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Evolution by Phenotype
a biomedical perspective
Kenneth M. Weiss *† and Anne V. Buchanan *
ABSTRACT Genes are widely assumed to play a major role in the epidemiology
of complex chronic diseases, yet attempts to characterize the genetic architecture of
such traits have been frustrating. Understanding that evolution works by screening phenotypes rather than genotypes can help explain the source of this frustration. Complex
traits are usually the result of long-term, often subtle, gene-environment interactions,
such that individual life histories may be as important as population histories in predicting and explaining these traits. Recognizing that the problem is not due to technological limitations can help temper expectations and guide the design of future work
in biomedical genetics, by allowing us to focus on better approaches where they exist
and on those problems most likely to yield a genetic solution.We may even be forced
to re-conceive complex biological causation.
“How extremely stupid not to have thought
of that!”
Thus is Thomas Huxley famously said to have bemoaned his failure to see something so obvious as evolution of species by descent from a common ancestor. It
is easy to be so programmed to think in a particular way that, like Huxley, we
miss things that, once pointed out, are obvious.
Departments of Anthropology* and Biology,† Penn State University, University Park, PA 16802.
Email: [email protected].
The authors wish to thank Joe Terwilliger, Charles Sing, and three reviewers for helpful suggestions,
whether or not they agree with the authors’ views. Financial support from NIH/NHLBI award HL
58239 is gratefully acknowledged.
Perspectives in Biology and Medicine, volume 46, number 2 (spring 2003):159–82
© 2003 by The Johns Hopkins University Press
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The assumption of the primacy of genes has dominated biology for 75 years.
Huge investment is being made in working out the genetic basis of common,
complex chronic diseases. To date, this attempt has been frustrated, due in large
part to excessive genetic determinism resulting from an insufficient appreciation
of fundamental aspects of evolutionary biology.The ideas are simple and in principle well known, but they are not yet well integrated into daily practice.
Evolution by Phenotype
Darwin and Wallace provided a plausible mechanism by which the external environment could generate the dynamic history and pattern of divergence among
organisms.This was a theory of evolution by phenotype, the notion that organisms
evolved by having spontaneously generated variation screened by natural selection.The theory itself left open the question of what exactly is inherited, and it
remained for Mendel to provide leads to the answer. Like Darwin, Mendel studied traits, not genes, but he carefully chose qualitatively varying traits that (we
now know) were closely tied to genes. By the early 20th century, it was shown
that the contributions of many genes with individually small effect could produce complex or quantitatively distributed phenotypes (like stature or weight),
and the genetic theory of evolution, known as the Modern Synthesis, was formulated (e.g., Mayr 1982, 1991). The subsequent discovery of DNA as a ubiquitous protein coding system, expressed in the Central Dogma of the one-way
transfer of coding information from nucleic acid to protein, reinforced this view
and provided an understanding of the nature of the informational molecule that
was transmitted across generations.These advances provided the basis for a powerful systematic genetic research program.
Biology today is thoroughly rooted in the Modern Synthesis, a theory in
which causation is regarded as ultimately gene-based. The implications of this
tenet have been considered in detail over the past century and more, in regard
to the varying units of selection, including organisms, populations, and species
(e.g., Gould and Lloyd 1999; Lewontin 2000; Mayr 1982, 1997). But while genes
are quasi-permanent units of biological information storage, the screening of
phenotypes by natural selection is only indirectly reflected in genotypes.
Important aspects of evolution by phenotype can be illustrated schematically
in Figure 1. Time moves downward from a common ancestor at the top. Each
point represents a position occupied by a single individual in phenotype “spacetime.”We won’t enter the endless debate about what appropriately constitutes the
“phenotype”: ultimately it is the reproducing organism, but in some cases it may
for all practical purposes be a particular trait, or even a community of organisms.
An individual phenotype is the result of the interagency of all the internal and
external factors encountered by the inherited genotype.The scatter at any given
time represents the existing range of diversity, whose viable limits are indicated by
ellipses.The bottom scatters show the range of variation in three modern species.
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Figure 1
Evolution in time and phenotype space. Points are individuals in X-Y phenotype space,
as this changes over time (vertical axis); ellipses represent acceptable phenotype space
for a species at any given time. A, B, C are present-day species. Highly schematic.
The denser scatter near the centroids at any time represents the usual pattern, in
which at most a few similar states are quite common, and there are numerous
variant states (the farther from the centroid, the rarer). Frequency and effect size
measured relative to the centroid are frequently confounded, an important point
in human genetics, where disease states are often characterized relative to a mean
“normal” state, archaically still typically referred to as “the wild type.”
The centroids change over time due to the various evolutionary factors at
work.The lines suggest the way we use the centroids as rather platonic abstractions in drawing phylogenies, but all that really exist are the more Aristotelian
sets of individuals (dots). Perhaps because of this Platonism, related metaphors
like “survival of the fittest” and our gene-centered theory lead to a widespread
tendency to view natural selection as prescriptive at the gene level. But there are
a number of reasons why we should consider selection instead to be more tolerant of variation.
The Lamarckian Temptation
The fit of organisms to their way of life can make it appear as if they have
been heading consistently or even purposefully towards their present state
through evolutionary time. This was Lamarck’s famous notion—the impression
that there has been a steady history of highly prescriptive genotypic specification. But this can be misleading. The most important reason is the “anthropic”
illusion: whatever exists today is the product of a 3.5-billion-year unbroken lineage of ancestors, each of which, by virtue of having reproduced successfully, was
“adapted.” As has been noted countless times, were we to look forward to some
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time millions of years from now, any particular future biological outcome would
be laughably improbable. But we must remember that of all the essentially
unlimited things that might have evolved, something had to do so in order to be
here.Yet a strongly adaptationist viewpoint leads to the assumption that essentially every trait must be the product of adaptive natural selection, with a specific
genetic explanation. The assumption that everything is being selected all the
time at the gene level can lead us to seek functional constraints or to infer strong
genetic effects that may not exist.
Imperfectly Precise Biological Processes
An individual experiences a variety of factors during life that affect his or her
phenotype. Individuals with the same inherited genotype, such as identical twins
or inbred laboratory animals, are not identical, in part because they do not have
identical life histories. Some factors that generate phenotypic variation during
life are genetic, caused by mutations that do not get repaired: for example, DNA
and RNA polymerases mis-incorporate nucleotides to introduce point mutations; DNA is nicked by UV light and not repaired; and gene duplication or
deletion may cause a variety of dose-related stresses. We usually think about
genetic variation due to germline mutations, but somatic mutations also occur,
turning a multicellular organism into a genotypic mosaic. Some somatic mutations may lead to cancer, but others are not associated with disease.
Numerous non-genetic factors may also lead to phenotypic variation. A substantial fraction of newly synthesized polypeptides fold incompletely and thus
cannot generate functional protein; cell division can be anomalous; mitosis and
other stochastic factors can lead individual cells to have aberrant (low or high)
numbers of required structures, which can induce gene expression changes in
the cell and its lineal descendants in the organism; and so on. Liability for these
kinds of random events to happen may or may not be inherited, but even if there
is an inherited effect, these factors add a potentially substantial element of realized variation in organisms.
In all of this there is a stochastic element. Chance affects whether an anomalous molecule is detected or a gene turned on in a particular cell; what a dividing cell differentiates into; whether a fertilized crocodile egg develops into a
male or female (a process that is temperature-dependent); whether a given plant
reproduces (if it depends on successful fertilization by external factors like wind
or bees); or whether it germinates at all. Structures like sensory bristles on flies
develop when random developmental factors lead one of a number of equally
prepared cells to initiate a structure, and to suppress development in a surrounding inhibition zone of cells. This patterning depends on the relative concentrations of transcription factors, as well as of inhibitory gene products, parameters
that have stochastic attributes.The role of chance is seen in the variability among
identical twins or inbred laboratory animals in traits ranging from fingerprints to
disease history to lifespan (Finch and Kirkwood 2000).
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From zygote to death, organisms have evolved considerable plasticity to
respond to what both internal and environmental factors, including chance, may
bring. Among these capabilities are a host of repair mechanisms, many of which
are so primary that they were probably present in the early evolution of cells
(Yasui and McCready 1998). Perhaps 2 percent of a cell’s energy is used in error
control (Scriver 2002), and an unknown amount is involved in the production of
errors in the first place. Enzymes that repair replication errors, somatic mutations,
mitotic errors, or errors caused by environmental degradation (e.g., UV exposure) are ubiquitous in cells. Endonucleases, exonucleases, proteases, tumor suppressors, perhaps even the immune system itself (because its job is to recognize
and destroy “foreign” proteins), all keep the error-prone cellular functions in line.
Nonetheless, not all errors are prevented or repaired, and tolerance has its limits. Internally produced errors left unchecked can eventually exceed what we
would consider normal, or what corrective mechanisms can correct, and appear
in the form of disease. Cancers can arise when cell-cycle regulation experiences
unrepaired damage, and neurodegenerative diseases such as Alzheimer’s, Parkinson’s, Huntington’s, or Creutzfeld-Jakob, may develop when cells do not rid
themselves of accumulations of misfolded proteins (Wickner, Maurizi, and Gottesman 1999). Homeostatic mechanisms work in the short run; an organism has
no way to anticipate the eventual damage incurred by environmental exposures
that are within homeostatic tolerance at any given moment, but which, over
time, become a major risk factor for complex chronic disease.The ultimate risks
incurred by decades of over-eating are one such example.
Despite these many sources of variation, we are surrounded by organized and
recognizable order. Organisms are protected by a kind of “law of large numbers”
or “central limit” effect. Even with developmental and somatic mosaicism in cellular phenotypes and genotypes within an individual, most individuals end up
close to their population mean for most traits. Billions of years of evolution have
kept the underlying genotypic variation, error-protections, and the like within
acceptable range (and many of those who fall out of that range never are born
to be observed).
In this sense, we can say that organisms have considerable self-organizing
properties. The fact that phenotypes are often modally distributed, with most
organisms near the mode, justifies idealizations such as equating the most common or modal state with the “wild type,” relative to variants or mutants. But this
can be misleading, because for some purposes, like understanding variation
among individuals within a species (such as with respect to susceptibility to disease), the amount of relevant variability may be high.
We acknowledge that in the foregoing we have been quite platonic ourselves.
Concepts of error and chance are judged relative to an assumed truth, mode, or
known process.Whether anything is truly stochastic, and whether our assessment
of “error” is correct, are important questions. In the end, what counts in life is
not an “ideal” state but sufficiency. Deterministic processes can for all practical
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purposes appear “random” (Wolfram 2002), but variation of the sort discussed
here, whatever its actual cause, will not be genetic in the sense of inheritance.
Several Forms of Evolutionary Drift
Variation in DNA sequence that has no function, or whose functional effects
do not push its bearers out of the part of the phenotype space that is not directly
affected by natural selection, is known as selectively “neutral” variation.This variation changes over time by the random aspects of life and death that are known
generally as genetic drift. Biologists have become comfortable with the notion of
selective neutrality of DNA sequence variation, and even with the fact that chance
affects the frequencies of alleles under selection. But despite the fact that similarly
incidental variation can be seen in almost every biological trait almost every day,
we have been curiously resistant to applying a similar notion to phenotypes. Such
a possibility is fully compatible with formal evolutionary theory but is not prominent, and instead there is a widespread tendency to offer adaptive explanations for
almost anything. Darwin acknowledged that some phenotypic variants may be
selectively neutral, but clearly his main legacy was the notion that “each slight
variation, if useful, is preserved, by . . . Natural Selection” (Darwin 1859).
The usual logic of adaptive explanations is important to state: My Favorite
Trait evolved stepwise, from some rudimentary origin, by adaptive selection
favoring each step for some reason that may, but need not, have been related to
the present function we are trying to explain.Thus, for example, vision evolved
stepwise from simple cell-adhesion receptors, to photoreceptors, and ultimately
to complex eyes.
But let us just look around us. Most variation we see every day has little if
anything to do with Darwinian fitness. As with DNA sequences, phenotypic drift
can occur, as phenotypic variation changes by chance over generations. We can
modify the classical Darwinian argument with the most minor of twists, by
removing the adaptive selection from the scenario: My Favorite Trait evolved
stepwise, from some rudimentary origin, by drifting in a direction that at each
step may, but need not, be related to the present function we are trying to
explain. Each step need merely have had what we might call “random suitability” relative to its circumstances. Step-by-step, facultative use of those stages can
become behavioral habit—but need not have involved Darwinian turmoil.
Again, it is by looking backward over eons of time, and implicitly equating
function with origin and assuming it arose via selection, that we experience the
Lamarckian temptation, assuming (in ironic agreement with religious fundamentalists) that complexity is simply too improbable to have arisen by chance
and must have been molded teleonomically (“for” some function; e.g., Reeve
and Sherman 1993; Thornhill 1990). Nothing in the theory of evolutionary
biology—including its focus on genes—suggests that phenotypic drift cannot, or
did not regularly, occur. Indeed, our hallowed principle of parsimony should
restrain us from invoking causal explanations when none are needed.
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Evolution by Phenotype
Phenogenetic relationships—that is, between genotype and phenotype—are
often many-to-many relationships. A genotype can be associated with a range of
phenotypes (e.g., quantitative measures such as blood pressure, or qualitative ones
like affected/unaffected); this is due to the imprecision of biological processes
discussed above, not to mention environmental effects (Schlichting and Pigliucci
1998). For similar reasons, the same phenotype can be found in individuals with
many different genotypes, a phenomenon we can refer to as “phenogenetic
equivalence.” When there is phenogenetic equivalence, genetic variation associated with the same phenotype will change over time by drift, even if the phenotype is being maintained by strong natural selection, because genotypes with
equivalent fitness are neutral relative to each other (Clark 1998; Hartl and
Campbell 1982). This dynamic heterogeneity can be referred to as “phenogenetic drift” (Weiss and Fullerton 2000).
Phenogenetic drift refers to the uncoupling of specific relationships between
functional genotypes and the same phenotype. Epistasis (gene-gene interaction)
and pleiotropy (multiple functions of a gene) may affect the pattern, rate, or
nature of phenotypic drift. For example, selection related to one function of a
pleiotropic gene can constrain traits associated with its other functions or lead to
a balance between the different selective effects. A biomedically important result
of phenogenetic equivalence or drift is that it may not be possible to infer the
genotype underlying an observed phenotype.
Here as before, it is difficult to resist the temptation to think platonically. Like
notions of error and chance, ideas of selective neutrality or equivalence may
themselves be ephemeral and context-specific.
The Amount and Pattern of Variation
Most new mutations are unique at the DNA sequence level, either themselves, or because of their haplotype background (the specific set of sequence
variants around them on the chromosome on which they arose). A newly arising variant will perforce initially be geographically localized; it may increase in
frequency, by chance or by selection, and may diffuse from its native region over
subsequent generations. Rare variants thus tend to be geographically localized,
while common variants tend to be older. Because humans reproduce slowly, this
applies in particular to globally distributed variants, which as a rule were present
in our common ancestors in Africa. Natural selection works on phenotypes but
can only affect the frequency of local genetic variation associated with those
phenotypes. This means that even the same selective force is likely to favor different and/or multiple genetic variants in different geographic regions, or within
the same region. As a result, phenotypic convergence (or conservation) is often associated with genotypic divergence.
The amount of variation in a given gene depends on demographic history:
population size, migration patterns, selective effects, birth and death of individuals, and mutation rates (e.g., Harpending et al. 1998; Hartl and Clark 1997;
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Jorde, Bamshad, and Rogers 1998; Jorde,Watkins, and Bamshad 2001; Jorde et al.
2000; von Haeseler, Sajantila, and Paabo 1996). Each of these has a stochastic element. Population bottlenecks and selection reduce variation and simplify the
sequence relationships among haplotypes. For single nucleotide positions as well
as haplotypes, the frequency distribution in a given population is usually rather
“modal,” comprising a small number of relatively common variants and a long
tail of rarer ones.These are the general properties expected of variation in selectively neutral DNA sequence, and because of phenogenetic drift similar characteristics generally pertain to sets of alleles that are associated with specific phenotypes (like disease), even if the latter are affected by selection.
It is often said that humans are not as variable as other globally distributed
species.This appears to be due to our history as a species recently formed from a
small initial population, rather than to a history of strong natural selection. On
average, if the sequences of two copies of a human chromosome region are compared, they will differ about once every 1,300 nucleotides.This may seem small,
but in a genome of 3.3 billion nucleotides, it comprises roughly 2 to 3 million
differences. If even a modest number of copies of the region are compared, rather
than just two, one may see a variant every hundred positions; most sites may show
variation in some human somewhere.The lack of variation in the human genome
relative to that in other species does not imply that we have insufficient variation
for there to be complex heterogeneity in genetic effects related to disease.
Biomedical Implications
We have considered various aspects of biological variation from the general perspective that evolution screens organisms by phenotype rather than by genotype.
Nothing in evolutionary theory guarantees extensive variation (except for special cases such as immune diversity), but mutational pressure and the tolerant
nature of selection suggest that variation can be expected, and that is consistent
with experience. Phenogenetic relationships are consequently likely to be less
precise and deterministic than seems generally appreciated.These simple principles are not new but are often overlooked, and they can help explain many of
the complexities and frustrations encountered by efforts to map and understand
genes associated with disease.
Biomedical ascertainment screens populations in interesting ways that resemble natural selection itself. Like natural selection, we screen our population by
phenotypes, via their appearance in clinics and registries. This effectively identifies the tail of the phenotype distribution (abnormal, early onset, hyper- or hypometabolic values or risk), and it is worth reflecting on just how distorted a view
from the tail of life can be.The biomedical ascertainment systems of the wealthiest countries of North America, Europe, and Asia screen at least a half-billion
people in whom a serious disease might be detected. When N = 500,000,000,
alleles that are very rare indeed get noticed.
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table 1
N UMBERS
OF
A LLELES
AT
S ELECTED D ISEASE -R ELATED G ENES
Gene (Trait)
TP53 (colon, other cancers)
CFTR (cystic fibrosis)
LDL receptor (heart disease)
PAH (PKU = phenylketonuria)
BRCA1 (br, ovarian cancer)
DMD (muscular dystrophy)
GBA (Gaucher’s disease)
BRCA2 (br, ovarian cancer)
Pax6 (eye problems)
TSD (Tay-Sachs disease)
Pax3 (hearing, pigmentation)
Mutations
~1,300
1,001
771
336
319
186
157
154
115
91
44
SOURCE: HGMD (2002).
Genetic effects in such individuals may not have the same properties as those
found in the bulk of the population.We can see the difference by comparing the
genetics of relatively “simple” traits, which tend to be rare, with that of “complex” traits, which may be common and where the ascertainment system is
somewhat different.
The Genetic Basis of Highly Deleterious Pediatric Diseases
An allele whose effects are strong and hence rare will generally be the only
such allele segregating in a given family.“Strong effect” is another way of saying
there is a high probability of an allelle’s being detected if present, or a large effect
on the phenotype relative to the mean, so that the trait will appear to be “Mendelian” (closely tied to the segregation of the underlying allele in families), and
causation will appear relatively straightforward. Such traits are easy to map genetically, and genes responsible for hundreds of them have been mapped (OMIM
2002).Yet even in these instances there are complex phenogenetic relationships
and considerable genetic heterogeneity (HGMD 2002).
Phenylketonuria (PKU) is a familiar and well-studied example. PKU is a disease of amino acid metabolism.That PKU is genetic in nature has been known
for decades, based on its classically Mendelian appearance in families. Most cases
result from reduced activity of a single gene product, the enzyme phenylalanine
hydroxylase (PAH).This leads to levels of phenylalanine that can be toxic, especially to the brain, and can result in severe mental retardation.
The mutational spectrum and phenogenetic relationships of PAH are typical
of most simple Mendelian diseases (Table 1; see OMIM 2002). Hundreds of alspring 2003 • volume 46, number 2
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Figure 2
Relative frequency of disease-associated PAH alleles. Distribution of 444 different observed
alleles.
S OURCE : PAH DB (2002).
leles at the PAH locus have been seen in patients, including a small number of
relatively common alleles and many rare ones (Figure 2). The presence and frequency of alleles vary among populations, with many alleles being specific to a
given regional origin (e.g., part of Europe).These characteristics reflect the geographically localized nature of most new mutations, as well as population history
and phenogenetic drift among alleles associated with similar phenotypes that are
roughly equivalent in the face of selection.
PKU is generally considered to be a “recessive” disease, but this is a legacy
term from Mendel’s two-state world that can have misleading connotations.
Classic PKU segregates in Mendelian ways in families, but we now know that at
the genetic level most persons affected with PKU are not homozygotes, but heterozygotes with two different alleles whose combination leads to clinically elevated phenylalanine levels. Now that we can go beyond a classical two-allele
(abnormal, wild-type) classification, we have discovered a quasi-continuous
genotype-phenotype distribution (Table 2), with considerable phenogenetic
equivalence, among the huge number of diploid genotypes that can result from
hundreds of alleles (Guldberg et al. 1996; Kayaalp et al. 1997; Scriver and Waters
1999). Recognition of variable severity has led to the specification of a second
clinical category, non-PKU hyperphenylalananemia (HPA), which arises in
genotypes whose alleles have less severe average effects. However, even the
revised clinical categories represent rather arbitrary subdivisions of a more continuous phenotype distribution, and are far from perfectly predictable, as the
table shows. Indeed, most effects are probably difficult even to estimate with replicable accuracy.
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table 2
PAH G ENOTYPE -P HENOTYPE R ELATIONSHIPS : O BSERVED
E XPECTED M ETABOLIC P HENOTYPES IN 184 I NDIVIDUALS
PAH D EFICIENCY
VERSUS
WITH
OBSERVED PHENOTYPE
PKU
Genotype
Score 1
Expected Phenotype 2
2
Classic PKU
42*
11
3
Moderate PKU
10
13*
4
Moderate/mild PKU
1
5 and 6
Mild PKU
1
Classic
Moderate
Mild
Total
4
—
57
73.7
6
—
29
44.8
6*
6*
—
13
92.3
3
23*
2
29
79.3
10*
20
100.0
35*
36
97.2
47
184
8
Mild PKU/MHP
—
—
10*
9–16
MHP
—
—
1
54
33
50
TOTAL
Obs=Exp
(%)
MHP
1 Sum of estimated effects of individual alleles, where 1 = classic PKU, 2 = moderate PKU, 4 = mild PKU,
8 = mild HPA (MHP) symptoms.
2 Determined, for each patient, on the basis of the sum of the observed alleles of the two PAH mutations.
*Groups in which the observed matched the expected phenotype.
SOURCE: GULDBERG,
ET AL.
(1996).
Because clinics (like natural selection) concentrate on persons with health
problems, the population distribution of severity is not well-known, but Figure
3 shows in schematic terms what such distributions may look like. Some genotypes have strong (PKU) and others, milder (HPA) effects. Genotypes with even
lesser and probably less predictive effects must exist in the unaffected population,
but we have no way to know a priori how frequent and complex these might
be, nor how many additional alleles or other risk factors may exist. Clinical samples aggregate all the sources of risk present in the population and hence may
make poor material from which to estimate these things.
Different sets of PKU-related alleles have arisen in Europe and Asia (Scriver
et al. 1996). PKU is very rare in sub-Saharan Africans (and their descendant populations, such as African-Americans).That there are hundreds of such mutations
in Eurasians suggests that there may have been some selection on phenotypes
related to PAH that elevated the frequency of the sets of relevant mutations occurring independently in the history of Europe and Asia, leading to a kind of
phenotypic convergence with genotypic divergence.What the selective factor(s)
may have been is unknown.
Strong Selection over Time: The Hemoglobinopathies
Under some circumstances strong natural selection can maintain mutations
with very harmful effect at high frequency in a population. The classic case is
genetic variation related to malaria. Mutations in the alpha- and beta-globin gene
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Figure 3
Frequency and effect size for PAH. Schematic of the distribution of the frequency and severity of
phenotypes associated with blood levels of phenylalanine. Most cases of true PKU are in the tail of the
allelic effects distribution. Many individuals have modestly elevated phenylalanine levels. Many other
individuals in the population have genotypes that may have small but subclinical and hence rarely
seen effects.
clusters can cause anemias of various types by interfering with oxygen—carbon
dioxide transport in red blood cells (Scriver et al. 2001). In the canonical instance
of balancing selection, homozygosity for the sickle-cell mutation in the beta-globin gene leads to severe anemia, which is strongly selected against, but homozygotes for the “normal” allele are severely affected by malaria which is also
strongly selected against; however, heterozygotes are relatively resistant to both
malaria and anemia.The result is an intermediate frequency of the two alleles.
This classical view suggests that selection works directly via genotype, but the
truth is more complex in view of evolution by phenotype. There is extensive
phenogenetic equivalence among individuals with the phenotype “malaria
resistance,” involving coding as well as regulatory variation in many different
genes, including glucose 6-phosphate dehydrogenase (G6PD) and the Duffy
blood type gene, as well as the genes in the alpha- and beta-globin clusters (Scriver et al. 2001;Vogel and Motulsky 1997; Weatherall 2001; Weiss 1999). As expected, this observed variation is geographically coherent, reflecting the local
histories of each mutation.
There are many mutations in the beta-globin genes. Sickle-cell hemoglobin
(HbS) is found from Africa across to western India, but Africa is also home to
HbD and HbC, and a variety of mutations and deletions in the other genes in
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the cluster.The predominant mutations in Asian populations—HbE in the betaglobin gene and a variety of mutations in the alpha-globin cluster—are essentially absent in Africa. At other loci, the relevant Duffy allele has reached very
high frequency in Africa, as G6PD mutations have in the Mediterranean. Thus,
even strong selection favoring a specific phenotype closely tied to specific genes
does not usually purify allelic variation, even within a specific population.
It might be objected that we are here lumping together different forms of
malaria to which there are different adaptations.That is so—and it is well known
that, in general, the more precisely or narrowly defined a phenotype is, the closer
it is to specific physiological pathways more likely to involve a limited number
of genes. That was Mendel’s trick, too. This knowledge is often honored in the
breach, in regard to the search for genes related to vaguely defined traits like diabetes, obesity, epilepsy, schizophrenia, or heart disease. Still, even for a given form
of malaria, it is common to find multiple genetic responses. And genetic resistance to malaria is at least as complex in mice (Fortin, Stevenson, and Gros 2002).
The Genetic Basis of “Complex” Diseases
We should always view with caution reasoning about a phenomenon that is
based on its extreme. But success in mapping rare pediatric diseases has given
human genetics hope that the same methods will work for common complex
chronic diseases like cancers, hypertension, and heart disease.
From a genetic point of view, complex traits involve many loci. In the simplest model for this, innumerable loci termed polygenes contribute in a dose-like
way to underlying risk of qualitative outcomes (heart attack) or quantitative phenotypes (blood pressure) (Hartl and Clark 1997). In this model, many genotypes
will be associated with similar phenotypes; adding environmental effects completes the many-to-many picture of phenotypic equivalence noted earlier. Observation and experiment have long supported this general model (e.g., Wright
1968–1978). However, alleles at some of the loci may have relatively stronger
individual effect (i.e., high penetrance) (Mackay 2001a, 2001b).These genes have
become known as quantitative trait loci (QTL), and in the disease context may
be associated with early onset, high severity, or idiosyncratic pathology.
Because of the confounding of frequency and effect size, such alleles are typically rare, act in families as “Mendelian” alleles, and account for successful applications of gene-mapping approaches to complex diseases.The alleles also tend to
be geographically localized; when the loci are examined in detail, a large number of additional alleles are usually found, with effects typically smaller and often
difficult to estimate precisely. Examples are the BRCA genes associated with
breast cancer, and TP53, which is associated with various cancers.
Despite a number of successes of this sort, many if not most complex diseases
have not been cracked even in this limited way. A common experience is that
different mapping studies identify a number of apparently relevant loci, but the
findings are difficult to replicate and the initial effect estimates are biased upspring 2003 • volume 46, number 2
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Figure 4
Many layers between genotype and phenotype. Illustrates with a chromosomal cluster of lipidrelated apolipoprotein genes (AI, CIII, AIV) how the effect of the inherited genotype on risk of coronary artery disease (CAD), or heart attack, a stochastic late-onset final event, is filtered through various levels, from primary gene-product level, risk variable (e.g., triglycerides, or HDL cholesterol levels),
to risk (top panel, risk inversely related to HDL cholesterol and directly to high triglyceride levels),
to outcome.
S OURCE : M ODIFIED
FROM A FIGURE PROVIDED COURTESY OF
C. F. S ING .
wards (Goring,Terwilliger, and Blangero 2001; Ioannidis, Schmid, and Lau 2000;
Ioannidis et al. 2001). In the end, loci with alleles of replicably strong effect usually account for only a small fraction of cases, leaving a larger residuum of unexplained familial aggregation.Tens of genes have been associated in this way with
diabetes, cardiovascular disease, cancer, retinitis pigmentosa, deafness, and many
other diseases. Recent tabulations report about 150 genes associated with obesity in humans and in laboratory animals (Brockmann and Bevova 2002; Perusse
et al. 2001; Rankinen et al. 2002).Genetic variation associated with disease compatible with reproduction (e.g., because of late onset), and hence not severely
constrained by natural selection, can be generally expected to be comparable to
variation in neutrally evolving regions or in genes associated with serious pediatric disease that are under strong natural selection.
A major objective of complex disease genetics is to use genotypes to predict
risk so that targeted preventive measures can be taken. But many factors intervene between an inherited DNA sequence and the final stochastic event of a
late-onset disease. These are shown schematically in Figure 4, relative to a cluster of apolipoprotein genes and heart attacks.The genes must be transcribed and
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translated, and because of the imprecision of biological processes described earlier, there will be variation in the cellular concentration of their protein products. These gene products are related to an intermediate phenotype, the levels of
lipoproteins that package cholesterol, triglycerides, and apolipoproteins for circulation in the blood. Lipoprotein levels constitute a more proximate concentration-related risk factor for cardiovascular disease (inverse with HDL cholesterol, direct with triglycerides), and heart attacks themselves have an additional
stochastic component. The levels of these risk factors are affected by external
environments and many other genes and internal feedback loops (not shown,
and usually unknown). Other biological risk factors, such as endothelial calcification may also be at work. The subtle complexities in such systems have been
analyzed extensively by Sing and colleagues (e.g., Perusse et al. 2001; Sing, Haviland, and Reilly 1996). It is no surprise that late-onset effects are difficult to
identify or estimate from inherited genotypes.
Despite disavowals, tacit genetic determinism is reflected in the naming of
genes for diseases such as breast cancer (BRCA), diabetes (NIDDM), and Alzheimer’s disease (presenilin). Here we see the effects of the Lamarckian illusion:
we name the gene as if the “function” through which we discover it is its evolutionary reason for being. Usually, that is not even its main function—many
genes “for” a particular complex trait are actually ubiquitously expressed. We
rivet attention on small susceptibility differences, of little relevance to the evolution of the genes involved.We seem to have little appreciation for our unprecedented good fortune that these diseases occur in large part because we are so
healthy that we live far longer than did our evolutionary ancestors. Even the
bearers of high-risk alleles at these loci typically thrive healthfully for many
decades.Yet, despite pro forma caveats, we still speak of and daily seek out genes
“for” these diseases.
One view of things is that this theoretical talk about complexity is fine, but
that in reality not so horribly many genes are involved. For the sake of discussion, let us grant that the loci affecting risk are not too numerous; that these loci
are identified and the relevant variants genotyped; that genotypic risk estimates
are accurate; and that a high-risk subset of the population can be identified that
might constitute a substantial fraction of all cases (Horrobin 2001a; Pharoah et
al. 2002). Even then, because the different genotypes affect risk in different ways,
intervention would typically have to be generic rather than genetic (e.g., on
cholesterol or blood pressure levels or early detection, much as we do now, rather
than on individually specific genetic pathways). And this would have to be done
taking into account the effect of diverse environmental factors that temper or
exacerbate genetic risk. Preventive intervention in the form of a low-cholesterol
diet, for example, might lower some people’s cholesterol, while others’ levels
don’t budge.
Common diseases do somewhat conceptually change the nature of ascertainment, however, because we tend to confuse two meanings of the word genetics.
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We have been discussing the classic meaning—that is, genes as the units of inheritance, and thus the effect of genotypic variation on phenotypic variation. This
usually means we study the patterns of correlation of phenotypes and genotypes
among relatives. From this point of view, only those people who inherit specific
alleles are particularly vulnerable to a disorder.These alleles are studied by ascertainment on the tail of the genetic effects distribution.
The second sense of genetics refers to genes as units of function, that is, the
mechanistic role of genes in developmental and physiologic pathways. From this
point of view, everyone may be vulnerable to chronic disease, for example, by
exposure to levels of substrates for those pathways that overwhelm homeostatic
mechanisms that everyone shares. In this case, a knowledge of the pathway may
indicate points at which effective intervention may be relevant for the general
population and hence for public health. For such traits, ascertainment is uncorrelated with genotype.
We cannot tell a priori whether a trait is common for the inherited or functional genetic reason. A disease may be common in a population because some
environmental factor affects a particular genetic pathway that is shared by everyone. Many chronic diseases have risen greatly in prevalence because of relatively
recent exposures to aspects of modern life like sloth, dietary excess, and chemical carcinogens (e.g.,Trowell and Burkitt 1981). In such cases, essentially everyone is susceptible if exposed. Alternatively, a disease could be common because
an underlying genetic variant is, if not shared by everyone, at least common in
the human species. (Here, too, the disease could have risen in prevalence because
such variants interact with changed environments.) How often common variants
are responsible for common complex disease has been the subject of heated
debate.The evidence to date—and there is a lot of it—clearly suggests that common variants with major effects on risk are the exception, not the rule, but the
“common variant for common disease” notion has itself become common, and
major funding and policy decisions have been made in service to it. However,
the evidence shows that most often a disease is common because many different, individually rare genetic variants are involved.
Under “Normal” Conditions: Gene or Environment?
It is important to consider the role of environments in this context. In terms
of public health benefits, it seems hard to avoid the inference that while rare
allelic variants can overwhelm other factors, environmental factors play a
major—probably the major—role in risk for common diseases. A few examples
can make the point.
A lot is being said these days about genetic influences on Alzheimer’s disease.
But anti-inflammatory agents like ibuprofen and cholesterol-lowering agents
like statins have been reported to have huge effects on the risk of Alzheimer’s
disease—reducing risk by up to around 80 percent (in t’Veld et al. 2001; Jick et
al. 2000;Wolozin et al. 2000). Possible mechanisms have been suggested (Simons
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et al. 2001); this is tentative (e.g., Koudinov, Berezov, and Koudinova 2002), but
if such results hold up they dwarf the effects commonly attributed to apolipoprotein (APOE) genotypes.A few alleles in the BRCA1 and BRCA2 genes have
dramatic effects on breast cancer risk.These are about as determinative as genetic
risk for complex disease can get. However, recent work has shown that even for
these dramatic, clear-cut, and severe mutations, lifetime risk varies greatly,
depending on the decade when the person was born (Welsch and King 2001);
also, among women with the BRCA1 mutation, risk is higher in women who
were exposed to oral contraceptives for more than five years, or who used them
prior to 1975 or when under age 30 (Narod et al., 2002). Clearly, environmental factors have a significant effect on risk. Further, it has been suggested that the
method of case ascertainment itself in these studies has led to overestimates of
genetic risk, because ascertaining on multiply affected families will include all
the sources of increased risk, most of which are probably unknown or unsuspected.This again raises epistemological issues concerning our understanding of
the genetics of disease (Begg 2002).
A pandemic of adult (type 2) diabetes and related diseases is in progress in
Amerindians and populations with Amerindian admixture (Weiss 1999). These
conditions have a particular natural history in Amerindian-related populations
that suggests a common genetic background. Numerous gene-mapping studies
have identified genes that may contribute to this risk (e.g., Arya et al. 2002a,
2002b; Duggirala et al. 2000, 2001; Ehm et al. 2000; Hanis et al. 1996; Hanson
et al. 1995; Horikawa et al. 2000), and the evidence suggests that unidentified
genetic factors contribute even more.Yet, 60 years ago these problems were rare
in the same populations. Clearly, the most important cause is change in environment or lifestyle—probably involving diet or physical activity—interacting
with predisposing genotypes that may comprise an unusual instance of high-frequency genotypes conferring major susceptibility on complex disease.
These examples point to a substantial amount of non-genetic inheritance in
humans. Children of mothers who had gestational diabetes have a higher risk of
disease, at younger ages, than children whose mothers were disease-free during
pregnancy, which suggests that something in the uterine environment may affect
a person’s risk decades later. But dietary habits are also inherited, leading to secular trends in risk and decreasing age of onset.
Those who prefer a gene’s eye view of the world must consider the use of genetic information.The above examples show that phenogenetic relationships can
be sensitive to rapid environmental changes. Biotechnology must be able not just
to genotype individuals but to estimate future risk, so that interventions can be
designed.Yet, risk can only be estimated from past experience, while future environments are impossible to predict—except that they will change unpredictably.
Will they evoke susceptibility in genotypes that today appear to be safe? For
whom can we confidently recommend difficult preventive measures (like “prophylactic” bilateral mastectomy)?
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Conclusion
In the 150 years since Huxley bemoaned his failure to see the simplicity of evolutionary explanations, the twin discoveries of evolution and genetics have transformed the life sciences and our understanding of life itself. As so often happens
when a science is transformed by sweeping new ideas or technology, strong signals are rapidly identified and a picture falls into place. This describes our success with Mendelian traits—but that success has lured us to extrapolate the
behavior of strong effects to that of all effects, and another general experience in
science is that extrapolation does not work with equal efficacy across the entire
scale-space of relevant phenomena.
A number of authors have considered the problem of causal genetic inference
for complex biomedical traits (e. g., Beaudet 1999; Emahazion et al. 2001; Holtzman 2001; Holtzman and Marteau 2000; Horrobin 2001b; Millikan 2002;
Scriver 2002; Sing, Haviland, and Reilly 1996; Strohman 1997, 2000a, 2000b,
2002; Terwilliger and Goring 2000; Wolf 1995, 1997). Proponents of current
approaches sometimes offer measured views, but often only in passing, and overall, the momentum behind strongly Mendelian thinking is difficult to deny or to
overcome.
The Modern Synthesis was a theory that united to divide.A conceptual union
of all biology was made by defining causation as essentially genetic, with the
environment and phenotypes something temporary that genes had to slog
through to get to the next generation. This gave universal scope to molecular
genetic reductionism and provided a systematic research program that suited the
traditions of the physical sciences. But that in turn led the conceptual union to
be re-divided, all the way down to single nucleotides.That’s where we are now,
and how or when a renewed, refreshed, or more effective holism will build
organisms back up to a more perfect union is unclear (Laubichler 2000).The job
will require perceptive re-thinking.
In the meantime, we have misremembered the Central Dogma. It is about the
mechanistic relationship of genes to polypeptides, not phenotypes, and it works
best when the latter two are closely related and predominate over other aspects
of causation.We got here via Mendel, who deliberately chose well-behaved traits
to work with. He was clever enough to avoid complex traits—and even then he
was a bit lucky (Weiss 2002).
If phenotypes can’t always be accurately predicted from genotypes, the converse presents even greater challenges, because successful gene mapping requires
the ability to predict underlying genotype (G) from observed phenotype (P)
(Weiss and Terwilliger 2000).The causal arrow (G→P) does not imply the inferential one (G←P). Weak G←P relationships explain much of the problem
encountered in complex disease mapping.This is a biological fact, not a technological limitation. The same is true with regard to natural selection: if scientists
cannot screen effectively on genotypes via ascertainment of phenotypes, neither
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can selection.We can’t expect selection to have refined genetic causation for our
convenience. This may go against the faith, but it is at least worth taking seriously as we endlessly launch ever more costly attempts at the Sisyphean mountain of complexity.
One justification proffered for current approaches is we are merely attempting to assess the reaction norms of disease-related genes across the array of
lifestyle environments. But even this modest objective is somewhat illusory. In
essence we are only studying responses to the particular limited range of the last
few decades of exposures, to rapidly and unpredictably changing environmental
variants usually not identified or measured in genetic studies.We routinely treat
genotypes and environments as separate, replicable entities whose effects can be
regressed on each other (Marx 2002; Strohman 2002; Willett 2002). The true
relationships are probably more subtle and seamless—not least because we modify our environments and transmit them culturally, such as by jogging or eating
less fat, based on knowledge that we think will alter disease risk itself (e.g., by
exercise or diet; Lewontin 2000; Moore 2001; Oyama 2000).
The points we raise are not new, and a common reaction is that “everybody
knows” them. But if they are not integrated into practice—and policy—how
well does everybody really know what “everybody knows”? There will always be
seductive exceptions, but an evolutionary perspective helps us see why genetic
variation may not adequately explain the subtle variation in complex traits that
is the target of so much of our research. That this is not really understood (or
honestly presented) is shown by the widespread promises of genetically personalized medicine (your risk on a chip).
These considerations have societal implications. From a public health point of
view, genetic approaches to complex disease should be pursued where they are
most appropriate or could have the most impact on the population that pays for
them. The caveats we have raised do not promise solutions, but they do lead to
a few suggestions:
1. It is potentially easier and less costly to alter environment than genotype,
especially on a population scale, a point made more than once in these
pages (e.g.,Weiss and Schull 2002).The residuum of recalcitrant cases that
really are genetic in the usual Mendelian sense are appropriate targets for
gene-based research investment.
2. A common justification for “hypothesis free” genome scanning is to find
pathways involved in complex traits, on the grounds that known candidate genes have not fared well.Yet candidate genes got that status for
good reasons, and what mapping studies do is generate more of them.
There are now more direct, tissue-specific, expression-based ways to find
pathways lurking within the genome.We no longer need to rely on the
caprices of phenotypic variation in families.
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3. There may be some traits whose basic biology remains so mysterious
that current approaches are the best available way to open the door.
Devastating early-onset psychiatric diseases may be examples. But we
can expect complexity to continue to bedevil our efforts.
4. Perhaps it is time to demonstrate with some accountability that genetic
knowledge can actually deliver the health miracles that we have been
promised. Proof of principle could be achieved by intense, programmatic
investment in a model complex trait for which we already have many
candidate genes. If genetic approaches actually work there, it will be
clearly justified to fund them for other traits. Should we start with, say,
the 150 obesity genes?
5. We are swimming in an epistemological maelstrom yet seem unwilling
to acknowledge the depths of the problem. Both our genetic approaches
and the questions we ask of them may be inapt.The situation is compounded by an essentially identical history of attempting to decompose
the same traits in terms of environmental risk factors. Current “complexity” schools of thought may be faddish (Lewontin 2000), but they
have a point, and they have amply shown that even relatively simple
causation can be inferentially problematic (Wolfram 2002).We lack
adequate theory for observational units that are each unique rather
than replicable (Molenaar, Huizenga, and Nesselroade, in press), or for
when unmeasured phenomena have as much causal impact as those
we know about, like unspecified secular trends in exposure and diagnostic criteria. Rather than continuing to tinker with existing approaches,
a mobilized effort to understand these epistemological rather than technical problems in the underlying notions of causation and inference
themselves would be warranted. Change will probably have to come
from the young.
We know that even this paper is largely reductionist in phrasing. We have
written of disease alleles, of environment, genes, and interactions, in rather loose
ways as separable, replicable causal elements. It is not easy to escape from a paradigm. At least, we have tried to state a tempering perspective in regard to complex traits based on simple and rather obvious aspects of evolution, in order to
show why current frustrations are explicable and even to be expected.
These are difficult problems to solve by any means, so impatience may be unfair. Certainly much knowledge comes from looking into the basic science
underlying human traits. But the cost is high, especially if we have good reason
to believe we are looking in the wrong place. In regard to the two meanings of
genetics: because of evolution, genetics is involved in everything; but because of
evolution by phenotype, not everything is genetic.
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