tyler-Smith and Molly Przeworski
Available online 19th October 2006
0959-437X/$ – see front matter
# 2006 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.gde.2006.10.002
Introduction
Alleles that contribute to the genetic susceptibility to
human diseases are the focus of intense attention in human
genetics. They can be studied by a variety of approaches
(e.g. linkage analysis and case-control studies) that aim to
identify their role in the etiology of clinically defined
phenotypes. Disease alleles are also a specific subset of
all genetic variation present in human populations. As
such, their fate is influenced jointly by the history and
strength of the selective pressures acting in human populations as well as by demographic history, including population size changes and geographic structure. Therefore,
the properties of disease alleles can also be investigated by
population genetics modeling based on evolutionary principles. An important difference between disease mapping
and population genetics studies is that the latter focus on
the effects of susceptibility alleles on evolutionary fitness
rather than focusing on parameters of clinical severity.
Fitness is only loosely correlated with clinical severity.
Current Opinion in Genetics & Development 2006, 16:630–636
This is because the relevant fitness effects are those that
acted in ancestral human populations, whereas clinical
phenotypes are assessed in contemporary populations.
In addition, clinical severity does not necessarily imply
effects on reproduction, viability and survival, especially
for diseases with late age of onset.
Disease mapping strategies rely — implicitly or explicitly
— on population genetics quantities, such as allelic
heterogeneity, allele frequency spectrum and inter-ethnic differentiation; for example, standard association
methods are most powerful for detecting common susceptibility variants with limited heterogeneity. Likewise,
the evidence for association is most likely to be replicated
in additional populations if the susceptibility variants are
the same and similarly common across populations.
Because these quantities are functions of the underlying
population genetics model, modeling studies might help
to optimize disease-mapping strategies to the outcomes of
specific evolutionary scenarios.
In general, modeling studies aim at analyzing a complex
biological process (e.g. the evolution of disease susceptibility) by reducing it to more basic components (e.g.
mutation rate or strength of selection etc). The ultimate
goal is to understand the properties of the process and to
make quantitative predictions about its outcomes. Even
though most models are too simple to be realistic, they
can still have practical utility. For example, they can
generate hypotheses that can be tested by empirical
studies of genetic variation or they can point to new
approaches to disease mapping. Conversely, as we learn
more about genetic susceptibility variants, modeling studies might use the available empirical information to fill in
the gaps of knowledge regarding the genetic architecture
of common diseases and how it was shaped by natural
selection. Hence, population genetics modeling informed
by empirical studies of risk variation — and the reverse —
will be necessary if a comprehensive understanding of the
genetic bases of common diseases is to be obtained.
Here, I review the results of modeling studies performed
to date, in light of the available data on susceptibility
variants for common diseases. Several hypotheses have
also been formulated about how changes in selective
pressures acted on susceptibility genes for common diseases. These hypotheses are providing a framework for
further empirical and modeling studies.
Mutation–selection balance: models and data
Different types of population genetics models are
likely to apply to Mendelian versus common diseases.
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Population genetics models of common diseases Di Rienzo 631
Mendelian diseases, which are usually due to highly
penetrant and deleterious alleles that segregate in
families, probably fit relatively simple equilibrium models
of mutation–selection balance in which disease alleles are
removed by purifying selection and continually replenished through the mutation process. Mutation–selection
balance predicts high allelic heterogeneity and low disease prevalence, which are both common features of
Mendelian diseases. Models of positive selection also
apply to a minority of Mendelian disease alleles, most
notably sickle cell anemia, G6PD deficiency and the
thalassemias, which are caused by the pleiotropic effects
of disease alleles on resistance to malaria. For other
diseases (e.g. cystic fibrosis), a selective advantage has
been invoked, but generating definitive evidence has met
with considerable difficulties [1–4].
When it comes to common diseases with complex patterns of inheritance, however, the formulation of evolutionary models is more complicated. Owing to incomplete
penetrance, late age of onset, gene-by-environment interactions and polygenic inheritance, the connection
between common disease susceptibility and fitness is
not clear. Moreover, although most recessive Mendelian
diseases are due to loss-of-function alleles, this expectation might not apply to common diseases, thus adding to
the uncertainty regarding the rate at which susceptibility
variants are generated by the mutation process. Perhaps
more importantly, part of the selective pressures acting on
susceptibility alleles for common diseases might have
changed dramatically during human history, as a result
of major cultural and/or environmental transitions, implying that non-equilibrium models might be needed.
The first models of common disease susceptibility based
on population genetics principles were formulated within
the mutation–selection balance framework. Pritchard [1]
modeled the evolution of a disease susceptibility locus in
a randomly mating population of constant size, incorporating the effects of mutation, genetic drift and selection
[5]. An important aspect of this analysis is that the total
frequency of the susceptibility allele class was treated as
an outcome of the evolutionary process and was not fixed
to a specified value. Under neutrality and for a broad
range of mutation rates, he found that the susceptibility
class is unlikely to be polymorphic (i.e. have a frequency
between 1% and 99%), but rather it tends to be either rare
or near fixation. Accordingly, neutral susceptibility alleles
are expected to make only a minor contribution to the
genetic variance for the disease. However, if susceptibility alleles are deleterious (and if the mutation rate is
relatively high), the probability that the susceptibility
class is polymorphic is much higher than under neutrality;
hence, deleterious alleles are likely to explain a larger
fraction of the genetic variance. These findings suggest
that common disease susceptibility is unlikely to be due
to selectively neutral variation.
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In a related study, Reich and Lander [2] also modeled
susceptibility alleles as being (slightly) deleterious and
showed that, conditional on a specified total frequency of
the susceptibility allele class (20%) at a single locus and
for a ‘typical’ disease mutation rate, there is a single
predominant allele within the susceptibility class [6].
Importantly, this study focused on the role of population
growth on the dynamics of disease alleles. More specifically, the authors showed that, although the number of
disease alleles increases very quickly if the frequency of
the susceptibility class before the onset of growth is low,
the allelic heterogeneity for loci with intermediate frequencies also increases, but much more slowly. For
plausible times for the onset of population growth, the
increase in allelic heterogeneity for loci with intermediate
equilibrium frequency is expected to be negligible.
Hence, if indeed common diseases are due to susceptibility loci with intermediate equilibrium frequencies,
the allelic heterogeneity is predicted to be relatively low.
Owing to the different parameterization, the conclusions
of these two studies cannot be directly compared. Nevertheless, they highlight important issues in modeling the
evolution of common disease susceptibility, and they
indicate new directions for further investigation. One
important issue is the choice of parameter values. For
example, an obvious difference between the two studies
concerns the assumptions about the rate at which new
susceptibility alleles are created by the mutation process.
Pritchard [1] considered a broad range of mutation rates
(i.e. 2.5 10–6 to 1.25 10–4 per gene per generation),
whereas Reich and Lander [2] considered a single and low
mutation rate (i.e. 3.2 10–6). The rate at which susceptibility alleles are generated and the rate at which they are
repaired or compensated for depend on the functional
effects of mutations causing common diseases, which in
turn depend on a variety of factors, including the disease
pathophysiology and the biological function of the gene.
For example, the mutation rate for variants leading to loss
of function is likely to be higher than the rate for variants
resulting in gain of function. Moreover, partial or complete loss of gene function can have similar phenotypic
effects and selection coefficients depending on the
redundancy of the gene. More importantly, because
the current disease allele frequency and heterogeneity
are the result of a long-term history, the phenotypic and
fitness effects relevant to population genetics modeling
are those existing in ancestral human populations and
might not be easily extrapolated from the effects of the
same mutations in contemporary populations.
The comparison of model predictions under constant and
growing population size, as discussed by Reich and
Lander [2], is an important reminder that susceptibility
loci evolve in populations with complex histories and
that demographic histories may differ across major
ethnic groups. To date, population genetics models of
Current Opinion in Genetics & Development 2006, 16:630–636
632 Genomes and evolution
disease susceptibility have relied on a small set of highly
simplified demographic models. Thus, though much is
still to be learnt about plausible models for human history,
future modeling should include some of the complexities
emerging from recent inferences about human demography. Several lines of evidence point to a history of rapid
recent growth from an equilibrium population for subSaharan Africans [7–9], whereas genetic variation data
from non-African populations are compatible with bottleneck models [8,10] and, possibly, serial founder-events
[11,12]. In addition to population expansion, human
variation data are also consistent with significant geographic structure on a continental scale as well as on a
finer scale [13]. Future modeling studies incorporating
some of these demographic complexities might show
that the predicted allelic heterogeneity and frequency
spectrum vary to an appreciable extent across human
populations — even if selective pressures were the same.
Despite the uncertainties regarding plausible parameters
values, the results of the above modeling studies have
stimulated much empirical and analytical work. Systematic re-examination and meta-analysis of 25 reported
disease associations with common variants revealed a
significant excess of replications of the original reports,
suggesting that some of these associations are real and
that common variants with modest effects — odds ratios
between 1.07 and 2.28 — do exist [14]. Evidence supporting a role for heterogeneous and rare susceptibility variants is also rapidly growing as strategies for the detection
of these alleles are being developed. Testing for heterogeneous and rare susceptibility variants presents considerable analytical challenges, because established disease
association methods are tailored to common susceptibility
variants and are unlikely to be powerful for the case of
multiple rare variants. A strategy specifically developed to
meet this challenge relies on performing variation discovery either in affected and unaffected subjects or in the
extremes of a distribution (e.g. the top and bottom 5%) for
a disease-related phenotype, followed by testing for an
excess of a class of variants in one group compared with
the other. The class of variants to be tested is defined
a priori on the basis of criteria that are likely to identify
the subset of all variation with effects on gene function
(e.g. nonsynonymous variants). When applied to candidate genes for variation in plasma lipid levels [15]
and risk to colorectal cancer [16], this approach detected
an excess of nonsynonymous variants with frequency
lower than 0.5%. Reliable identification of the class
of variants with functional effects is a critical aspect
of this approach. For this reason, to date it has been
applied to coding regions and nonsynonymous variants,
for which information on structural and sequence
conservation can be used to calculate the probability of
an effect on function [17,18]. In principle, this approach
could be extended to non-coding sequences, but the
prediction of functional effects for variants affecting
Current Opinion in Genetics & Development 2006, 16:630–636
regulatory sequence elements is far less reliable than
for nonsynonymous variants.
Because standard approaches based on typing common
variants are thought not to be powerful for the identification of rare risk-alleles, the development of new statistical
methods is an area of active investigation. A promising
new method exploits the property that rare variants are on
average younger than common variants and, hence, are
associated with longer homogeneous haplotypes [19].
Because the method considers all haplotypes of all
lengths, it uses the information on the long-range haplotype structure typical of rare variants. Computer simulations showed that this method has reasonable power for
variants with frequency as low as 1%, as long as the size of
the phenotypic effect is at least moderate. Additional
work is necessary to investigate the power of this method
for less common variants and as a function of the relevant
evolutionary parameter values, such as selection coefficient, mutation rate and demographic parameters.
Advantageous disease variants and
changes in selective pressures
Several additional non-neutral frameworks have been
proposed to explain the evolution of common disease
susceptibility. Although these were not developed into
formal population genetics models of disease susceptibility, they provide a useful set of hypotheses for further
investigation. In one scenario, which can be thought of as a
case of antagonistic pleiotropy, alleles that increase risk to
common late-onset diseases are advantageous in early life
[20]. Because the strength of selection decreases with the
age of onset of the phenotype, the allele will be favored
and selection will maintain the optimum for younger ages.
This scenario, originally proposed to explain the evolution
of senescence, might also apply to some of the more
common late-onset diseases (e.g. type 2 diabetes and heart
disease). The finding that mutations affecting the insulin–
IGF (insulin-like growth factor) signaling pathway lead to
an increased lifespan in worms, flies and mice supports a
link between type 2 diabetes and ageing.
The evolutionary scenarios discussed so far envision
selection regimes in which selective pressures do not
change substantially over time; hence, they are essentially
equilibrium models. However, decades of anthropological research have established that the environment in
which we now live is radically different from the one that
ancestral human populations adapted to. A number of
transitions have resulted in shifts in the selective pressures acting on the biological processes that, in contemporary populations, underlie major classes of common
diseases. Crucial junctures include the dispersal of modern humans out of Africa, and the shift away from a
hunting and gathering life-style to a subsistence based
on agriculture and animal husbandry, in addition to the
more recent transitions associated with industrialization
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Population genetics models of common diseases Di Rienzo 633
and globalization. These transitions were associated with
major changes in environmental factors — including diet,
pathogen exposure, life style and social organization etc
— that are likely to have shaped the evolution of susceptibility genes for common diseases.
The impact of these selective shifts on susceptibility
genes for many common diseases has been formalized
in a series of hypotheses, which are based on epidemiological trends and the disease pathophysiology. Perhaps
the most popular is the ‘thrifty genotype’ hypothesis first
proposed in 1962 to explain the high prevalence of type 2
diabetes and obesity [21]. This hypothesis posits that
since ancestral hunter-gatherer populations underwent
seasonal cycles of feast and famine, they would have
benefited from being ‘thrifty’ (i.e. having extremely efficient fat and carbohydrate storage). With changes in
subsistence strategies, this ‘thriftiness’ became detrimental as food production, processing and storage resulted in
more reliable availability across seasons and changes in
dietary patterns. More recently, it was proposed that
inter-ethnic differences in the prevalence of type 2 diabetes and obesity, in particular the low prevalence in
Europeans, might be due to adaptations to cold climates,
whereby adaptive variation in a subset of susceptibility
genes results in increased thermogenesis and lower risk to
type 2 diabetes and obesity [22]. Consistent with the idea
that cold stress was an important selective force during
human dispersal, there is substantial evidence that body
mass index (BMI) and basal metabolic rate (BMR) vary
clinically around the globe [23,24]; high BMI and low
BMR are correlated with increased risk of type 2 diabetes
and obesity. A related scenario, referred to as the ‘sodium
retention’ hypothesis, was proposed to explain the interethnic differences in the prevalence of hypertension,
particularly the salt-sensitive type [25]. Under this scenario, ancestral populations living in equatorial Africa
adapted to hot and humid climates by increasing the rate
of sodium and water retention; as populations moved out
of Africa into cooler, drier environments, this high level of
sodium retention probably became deleterious. Energy
metabolism and sodium homeostasis are not the only
biological processes associated with currently common
diseases that have undergone major selective shifts. Parasite loads and pathogen exposure are likely to have
increased dramatically with the switch to the sedentary
life-style, higher population densities and greater proximity with domesticated animals resulting from the spread
of agriculture and animal husbandry [26]. The reduced
exposure to childhood infections, and the overall higher
sanitary conditions typical of industrial societies are
thought to be responsible for the rapid increase in inflammatory and allergic diseases, such as asthma and inflammatory bowel disease [27].
What is common to the above scenarios is the notion that
the observed increase in disease prevalence associated
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with the adoption of a Western life-style and diet is too
rapid to be due to a change in the gene pool. Rather, it is
likely that the environmental change simply uncovers the
latent genetic susceptibility of human populations to
diseases that today account for a disproportionate fraction
of the public health burden. Consistent with the idea that
common disease susceptibility might be the result of
ancient human adaptations to a long-term steady environment, a number of alleles that increase risk to common
diseases are ancestral (i.e. they are identical to the allele
found at the orthologous position in chimpanzee [28,29]).
The new (derived) allele at these variants confers protection against the disease. A possible population genetics
model for this class of variants is that the ancestral alleles
were maintained by purifying selection in the ancestral
environment, whereas the derived alleles were slightly
deleterious (see Figure 1). Upon the change in selective
pressures, the ancestral alleles increase risk to disease
whereas the derived alleles become either neutral or
advantageous.
A signature of recent positive selection has been reported
for several derived alleles that have protective effects
against heart disease and diabetes [30,31,32,33]; this
observation is somewhat surprising because, given the
late age of onset of these diseases, protective alleles are
expected to have little or no fitness consequences. One
possible explanation is that alleles that protect against the
primary disease phenotypes also protect against phenotypes with stronger fitness effects, such as those that
affect reproductive function more directly. For example,
the ancestral Thr235 allele in the Angiotensinogen (AGT)
gene was reported to increase risk to both hypertension
[34,35] and pre-eclampsia [36]; accordingly, the haplotype carrying the derived (protective) allele exhibits a
haplotype structure consistent with the action of recent
positive selection [31]. Moreover, as predicted by the
sodium retention hypothesis, the worldwide distribution
of allele frequency at the AGT Thr235Met variant, as well
as other variants influencing risk to hypertension, is
consistent with the action of spatially varying selection:
the protective alleles decrease gradually in frequency
with increasing distance from the equator [32,37]. A
selective advantage for protective alleles through direct
effects on reproduction might also be at work in type 2
diabetes. Over the past 25 years, a decline in diabetes
prevalence has been reported in the Pacific island of
Nauru, which experienced a dramatic epidemic starting
in the 1950s. The lower number of live births observed in
Nauruan women with diabetes or impaired glucose tolerance suggests a link between diabetes risk and reduced
fertility that is consistent with the observed rapid decline
in diabetes prevalence [38].
The above frameworks open the appealing prospect that
approaches based on detecting the signature of selection
might be added to the arsenal of disease mapping
Current Opinion in Genetics & Development 2006, 16:630–636
634 Genomes and evolution
Figure 1
The ancestral susceptibility model as it might apply to the ‘thrifty’ genotype and the sodium-retention hypotheses. In ancestral human
populations, those alleles that today increase risk to type 2 diabetes or hypertension were maintained by purifying selection. As human
populations transitioned to a Western life-style and diet or as they moved out of Africa to colder climates, the ancestral alleles were no longer
advantageous and increased disease susceptibility. The derived alleles at those sites were slightly deleterious in ancestral populations. Since the
environmental change, they have conferred protection against the disease and have been either neutral or advantageous. Neutral variation also
segregates throughout the history of human populations.
strategies. Because population genetics studies and
disease association studies are based on independent
theoretical frameworks, evidence converging on an overlapping set of candidate susceptibility variants will
increase the confidence that the true causative variation
has been identified. Consistent with this proposal, a scan
for selection signals in 132 genes involved in inflammation, blood clotting, and blood pressure regulation found
signatures of selection in several genes previously implicated in common disease risk [39]. A practical advantage
of population genetics studies over disease mapping
studies is that they can be performed on random samples
of unrelated individuals and do not require the costly and
time-consuming collection of extensive phenotypic data.
For example, to the extent that susceptibility to common
diseases might be related to phenotypes for which selection varies according to measurable aspects of geography
(e.g. phenotypes related to sodium homeostasis or energy
metabolism), mapping susceptibility alleles could be
conducted by genotyping individuals from geographically
diverse populations and testing for correlations between
population allele frequencies and environmental variables. Along the same lines, a recent genome-wide scan
for signals of recent positive selection developed a set of
SNPs that tag the haplotypes carrying the strongest
signatures of selection; it was proposed that this set of
tag SNPs be included in whole-genome scans for complex
traits [40]. There is, however, uncertainty regarding
Current Opinion in Genetics & Development 2006, 16:630–636
the assessment of statistical significance for signals of
selection [41].
Conclusions
Although the development of formal population genetics
models for common diseases is still in its infancy, it is
already clear that no single model can explain the evolution of susceptibility alleles even for the same disease or
the same susceptibility gene. For example, analyses
aimed at detecting rare susceptibility variants for a given
phenotype showed that there is strong evidence for such
variants in some candidate genes, but not in others
[15,16]. Moreover, both common and rare variants
have been shown to contribute to the susceptibility to
the same common disease. This might even apply to
variants within the same gene; for example, the ABCA1
(ATP-binding cassette A1) gene, which codes for a cholesterol efflux pump, was shown to harbor multiple rare
variants as well as a common variant (Ile883Met) that
influence plasma HDL cholesterol levels [15]. Given
the complex history of selective pressures acting on
humans, and the composite pathways underlying the
pathophysiology of common diseases, it is not surprising
that a diverse spectrum of plausible evolutionary models
is emerging. Further modeling studies rooted in population genetics theory and data are necessary to achieve a
more complete understanding of the evolutionary causes
of common diseases. Moreover, by broadening the range
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Population genetics models of common diseases Di Rienzo 635
of plausible models, they might lead to mapping
approaches that are powerful for the identification of
the full gamut of susceptibility variants.
One of two studies that abandoned the standard linkage disequilibriumbased approach to disease mapping and used instead a study design
that enables the identification of heterogeneous and rare susceptibility
variants. The first of several studies showing a role for rare variants in the
susceptibility to common diseases.
Acknowledgements
16. Fearnhead NS, Wilding JL, Winney B, Tonks S, Bartlett S,
Bicknell DC, Tomlinson IP, Mortensen NJ, Bodmer WF: Multiple
rare variants in different genes account for multifactorial
inherited susceptibility to colorectal adenomas. Proc Natl Acad
Sci USA 2004, 101:15992-15997.
Together with the study by Cohen et al. [15], this is an important study
based on resequencing of cases and controls to identify an excess of rare
variants with functional effects in affected individuals.
I would like to thank M Przeworski, J Cohen, N Freimer, H Hobbs,
D Nicolae and J Pritchard for comments on the manuscript. I was
supported in part by National Institutes of Health grant DK56670.
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