Numbering the hairs on our heads: The shared David Houle

Numbering the hairs on our heads: The shared David Houle
Numbering the hairs on our heads: The shared
challenge and promise of phenomics
David Houle1
Department of Biological Science, Florida State University, Tallahassee, FL 32306-4295
Edited by Diddahally R. Govindaraju, Boston University School of Medicine, Boston, MA, and accepted by the Editorial Board September 21, 2009
(received for review July 22, 2009)
Evolution and medicine share a dependence on the genotype–
phenotype map. Although genotypes exist and are inherited in a
discrete space convenient for many sorts of analyses, the causation
of key phenomena such as natural selection and disease takes place
in a continuous phenotype space whose relationship to the genotype space is only dimly grasped. Direct study of genotypes with
minimal reference to phenotypes is clearly insufficient to elucidate
these phenomena. Phenomics, the comprehensive study of phenotypes, is therefore essential to understanding biology. For all of
the advances in knowledge that a genomic approach to biology has
brought, awareness is growing that many phenotypes are highly
polygenic and susceptible to genetic interactions. Prime examples
are common human diseases. Phenomic thinking is starting to take
hold and yield results that reveal why it is so critical. The dimensionality of phenotypic data are often extremely high, suggesting
that attempts to characterize phenotypes with a few key measurements are unlikely to be completely successful. However, once
phenotypic data are obtained, causation can turn out to be unexpectedly simple. Phenotypic data can be informative about the
past history of selection and unexpectedly predictive of long-term
evolution. Comprehensive efforts to increase the throughput and
range of phenotyping are an urgent priority.
disease genotype–phenotype map
| natural selection | G matrix |
edicine emphasizes proximal cause, for example, in the
case of infectious disease, exposure to disease-causing
microbes, environmental and genetic factors that have shaped
the properties of exposure, antimicrobial therapy, and treatment
of symptoms. Evolutionary biology approaches these same factors retrospectively in terms of the evolutionary history of the
microbe and human host, that is the factors that have shaped the
niche of microbes and humans, the evolutionary factors that
allow or promote the existence of genetic variants, and prospectively the potential for the microbe to evolve in response to our
therapies. The two approaches are reciprocally illuminating.
I want to point out another point of contact between medicine
and evolutionary biology that is less appreciated: they both
depend on our knowledge of the relationship between genotype
and phenotype, the genotype–phenotype (G-P) map. This concept has a long history in evolutionary thinking. An early and
influential statement of the importance of the G-P map in
evolutionary biology is that of Lewontin (1), whose map is
redrawn in Fig. 1A. The evolutionary process takes place in two
“spaces.” The first is the genotype space (G space), which
consists of all possible genotypes. Populations move in this space
over generations in response to natural selection and genetic
processes. Natural selection, however, takes place in continuous
phenotype space (P space), the space of all possible phenotypes.
The genotype of an individual strongly influences the location in
P space through the process of epigenesis, the totality of interactions of genes and environment, including all aspects of
development (2). The properties of the phenotype produced
influence its probability of survival and success at reproducing its
genotype. This process of weighting genotypes by phenotypic
success (and potentially epigenetic inheritance) then indirectly
changes the mean genotype. Finally, transmission influences the
mean of the next generation through the processes of segregation, mutation, and recombination. This process is repeated over
many generations.
Medical genetics seeks to understand the genetic causes of
variation in human morbidity and mortality. As represented in
Fig. 1B, doing so involves unraveling the same transformations in
and between G and P spaces as does understanding the process
of evolution. Epigenesis and transmission are the same in both
realms; the determination of disease state from phenotypes is
precisely analogous to natural selection, although disease state
may or may not influence reproductive success. Proximal causation of disease state takes place in P space and must ultimately
be studied there. Current methods for explaining G-P relationships are, however, based almost entirely on determining the
positions of subpopulations in G space, bypassing P space except
as a classifier. For disease genetics, individuals are rather crudely
sorted into diseased and healthy subpopulations so that their
genetic differences can be compared. Analogous approaches are
commonly used for simple continuous phenotypes, such as
human height. The techniques of Mendelian analysis, candidate
gene studies, and association studies are in this sense all association studies.
Thanks to genomics, we now have, or can readily obtain,
abundant population data on genotypes. In addition, efforts to
extend high-throughput techniques to aspects of the epigenetic
process relatively close to the genome, such as gene expression,
protein interactions, and metabolism, have greatly increased our
ability to detect genetic influences of these subcellular phenotypes. If we consider, however, multicellular phenotypes, such as
morphology, physiology, and behavior, our capabilities have
remained relatively unchanged over the last 20 years. Commercially available gene chips now allow the simultaneous assay of
the expression of an entire genome, but the average investigator
of variation in whole organism phenotypes is not far removed
from previous generations who took out the calipers, made a
single measurement, and wrote it down in a notebook with a
pencil. As a result, the depth of our knowledge of genomes is
approaching completeness, whereas our knowledge of phenotypes remains, by comparison, minimal. Part of the explanation
for this strong imbalance is certainly that P space is vastly more
vast than G space.
All biologists need the problem of G-P relationships to be
solved, or at least thoroughly described, but the need in evolu-
This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences,
“Evolution in Health and Medicine” held April 2–3, 2009, at the National Academy of Sciences
in Washington, DC. The complete program and audio files of most presentations are available
on the NAS web site at
Author contributions: D.H. designed research, performed research, analyzed data, and
wrote the paper.
The author declares no conflict of interest.
This article is a PNAS Direct Submission. D.R.G. is a guest editor invited by the Editorial
PNAS | January 26, 2010 | vol. 107 | suppl. 1 | 1793–1799
all with individually small effects. For these phenotypes we need
alternative approaches to the G-P map.
Making sense of the evolutionary process requires that the
phenotype as a whole be approached; understanding the causation of disease in human does as well. The large-scale study of
high-dimensional phenotypes is phenomics; phenomics is the
natural and inevitable complement to genomics. Implementation
of a phenomic approach faces two critical challenges. One is
obtaining comprehensive phenotypic data, and the second is
learning how to use such data. The title of this article includes a
quotation from Luke 12:7, where God is ascribed the power to
evaluate the tiniest details of existence, not only to number the
hairs of our heads, but to understand their meaning. Can we
hope to do as well?
Fig. 1. G-P maps in evolution and medicine. Circles represent population
mean genotypes and phenotypes, and arrows indicate the processes by which
genotypic and phenotypic means are interconverted. (A) In the evolutionary
realm, epigenesis (transformation t1) transforms genomic information into
the whole-organism phenotype. Natural selection (transformation t2) alters
the proportions of types within the population of phenotypes, potentially
changing the phenotypic mean. This process alters the frequency of genotypes
by transformation t3, the inverse of epigenesis. Finally, reproduction results in
transmission (t4) of genotypes to the next generation, possibly again altering
the mean genotype as a result of mutation and recombination. This process is
repeated over many generations, moving both the population genotype and
phenotype through their spaces. (B) The medical realm shares the process of
epigenesis (t1). Any influence of the phenotypes on the likelihood that an
individual will be healthy or diseased is reflected in the mean phenotype of
healthy (PH) and diseased (PD) individuals, in a process precisely analogous to
natural selection. This differential sorting of genotypes depending on the
phenotype they produce affects the genotypic means of healthy and diseased
individuals through t3. Differences between healthy and diseased subpopulations in G space are detected in association studies. Proximal causation
of disease is studied in the P space.
tionary biology is particularly acute, because no predictive
science of evolutionary dynamics can emerge without such
understanding. The study of natural selection is even more
primitive than our knowledge of phenotypes, but only by combining a G-P map with detailed knowledge of natural selection
can one predict what aspects of the genome can evolve in
response. As of now, however, this effort is pinned to the type
of association studies diagrammed in Fig. 1B, which rely on
crude, simplistic phenotypic measures to categorize individuals,
and conduct the remainder of the analysis in G space.
Does this relative lack of phenotypic information matter?
There is a growing realization that phenotypically naive association studies are unlikely to explain more than a minority of
genetic causation (3, 4). For some phenotypes, even driving an
association project to complete description seems likely to give
us a list of thousands of genes and perhaps millions of variants,
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Studies in G Space
The thrust of modern biology and much of medicine is that the
most effective way to understand phenotypes, including disease
state and mortality, is to understand how genes function. The
assumption is that failures in gene function either directly cause
failures of organismal function or mimic the effects of failures
with other causes. For example, epidemiologists have explicitly
turned to the concept of “Mendelian randomization” (5) to test
hypotheses about even environmental causes of disease. This
approach exploits genetic variants that manipulate the effective
factor hypothesized to be responsible for altered risk. Because
relatives that share exposure to environmental factors may nevertheless differ in their relevant genotypes, we may get a less
confounded picture of causation. Furthermore, we know that
virtually all common diseases show evidence of inheritance. As
the human genome was being published, optimism that these
genetic and genomic approaches would unravel the majority of
disease causation ran high (e.g., ref. 6).
This G-space approach to disease has undoubtedly had great
successes. As of June 2009, The Online Mendelian Inheritance
in Man database (7) listed 2,908 disorders that can be traced to
defects at particular human genes. More than 3,700 additional
disorders show evidence for inheritance, although they have not
yet been traced to a precise genomic location, so these numbers
are very likely to continue to increase. If progress along these
lines can continue, then maybe we do not need to worry about
our relative inability to measure phenotypes.
The available methods for detecting genetic association have
important limitations that give reason to doubt how far we can go
in understanding phenotypic causation (8). For example,
traditional Mendelian analyses of disease state have been supercharged by the availability of abundant markers, but require
that the overall probability of disease in the random population
be low, a set of candidate loci be in hand, and both the
penetrance (probability of developing the disease state when the
variant is present) and effect size (detectability of the disease
phenotype) of an allele be very high. When these assumptions
are met, first-degree relatives, such as siblings, of those with the
disease are at a high relative risk of disease (λ), and therefore
have a high odds ratio (OR), a ratio of the probability of being
affected when an allele is present to the probability of being
affected when it is absent, which is typically approximately twice
λ. Such analyses, therefore, only effectively uncover causation of
rare syndromes that can be caused by single genes. Typical
relative risk values for mapped Mendelian variants are well over
10. Some examples of Mendelian genetic diseases are cystic
fibrosis (λ = 500), phenylketonueria (λ = 500), and sickle cell
anemia (λ = 18) (7).
The relative risks of common, chronic, and late-onset diseases
are typically much lower. The two leading causes of death in the
United States are heart disease and cancer (9), but the average
λ for 28 types of cancer was just 2.2 (10), whereas a typical
number for heart disease is 3 (for the presence of coronary artery
disease in sibs of heart attack patients; ref. 11). Finding causation
in such cases requires candidate gene or genomewide association
(GWA) studies. GWA studies (e.g., ref. 12) are powerful when a
causal allele is common enough to be present in multiple individuals in a sample and penetrance is as low as 10%, enabling
alleles with small ORs of 1.1 or so to be detected, if sample size is
very large. Common alleles, however, almost never have large
effects (detected alleles usually have ORs between 1.2 and 1.5;
ref. 8), and therefore explain little of the variance in disease. The
candidate gene or “rare variant” approach intensively screens for
variants in samples with and without the disease. Enrichment of
rare variants in the diseased population, followed by verification
that the function of the candidate gene is altered, indicates that
altered function increases disease. Typical rare variants have
ORs between 2 and 10, but individually explain little of the
variance in disease susceptibility because of their rarity. The
discovery that de novo copy-number variants are commonly
associated with disease in a wide variety of syndromes (see, e.g.,
refs. 13 and 14) provides hope that these readily detected
mutations will lead us to large numbers of new candidate genes.
The GWA and candidate gene approaches are now being
brought together by intensive resequencing of case-control populations (see, e.g., ref. 15).
This combination of available techniques is thus incapable of
detecting variants with low penetrance or those at loci that are
not yet candidates. These classes of variation appear to be quite
common as only a small proportion of the total genetic causation can
so far be assigned to a genomic location in most syndromes (3, 4),
a result that suggests that low-penetrance alleles explain a substantial
proportion of disease susceptibility. One response to this problem
is to improve our methods, by explicitly addressing the mechanisms
of low penetrance, such as genotype–environment interaction and
epistasis (16).
Evidence is strong, however, that these holes in our understanding signal a deeper problem that cannot be fully addressed
by better association studies. A particularly revealing set of
GWA studies recently discovered multiple new regions with
effects on height in human populations (17–19). The total
sample size of genotyped individuals over those three studies was
≈85,000; each study accumulated large samples by combining
data from many different GWA studies that incidentally recorded height. The studies collectively identified 52 loci that
affect height, 40 of which were previously unknown. On one
level, this result seems to confirm the usefulness of GWA studies
(20), and each article proudly points out clustering of the
associations near loci known to influence bone growth. The
more important message, however, is the proportion of variation
in height explained; the studies explain just 2.9%, 2.0%, and
3.7%, respectively of the variation in human height in populations of European ancestry. These figures suggests the distinct
possibility that something approaching the entire genome is
capable of influencing height (4), a conclusion supported by the
finding that one-third of nonlethal mouse gene knockouts affect
body weight (21).
Given these results, the goal of understanding GP relationships can probably advance only partway by association mapping.
Many voices of caution have argued for a scaling back of the
genomic rhetoric to match diminished expectations (8, 16).
Others are ready to counsel that the entire enterprise of association mapping should be abandoned (e.g., refs. 3 and 22). To
those focused on the overriding G-P problem the first response
is eminently sensible, but unsatisfying, because it leaves no
prospect of a solution. The second is defensible only if an
alternative is available.
A Phenomic Alternative?
Fig. 1 suggests a natural alternative to G space studies that
incorporates some concepts from quantitative genetics and
evolutionary biology. These concepts are relatively straightforward if a single trait is involved, whose value can be symbolized
p, with population mean P. First consider the study of natural
selection during evolution, represented in Fig. 1A. Fitness is a
function of trait value, f(p). When this function is standardized
to a value of 1 at the population mean [i.e., f(P1) = 1] the
derivative at the population mean is the selection gradient, β.
The gradient is approximated by the ratio between the covariance between f and p and the population variance in p, β =
COVf,P/VP (23), and gives the rate at which relative fitness
changes for a unit change in the trait. It is readily estimated as
the regression of relative fitness on trait value. The gradient
allows the calculation of the change in mean phenotype after one
round of natural selection (P1 and P1′ in Fig. 1A) as P1′ − P1 =
Vpβ = COVf,P.
A very similar approach could be used to examine the function
expressing the change in disease (or health) probability as a
function of phenotype f(p). In this case, the means of healthy and
diseased individuals (PH and PD; Fig. 1B) are readily calculated,
and the gradients that transform the population mean to either
PH or PD can be obtained as e.g., βH = (PH − P)/VP. This
procedure is equivalent to using an indicator of disease state as
an analog for fitness. For example, to obtain the health gradient
βH we could use an indicator, x, that has a value of 1 for a healthy
individual and 0 in a diseased one. If the proportion of healthy
individuals is h, regressing x/h on p gives the disease gradient that
expresses the change in relative probability of health for a unit
change in trait value. When a large change in disease probability
occurs in the range of the data, logistic regression will provide
a better estimate of βH (24).
This thinking is most useful when generalized to multiple traits
(25), that is to P space, where the phenotype is a vector p. The
weighting function, f(p) (which can be either a fitness function
or a disease function) is a multidimensional surface. The derivative of this function is a gradient vector β with elements that
summarize the direction in which f(p) fitness or disease state
probability increases the most rapidly. Quadratic (or even higherorder) terms can also be fit, capturing the curvature of f(p)
around the population mean. The resulting matrix of quadratic
terms is called γ (25).
Estimation of β and γ is by multiple regression and has the
same advantages in this context as any other: if the function is
well-behaved and the traits that actually cause the dependent
variable to vary are in the analysis, the elements of β and γ will
reveal the relative importance of each phenotype in determining
the outcome. This could, for example, reveal which of a large
number of possible phenotypes are most predictive of disease. If,
however, some or all of the causal traits are missing from the
analysis, because they are almost certain to be when only one
trait is analyzed, the estimated β can underestimate or overestimate the importance of each trait, perhaps giving a misleading picture of which phenotypes matter (25, 26). Neither
medical researchers nor evolutionary biologists currently have
access to anything approaching complete phenotypic data, so full
use of this approach awaits widespread implementation of phenomic-scale measurements.
In the evolutionary realm, the results of natural selection are
transmitted back to the genotypes passed on to the next generation. The process of epigenesis (t1 in Fig. 1) is to some degree
indeterminate so individuals with the same genotype produce a
variety of phenotypes. This variation itself may be partly predictable from the study of interactions between genotype and
environment. As a result, some of the change in phenotype
brought about by applying function f(p) is caused by the deviations of an individual from the average phenotype that its
genotype would produce, and only some of the weighting
function is transmitted back to G space in transformation t3. The
amount that is transmitted for a single trait is captured by the
PNAS | January 26, 2010 | vol. 107 | suppl. 1 | 1795
additive genetic variance, VA, which is that part of VP that causes
offspring to resemble their parents. The expected change in
mean phenotype in a single trait between generations (neglecting the transformations caused by mutation and recombination,
which are usually small) is then P2 − P1 = VAβ, which can be
rearranged to give the more familiar form P2 − P1 = VA/
VP·COVf,P = h2S. In the multivariate case, inheritance is captured
by a matrix of variances and covariances, G, where the diagonal
elements are the additive genetic variances for each trait, and the
off-diagonal elements are the additive genetic covariances between the traits. The resulting transformation across generations
is then P2 − P1 = Gβ.
Steps Toward Phenomics
Assessment of variation at a few locations in the genome was not
enough to characterize location in G space, so we have turned to
genomics. Similarly, the logic of natural selection and disease
causation in P space makes clear that studying a few traits cannot
be enough. In the last 10 years, calls for enhanced phenotyping
have become increasingly common, although the logic behind
these arguments has been varied and not always explicit (27–33).
These calls have increasingly been taken up and led to concrete
increases in our phenotyping ability (e.g., refs. 32 and 34–38) of
differing scale and complexity.
Clearly, phenomic measurements must be extensive, covering
many different aspects of the phenotype, such as morphology,
behavior, physiology, etc. Less obviously, phenomics must also
be intensive; that is, it must lead to detailed characterization of
each major aspect of the phenotype. For example, the genetic
variants that affect function of the human heart are very likely
to have pleiotropic effects on other body parts and functions,
calling for extensive measurements of other systems. In addition,
the heart itself cannot be adequately characterized by a small
number of summary parameters like cardiac output, but must be
approached in terms of the full complexities of physiological
capacities, morphology, etc., calling for intensive measurements
of the heart. Phenomic efforts are rising to both challenges. For
example, the mouse research community is adopting a standard
set of protocols for extensive measurement covering many
different aspects of the phenotype (37, 38), and intensive
measurements of mouse morphology are being pursued by other
groups (e.g., ref. 39). Most important, the mouse community is
focused on associating this detailed phenomic data with particular genotypes and their recombinants.
An easy objection to putting resources into phenomics is that
most of what we might measure may prove irrelevant. Although
the genotype has a finite extent and discrete content and can
therefore be measured exhaustively, the phenotype is both
continuous in multiple dimensions and infinitely divisible in
some dimensions. For example the state of the phenotype can be
measured at an infinitely great number of time points. If the goal
is exhaustive measurement of the phenotype, it will forever
remain beyond our reach. Rather, the goal must be defined in
terms of understanding. How intensively we need to measure the
phenotype to achieve goals like understanding the proximate
causation of natural selection or disease is an open question that
must be addressed with respect to a particular goal, such as
predicting susceptibility to a particular disease, or response to a
particular selection pressure. Both the genotype and especially
the phenotype are immensely complex; our hope must be that
any particular problem becomes simpler when viewed from a
favorable perspective. Buchanan et al. (22) nicely summarized
this hope with the metaphor of an hourglass with the full
genotype at one end and the full description of the phenotype at
the other. In between, we hope, is the waist of the hour glass,
where measurement of just a few key aspects of the organism
(which could be any combination of genetic, environmental, and
phenotypic measurements) are maximally informative about the
1796 |
Fig. 2. Proportion of additive genetic variation on each principal component (PC) axis of the G matrix for three sets of morphological data: 30
measurements of baboon skulls (ref. 57 and C. Roseman, personal communication), 39 skull measurements of a combined estimate from two tamarin
species, Saguinus fusicollis and Saguinus oedipus (refs. 58 and 59 and J. M.
Cheverud, personal communication), and 12 Drosophila melanogaster wing
vein intersections (42).
problem we want to address, for example, fitness or disease
susceptibility. This task is to increase the range of data that can
be applied to a problem in hopes that, once the key pieces are in
hand, we can build simplified, but powerful, models of causation.
Early Lessons from Phenomic Data
Evolutionary biologists have been increasingly dealing with
intensive datasets where a single aspect of the phenotype is
subjected to detailed characterization, and the outcomes of these
studies can give a taste of what we might learn by expanding such
studies to more be more comprehensive. I discuss four areas
where we can draw tentative conclusions not accessible from a
genomic viewpoint alone.
Phenotypic Datasets Have High Dimensionality. Perhaps the largest
class of highly multivariate datasets are obtained from studies of
morphological form, assessed by means of the spatial locations
of landmark points that are recognizable across a series of
specimens or the curves that connect such points. In a relative
handful of instances, such data have been subjected to a genetic
analysis, in which the variation was partitioned into additive
genetic variance and all other sources of variation. This process
yields a G matrix with as many rows as the number of measurements, minus a few degrees of freedom for estimating the spatial
orientation of each specimen relative to the others (40). When
each G matrix is subjected to a principal components analysis
(PCA), we can see just how much variation will be missed in
studies that only measure of handful of traits. PCA rotates the
G matrix to a new set of directions (the eigenvectors). Each
direction has an amount of variation associated with it, its
eigenvalue. The eigenvectors are chosen to maximize the range
of the eigenvalues, so PCA allows us to measure the amount of
variation in the least variable combinations of the original traits.
Three eigenvalue distributions are shown in Fig. 2, for measurements of baboon and tamarin and for Drosophila wing vein
intersections. The analyses of these data remove size, leaving
only variation in shape (41). On a log scale, the decrease in the
amount of genetic variation in shape explained is approximately
linear, suggesting an exponential distribution. What is most
remarkable is how slowly the amount of variation falls from the
kth most variable direction to the k + 1th direction. For the
tamarin and baboon skulls, the variation falls by an average of
18% with each dimension, whereas for the flies it falls at the rate
of 29% per dimension. No single summary measure can capture
even 50% of the variation in the shape of these structures.
A second question is just how many aspects of form must be
measured to characterize the genetic variation fully, that is, what
is the dimensionality of the genetic variation. The fly-wing study
used a particularly large number of families (800) and individuals
(17,000), and demonstrated that at least 17 of the 20 possible
dimensions had significant genetic variation (42). Further analysis using a restricted maximum-likelihood approach (43) revealed significant variation in all 20 directions. No similar
analyses of the primate skull datasets have been done, and the
sample size in each of those studies was substantially smaller.
Nevertheless, the overall pattern of decrease in variation is quite
similar and suggests that the genetic dimensionality in each of
these species is also quite large. Full characterization of the
genetics of these phenotypes cannot be undertaken from a small
sample of measurements.
Studies of Selection Can Reveal That Only Some Combinations of
Traits Are Important. Although the dimensionality of genetic
variation is high, the selection-gradient analysis described above
may well turn up some low-dimensional combination of traits
that predicts an important outcome, either fitness or disease.
Such a result could potentially indicate the narrow waist of a
causal hourglass (22). The ability to attract a mate is an
important component of fitness, and attractiveness can be readily
assayed by directly observing matings, allowing selection gradient analyses to be performed. Blows and colleagues (44, 45)
characterized the relative abundances of nine cuticular hydrocarbons (CHCs) in a population of Drosophila serrata, then
compared the compositions of males to their success in competitive mating trials. The standardized selection gradient was
extremely strong (change in relative fitness of 76% >1 SD
change in the relative proportions of different CHCs), suggesting
female preferences for higher proportions of several CHCs and
antipathies toward others.
This logic of simplification extends to sets of phenotypes near
a fitness optimum, where fitness decreases away from the
population mean. In such cases, the important parameters are
the quadratic coefficients in the γ matrix. These can be manipulated to allow interpretation, even in very high dimensional
space (46). Brooks et al. (47) studied sexual selection on the calls
of a cricket by synthesizing variation in five aspects of the call so
that they could assess attractiveness of phenotypes not actually
found in nature. They found that mate choice favored an intermediate optimum phenotype and that females paid strong
attention to just two of the possible directions in P space. These
cases suggest that a phenomic approach that begins with extensive and intensive measurements can then turn around to indicate some low-dimensional subset of these that is actually
important in a particular context. The advantage of passing
through a phenomic phase is that which combinations of traits
are actually important is not apparent at the start.
Studies of Selection Can Suggest Past History of Trait Evolution. Both
of the sexual-selection studies cited above went further and
compared the pattern of selection on phenotypes to the pattern
of genetic variation in the population studied. In each case, those
aspects of the phenotype that were most strongly selected also
had little genetic variation (45, 48). This pattern suggests a
persistent mismatch between the phenotypes that females prefer
and the ability of the males to produce them. The result is that
the genetic consequences of female choice are very small;
successful males are those that happen differ from normal in the
favored direction, perhaps because they have experienced a
favorable environment. Fig. 3A represents this relationship
between population variation and selection schematically. The
gray ellipse represents the expected phenotype (averaged over
environmental factors) of the genotypes in the population. There
Fig. 3. Relationship between genetic variation in P space and the probability
of disease or of fitness. Gray ellipses represent the distribution of genotypic
variation in phenotype. + indicates the phenotype with the lowest probability
of disease (or highest fitness), and the black lines are iso-lines that mark a
particular level of probability of disease. (A) Constraints on the possible
genotypes. The heavy diagonal line represents the constraint. A genotype
above and to the right of this line cannot evolve. (B) Population mean is near
the optimum, but mutation creates variation around that optimum. (C) A
population in a novel environment, evolving toward a new optimum. (D) An
aging population, where deterministic changes in phenotype caused by senescence drive the population away from the optimum.
is plenty of variation in the genetic basis of the phenotype, but it
is oriented orthogonal to the direction of selection.
This finding suggests that the comparison of the pattern of
genetic variation for phenotype with the probability of disease
could be very informative about the nature of the genetic
variation in human disease. If the contours in Fig. 3 are now
taken to represent disease probabilities instead of fitnesses (with +
indicating a healthy phenotype with low disease probability),
several possible scenarios might be found. Fig. 3A would represent an outcome with low λ, such as our inevitable demise caused
by aging. Mutation-selection balance might produce a distribution like that in Fig. 3B, where the population generally matches
the optimum state, but individuals with extreme phenotypes
have increased probability of disease. Note that this disease may
not be the same in each direction. The emerging hypothesis that
many psychiatric disorders represent overexpression or underexpression of continuous personality traits provides a possible
example, in which deviation in one direction leads to autism and
deviation in another leads to schizophrenia (49). Diseases of
civilization might lead to a pattern like that in Fig. 3C, where
there is ample variation that has not yet been removed by a long
history of natural selection. In the environment of evolutionary
adaptedness, the selective pattern on the same variation might
have been like that in Fig. 3 A or B. The shift from those patterns
to that shown in Fig. 3C would be caused by genotype–environment
interactions that alter the relative consequences of genetic variation. A second kind of alteration in the probability landscape might
occur with age. In this case, Fig. 3B might represent the probability
landscape during the reproductive years, and Fig. 3D might represent the landscape in the same population at an advanced age.
Genetic Variation Predicts Long-Term Evolution. There are many
reasons to believe that the genetic variation that segregates
PNAS | January 26, 2010 | vol. 107 | suppl. 1 | 1797
within a population might be irrelevant to long-term evolution.
For example, most variation could be in the form of unconditionally deleterious mutations destined for quick elimination
from the population, and conversely those rare mutations that
will lead to major phenotypic changes might not be polymorphic
for long. Contrary to this expectation, comparison of standing
genetic variation in phenotype with patterns of among-species
divergence suggests the relationship can be reasonably strong.
Most work along these lines has relied on the relationship
between the direction with the most genetic variance in P space,
the first eigenvector of G, called gmax, and the direction of
evolutionary change (50). In most cases, the angle between these
directions is less than expected under random models of change.
Recent work has widened the scope of such comparisons to
include all possible directions in multivariate P space, and here
again those directions that show evolutionary change tend to
have the most variation (51–53). The existence of relationships
between variation and evolution suggests that the variation
present in populations reflects deep conserved properties of the
G-P map in ways that are not fully understood.
typing always far short of these ideals, however. Biologists use a
huge variety of different, often ad hoc techniques for dealing
with such data. In many cases, automation is restricted to use of
a computer mouse. Sophisticated approaches are often applied
to reduce the complexity of the phenotype measured to just a few
dimensions, rather than to acquire intensive phenotypic data. My
own approach to Drosophila wing measurement (34) reduces
handling time of a live specimen to about a minute for all
operations, but could readily be improved in various ways. For
example, the low resolution and depth of field in the images
prevents us from characterizing the cells and hairs clearly visible
on the wing; the software we depend on was written to recover
the locations, but not the thicknesses of veins. Because we
already have an immobilized specimen, why not characterize
body parts other than the wing?
The fundamental problem for phenomics is that the need for
expertise is truly transdisciplinary (33). We need engineers,
computer scientists, mathematicians, and statisticians as much as
all flavors of expertise in biology (56). The time for the Human
Genome Project did not arrive until fast and inexpensive methods were developed. Coordinated large-scale efforts to develop
such approaches are what is currently missing from phenome
efforts. As in the case of images, general approaches to phenotyping applicable across many organisms are surely possible for
groups with the right expertise.
Therefore, although biologists continue valuable piecemeal
efforts toward phenomics, we need large-scale efforts with the
following aims: (i) further development of robust, general highthroughput phenotyping techniques; (ii) combined sequencing
and phenotyping efforts that expand from the handful of genotypically controlled model systems, such as mice, to encompass
natural population variation; and (iii) further development of
analytical approaches that can use high-dimensional genotypic,
endo-phenotypic, and end-phenotypic data to generate wellsupported hypotheses for further testing.
Short of the ideal project outlined above, humans are clearly
the one outbreeding species where the prospects for informative
phenomics are the greatest. We have the peculiar tendency to
measure our own species obsessively; the biomedical community
is the one best positioned to provide the most complete phenomic data. The ultimate reward is to understand the G-P maps
needed to turn biology and medicine from descriptive to predictive sciences.
We did not begin to study genomes because we care about
genotypes; we study genomes because we care about phenotypes,
the health and well-being of humans and the diversity of life on
Earth. Now is the time to begin to take the study of the phenotype as
seriously as we take the study of the genotype. We must number,
locate, and measure even the hairs of our heads, the details of the
phenotype, so that we can understand which of those details matter.
Phenomics: What Needs To Be Done
The foremost reason that G space is the favored locale for G-P
studies is clear: “Collecting phenotypic data . . . is expensive and
time consuming . . . ” (54). Fifty years ago few could have
imagined how our ability to obtain molecular data would increase; 20 years ago few could have imagined the scale at which
we can now collect genomic information; 10 years ago few
anticipated that genome-scale data could become as cheap as it
now is. A key to this set of transformations was the vision of the
Human Genome Project, which brought intellectual, technical,
and financial resources to bear on genomes. Now is the time for a
phenome project bringing the same kinds of gains in throughput and economic efficiency to the study of the phenotype.
Many biologists share my enthusiasm for the prospects of
phenomics. There are increasing numbers of self-described
phenome projects that should be wholeheartedly supported. The
most useful of these take advantage of species where differentiated genotypes already exist as a scaffold onto which phenotype
information can be added (37–38, 55). Inspection of the details
of these projects, however, reveals that they are makeshift,
shoestring operations compared with the magnitude of the
challenges. We are pursuing phenomics as a piecemeal, smallscience endeavor.
The need for a bigger-science approach is most apparent in the
development of high-throughput approaches to phenotyping. To
take one example, imaging is an extremely promising source of
phenotypic data. The analysis of images should be generalizable
across many different organisms and many different sorts of
phenotypes (morphology of course, but also flows, spatial locations of metabolites, etc.). To maximize throughput, one would
obviously optimize hardware for rapid, repeatable imaging, but
also optimize specimen handling, automate phenotyping in software, and solve database issues to allow the handling of the
massive amount of data that would result, among other challenges. The efforts of biologists who exploit imaging for pheno-
ACKNOWLEDGMENTS. I thank the organizers Randy Nesse and Raju Govindaraju for the invitation to participate in the symposium, Charles Roseman
and Jim Cheverud for sharing unpublished data, and Stevan J. Arnold and an
anonymous reviewer for detailed comments. This work was supported by
National Science Foundation Grants DEB-0344417 and DEB-0129219 and the
National Institutes of Health through National Institutes of Health Roadmap
for Medical Research Grant U54 RR021813.
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