Download Quantitative trait locus mapping in natural populations

Molecular Ecology (2005) 14, 363– 379
doi: 10.1111/j.1365-294X.2004.02378.x
INVITED REVIEW
Blackwell Publishing, Ltd.
Quantitative trait locus mapping in natural populations:
progress, caveats and future directions
QTL MAPPING IN NATURAL POPULATIONS
JON SLATE
Department of Animal and Plant Sciences, University of Sheffield, S10 2TN
Abstract
Over the last 15 years quantitative trait locus (QTL) mapping has become a popular method
for understanding the genetic basis of continuous variation in a variety of systems. For
example, the technique is now an integral tool in medical genetics, livestock production, plant
breeding and population genetics of model organisms. Ten years ago, it was suggested that
the method could be used to understand continuous variation in natural populations. In
this review I: (i) clarify what is meant by natural population in the QTL context, (ii) discuss
whether evolutionary biologists have successfully mapped QTL in natural populations,
(iii) highlight some of the questions that have been addressed by QTL mapping in natural
populations, (iv) describe how QTL mapping can be conducted in unmanipulated natural
populations, (v) highlight some of the limitations of QTL mapping and (vi) try to predict
some future directions for QTL mapping in natural populations.
Keywords: additive genetic variance, complex traits, fitness, gene mapping, microevolution,
quantitative trait loci
Received 15 July 2004; revision received 22 September 2004; accepted 22 September 2004
Introduction
Evolutionary geneticists have a long-standing interest in
phenotypic variation between individuals, either of the
same or of different species. Understanding this variation
can inform us how species adapt to their environment, and
how this adaptation can lead to speciation. For example, to
what extent are individual differences due to environmental
effects, genetic effects or both? Before and after the synthesis
of population genetics during the early 20th century the
nature of genetic variation of continuous traits was keenly,
sometimes acrimoniously, debated (Provine 1971; Barton
& Keightley 2002). Questions that have fascinated evolutionary geneticists over the last century include: ‘How many
genes determine quantitative genetic variation?’, ‘Are major
genes (those that individually explain a large proportion
of phenotypic variance) common?’, ‘Do individual genes
explain variation in several traits (pleiotropy)?’, ‘Are the
same genes responsible for trait variation across populations,
and even across species?’, ‘Is gene action dependent on the
environment?’, ‘What are the actual genes responsible for
Correspondence: J. Slate. Tel.: 44 0114 2220048; Fax: 44 0114
2220002; E-mail: [email protected]
© 2005 Blackwell Publishing Ltd
phenotypic variation, adaptation and speciation?’, and
perhaps the most challenging of all, ‘What are the evolutionary forces that maintain genetic variation?’ (Barton &
Turelli 1989). In the late 1980s, the advent of molecular
markers and genetic maps meant it was possible to map
the genes that explained continuous variation. While most
mapping studies were focused on humans and agriculturally
important organisms, evolutionary biologists were quick to
initiate quantitative trait locus (QTL) studies, especially in
classical model organisms such as Drosophila melanogaster
(Shrimpton & Robertson 1988; Mackay 1995).
In this review I will describe how QTL mapping can be
used to elucidate the genetic architecture of continuous
traits in natural populations. There are already a number of
excellent reviews of QTL discovery in Drosophila (Mackay
1995, 2001, 2004), domestic animals (Haley 1995; Haley
1999; Andersson 2001; Andersson & Georges 2004) and
plants (Kearsey & Farquhar 1998; Mauricio 2001), as well
as others with a broader taxonomic focus (Orr 2001; Barton
& Keightley 2002). However, it is almost a decade since the
only review of mapping in natural populations (MitchellOlds 1995) was published. At that time the major conclusion
was that we were largely ignorant of the molecular quantitative genetics of natural populations, and Mitchell-Olds’
364 J O N S L A T E
review can be largely regarded as a call-to-arms. The purpose
of this review is to consider whether evolutionary biologists
have been successful in carrying out QTL mapping in
natural populations. I deliberately highlight some recent
studies that have proved particularly useful in addressing
some of the questions posed in the opening paragraph. I
also discuss, at some depth, how QTL mapping can be performed in unmanipulated natural populations, as opposed
to experimental crosses.
QTL mapping: historical background and
underlying principles
I provide only a brief historical and methodological overview
of QTL mapping as detailed descriptions are available
elsewhere (Liu 1997; Lynch & Walsh 1998; Ott 1999; Weller
2001). The acronym QTL was first coined by Geldermann
(1975). However, the underlying concept is older, having
originated in the early 1900s (reviewed in Lynch & Walsh
1998). The basic premise that underlies all QTL mapping
methods is straightforward. Genetic markers dispersed
over an organisms’ genome are typed within a mapping
population of individuals, for whom phenotype data are
available. If a marker is in close physical linkage with a
QTL the two will be in linkage disequilibrium within the
mapping population, generating a statistically significant
association between the marker genotype and trait variation.
An excellent overview of experimental designs is given by
Lynch & Walsh (1998), who make the distinction between
inbred line crosses and outbred (intrapopulation) designs.
The latter category can be separated into crosses deliberately
created to maximize power to detect QTL (e.g. by creating
large families of half-sibs) and those that are conducted on
unmanipulated populations. Below, I briefly describe the
different types of mapping population.
Inbred line crosses
The simplest and most efficient way to detect QTL is by
using inbred line crosses. By crossing two inbred parental
lines, populations or species, the progeny will exhibit a
fixed difference between every marker and trait locus.
Thus, all linked loci in the progeny (F1 generation) are in
linkage disequilibrium. These F1 progeny are in turn used
to create a mapping population. The two most commonly
employed mapping populations are the F2 design
(whereby F1 individuals are interbred) and the backcross
design (whereby F1 individuals are mated to one of the
parental populations. The major advantage of the F2 design
over the backcross (hereafter BC) is that three genotypes are
present at every marker (and QTL) in the mapping population. BC populations only have two possible genotypes at
a locus. Thus, the F2 design enables estimation of dominance
components of a QTL but the backcross design does not.
Outbred populations
The use of inbred line crosses to detect QTL in natural
populations is unlikely to provide an accurate description
of the genetic architecture of the focal trait in the parental
lines in their natural environment (see section: What is
meant by natural population?). Furthermore, the creation
of specific mapping populations may be logistically
impossible in some species. Thus, it is sometimes desirable
to conduct mapping experiments on outbred populations.
This is a difficult, although a possible task. An excellent
discussion of the major difficulties, and of how they can be
overcome is provided by Lynch & Walsh (1998). One of the
major aims of this review is to illustrate how methods and
software commonly employed in domestic livestock and
human mapping programs can be of great value to ecologists
and evolutionary biologists. Broadly speaking, outbred
populations can be mapped using either sibships (half-sibs
or full-sibs) or using general pedigrees spanning several
generations. The distinction between these experimental
designs is discussed later in the review.
Regardless of experimental design, three basic requirements must be met to map QTL. The first is a genetic map
of variable markers. The second is a pedigree with which
to follow the segregation of those markers. The third is
phenotypic data (trait measurements) on the individual
members of that pedigree. Strategies to obtain all three are
discussed (see section: How to map QTL in unmanipulated
populations).
What is meant by natural population?
For the purposes of this review, I regard a natural population as one in which the individuals used in a mapping
study are descended from recently sampled individuals
of a nondomesticated organism. This definition excludes
model organisms that have been reared in the laboratory
for many generations. I also exclude humans from this
review, although I accept that they are regarded as natural
populations by some biologists. The definition of natural
population I use can be further categorized as those in which
the mapping population is created by the experimenter
(e.g. by crossing two individuals from different populations/
ecotypes/species) and those where an unmanipulated
pedigree is used. A further distinction I make between
mapping populations is between those in which the population is reared in a laboratory/glasshouse environment
and those in which phenotype values are measured in
the wild. It is important to make the distinction between
the different types of mapping population, because the
preferred experimental design very much depends on the
question that is being addressed (Fig. 1).
If one is interested in the number and distribution of
effect sizes of QTL involved in speciation or reproductive
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363– 379
Q T L M A P P I N G I N N A T U R A L P O P U L A T I O N S 365
Fig. 1 QTL mapping in natural populations: strategies and examples. A mapping experiment can be conducted in either interspecies or
intraspecies populations. Further distinctions between experiments can be made depending on whether they are conducted in the natural
environment or in laboratory/glasshouse conditions, and on whether they are conducted in specially created mapping crosses or in
unmanipulated pedigrees. The preferred experimental approach depends on the question being addressed, although there is overlap
between strategies. For example, an experiment conducted in an interspecies cross in a laboratory would be appropriate to investigate
speciation genetics but inappropriate to learn about microevolutionary change in either parental population. 1. Verhoeven et al. (2004); 2.
Slate et al. (2002b); 3. Lexer et al. (2003); 4. Gockel et al. (2002), Calboli et al. (2003); 5. Ungerer & Rieseberg (2003); 6. Bradshaw et al. (1995,
1998); 7. Peichel et al. (2001), Colosimo et al. (2004); Shapiro et al. (2004); 8. Hawthorne & Via (2001).
isolation, then it is usually necessary to create a mapping
population from two parental species, races or populations. In theory, such an analysis could be performed in an
unmanipulated population in a hybrid zone (if one exists),
but understanding the genetic basis of species differences
is nearly always more tractable in specially created mapping crosses. Similarly, when trying to establish whether
the magnitude and direction of QTL effects are constant
across environments, it is usually desirable to create replicates of a mapping population that can be studied in
heterogeneous environments (either in the laboratory or the
natural environment). Again, this type of study requires a
specially created mapping population. However, if one is
interested in studying the genetic architecture of continuous
traits of a particular population, then the use of an artificially created mapping population that is reared in the
laboratory presents some difficulties. First, any QTL
that are detected represent fixed differences between the
parental lines, rather than the standing variation segregating
within the parental lines. Second, environmental variance
tends to be greater in the wild relative to a carefully controlled
laboratory environment, so that heritability (and therefore
the proportion of trait variance explained by a QTL) is
likely to be lower in the wild than in the laboratory
(Hoffmann 2000). Only by measuring QTL magnitude in
an ecologically realistic setting can the distribution (and
intensity of selection) of allelic effects be estimated.
Although I make the distinction between the different
categories of natural population used in QTL mapping I
do not intend to present some as being ‘better’ than others.
This review does pay particular attention to unmanipulated
pedigrees, primarily because this is an area in which relatively little progress has been made.
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363–379
How common are QTL mapping experiments in
natural populations?
I conducted a Web of Knowledge (http://wok.mimas.ac.uk/)
search on the keywords ‘QTL’ combined with either ‘natural’
or ‘wild’. By the end of 2003 there were 416 published
articles that used these terms in the title, keywords or
abstract, which suggests that QTL mapping experiments in
natural populations are now commonplace. The number of
articles published per annum has risen every year since
1992 and shows no sign of levelling off (Fig. 2). At the time
Mitchell-Olds’ 1995 review appeared, just 19 articles had
Fig. 2 Publications on QTL mapping in natural populations since
1990. The number of publications using the terms ‘QTL’ and either
‘natural’ or ‘wild’ was obtained using Web of Knowledge. Shaded
bars are yearly totals and open bars are cumulative total. Note that
the number of publications per annum has risen since the early 1990s.
366 J O N S L A T E
been published. Thus, at face value, the last decade has
witnessed a dramatic uptake of QTL mapping to investigate
the genetic architecture of continuous traits in natural
populations. However, only 15 articles contain these search
terms in the title. The vast majority of these studies have
been conducted in inbred line crosses, sometimes using
parental lines that have been removed from the wild for
many generations. The next most common type of study is
that in which mapping populations are recent descendants
of wild-sampled individuals. A small subset of these mapping populations has been reared in the natural environment.
In contrast, I am aware of only one study where QTL have
been mapped in an unmanipulated wild population (Slate
et al. 2002b). In other words, QTL mapping in natural
populations has taken off, but some experimental designs
have been largely ignored. The imbalance between studies
of experimental and free-living populations presumably
reflects the fact that all three requirements for QTL
mapping have been, until recently, unavailable for most
free-living natural populations. A goal of this review is
to encourage more biologists to initiate QTL studies in
unmanipulated populations in the natural environment.
Recent progress
The second aim of this review is to describe how QTL
mapping in natural populations can be used to address
some of the questions posed in the opening paragraph.
Most of the examples I discuss have been published in
the last three years. Older studies have been reviewed
elsewhere, or tend to focus on the number and magnitude
of QTL. Although the vast majority of studies have been
conducted in specially created mapping populations, an
obvious way in which these studies can be distinguished
from each other is on the grounds of whether parental lines
are from the same or from different species.
Inter-species crosses and the genetics of reproductive
isolation
‘Historically, however, the most contentious question has concerned whether major genes play a major
role in species differences. It is now clear that the
answer is yes, sometimes’ (Orr 2001).
One area in which QTL mapping has been particularly
illuminating is in understanding the genetic architecture of
reproductive isolation. Orr (2001) highlighted some of the
main conclusions from interspecies crosses (notably, that
some species differences can be explained by major genes,
and the number of QTL underlying differences is highly
variable) as well as some as the caveats from this type of
study (distinguishing between factors that arose before
and after isolation is not straightforward; the importance
of comparing the magnitude of QTL effects to phenotypic
variation within parental lines). Since Orr’s review, the
number of QTL studies in natural populations has doubled.
The overall conclusions reached by Orr remain unchanged.
However, a handful of recent studies have advanced our
understanding of the genetics of reproductive isolation,
addressing questions beyond the somewhat equivocal,
one of, ‘Are species differences attributable to major effect
genes?’. Here, I focus on three systems, which have proved
particularly informative.
Threespine sticklebacks. At the end of the last ice age,
approximately 15 000 years ago, marine threespine sticklebacks (Gasterosteus aculeatus) colonized new freshwater
lakes, and rapidly adapted to a diverse array of new
environments. Sympatric speciation has occurred in at
least six lakes in British Columbia, Canada. Within these
lakes, sympatric species have adapted to a different ecological niche — benthic forms are invertebrate feeders found
close to the shore and, relative to their marine ancestor,
they have a reduced body armour, increased body depth
and a decreased number of gill filters used to filter food.
Limnetic forms more closely resemble marine forms with
a streamlined body, extensive armour and a large number
of gill rakers. Benthic and limnetic forms are reproductively
isolated, although fertile F1 crosses can be generated in an
aquarium. In a series of beautiful experiments, David
Kingsley (Stanford University), Dolph Schluter (University
of British Columbia) and colleagues have dissected the
genetic architecture of the traits that cause reproductive
isolation, many of which have arisen by independent parallel
evolution in a number of different populations. The key
to this work was the construction of a medium density
microsatellite map (Peichel et al. 2001) containing 227 loci
at a mean intermarker interval of 4 cm centimorgans.
To achieve this notable feat, the authors sequenced a
staggering 3560 clones and identified over 1000 loci before
designing primers to amplify the most promising 428 loci.
A decade ago, an exercise on this scale would only have
been performed in humans (Weissenbach et al. 1992) or model
organisms (Dietrich et al. 1994). Microsatellites are the
marker of choice for this system because they are variable
in different populations, eliminating the need for a new
marker set for alternative populations. The mapping population was derived from a benthic female and a limnetic
male captured at the Priest Lake, British Columbia. An F1
male was then backcrossed to a benthic female to produce
a mapping population of 92 progeny. The progeny were
measured at several gill raker and body armour traits and
interval mapping revealed major QTL for every trait, each
explaining between 17% and 37% of the phenotypic variance
observed in the progeny. Of course, in a relatively modest
mapping population (n = 92), any QTL that is detected
would inevitably explain a large proportion of phenotypic
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Q T L M A P P I N G I N N A T U R A L P O P U L A T I O N S 367
variance to reach statistical significance (see section: Caveats).
However, it is the subsequent experiments that reveal the
beauty of the stickleback system.
The observation that genetic variation for a quantitative
trait can be explained by several loci, some of which are of
relatively large effect is hardly surprising or even novel.
More challenging questions that arise from a mapping
experiment of this kind include: (1) ‘Do QTL of large effect
actually represent the effects of more than one linked
gene?’; (2) ‘Are the same QTL segregating in different populations?’, and (3) ‘What are the actual genes responsible for
phenotypic variation in a mapping population’. Colosimo
et al. (2004) created a new stickleback mapping population,
using a marine female (from Japan) and a benthic male
(from a second British Columbia lake, Lake Paxton) to
investigate the genetics of armour plate reduction (benthic
fish have reduced armor plates relative to limnetic and
marine species). For this paper, an F2 design was used
(enabling the measurement of dominance effects at QTL),
and the mapping population was relatively large (n = 360).
Four lateral plate number QTL were identified, one of which,
on linkage group 4, explained 75% of the phenotypic variance. A second cross was then created from benthic and
limnetic fish at another geographical location (Lake Frient,
California). Three of the QTL (including the major one)
were segregating in this second mapping population. The
authors also conducted an elegant ‘complementation’ cross
to test whether the same gene was responsible for low plate
number in benthic fish from the Paxton and Frient populations. At the major QTL, the high plate number allele is
dominant over the low plate number allele (i.e. heterozygotes have high plate number). Therefore, F1 fish from
a Frient benthic × Priest benthic cross can only have a low
plate number if the same gene is responsible for the QTL in
each population. Eighty-four progeny were examined
and all had low plate number. Thus, the authors convincingly showed that a single locus can cause a major shift in
phenotype, and that the same locus (but not necessarily the
same mutation or even the same gene) can explain cases of
parallel evolution. It is also notable that one of the other
lateral plate number QTL that was identified in both the
Frient and Paxton populations was also segregating in the
original mapping population from Priest Lake (Peichel
et al. 2001). Indeed, the colocation of QTL in different mapping populations appears to be a fairly regular occurrence
in the stickleback system, although I am unaware of this
being formally tested.
Remarkably, the same Japanese marine fish × Paxton
benthic fish mapping population has been used to illustrate a second case of a (different) major gene appearing to
cause the parallel evolution of a dramatic morphological
change (Shapiro et al. 2004). Here the authors examined the
genetic basis of pelvic reduction — the complete loss of
pelvic spines and structures — that is observed in some
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363–379
benthic stickleback populations. Again, the phenotypic
variance within the F2 was largely explained by one major
QTL and a handful of minor QTL. The authors then identified
candidate genes from the extensive literature on hindlimb
developmental genetics in model systems. One candidate,
Pitx1, causes a similar phenotype in mice, most notably
asymmetry in pelvic limb reduction, a trait that is also
characteristic of benthic sticklebacks. Pitx1 was mapped in
sticklebacks to exactly the same location (on linkage group
7) as the major QTL, confirming it as a strong candidate.
However, when Pitx1 was sequenced in marine and benthic fish, no amino acid-changing nucleotide differences
were observed. The authors next compared gene expression of Pitx1 in developing larvae of marine and benthic
fish. In benthic fish, Pitx1 was not expressed in pelvic
tissues but expression was normal elsewhere. Marine fish
showed normal expression in pelvic tissue. Thus, the QTL
is likely to be explained by an unknown regulatory mutation within the noncoding part of Pitx1. Complementation
tests indicate that a similar (or the same) mutation is
responsible for the loss of pelvic structures in an Icelandic
population. In summary, studies of sticklebacks have
shown that parallel evolution may frequently be caused
by independent mutations of the same gene. Several
important lessons can be learned from the stickleback
experiments. For example, QTL mapping experiments that
can be conducted in replicate populations offer considerable
power for understanding evolutionary change. Second,
candidate gene analyses may help identify the loci responsible for quantitative variation. Finally, QTL are not necessarily determined by mutations that alter the amino acid
sequence of a gene. It should, however, be noted that the
detection of QTL responsible for evolutionary change is
not the same as identifying the genes and mutations that
underlie the QTL.
Pea aphids. Another notable example of QTL mapping helping to elucidate the mechanisms of reproductive isolation
has been conducted in pea aphids, Acyrthosiphon pisum
pisum (Hawthorne & Via 2001). Pea aphids have two races
that are specialized to alfalfa and red clover hosts. The two
races are reproductively isolated because mating only
occurs on the host plant. Hawthorne & Via (2001) tested the
prediction that genetic correlations between mate choice
and resource use had promoted speciation. An F2 population
was created, and the genetic correlations between fecundity on each host (a surrogate for resource use) and host
acceptance (a surrogate for mate choice) were measured by
traditional quantitative genetics methods. Positive genetic
correlations were observed between resource use and mate
choice, while negative genetic correlations were observed
between acceptance of one host and fecundity on the other.
These genetic correlations are in exactly the direction
required to promote reproductive isolation. It might then
368 J O N S L A T E
be asked, why perform a time-consuming and relatively
expensive mapping exercise when quantitative genetics
can adequately describe important features of the genetic
architecture of the focal traits? However, quantitative
genetics cannot pinpoint the loci responsible for trait
variation, so it cannot reveal how the genetic correlations are
maintained. More specifically, are correlations caused by
tight linkage or pleiotropy of the QTL, in which case they may
be the cause of reproductive isolation? Alternatively, are
they explained by gametic disequilibrium between unlinked
loci? If the latter is true, these ephemeral associations must
be maintained by selection or population structure and are
likely to have arisen post reproductive isolation. By typing
194 F2 progeny at 173 AFLP (amplified fragment length
polymorphism) loci Hawthorne & Via (2001) detected
colocalized QTL for host acceptance and fecundity, indicating that the former scenario (linkage and/or pleiotropy)
was acting in pea aphids and may be an evolutionary force
that commonly drives reproductive isolation.
Monkey flowers. Perhaps the best known QTL mapping
studies of reproductive isolation have been conducted
within the monkey flower species, Mimulus lewisii and
Mimulus cardinalis. M. lewisii has pink flowers with a shape
and high nectar concentration that is attractive to its
bumblebee pollinator. M. cardinalis has yellow flowers with
a tubular corolla and a high nectar volume preferred by its
hummingbird pollinator. The two species are found in
sympatry in the Sierra Nevada Mountains of California but
hybrids are absent (making analysis of unmanipulated
populations impossible). Bradshaw and colleagues created
an F2 mapping population and a RAPD marker map which
they used to identify QTL for eight floral traits. Every trait
had a QTL that explained at least 25% of the F2 phenotypic
variance (Bradshaw et al. 1995). A follow-up study used a
larger mapping population (n = 465) to investigate 12 traits
and confirmed the findings of the earlier study (Bradshaw
et al. 1998). The largest effect QTL (named the yup locus)
explained ∼80% of F2 carotenoid pigment variation, a trait
that largely controls flower colour. Recently, the authors
have conducted a series of elegant backcrosses and field
experiments to examine the effects of an allelic substitution
at the yup locus in an otherwise uniform background
(Bradshaw & Schemske 2003). M. cardinalis plants with an
introgressed M. lewisii yup allele had dark pink rather than
yellow flowers and were visited by bees 74 times more
frequently than the wild type. Similarly, the reciprocal
cross resulted in yellow-orange rather than pink flowers
and a 68-fold increase in hummingbird visits. Thus, a
single gene (or more accurately, single locus — close linkage
of several genes cannot be excluded) substitution can alter
phenotype sufficiently to cause a dramatic shift in pollinator
preference. However, this adaptive change may have arisen
after reproductive isolation, i.e. by reinforcement, and it
should not yet be regarded as a ‘speciation gene’. If the
causative mutation is eventually identified, its age can be
estimated by population genetic methods to determine
whether it arose before or after reproductive isolation.
Of course, identification of a mutation responsible for a
QTL is not trivial in a nonmodel organism, unless a good
candidate gene has been isolated in other model species.
Sunflowers. One example where performing QTL analysis
in the natural environment has helped understand the
genetics of speciation is within the sunflower genus Helianthus.
Helianthus paradoxus is a salt-tolerant homoploid species
derived from Helianthus annuus and Helianthus petiolaris.
Neither parental species is tolerant of the saline marshes to
which the hybrid species is adapted. Lexer et al. (2003)
created a backcross mapping population from the parental
species and transplanted seedlings to the habitat in which
H. paradoxus is found. A number of QTL for mineral ion
uptake were identified as well as three survivorship
QTL. Co-localization of QTL suggests that survivorship is
associated with increased calcium ion uptake and exclusion
of sodium ions. Furthermore, QTL alleles associated with
survivorship were derived from both parents, indicating
that phenotypes that are more extreme than those observed
in either parent species are possible in hybrids. Selection
coefficients at survivorship QTL were large, suggesting
that the homoploid species could have become established
rapidly, even in the presence of gene flow from the parent
species. Although, H. paradoxus is found in the wild, this
experiment could only have been conducted by creating a
mapping population from the parental lines and transplanting it to the wild. Because of the strong selection acting
at the survivorship QTL, they would not be segregating in
adapted H. paradoxus populations. Furthermore, in a replicate
glasshouse population, only four of the 14 QTL found in
the natural environment were detected. Thus, conducting
the experiment with an unmanipulated population or
in the glasshouse may have yielded fundamentally different
results.
Results from intraspecies crosses
QTL mapping is also beginning to illuminate our understanding of the architecture of intraspecific genetic variation.
Here I will only focus on studies that have been conducted
on mapping populations directly generated from wild
caught/sampled individuals. Mirroring the case of interspecies crosses, a handful of very recent studies have proved
particularly illuminating, addressing issues beyond the
magnitude of QTL effects. Linda Partridge and colleagues
(University College London) have examined the question
of whether the same genes are involved in adaptation in
different populations, by studying the genetic basis of
variation in body size in Drosophila melanogaster which
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Q T L M A P P I N G I N N A T U R A L P O P U L A T I O N S 369
shows clinal variation in both Australia & South America.
Gockel et al. (2002) derived an Australian mapping population from low latitude (small bodied) and high latitude
(large bodied) wild caught flies. Composite interval mapping
using 41 microsatellite loci revealed QTL on chromosome
3 and the distal end of chromosome 2. A similar experiment conducted on flies sampled from a South American
cline revealed almost identical results (Calboli et al. 2003),
indicating that the same loci may have been involved in
adaptation to climatic variation on different continents.
However, it should be noted that Drosophila have only three
major chromosomes and one minor chromosome, and so
the probability of observing QTL in similar locations by
chance is greater than for species with a larger number of
linkage groups. Furthermore, there is increasing evidence
that Drosophila QTL are actually explained by multiple
linked QTL (Mackay 2004). There is no a priori reason to
assume this phenomenon is unique to Drosophila.
A detailed understanding of quantitative genetic variation must also address whether QTL effects are dependent
on both the environment and the genetic backgrounds in
which they are segregating. Verhoeven et al. (2004) have
recently described an extensive QTL mapping study of
fitness-related traits in natural populations of the wild
barley, Hordeum spontaneum. Crosses were made between
plants adapted to coastal and steppe environment and
the fitness of the progeny was measured in both parental
environments as well as in a common-garden experiment
involving high and low nutrient conditions. For most fitness traits, between 1 and 5 QTL were identified and they
explained between 9% and 76% of the interline phenotypic
variance. This pattern is similar to that observed in many
other studies — an exponential distribution of QTL effects.
Interestingly, a high proportion of QTL were identified in
only one environment. Where QTL were identified in both
environments, their effect (but not necessarily magnitude)
was in the same direction across environments, i.e. adaptive evolution to different environments has not resulted in
genetic trade-offs. These findings are broadly similar to
those of Lexer et al. (2003) who found that only four out of
14 Helianthus paradoxus mineral tolerance and survivorship
QTL that were detected in the natural environment could
be detected in the glasshouse. Thus, evidence that QTL
effects are dependent on the environment is beginning to
accumulate.
Recent experiments in Arabidopsis thaliana have examined
whether QTL effect can be influenced by genetic background. Ungerer et al. (2003) created two populations of
genetically variable lines derived from different ecotypes
and performed a multigenerational selection experiment.
One population comprised plants that contained approximately 7/8 genome from one ecotype and 1/8 from the
other. Ecotype proportions were reversed for the replicate
population. By typing 60 randomly selected plants of each
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363–379
population at 31 markers at the end of the selection experiment, it was possible to detect deviations from expected
allele frequencies, and hence determine the genomic regions
that responded to selection. The same genomic regions
showed the greatest response to selection in both genetic
backgrounds, indicating that gene by gene interactions
(epistasis) were unimportant in this experiment. In a
follow-up experiment, Ungerer & Rieseberg (2003) created
an F2 population from the same parental ecotypes and
measured them for a series of fitness-related traits (e.g.
time until flowering, longevity and biomass). Composite
interval mapping on 294 progenies indicated that those
regions that responded to selection in the initial experiment colocalized to regions where QTL were discovered in
the second experiment. For each trait, between 1 and 4 QTL
were detected (experiment-wide P < 0.05) that explained
between 3% and 51% of the phenotypic variance. Epistasis
between the QTL was of limited importance compared to
the additive effects of these QTL. In contrast, analyses
of recombinant inbred lines of A. thaliana have revealed
epistasis in the natural environment (Weinig et al. 2003)
and the laboratory (Kearsey et al. 2003), although these
populations would not be considered natural under the
definition provided earlier. Studies of model organisms,
especially Drosophila suggest that epistatic interactions
between QTL are common (Mackay 2004) and further work
to address this question in natural populations is required.
In summary, recent experiments such as those described
above add an extra layer of sophistication to the relatively
straightforward task of finding QTL and estimating their
effect size. Thus far, the findings are relatively uncomplicated in the sense that the same QTL might be responsible
for adaptive evolution across populations (within a species), and the action of these QTL is not greatly influenced
by gene–gene interactions. Whether these interpretations
are more broadly applicable requires further testing and
care should taken not to oversimplify the interpretations
of these data. Lessons might be learned from studies of
Drosophila, where it is apparent that the last decade of QTL
research has revealed an unanticipated complex genetic
architecture of continuous traits (Mackay 2004).
Results from unmanipulated populations
Earlier in this review, it was described how most QTL
analyses in natural populations have been conducted
in specially created mapping crosses. Studies conducted
within these populations have undoubtedly enhanced our
understanding of adaptation, reproductive isolation and
speciation. However, the extent to which they can tell us
about microevolution within populations is questionable
for several reasons. Crosses invariably have elevated levels
of phenotypic and genetic variation relative to parental
strains. Even if both parental lines are from the same
370 J O N S L A T E
population and genetic variance is not elevated, the genetic
architecture of mapping populations may have been shaped
by genetic drift, mutation and adaptation within the
laboratory environment (Hoffmann 2000). A further problem
is that laboratory populations are often reared in a constant
environment, whereas natural populations are typically
found in a fluctuating environment, which very often
encompass harsher conditions than are found in the lab.
This elevated environmental variance may result in QTL–
environmental interactions as well as a relative reduction
of QTL magnitude (when expressed as a proportion of trait
variance explained). In short, we need to know whether conclusions reached from QTL experiments that are
performed in unnatural conditions apply to truly natural
populations. To date, only one QTL study has been conducted in an unmanipulated wild population, the red deer
(Cervus elaphus) on the Scottish island of Rum (Slate et al.
2002b). Three QTL for birth weight (a trait positively
correlated with fitness components) were identified in a
pedigree containing 365 deer. All QTL were of large effect,
and interestingly, one appeared to be paternally silenced,
i.e. the QTL effect was only present when inherited from the
mother. Because a major goal of this review is to highlight
how QTL mapping can be conducted in unmanipulated
populations, I will return to this example in some depth.
Similar exercises are now underway in other species,
although none are yet published.
How to map QTL in unmanipulated populations
In this section, two alternative strategies for QTL detection
in unmanipulated pedigrees are presented: mapping
within sibships and mapping in general pedigrees. Before
describing these approaches, it is useful to reflect on recent
developments in quantitative genetic analysis in the wild.
The last five years has witnessed the uptake of a favourite
tool of plant and animal breeders, the ‘animal model’, to
examine microevolutionary change in natural populations.
Briefly, the animal model is a mixed effects model in which
components of variance (e.g. additive genetic variance,
maternal effects, environmental variance) can be estimated
from a pedigreed population of individuals of any structure.
Variance components are usually estimated by restricted
maximum-likelihood (REML). Interested readers are referred
to Kruuk (2004) for an excellent review of the application
of the animal model to natural populations, and to Lynch
& Walsh (1998) for a more general description of the
underlying methodology.
Fisher’s fundamental theorem of natural selection
(Fisher 1958) has commonly been interpreted to mean that
selection will deplete additive genetic variation fastest for
traits most closely related to lifetime fitness (see also Frank
& Slatkin 1992; Walsh & Lynch, unpublished). By extension, fitness traits should be less heritable than others. Initial
investigations in natural populations supported this idea
(Gustafsson 1986; Mousseau & Roff 1987). More recent
analyses using the animal model on long-term data sets of
marked individuals find the same pattern but have shown
that a low heritability of fitness traits is not necessarily
explained by a low additive genetic variance (Kruuk et al.
2000; Merilä & Sheldon 2000). In fact, traits closely related
to fitness often have more additive genetic variance than
traits less closely related to fitness (Houle 1992; Merilä
& Sheldon 1999). However, fitness traits also have a high
environmental variance (a component of the denominator
when measuring heritabilities), which results in a low
heritability (Kruuk et al. 2000). The animal model has
subsequently been employed to investigate the causes of
evolutionary stasis (Merilä et al. 2001; Kruuk et al. 2002), the
importance of maternal effects (Kruuk 2004), and to measure genetic correlations (Coltman et al. 2001; Charmantier
et al. 2004). In short, the animal model has greatly enhanced
our understanding of the evolutionary quantitative genetics
of natural populations. Of course, classical quantitative
genetics cannot identify the genes that underlie quantitative
variation. However, the animal model can be adapted to
identify QTL, as outlined later in this section.
Consider the three requirements to identify QTL in an
unmanipulated natural population. The first is a population of individuals of measured phenotype. The second
is that the population is pedigreed, and the third is the
availability of a genetic map of variable markers (discussed
further in succeeding sections). Measuring individual
fitness in the wild is notoriously difficult (Endler 1986), especially over several generations of a long-lived organism.
However, the growth in animal model analyses (Kruuk 2004),
which shares the first two requirements outlined above,
indicates that appropriate data sets for QTL mapping are
available for an increasingly broad range of taxonomic
groups.
Mapping in sibships
Readers of Molecular Ecology will be well aware that the
application of microsatellite markers to infer parentage in
natural populations has become widespread. In many wild
populations, an impressively large number of parents have
been genetically assigned, typically using likelihood-based
methods implemented in well-known freeware packages
(Marshall et al. 1998; Duchesne et al. 2002; Signorovitch
& Nielsen 2002; Gerber et al. 2003; reviewed in Jones &
Ardren 2003). Often, relatively large half or full-sibships
can be constructed, especially in species that exhibit a large
variance in male reproductive success. These sibships
may be suitable for QTL mapping and can be considered
analogous to domestic livestock pedigrees. For example,
large paternal half-sibships are well established as suitable
mapping populations in domestic sheep (Montgomery
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363– 379
Q T L M A P P I N G I N N A T U R A L P O P U L A T I O N S 371
et al. 1993) and cattle (Georges et al. 1995). Similarly, full sib
families are often used in chicken and pig gene mapping
experiments.
Outbred half-sib and full-sib families are similar to BC
and F2 populations used in inbred line crosses. The fundamental difference is that F1 parents derived from an inbred
line cross are heterozygous at every segregating QTL and
marker. Furthermore, if the parental lines exhibit a fixed
difference at marker and QTL, the phase between markers
and linked QTLs is identical in all F1s. Outbred sibships
differ in several important respects. First, there is no guarantee that every parent will be heterozygous at both
marker loci and QTL. Thus, many families will be uninformative with respect to QTL detection. By using highly
variable markers such as microsatellites, the power to
detect QTL can be enhanced, but power is always limited
by the heterozygosity of the QTL. A second important consideration is that the phase between marker and QTL is not
necessarily consistent across families. Thus, marker effects
must be considered independently within each sibship
(whereas all sibships can be treated as a single large family
in inbred line designs), for example in a nested anova
design. Note that in inbred line designs QTL effects are
usually expressed as a difference in means of different
genotypes (a fixed effect), whereas in an outbred design, QTL
effects are typically expressed as a proportion of trait
variance explained (a random effect). Mapping in outbred
sibships commonly uses the Haley-Knott regression; a
method that was initially developed for inbred line crosses
(Haley & Knott 1992), but has been extended to detect QTL
in both half-sib (Knott et al. 1996) and full-sib outbred
families (Haley et al. 1994). Haley-Knott regressions can be
readily implemented via qtl express (Seaton et al. 2002), a
suite of programs available via a web server housed at the
University of Edinburgh.
Mapping in general pedigrees by variance components
Of course, not every natural population contains sufficiently
many or sufficiently large sibships to detect QTL by the
Haley-Knott regression. The closest analogy to this sort
of population might be a human pedigree used to detect
disease QTL. Such a pedigree may span many generations,
contain some inbreeding and may even be comprised of
a series of related sibships. Fortunately, QTL can still
be detected, by estimating variance components, including
those associated with a QTL. QTL mapping by variance
components in general pedigrees has been developed
independently by both the human genetics (Almasy &
Blangero 1998) and animal breeding (George et al. 2000)
communities. The animal breeders’ method is essentially
an extension of the animal model, and is the approach used
by Slate et al. (2002b) to detect QTL in a wild red deer
population.
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363–379
Consider the polygenic (‘animal’) model used to estimate additive genetic variance in a pedigreed population.
In matrix algebra form, the animal model can be written:
y = X β + Za + e
where:
y is a vector of phenotypes of all pedigreed individuals
β is a vector of fixed effects
X is a design matrix relating the appropriate fixed effects to
each individual
a is a vector of random effects (polygenic additive effects)
Z is an incidence matrix relating the appropriate random
effects to each individual
e is a vector of residual errors.
For any pair of individuals in the pedigree, the genetic
covariance between them is a function of 2Θij where Θij is
the coefficient of coancestry, the probability that an allele
randomly drawn from individual i is identical-by-descent
(IBD) with an allele randomly drawn from individual j.
Note that the coefficient of coancestry is obtained from the
pedigree structure of the individuals concerned, rather
than, for example, any marker data.
This model returns an estimate of the heritability of the trait,
in addition to a likelihood value ( l0) for the REML solution.
Consider now, a second linear mixed model, containing
the same terms as in the first model, plus a QTL effect at a
location of interest. This model, termed the ‘polygenic +
QTL’ model can be written as:
β + Za + Zq + e
y = Xβ
where q is a vector of additive QTL effects.
To obtain an REML solution of this model, marker data are
used to infer Rij, the proportion of alleles that two individuals
i and j actually share IBD at a chromosomal location. The
use of all markers on a chromosome to estimate Rij at each
location is known as multipoint mapping. Note that Rij is an
estimate rather than a probability, and varies at each test location (Fig. 3). Estimating Rij is time-consuming in large pedigrees, especially as it must be estimated for every test location
(e.g. every 2 cm) along a chromosome. A number of different
programs are now available to estimate Rij (see Table 1).
The ‘polygenic + QTL’ model returns estimates of the
additive genetic variance, the variance attributable to a QTL
at the test location and the likelihood value of the REML
solution (l1). A likelihood ratio test statistic (LRT) can be
obtained from the two models by:
LRT = −2 ln( l0 − l1)
Under the null hypothesis of no QTL at the test location
the test statistic follows a 50: 50 mixture distribution, where
372 J O N S L A T E
Fig. 3 QTL mapping in general pedigrees. A simplified version of a general pedigree is illustrated. The pedigree contains 10 individuals
(1–10), all of whom are typed at a microsatellite locus with four alleles (A–D). 2Θij (twice the coefficient of coancestry) and Rij (the IBD
coefficient at the marker) between each pair of individuals is shown. An animal model (variance components) analysis requires the former
to estimate additive genetic variance and the latter to estimate the variance component explained by a QTL. Note that individual 10 is the
grandchild of individuals one to four. 2Θij is, by definition 0.25 between a grandparent and grandchild. However, the number of alleles
actually shared IBD varies across the genome. In this example, individual 10 shares half of its alleles IBD with grandparents 1 and 4 but no
alleles IBD with grandparents 2 and 3.
one component is a point of mass 0 and the other mixture
component is a χ 12 distribution (Almasy & Blangero 1998;
George et al. 2000). Under the subtly different null
hypothesis of no QTL anywhere on the chromosome, the
test statistic appears to approximate to a χ 12 distribution
(George et al. 2000).
It should be pointed out that QTL detection by this process
is time-consuming and does have some other disadvantages.
Most notably, statistical significance testing by permutation testing cannot be readily employed in the way that it
can in more familiar mapping designs. However, mapping
in general pedigrees does utilize more pedigree information than half-sib or full-sib designs, and therefore appears
to have greater power to detect QTL (Slate et al. 1999;
George et al. 2000). In their study of birth weight QTL in
red deer, Slate et al. (2002b) used both a general pedigree
and a half-sib approach to identify QTL. Two out of three
QTL were detected by both methods. In summary, natural
populations for which the animal model has been used to
measure additive genetic variance components can also be
used to detect QTL (sample size permitting), provided a
map of variable markers is available.
Maps and markers
Any mapping project is, of course, impossible without
a linkage map of variable markers. The choice of marker
largely depends on the type of mapping population. If
inbred line crosses are used, then the ideal marker will be
biallelic, reflecting a fixed difference between the parental
lines. Thus, AFLPs or single nucleotide polymorphisms
(SNPs) are appropriate. The advantages of AFLPs are that
they can be generated for any organism, and that numerous
genotypes can be obtained rapidly and cheaply. Disadvantages of AFLPs are that: (i) bands are usually unique to a
particular mapping population, making them inappropriate
for comparative studies with other populations; (ii) bands
are dominant, meaning heterozygotes cannot be distinguished from one of the homozygote genotypes, e.g. in an
F2 population; (iii) AFLPs cannot be identified in a targeted
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Q T L M A P P I N G I N N A T U R A L P O P U L A T I O N S 373
Table 1 Software to aid QTL detection in natural populations
Program
URL
Comments
Linkage map construction
mapmaker
ftp://ftp-genome.wi.mit.edu/distribution/software/mapmaker3/ Linkage map construction and QTL analysis
in experimental populations.
crimap
http://compgen.rutgers.edu/multimap/crimap/index.html
Linkage map construction in general
pedigrees. Requires UNIX operating system.
QTL mapping in experimental crosses
bqtl
http://hacuna.ucsd.edu/bqtl/
mapmanager qtx
mapqtl
http://www.mapmanager.org/mmQTX.html
http://www.kyazma.nl/index1.php
multimapper
http://www.rni.helsinki.fi/∼mjs/
plabqtl
http://www.uni-hohenheim.de/∼ipspwww/soft.html
qtlcartographer http://statgen.ncsu.edu/qtlcart/index.php
qtl express
http://qtl.cap.ed.ac.uk/
r/qtl
http://www.biostat.jhsph.edu/∼kbroman/qtl/
IBD coefficient estimation
simwalk
http://watson.hgen.pitt.edu/docs/simwalk2.html
loki
IBD coefficient estimation and haplotype
inference. IBD coefficients can be used in
mixed effects model to detect QTL in general
pedigrees.
IBD coefficient estimation and QTL analysis
in general pedigrees. IBD coefficients can be
used in mixed effects model to detect QTL in
general pedigrees. Requires UNIX or LINUX
operating system.
http://loki.homeunix.net/
QTL mapping in general pedigrees
solar
http://www.sfbr.org/solar/index.html
merlin
QTL mapping by variance components in
general populations. IBD coefficient
calculation implemented within program.
Requires UNIX or LINUX operating system.
QTL detection and IBD coefficient estimation
in general pedigrees.
http://csg.sph.umich.edu/pn/index.php?furl=/
abecasis/Merlin/index.html
Pedigree management software
grr
http://qtl.well.ox.ac.uk/GRR/
pedviewer
http://www-personal.une.edu.au/∼bkinghor/pedigree.htm
pedcheck
http://watson.hgen.pitt.edu/register/docs/pedcheck.html
way in the sense that other markers can. By this, I mean
that if a particular chromosome has poor marker coverage,
identifying additional AFLPs to enhance coverage is not
straightforward. SNPs are becoming increasingly widely
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363–379
Bayesian QTL analysis in line crosses.
Requires R package (open source).
QTL mapping in experimental populations
QTL detection in experimental crosses.
Commercial package. joinmap software also
available for map construction.
Bayesian QTL analysis in line crosses and
outbred sibships. C source code provided.
QTL analysis in line crosses by composite
interval mapping. Popular with plant breeders.
Interval, composite interval and multi-trait
mapping in experimental populations. Very
widely used.
QTL mapping in inbred and outbred
experimental crosses. Implements HaleyKnott regression. Data submitted to web
server. QTL mapping in general pedigrees
expected shortly.
QTL mapping in experimental crosses. Free
add-on package for the R and S languages.
Detecting pedigree errors from marker data.
Uses identity by state allele sharing.
Draws pedigree diagrams. Can also be used
to calculate inbreeding coefficients.
Detects pedigree errors from family and
marker data.
used in ecology (Morin et al. 2004), although they have
yet to be used to map QTL in natural populations. The
advantages of SNPs are that they are more abundant than
any other marker, they can be obtained in coding or
374 J O N S L A T E
noncoding regions, they can be targeted to particular genes
or regions of the genome (Aitken et al. 2004), they are
codominant and they may be conserved between mapping
populations. Disadvantages include: (i) SNP discovery
requires genome sequence data from the study species or
a related model organism; (ii) heterozygosity can be low;
(iii) SNP discovery is relatively expensive compared to
AFLP genotyping, although subsequent genotyping can be
cost-effective on some systems (Morin et al. 2004).
For outbred mapping populations, the marker of choice
is usually the microsatellite. Unlike populations that are
derived from inbred lines, parents are not by definition
heterozygous at a marker. Biallelic markers such as SNPs
or AFLPs can never have an expected heterozygosity
greater than 0.5 in a randomly mating population. In outbred
populations, the proportion of parents that are informative
at a marker is maximized by genotyping with multiallelic
microsatellites. It is notable that many of those species
where quantitative genetics has been conducted on
pedigreed natural populations already have microsatellite
maps. For example, medium density maps already exist
for red deer (Slate et al. 2002a), sheep (Maddox et al. 2001)
and Atlantic salmon (Gilbey et al. 2004; Moen et al. 2004).
Geneticists may also take advantage of the fact that microsatellites are often conserved between related organisms.
For example, PCR (polymerase chain reaction) primers for
markers developed in livestock, humans, Arabidopsis thaliana
and laboratory rodents have all proved useful in natural
populations of related species (Morin et al. 1994; van
Treuren et al. 1997; Slate et al. 1998; Rogers et al. 2000; Peakall
et al. 2003; Kuittinen et al. 2004). Passerine bird species have
been the focus of more longitudinal quantitative genetics
studies than any other taxonomic group (Kruuk 2004).
Approximately 500 passerine microsatellites have been
cloned and many of these coamplify in related species
(Primmer et al. 1996; Dallimer 1999; Dawson et al. 2000;
Richardson et al. 2000). However, the degree of cross-utility
declines with time since common-ancestry, and in no
single species are the majority of available markers
useful. Fortunately, the first draft of the chicken genome
has recently been made publicly available (see http://
www.genome.wustl.edu/projects/chicken/), providing a
bioinformatics resource to aid the discovery of SNPs in
passerine species. Thus, it should be possible to construct
passerine maps using a combination of microsatellites and
SNPs.
Having generated a suite of variable markers constructing
a linkage map is deceptively simple. The only requirement
is a pedigreed population for which the segregation of
marker alleles can be followed. The process of genotyping
a mapping population to identify QTL generates exactly
the data required to construct a linkage map. Several software packages are readily available to create linkage maps
from inbred line crosses, outbred populations and even
multigenerational pedigrees ( Table 1). The optimal number
of markers required to conduct a QTL analysis is a function
of genome size, degree of linkage disequilibrium and
recombination rate. Depending on the organism, an average intermarker interval of 10 cm can usually be achieved
with between 50 and 200 markers. For nonmodal organisms, it is useful to have at least a rudimentary knowledge
of the karyotype to establish whether any chromosomes
remain unmapped.
Pedigrees of natural populations are inferred by behavioural methods, genetic profiling, or in most cases, a
mixture of both. Of course, some cases of inferred parentage
are likely to be wrongly assigned. However, once the
mapping genotype data has been accumulated, it should
be straightforward to identify cases of misassigned parentage due to numerous mismatches between parent and
offspring, e.g. Slate et al. (2000). Failure to account for
pedigree error will cause map distances to be incorrectly
estimated, and may even lead to the linkage being incorrectly assigned (Type I error) or missed entirely (Type II
error). Many of the programs listed in Table 1 will fail to
run in the presence of parent-offspring mismatch. Pedigree
error checking packages are listed in Table 1. When
designing mapping experiments, it is prudent to increase
the sample size to allow for the subsequent ‘culling’ of misassigned individuals in the pedigree.
Caveats
Having typed a pedigree, built a map and detected QTL
there are a number of further issues to consider. Some are
discussed here, but where necessary, the reader is guided
to more in-depth discussion of some of the well-known
difficulties and biases associated with QTL discovery. It
should be remembered that the majority of these issues
relate to all QTL mapping experiments, not just those
conducted in natural populations.
Are my QTL real?
Any QTL mapping experiment involves a large number of
statistical tests. Clearly, using nominal significance levels
would result in a large number of false positive QTL.
Conversely, very stringent criteria for declaring linkage
will cause some QTL to be missed. QTL mappers are well
aware of the problem, and guidelines have been proposed
to declare linkage (Lander & Kruglyak 1995). For mapping
in general pedigrees, the appropriate test statistic thresholds
for declaring linkage can be calculated as a function
of genome length, the number of chromosomes and the
recombination rate. Formulae to obtain appropriate
thresholds are provided by Lander & Kruglyak (1995). In
line crosses or in outbred sibships, significance testing can
be determined by permutation testing (Churchill & Doerge
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Q T L M A P P I N G I N N A T U R A L P O P U L A T I O N S 375
1994; Doerge & Churchill 1996). This method makes no
assumptions about the null distribution of the test statistic,
and knowledge of parameters such as the genome length is
not required. Thus, permutation testing provides a robust
method for significance testing.
QTL detected in natural populations may often be of
marginal significance. Sample sizes and marker density
are often modest. The power to detect QTL in unmanipulated populations will be further reduced if environmental
sources of variation provide ‘noise’. Repeating experiments
on an independent sample may be impractical or impossible
in some populations. However, there have been examples
of QTL being replicated in independent mapping populations, e.g. in monkey flowers and threespine sticklebacks.
Clearly, mappers have to be wary of detecting false positives, especially when independent evidence to verify their
conclusions is lacking.
Are my QTL effects overestimated?
Not only can false QTL be spuriously identified and real
QTL missed in genome scans, but the estimate of QTL
magnitudes are commonly upwardly biased. The problem
was first described by Beavis (1994), and is known as the
‘Beavis Effect’. Consider two QTL of equal magnitude. If
environmental variance causes the magnitude of one QTL
to be overestimated and the other to be underestimated,
the overestimated QTL is more likely to provide a test
statistic that exceeds significance thresholds. The problem
is exacerbated with smaller sample sizes (e.g. n = 100),
although the degree of upward bias is believed to be small
when sample size exceeds 500 individuals (Beavis 1994).
Further discussion of the Beavis Effect is provided in
Roff (1997), Orr (2001), Allison et al. (2002) and Barton &
Keightley (2002). It is worth noting that many studies of
natural populations have used considerably fewer than
500 individuals. Studies that conclude that individual
QTL account for a large proportion of phenotypic variation
must be treated with some caution. The problem can be
avoided by replicating experiments, or by estimating QTL
magnitude in a different sample of individuals to those
where the QTL was identified.
An additional issue when estimating QTL magnitude is
described by Orr (2001). If a mapping population is derived
from two inbred lines, or from reproductively isolated
populations, the amount of phenotypic and genetic variation within the mapping population (e.g. an F2) is often
huge in comparison to the variation within the parental
populations. QTL effects are commonly described by the
proportion of trait variation they explain in the F2 generation. However, it can sometimes be more informative to
describe a QTL effect relative to the variation in the parent
population in which a new mutation first appeared (the
standing variation). Measuring QTL effects in this way
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363–379
reduces the confounding influence of time since divergence
of the parental lines and provides a more accurate reflection
of the strength of selection on the QTL (Orr 2001).
How do I find my QTN?
The term QTN refers to quantitative trait nucleotide; in
other words the mutation that is responsible for a QTL.
Mapping studies in livestock, humans and model organisms
are usually regarded as an initial step towards identifying
a QTN. A variety of strategies have now been proposed to
aid QTN discovery including association mapping (linkage
disequilibrium mapping) on very large samples of unpedigreed individuals (Kruglyak 1999; Risch 2000), combining
QTL with microarray data on gene expression (Schadt et al.
2003), candidate gene analysis (see for example Galloway
et al. 2000; Shapiro et al. 2004), and, in model systems,
mutagenesis (Flint & Mott 2001; Mackay 2001). It should
be noted, however, that the process of QTN discovery is
time-consuming, expensive and thus far has resulted in
surprisingly few successes. Clearly, scientists studying these
systems have greater resources (financial and technological)
than those studying natural populations. At this stage, QTN
detection in the wild is unlikely unless very convincing
candidate genes are available. However, the integration of
microarray and QTL mapping methodologies does appear
promising, provided good quality RNA from appropriate
tissues can be obtained from individuals in natural
populations.
Some QTL mapping studies in natural populations have
sought to test specific hypotheses that do not necessarily
require the identification of QTN. For example, Hawthorne
& Via’s pea aphid experiment examined whether genetic
correlations were ephemeral (maintained by selection) or
were constrained by pleiotropy/linkage disequilibrium.
Thus, while QTN detection may be difficult in natural
populations, it may not be an overall objective of a QTL
mapping study.
Lessons from model organisms?
Clearly, one of the reasons for conducting mapping studies
in natural populations is to establish whether findings from
earlier studies in model organisms apply more generally.
While many of the results described in this review appear
to be uncomplicated, it is useful to make a comparison with
the experiences of those conducting studies in laboratory
populations of Drosophila or other model organisms. Upon
reading the experiences of Drosophila QTL mappers, it
is apparent that QTL effects are dependent on genetic
background (dominance and epistasis are common), environment and sex, and that many mutations have pleiotropic
effects (Mackay 2004). A decade earlier it was anticipated
that the picture would be less complex (Mackay 1995). A
376 J O N S L A T E
similar picture is emerging from studies of Arabidopsis
(Kearsey et al. 2003; Weinig et al. 2003). Given that natural
populations will generally harbour more genetic and
environmental variance than their equivalent model
systems, it seems probable that the situation will be at
least as complex in the wild. Thus, results from natural
populations should be interpreted with some caution.
Particular caution should be employed when QTL colocalize to the same genomic region. Even in genome-scans
conducted on very large sample sizes, confidence intervals
for QTL location are wide, with tens or hundreds of genes
lying beneath a QTL peak. Thus, it is dangerous to assume
that a QTL is determined by a single gene or that colocalized
QTL are explained by the same gene. In one notable study,
an approach termed reciprocal hemizygosity analysis was
used to dissect the molecular basis of a QTL in yeast, Saccharomyces cerevisiae (Steinmetz et al. 2002). The QTL was in
fact attributable to three tightly linked genes. Similarly,
QTL in Drosophila have been shown to be, in fact, caused by
linked genes acting cumulatively (Mackay 2004). At this
stage it is too early to say whether this phenomenon is
widespread, although data from model organisms suggest
that selection has favoured the clustering of genes of
related function (Cohen et al. 2000; Pal & Hurst 2003).
Given these findings, it would be premature to argue that
the colocalization of QTL affecting the same trait in different populations is the product of parallel mutations, or that
the colocalization of QTL affecting different traits in the
same population is the result of pleiotropy (rather than
linkage).
Future directions and conclusions
What does the future hold for QTL mapping in the natural
populations? Given that the number of mapping experiments in the wild continues to grow exponentially, it seems
likely that many more will be conducted in the next few
years. One of the most encouraging signs is the creation of
consortia to create genetic tools in ecologically interesting
organisms. For example, the Daphnia Genomics Consortium
(DGC), http://daphnia.cgb.indiana.edu/, has facilitated
genome sequencing, linkage map construction and microarray development in Daphnia pulex and related organisms.
As more model organisms become mapped and sequenced,
it will become easier to create maps in related nonmodel
species. For example, mapping studies in red deer and
Arabidopsis lyrata have benefited from resources created
for cattle and Arabidopsis thaliana. In the UK, the Natural
and Environmental Research Council (NERC) has invested
heavily in an Environmental Genomics program (http://
www.nerc.ac.uk/funding/thematics/envgen/), which
includes QTL mapping studies of natural populations of
Soay sheep, wild brassicas, wild relatives of Arabidopsis
and Senecio species.
It is apparent that SNP markers are becoming increasingly useful in ecological genetics (Luikart et al. 2003;
Morin et al. 2004). The decreasing cost of SNP genotyping
is also likely to make genetic map construction feasible in
nonmodel organisms, although it should be remembered
that SNPs are not ideal for mapping in outbred populations.
Comparative anchor tagged sequences (CATS) primers
may prove to be particularly useful. CATS primers anneal
to conserved exonic regions, enabling the amplification and
sequencing of adjacent introns. Because the primers are in
conserved regions, CATS loci can be useful to identify
SNPs for comparative genomics projects. Initial investigations suggest that mammalian CATS primers designed
from primates and rodents generate a useful PCR product
across a wide range of mammals (Aitken et al. 2004).
Cheaper SNP genotyping should also result in the wider
uptake of population genomics. Population genomics
involves the genotyping of many neutral loci in a large
number of unpedigreed individuals, and then estimating
population genetic statistics on each locus (Luikart et al.
2003). Outlier loci, those with unusual statistical properties
relative to others in the population, are likely to be linked
to genes under selection. Population genomics can be conducted in its own right (Wilding et al. 2001; Campbell &
Bernatchez 2004) or in tandem with a more conventional
QTL approach (Ungerer & Rieseberg 2003). It has the
advantage that pedigrees or phenotype data are not required.
However, while it may identify loci under selection, it does
not relate this to any particular trait or to a proportion of
quantitative genetic variation explained by that region of
the genome.
It is anticipated that more unmanipulated natural populations will be subject to QTL analysis. The application of
the animal model to quantitative genetic analysis in the wild
has seen a dramatic gain in popularity in just a few years.
QTL analysis in many of these populations is a logical next
step, provided a map is available. Generating passerine
linkage maps would be a particularly useful development,
because in some passerine species, several populations
have been the focus of long-term study (Charmantier et al.
2004). It would therefore be possible to determine whether
the same QTL are associated with microevolution in distinct
populations of the same species, and would also provide
the opportunity to confirm QTL in independent samples.
One of the aims of this review has been to highlight how
QTL mapping in natural populations has matured in
recent years. Initial studies provided descriptions of the
number and magnitude of QTL determining phenotypic
variance. However, it could be argued that results were
equivocal (Orr 2001; Barton & Keightley 2002), and may
even be confounded by problems such as Type I error and
biased estimates of QTL magnitude. It is questionable
therefore the extent to which individual studies have dramatically enhanced our understanding of how populations
© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 363– 379
Q T L M A P P I N G I N N A T U R A L P O P U L A T I O N S 377
evolve or how species arise. However, recent studies have
begun to explore increasingly sophisticated issues such
as genetic correlations, gene-by-environment interactions,
epistasis and the adaptive importance of particular genes.
As more studies are conducted, a clearer picture should
emerge. Furthermore, QTL studies have now begun to be
conducted in unmanipulated populations where natural
selection is operating, and where genetic architectures may
differ from the lab. It seems inevitable that the field will
continue to grow when the latest tools to identify QTL and
QTN become more readily applicable to natural populations. The future of QTL mapping in natural populations
as an approach to understanding microevolution, adaptation and speciation looks rosy.
Acknowledgements
I thank Josephine Pemberton, Allan McRae, Jake Gratten, Gavin
Hinten, Harry Smith and two anonymous referees for their
insightful comments that greatly improved the manuscript. My
interest in QTL mapping in natural populations has been inspired
by several years of collaboration and discussion with Terry Burke,
Dave Coltman, Allan Crawford, Ken Dodds, Jake Gratten, Loeske
Kruuk, John McEwan, Allan McRae, Josephine Pemberton, Mike
Tate and Peter Visscher.
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Jon Slate is a Lecturer in population genetics at the University of
Sheffield. Research interests of the Slate Group are based around
the broad theme of evolutionary genetics of natural populations
using comparative genomics, linkage analysis and molecular
evolution tools. Other interests include the molecular evolution of
mitochondrial DNA and the use of molecular markers to infer
inbreeding depression.