Download how mechanistic biology can inform molecular ecology

Molecular Ecology (2009) 18, 4997–5017
doi: 10.1111/j.1365-294X.2009.04427.x
INVITED REVIEW
Linking genotypes to phenotypes and fitness: how
mechanistic biology can inform molecular ecology
A N N E C . D A L Z I E L , * S E A N M . R O G E R S † and P A T R I C I A M . S C H U L T E *
*Department of Zoology, University of British Columbia, 6270 University Blvd, Vancouver, British Columbia, Canada V6T 1Z4
†Department of Biological Sciences, University of Calgary, 2500 University Drive N.W., Calgary, Alberta, Canada T2N 1N4
Abstract
The accessibility of new genomic resources, high-throughput molecular technologies and
analytical approaches such as genome scans have made finding genes contributing to
fitness variation in natural populations an increasingly feasible task. Once candidate
genes are identified, we argue that it is necessary to take a mechanistic approach and
work up through the levels of biological organization to fully understand the impacts of
genetic variation at these candidate genes. We demonstrate how this approach provides
testable hypotheses about the causal links among levels of biological organization, and
assists in designing relevant experiments to test the effects of genetic variation on
phenotype, whole-organism performance capabilities and fitness. We review some of the
research programs that have incorporated mechanistic approaches when examining
naturally occurring genetic and phenotypic variation and use these examples to highlight
the value of developing a comprehensive understanding of the relationship between
genotype and fitness. We give suggestions to guide future research aimed at uncovering
and understanding the genetic basis of adaptation and argue that further integration of
mechanistic approaches will help molecular ecologists better understand the evolution of
natural populations.
Keywords: adaptation, candidate gene, genetic variation, physiology, quantitative genetics
Received 19 August 2009; revision received 15 October 2009; accepted 19 October 2009
Introduction
Recent advances in high-throughput molecular biology
have made it possible to rapidly characterize a large
number of genetic polymorphisms in virtually any natural population (reviewed by Bouck & Vision 2007; Lister
et al. 2009; Mardis 2008). In addition to providing a
wealth of putatively neutral markers that can be used to
study ecological and evolutionary processes in natural
populations, these high-throughput techniques also
facilitate the search for adaptively significant genetic
variation. The value of using a combination of population genomic and quantitative genetic methods to identify the genes underlying ecologically important traits in
multicellular eukaryotes has been widely reviewed (e.g.
Luikart et al. 2003; Storz 2005; Vasemagi & Primmer
2005; Ehrenreich & Purugganan 2006; Jensen et al. 2007;
Correspondence: Anne C. Dalziel, Fax: 604-822-2416;
E-mail: [email protected]
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Hoffmann & Willi 2008; Naish & Hard 2008; Pavlidis
et al. 2008; Schmidt et al. 2008; Stinchcombe & Hoekstra
2008; Mackay et al. 2009; Slate et al. 2009), and we will
not revisit these issues here. Instead, the purpose of this
review is to highlight the benefits of incorporating a
mechanistic perspective when attempting to find the
genetic variants associated with ecologically relevant
phenotypic variation, predicting the potential impacts of
this variation across levels of biological organization,
and ultimately testing these predictions.
We define a mechanistic perspective as one that
incorporates a priori knowledge about the function of
genes, proteins, biochemical networks and pathways,
and their resulting effects on phenotypic traits, wholeorganism performance and fitness. Quite simply, incorporating a mechanistic perspective means thinking
about how organisms ‘work’. Although the benefits of
incorporating a mechanistic perspective into evolutionary studies have long been recognized (reviewed by
Autumn et al. 2002; Dean & Thornton 2007; Watt 1985,
4998 A . C . D A L Z I E L , S . M . R O G E R S and P . M . S C H U L T E
2000; Watt & Dean 2000), the ability to perform truly
integrative studies has been limited by technical and
analytical hurdles. We are now at a time where the
widespread availability of molecular techniques, and
recent advances in this field (both empirical and analytical), are making truly integrative studies more practical.
In this review, we begin by showing how mechanistic
knowledge can help molecular ecologists to formulate
testable predictions about the effects of genetic variation
on phenotype, performance and fitness. We then review
three classic research programs that successfully
exploited a mechanistic approach to choose candidate
genes and then test the phenotypic (and fitness) consequences of genetic variation at these loci. We conclude
by providing an outline of the methods that will be
required to carry this approach into the future.
Using mechanistic biology to predict the effects
of genetic variation
Biological systems can be divided into a series of hierarchical levels, with genetic variation (at the base of this
hierarchy) affecting processes at higher levels (Fig. 1).
Whole-organism performance
(Behavior
Morphology
Sel
ect
ion
Physiology)
Biochemical pathway
& network
Fitness
Environment
†
Cellular function
Interactions among proteins
Protein function
and/or ammount
Genetic variation *
Fig. 1 Connections across levels of biological organization.
Genetic variation may affect phenotype at a number of levels
of biological organization to ultimately influence whole organism performance capabilities and fitness. *Genetic variation
may result from a variety of types of polymorphisms [e.g. single nucleotide polymorphisms (SNPs), small insertions or deletions (indels), or copy number variation (CNV)] in a variety of
gene classes (e.g. protein-coding genes, ncRNAs, or regulatory
elements). †‘Environment’ includes all of the biotic and abiotic
factors that can influence traits at any level of this hierarchy.
Thus, variation at the genetic level can affect the function and ⁄ or amount of proteins, which may then alter
protein–protein interactions, influence biochemical pathways and networks and eventually modify cellular
function, organismal phenotype, whole-organism performance capabilities and fitness. The value of examining multiple levels of biological organization has been
clearly summarized by the prominent ecological physiologist George Bartholomew, who wrote ‘there are a
number of levels of biological integration and… each
level finds its explanations of mechanism in the levels
below and significance in the levels above it’ (Bartholomew 1966). Here we show how incorporating available
mechanistic information can help to generate predictions about the impacts of variation at each level of biological organization and guide experiments at higher
levels of organization. Decades of mechanistic investigation have provided a wealth of information that can be
used to guide these predictions. Box 1 summarizes
some of the online resources that can be used to access
this information. We argue that paying attention to as
many levels of biological organization as possible
should enhance our ability to understand the consequences of genetic variation, and to detect the genes
that are subject to natural selection in the wild.
Genes
There are many types of genetic variation that can affect
the function and ⁄ or amount of proteins, including single nucleotide polymorphisms (SNPs), small insertion–
deletion polymorphisms (indels), microsatellites and
larger copy number variants (Feder 2007). All of these
types of genetic variation have been shown to contribute to variation in ecologically relevant traits (e.g. Hammock & Young 2004; Perry et al. 2007; Aminetzach
et al. 2005; Schlenke & Begun 2004; and supporting
information Table S1). To make a prediction about the
functional consequences of any of these types of genetic
variation from sequence information alone, it is first
necessary to determine whether the variant occurs in a
protein-coding or non-protein-coding sequence region.
If the genetic variant occurs in a protein-coding gene, it
is often possible to make predictions about its function
(see Proteins). However, less than 2% of a typical mammalian genome codes for proteins (Amaral et al. 2008),
so many genetic variants will occur in non-coding
regions of the genome. For example, variants may fall
within a regulatory element (e.g. transcription factor
binding site) that does not need to be transcribed to
perform its function. Many non-transcribed regulatory
elements have been functionally characterized and there
are several examples of variation in regulatory elements
that contribute to ecologically important variation
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MECHANISTIC BIOLOGY AND MOLECULAR ECOLOGY 4999
Box 1 Selected online resources to identify candidate genes and assess their function
Online databases provide a rich source of information about genes and their biological functions. The list below is
organized by level of biological organization, but many of these resources are useful across multiple levels. This
list focuses on resources for protein coding genes. Resources for detecting and interpreting the functional consequences of variation in ncRNA genes and regulatory sites are also available, but the complexities of these analyses
are beyond the scope of this review (see Mituyama et al. 2009; Nardone et al. 2004; Portales-Casamar et al. 2009;
Wasserman & Sandelin 2004 for further information).
Finding genes present in a genomic region
If the results of population genomic or quantitative genetic screens highlight a particular genomic region, this
region can be scanned for genes of interest using a number of genome browsers. The Ensembl (http://www.
ensembl.org) and UCSC (http://genome.ucsc.edu/) genome browsers are useful for researchers studying animals
or fungi (Kent et al. 2002; Hubbard et al. 2009). For plants, NCBI’s plant genome central (http://www.ncbi.nlm.
nih.gov/genomes/PLANTS/PlantList.html) or species-specific sites such as the genome browser at The Arabidopsis
Information Resource (TAIR) (http://www.arabidopsis.org/cgi-bin/gbrowse/arabidopsis/) (Swarbreck et al. 2008)
can be used. These browsers allow users to examine the genome of your species of interest (if available) or that of
a closely related species. Levels of annotation vary among genomes, and the locations of ncRNA and regulatory
sites are not normally included.
Gene function
Once a gene of interest has been identified, NCBI’s Entrez Gene (http://www.ncbi.nlm.nih.gov/sites/
entrez?db=gene) is a very useful resource for obtaining mechanistic information about protein-coding genes
(Maglott et al. 2007) because it summarizes information from multiple sources. Entrez Gene provides intron ⁄ exon
structure, alternative splice variants, gene ontology information (from http://www.geneontology.org/), a text
summary of the protein’s functions, and links to other NCBI and external resources (Sayers et al. 2009). NCBI’s
conserved domain database (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) can help identify protein
domains within protein-coding regions and is particularly helpful for identifying gene function in cases where the
function of the complete protein is unknown (Marchler-Bauer et al. 2009).
Protein structure
NCBI’s molecular modeling DataBase (MMDB) (http://www.ncbi.nlm.nih.gov/Structure/MMDB/mmdb.shtml),
coupled with their Cn3D structure viewer, can display the location of mutation within the 3D structure of a protein (Wang et al. 2007). These 3D crystal structures are obtained from the RCSB Protein data bank (PDB) (http://
www.rcsb.org/pdb/home/home.do) (Berman et al. 2000). If a protein is part of a multi-subunit protein complex,
these databases also include interactions among subunits.
Pathways and networks
If a gene is part of a well-studied pathway, resources such as WikiPathways (http://www.wikipathways.org/
index.php/WikiPathways), Pathway commons (http://www.pathwaycommons.org/pc/), the Kyoto encyclopaedia
of genes and genomes (KEGG) (Kanehisa et al. 2008)(http://www.genome.jp/kegg/pathway.html), PANTHER
(PRotein ANalysis THrough Evolutionary Relationships) (http://www.pantherdb.org/), the Reactome database
(http://www.reactome.org/) or the Plant metabolic network (http://www.plantcyc.org:1555/ARA/server.html)
can provide a way to assess the relationship between a candidate gene and other genes within the pathway or network. BioCarta (http://www.biocarta.com/) provides another, very different, compendium of biological pathways.
Although the BioCarta compendium lacks the sophisticated functionality and broad coverage of the other pathway
databases, it provides extremely accessible ‘cartoon’ versions of the pathways that it covers, accompanied by
explanatory text at the level of an introductory textbook.
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Effects on organismal phenotype
NCBI’s Online Mendelian Inheritance in Man (OMIM) (http://www.ncbi.nlm.nih.gov/omim/) is a collection of
information about human genes often coupled to information about phenotype, particularly those related to Mendelian disorders in humans (Amberger et al. 2009). OMIM often provides helpful clues as to the function of a particular gene in the context of a whole organism. Online Mendelian Inheritance in Animals (OMIA) (http://
www.ncbi.nlm.nih.gov/sites/entrez?db=omia&tool=toolbar) is a newer resource that contains some additional
information (Lenffer et al. 2006) on other animal species. For plant biologists, TAIR (http://www.arabidopsis.
org/) contains some information about the relationship between genotype and phenotype (Swarbreck et al. 2008).
The list above is not comprehensive, and new databases are regularly developed. The journal Nucleic Acids
Research publishes an annual ‘Database Issue’ which contains updated information on these and many other genomic resources (e.g. http://nar.oxfordjournals.org/content/vol37/suppl_1/index.dtl for the January 2009 listing).
(reviewed by Wray 2007). However, there are still no
general rules that can be used to predict how sequence
changes in regulatory regions affect function (e.g. Wray
et al. 2003; Segal & Widom 2009), so the effects of
sequence variation in regulatory elements often must be
determined experimentally in the absence of a priori
predictions. Alternatively, a genetic variant may fall
within a non-coding RNA (ncRNA), which is transcribed but not translated into a protein (e.g. tRNAs,
rRNAs snoRNAs, piRNAs and microRNAs). Variation
in ncRNA genes is likely to have important ecological
consequences, given that almost 70% of the genome is
transcribed (Amaral et al. 2008; Mattick 2009). Predicting the effects of variation in ncRNAs will unfortunately remain difficult until mechanistic information
about their functions improves (Mattick 2009). However, the difficulty of making mechanistic predictions
about some of these ncRNAs from sequence data alone
does not reduce the importance of taking a mechanistic
approach to study the impacts of genetic variation in
these genes; it simply increases the difficulty of designing refined experimental tests of functional impacts at
the next level of biological organization.
Proteins
Genetic variation must ultimately affect the amount or
function of a protein to have consequences at higher
levels of biological organization. As discussed above,
when examining protein-coding genes, it is often possible to make predictions about the effects of genetic variation from primary sequence information. There are
many online resources through which available mechanistic information about protein structure and function
can be accessed (highlighted in Box 1). These resources
can help to identify the probable function of a proteincoding gene and the specific functional domains within
this protein. If further information is available about the
roles of particular amino acids within a domain, more
refined predictions about how a genetic variant effects
biochemical phenotype can be made.
For example, Johns & Somero (2004) used mechanistic
knowledge about the links between lactate dehydrogenase-A (LDH-A) primary sequence, tertiary structure
and enzyme function to predict which genetic polymorphisms were responsible for observed differences in
LDH-A kinetics among temperate and tropical damselfish (genus Chromis) that were hypothesized to be
important for thermal adaptation. They predicted that
one polymorphic site (T219A), would be of particular
importance because of its location in a key ‘hinge’
region of the protein and because this site was correlated with low temperature adaptation in Antarctic nototheniod fishes (Fields & Somero 1998). They tested this
prediction using site-directed mutagenesis followed by
in vitro expression and biochemical tests of protein
function. As predicted, all fixed differences between
tropical and temperate fishes had some effect, but only
the T219A mutation was sufficient to produce the biochemical changes that occur among species. Other
excellent examples in which mechanistic predictions
have been used to design relevant experiments to test
the impacts of genetic variation have been comprehensively reviewed by Dean & Thornton (2007).
Biochemical pathways and networks
Information about the function of a protein in isolation
can provide some insights into the impact of protein
sequence variation on phenotype. However, most proteins perform their functions through interactions with
other proteins, or as part of biochemical pathways and
networks. Understanding these higher-order interactions
is critical for predicting the linkages from genotype
to phenotype to fitness. For example, the enzyme
cytochrome c oxidase (COX) is made up of 13 protein
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MECHANISTIC BIOLOGY AND MOLECULAR ECOLOGY 5001
subunits in animals. Mutations in any one of these
subunits can affect interactions among subunits and thus
change the functional properties of the enzyme. The
multi-subunit COX enzyme is itself part of the mitochondrial electron transport chain, which is made up of four
multi-subunit protein complexes and the protein cytochrome c, which interacts directly with COX (reviewed
by Rand et al. 2004). The importance of the interactions
between COX and cytochrome c can be clearly seen in
the intertidal copepod, Tigriopus californicus. When divergent populations of this species are crossed, there is an
incompatibility between the nuclear encoded cytochrome
c gene and a mitochondrially encoded COX subunit that
results in mitochondrial dysfunction in hybrids (Rawson
& Burton 2002; Ellison & Burton 2008). Electron transport
chain function is also dependent on the proper functioning of other biochemical pathways, such as the Citric acid
cycle, which produces the reducing equivalents needed
for electron transport chain function, and ultimately
those pathways that produce the substrates for the citric
acid cycle, such as glycolysis and the b-oxidation pathway (Hochachka & Somero 2001). Thus, mutations that
directly affect the function or amount of a protein and
mutations that affect a protein’s interactions with other
pathway and network members have the potential to
affect the phenotype. There are now a number of databases that summarize the roles of specific genes in biochemical pathways and networks so that this knowledge
can be more easily incorporated into predictions about
the effects of genetic variation (Box 1).
As the proteins within a network play a collaborative
role in generating the phenotype, changes in several
different genes within a network could result in similar
changes in ecologically relevant phenotypes (e.g. coat
colour in mice; Steiner et al. 2009). Alternatively, specific genes within a network may be repeatedly targeted
by evolution in multiple taxa because of their role and
location in the network (Derome & Bernatchez 2006;
Carroll 2008; Stern & Orgogozo 2008, 2009; Erwin &
Davidson 2009; Streisfeld & Rausher 2009). Functional
and physical interactions among proteins in multi-protein complexes, biochemical pathways and networks are
the underlying cause of the genetic phenomenon of
epistasis (Phillips 2008; Tyler et al. 2009). A network
perspective is therefore necessary to understand why
some genes and not others are involved in generating
adaptively significant phenotypic variation (Carroll
2008; Stern & Orgogozo 2008, 2009; Erwin & Davidson
2009). This perspective also helps to illuminate the distinction, if any, between convergent and parallel evolution (see Abouheif 2008; Arendt & Reznick 2008).
Viewed narrowly, true parallelism only occurs when
there are independent origins of the same mutation,
causing the same amino change, in the same protein in
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two taxa that have the same underlying biochemical
networks. It is more useful, however, to mechanistically
determine the level of biological organization at which
parallelism ends and convergence begins. For example,
similar traits in different taxa could arise via changes in
the same gene, in different genes within the same pathway, or changes in different pathways that interact
within a network; a trait might have evolved in parallel
when viewed at one level of biological organization, but
by convergence at an underlying level.
Organismal phenotypes, whole-organism performance
and fitness
Changes in biochemical pathways and networks result
in cellular-level changes that can influence the structure
and function of tissues, organs and organ systems, and
thus alter a wide variety of complex organismal traits,
including morphology, behaviour and physiology
(Fig. 1). Predicting the effects of genetic variation on
morphology, behaviour and physiology requires an
understanding of the interactions among the many
underlying levels of biological organization. Most of the
databases relating genotype to organismal phenotype
contain information compiled from naturally occurring
(e.g. human disease phenotypes) and laboratory-produced mutants (Box 1). In natural populations, organism-level traits interact to influence the performance
capacity of an organism. Whole organism performance
capacity can be defined ‘as the ability of an animal to
conduct an ecologically relevant task’ (Irschick et al.
2008), and is a metric of how well a task is done. Such
tasks may include foraging ability, dispersal or predator
avoidance ⁄ resistance. Knowledge about the interactions
among organism-level traits can often be used to make
predictions about their individual impacts upon wholeorganism performance.
Finally, the ability of an organism to perform all the
various tasks necessary for it to survive and reproduce
can ultimately influence fitness (Irschick 2003). It is only
in those cases in which genetic variants have differential effects on whole-organism performance that fitness
effects will occur. The set of potential relationships
between phenotypic traits, performance and fitness has
been clearly outlined by Arnold (1983), using path analysis to statistically model these associations. Similar
associations could, in principle, be drawn between processes at any level of biological organization from
genetic variation through the intervening levels to
fitness.
Empirically, determining if a gene or trait has evolved
by natural selection is best accomplished by directly measuring the contribution of alternate alleles to the next
generation (e.g. Endler 1986; Barrett et al. 2008).
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Miller et al. 2007); for additional examples, see supporting information Table S1]. In addition, several groups
have already identified, or are on the cusp of identifying, candidate genes for ecologically important traits in
a variety of plant and animal species [e.g. whitefish
(Rogers & Bernatchez 2007; Whiteley et al. 2008; Jeukens et al. 2009; Nolte et al. 2009); sunflowers (e.g. Kane
& Rieseberg 2007; Sapir et al. 2007; Lai et al. 2008); Bochera stricta, a close relative of Arabidopsis (e.g. Schranz
et al. 2009); wild tomatoes (e.g. Moyle 2008); marine
snails (e.g. Wood et al. 2008; Galindo et al. 2009); additional examples reviewed in Karrenberg & Widmer
(2008)]. Given this, we expect that many more candidate genes associated with ecologically important traits
will be characterized in the near future, although this
will remain difficult for genes of small effect. Below, we
discuss in more detail three classic pre-genomics
research programs that have integrated across levels of
biological organization to study the effects of genetic
variation at a selected locus (Fig. 2). Each of these
Classic examples of mechanistic approaches
There are many research programs that have made tremendous progress in characterizing genes of large effect
associated with ecologically important traits [e.g. flowering time in Arabidopsis thaliana (reviewed by Ehrenreich & Purugganan 2006; Roux et al. 2006; Shindo et al.
2007); body armour and colouration in threespine stickleback (e.g. Shapiro et al. 2004; Colosimo et al. 2005;
(a)
Killifish (F. heteroclitus)
(b)
(c) Garter snake (T. sirtalis)
Sulphur butterfly (Colias spp.)
Flight capabilities*, survival*,
lifespan, female fecundity*,
male mating success* & dispersal
Metabolic rate in
embryos & adults
Body temperature &
maximal flight metabolic rate
Temperature
?
?
?
?
Enzyme kinetics
at low temperature
& enzyme concentration
Enzyme kinetics &
stability over a range
of temperatures & pHs
LDH-B
(SNP in coding region
& variation in promoter)
PGI
heterozygote*
(SNPs in coding region)
Fitness
Temperature
n
glucose &
lactate metabolism
tio
ATP concentration
& Hb-O 2 binding
lec
Se
Swimming ability,
development rate
& hatching time
No effect of prior TTX exposure or rearing environment
However, indirect methods, such as examining molecular data for signatures of selection (reviewed by Jensen
et al. 2007; Nielsen 2005) and comparative phylogenetic
analyses (reviewed by Garland et al. 2005; Leroi et al.
1994) may also be used to provide evidence for selection.
These methods are complementary, so a combination of
these approaches, with mechanistic evidence of a connection between genetic and phenotypic variation, provides
an accumulation of evidence that strongly supports the
hypothesis of adaptive evolution of a trait.
Crawling abiltiy after
TTX ingestion
Muscle
contraction
Propagation of
action potentials
?
?
Binding of TTX to
Nav1.4
Resistant allele of
Nav1.4
(SNPs in coding region)
Fig. 2 Three integrative research programs linking genotype to phenotype, performance and fitness. Connections across levels of
biological organization follow the structure of Fig. 1. Links that have not yet been studied are denoted with question marks. If the
link from performance to fitness has not yet been studied directly, the arrows are excluded. See the accompanying text for further
details and references. (a) Lactate dehydrogenase-B (LDH-B) in Fundulus heteroclitus. (b) Phosphoglucose isomerase (PGI) in Colias
spp. Similar evidence for the effects of PGI genotype on phenotype and performance has been found in the Glanville fritillary butterfly (Melitaea cinxia); this evidence is listed in blue and traits which are linked to PGI genotype in both Colias spp. and M. cinxia are
noted with a blue asterisk. (Photo provided by W. B. Watt). (c) The voltage-gated sodium channel (Nav1.4) in Thamnophis sirtalis.
(Photo provided by E. D. Brodie III).
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MECHANISTIC BIOLOGY AND MOLECULAR ECOLOGY 5003
research programs used knowledge of mechanism to
provide clear, testable hypotheses about the links
between genotype, phenotype and fitness.
Lactate dehydrogenase and thermal adaptation of
metabolism in killifish
The common killifish (Fundulus heteroclitus) is a small
teleost fish that lives in marshes and estuaries along the
Atlantic coast of North America. There is a steep latitudinal thermal cline over this species’ range such that
northern populations experience temperatures that are,
on average, 13 "C colder than those experienced by
southern populations at the same time of year
(reviewed by Powers & Schulte 1998). Dennis Powers
and colleagues initiated the search for genes that are
differentially selected between northern and southern
populations of this species over 30 years ago, using allozyme screening; the ‘genome scans’ of the pre-genomic era (Place & Powers 1978; Powers & Place 1978).
Analyses of allozyme frequencies detected clines at a
number of loci, including LDH-B, an enzyme that catalyzes the interconversion of pyruvate (a fuel for aerobic
respiration) and lactate (the end product of anaerobic
glycolysis). When Place & Powers (1979, 1984a, b)
examined the kinetics of purified LDH-B enzymes in vitro they found that the northern LDH-B enzyme (LDHBNN) had a higher catalytic efficiency at low temperatures, as would be predicted if local adaptation to low
temperatures had occurred in northern populations, but
did not find evidence for local adaptation of the southern LDH-B (LDH-BSS) genotype to warmer temperatures. Sequence analyses of LDH-B alleles suggested
that a particular amino acid variant at site 311
(Ala fi Asp) was responsible for functional differences among LDH-B alleles (reviewed by Powers &
Schulte 1998).
Powers et al. (1979) also discovered that ATP concentrations ([ATP]) in red blood cells are correlated with
LDH-B genotype. Since ATP decreases haemoglobinoxygen binding affinity, DiMichele & Powers (1982b)
predicted that LDH-BNN fish, which have higher [ATP],
would have a lower haemoglobin-oxygen binding affinity, allowing for more efficient unloading of oxygen at
the working muscles and an improvement in endurance
swimming performance (a trait that is highly dependent
upon oxygen availability, transport and use). As
expected, LDH-BNN fish had higher [ATP], lower haemoglobin-oxygen affinity and superior endurance
swimming performance at 10 "C, a temperature rarely
experienced by southern fish. However, there were no
differences in haemoglobin-oxygen affinity, [ATP] or
swimming performance at 25 "C, which is consistent
with in vitro studies of LDH-B kinetics that find no
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differences among genotypes at warm temperatures
(DiMichele & Powers 1982b).
Powers and colleagues next examined a suite of other
performance traits that are influenced by environmental
temperatures, including embryonic metabolic rate,
growth rate and hatching time. Since killifish lay eggs
at the peak of the highest spring tide, their eggs must
develop in air and hatch at the next high tide 2 weeks
later to survive (reviewed by Dimichele et al. 1986).
Thus, these traits were expected to be under strong
divergent selection between northern and southern
Fundulus populations that are developing at different
temperatures in the wild. They found that LDH-BNN
embryos had lower metabolic rates, slower development, later hatching times, decreased lactate metabolism
and decreased glucose production (reviewed by Dimichele & Powers 1982a, 1984a,b; Dimichele et al. 1986;
Paynter et al. 1991). Note that these differences in
metabolism, hatching and growth are opposite to what
would be expected for local adaptation to a colder environment (where faster development would be expected
to evolve to counter a slowing of metabolism due to
colder temperatures). These correlations between genotype and cellular and organismal phenotype were then
directly tested by exchanging the native LDH-B
enzymes of an egg (e.g. LDH-BNN) with the alternate
LDH-B enzyme (e.g. LDH-BSS). Dimichele et al. (1991)
found that the injected LDH-B enzyme determined the
metabolic rate and glucose use of the egg, showing that
it was LDH-B, and not a linked locus, that caused
the observed differences in cellular metabolism and
embryonic development.
All of the in vivo experiments described above were
performed on Fundulus from the hybrid zone between
northern and southern genotypes, near the middle of
the LDH-B cline [In Delaware, which is also near the
centre of other allozyme clines (Powers & Place 1978)].
Therefore, LDH-BSS and LDH-BNN genotypes were
tested in individuals with a mixture of northern and
southern alleles at other loci and observed phenotypic
variation between these two genotypes could be attributed to their LDH-B genotype (or to closely linked
loci) rather than to correlated variation at other
(unknown) loci. In addition, collecting fish from a single locality controlled for prior thermal history. However, more recent work on Fundulus from extreme
northern and southern populations found either no
differences in performance associated with LDH-B
genotype [swimming performance (Fangue et al.
2008)], or differences in the opposite direction to the
effects of LDH-B genotype alone [development rate to
hatching (DiMichele & Westerman 1997), growth rate
following hatching (Schultz et al. 1996) and adult metabolic rate (Podrabsky et al. 2000; Fangue et al. 2009)].
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These observations are consistent with experiments on
fish from the centre of the LDH-B cline in Delaware
that examined multilocus genotypes at several allozymes, as opposed to LDH-B in isolation. Dimichele &
Powers (1991) found that fish bearing the most common northern multilocus genotype had faster development, despite the fact that at the single locus level fish
bearing the LDH-BNN genotype developed more
slowly (Dimichele & Powers 1991). Thus, for at least
growth rate post-hatch, examining LDH-B alone does
not give a true picture of the differences among populations.
The search for the other loci influencing metabolism,
hatching and growth is now underway (e.g. Whitehead
& Crawford 2006). Interestingly, there are also differences in the amounts of LDH-B enzyme among northern and southern genotypes that are mediated by
differences in transcriptional regulation (Crawford &
Powers 1992). A combination of comparative sequence
analyses, in vitro experiments and in vivo tests of promoter action found that differences in transcription are
largely because of sequence variation in a cis-regulatory
region upstream of the Ldh-B gene (Schulte et al. 1997,
2000) and SP1 sites in the proximal promoter (Segal
et al. 1999). Analyses of molecular signatures of selection (Schulte et al. 1997) and phylogenetic comparative
studies (Pierce & Crawford 1997) suggest that natural
selection shaped these transcriptional differences
(reviewed by Schulte 2001). In addition, Whitehead &
Crawford (2006) have used comparative phylogenetic
methods to identify 13 other metabolic genes that show
evidence of selection for changes in expression in
response to habitat temperature (or environmental factors correlated with temperature).
Phosphoglucose isomerase, flight and thermal
adaptation in Colias butterflies
Ward Watt and colleagues began their research on
phosphoglucose isomerase (PGI) polymorphisms in Colias butterflies by selecting this gene as a candidate
underlying local adaptation to environmental temperature (Watt 1968; Sherman & Watt 1973). Colias butterflies eat nectar, a mixture of simple sugars, to fuel their
flight. Based on this observation, Watt (1977) hypothesized that selection for optimal flight performance could
act to fine tune glycolysis, a pathway involved in sugar
metabolism, to environmental temperature. More specifically, he hypothesized that PGI would be the target of
selection in response to environmental temperature as
this homodimeric enzyme catalyzes the reversible conversion of fructose-6-phosphate to glucose-6-phosphate,
and sits at a key branch point in glycolysis that links
substrates into other pathways such as gluconeogenesis.
Watt (1977) surveyed populations of four species of Colias butterflies (Colias meadii, Colias alexandra, Colias philodice eriphyle and Colias eurytheme) for allozyme variation
at PGI, and found a number of allelic variants or electromorphs (EM). Interestingly, there was an excess of heterozygotes in older butterflies when compared with
younger butterflies, suggesting differential survival of
genotypes (Watt 1977). Identical-by-descent laboratoryraised populations for each of the four most common
allelic variants for C. eurytherme were produced, so that
genotypes could be tested for differences in in vitro biochemical functioning. There were a number of biochemical differences among the alleles, including thermal
stability and substrate-binding affinity (Km) (Watt 1977,
1983). The most striking result from these biochemical
measurements was that homodimeric enzymes showed a
trade-off between thermal stability and enzyme kinetics,
whereas heterodimeric enzymes did not (Watt 1977,
1983). Thus, heterodimeric enzymes, with one allele optimized for stability and the other for catalytic efficiency,
functioned better than homozygotes over a wide range of
temperatures. Watt et al. (1996) found that PGI enzymes
in C. meadii, although unique in origin and sequence, also
show similar trade-offs between thermal stability and
kinetics as in C. eurytheme enzymes.
These remarkable differences at the biochemical level
generated clear predictions about how these alleles
might affect whole animal physiology and performance
in the wild. Watt predicted that heterozygotes should
be able to fly at a wider range of environmental temperatures because their metabolic pathways would be able
to function well across a range of temperatures. Indeed,
C. p. eriphyle, C. eurytheme and C. meadii heterozygotes
were able to fly earlier in the day (when it is cold), and
fly for a longer overall time each day (Watt 1983; Watt
et al. 1983). These differences in performance were also
hypothesized to affect fitness components that depend
on capacity for flight or thermal tolerance, such as mating success and ⁄ or fecundity. As predicted, survival
during heat stress in C. p. eriphyle, male mating success
in C. p. eriphyle, C. eurytheme and C. meadii, and female
fecundity in C. p. eriphyle were all highest for heterozygous butterflies; thus, heterozygotes had a greater net
fitness (Watt 1983, 1992; Watt et al. 1983, 1985, 1996,
2003; Carter & Watt 1988).
Phosphoglucose isomerase alleles have now been
sequenced from C. eurytherme and C. meadii, and there
are multiple amino acid changes among and within EM
classes and among species that display evidence of evolution via natural selection (Wheat et al. 2006). While
the exact mutation(s) underlying differences in thermal
adaptation is still unknown for Colias, the most promising candidates lie in the region of PGI’s tertiary protein
structure that links the two monomers to form a
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MECHANISTIC BIOLOGY AND MOLECULAR ECOLOGY 5005
functional enzyme. Interestingly, the exact sites mutated
vary from species to species, but occur in the same protein region (Wheat et al. 2006; Wang et al. 2009).
Similar evidence for the effects of PGI genotype on
phenotype and performance has been found in the
Glanville fritillary butterfly (Melitaea cinxia). M. cinxia
heterozygote individuals have a higher body temperature, flight metabolic rate and dispersal distance at
colder temperatures, which results in higher fecundity
(e.g. Haag et al. 2005; Niitepõld et al. 2009; Saastamoinen & Hanski 2008) and population growth (Hanski &
Saccheri 2006). Heterozyotes also have increased survival (Orsini et al. 2009) and a longer lifespan (Saastamoinen et al. 2009). The impacts of PGI genotype on
thermal adaptation are not limited to butterflies. For
example, there is evidence for local adaptation of PGI
alleles to temperature in the willow beetle (Chrysomela
aeneicollis) (Dahlhoff & Rank 2000) and the sea anemone
Metridium senile (Zamer & Hoffmann 1989). These studies, in combination with strong empirical evidence (i.e.
measuring genotype frequencies across life history
stages, measuring fitness components, and testing for
genetic signatures of selection) from Colias butterflies,
support the hypothesis that PGI evolves by natural
selection in Colias spp., and is a gene with major effects.
Voltage-gated sodium channel (Nav1.4), poison
resistance and locomotion in garter snakes
Garter snakes (Thamnophis siralis) feed on roughskinned newts (Taricha granulose) in the regions of
western North America where these two species overlap. To defend themselves from predators the newts
contain a toxin, tetrodotoxin (TTX), in their skin (Wakely et al. 1966; Brodie et al. 1974). TTX is a very potent
neurotoxin, which binds to, and blocks, the outer pore
of voltage-gated sodium channels (Nav) in neurons and
muscles. At the cellular level, blocking these channels
inhibits the initiation of action potentials, which are
necessary for nerve and muscle function. When even
minute amounts of TTX are ingested, muscles become
paralyzed and poisoned animals usually die by
suffocation (reviewed by Soong & Venkatesh 2006).
These devastating consequences of ingesting TTX are
expected to strongly select for the evolution of TTX
resistance, and as predicted, garter snakes from newteating populations have been shown to have greater
resistance to TTX (Brodie & Brodie 1990; Brodie et al.
2002). As well, variation in TTX levels in newts is geographically correlated with levels of resistance in snake
populations (Brodie & Brodie 1991; Hanifin et al. 1999),
making this system a classic example of a co-evolutionary ‘arms-race’ (Brodie & Brodie 1999; Brodie et al.
2002).
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Resistance to TTX was originally measured by
injecting snakes with TTX and then testing muscle
contraction ability via crawling, a performance trait
important for escape from predators and prey capture
(Brodie & Brodie 1990). TTX resistance, measured
using this performance trait, did not vary when
young snakes were repeatedly injected with TTX
(Ridenhour et al. 1999) or among laboratory-reared
and field-caught snakes (Ridenhour et al. 2004), which
suggested that TTX resistance had a genetic basis and
was not dependent on environmental factors. Crawling performance, a whole-animal performance measure of TTX resistance, was strongly correlated with a
cellular measure of resistance: the ability for action
potentials to propagate when an animal was exposed
to TTX (Geffeney et al. 2002). A priori knowledge of
the mechanism of action of TTX (i.e. that it binds to
sodium channels) suggested that this resistance might
be mediated at the biochemical level by the presence
of TTX-resistant sodium channels (reviewed by Soong
& Venkatesh 2006). Geffeney et al. (2005) tested this
hypothesis by looking for sequence variation in the
voltage-gated sodium channel gene, Nav1.4 (the isoform expressed in muscle), between resistant and susceptible garter snakes and assessing the impacts of
variants on protein function in vitro. Knowledge of
the structural interaction between TTX and the outer
pore of the Nav1.4 enzyme allowed for clear predictions about the location of these mutations in the protein sequence. In vitro assays of TTX binding to the
sodium channel (Nav1.4) demonstrated that a single
mutation, found in all resistant populations, could
decrease TTX binding to Nav1.4, and that the 1–3
additional non-synonymous changes found in the
most resistant populations further decreased TTX
binding and increased resistance (Geffeney et al.
2005). These data suggest that a great deal of the variation in TTX resistance in garter snakes can be
explained by the four amino acid changes in the
outer pore of the Nav1.4 enzyme, and were consistent
with biochemical knowledge of this sodium channel
(Fig. 2).
Feldman et al. (2009) have recently expanded this
work to examine two congeners of T. siralis, Thamnophis
atratus and Thamnophis couchii, that also contain TTXresistant populations. They found that SNPs in the protein regions which form the outer pore of the Nav1.4
channel also correlate with TTX resistance in these species. However, the specific mutations that confer resistance varied among species, suggesting that resistant
alleles have evolved independently (Feldman et al.
2009), and thus represent a case of convergent evolution
at the nucleotide level with parallel evolution at higher
levels of organization. Polymorphisms in the sodium
5006 A . C . D A L Z I E L , S . M . R O G E R S and P . M . S C H U L T E
channel gene also underlie resistance to a structurally
and functionally similar neurotoxin, saxitoxin, in a
wild-clam population (Bricelj et al. 2005).
Lessons learned from these examples
The examples discussed above, and listed in Table S1,
provide a number of valuable lessons. The first and
over-riding lesson is that mechanistic knowledge can be
used to generate testable hypotheses about the effects of
a particular genetic polymorphism at higher levels of
biological organization. Two of the examples (i.e. LDH
and PGI) discussed in detail above started at the biochemical level and worked ‘up’ to cellular phenotypes,
organismal phenotypes and fitness, but in principle,
such hypotheses could be generated beginning at any
level in the biological hierarchy. The second important
lesson is that a purely mechanistic approach has its limits. In particular, this approach has the potential to bias
the search for genes underlying ecologically important
traits towards well-understood biochemical pathways
and miss other genes-affecting fitness. The incorporation of a top-down approach, first elucidating
phenotype-environment associations followed by complementary marker-based approaches (e.g. quantitative
trait locus (QTL) mapping, linkage disequilibrium mapping and ⁄ or genome scans) is likely to reduce the
impacts of this ascertainment bias (e.g. Rogers & Bernatchez 2007; Whiteley et al. 2008), and detect other loci
underlying a trait of interest. Once these loci are
detected, available mechanistic knowledge can give
insight into the molecular basis of genetic interactions,
if present. For example, knowledge of the genes that
underlie ecologically important differences in flowering
time in A. thaliana (e.g. Ehrenreich et al. 2009; Flowers
et al. 2009) coupled with extensive knowledge about
the biochemical pathways underlying flowering time
has guided studies on the epistatic interactions among
loci, such as the interactions between flowering locus C
(FLC) and FRIGIDA (FRI) genotype (Caicedo et al.
2004; Michaels & Amasino 2001; reviewed by Ehrenreich & Purugganan 2006; Mitchell-Olds & Schmitt 2006;
see Table S1 for further references).
The third important lesson is that it is critical to
ensure that experimental conditions are as ecologically
relevant as possible when assessing the effects of genetic
variation on organismal phenotypes and fitness (discussed by Ungerer et al. 2008). For example, differences
in PGI and LDH function were only seen when these
enzymes, and animals, were studied at certain temperatures (e.g. Watt 1977, 1983; DiMichele & Powers 1982b).
The final and perhaps most critical lesson from these
examples is that isolating the impacts of a single gene
in natural populations with high background genetic
variation (i.e. variation at other loci throughout the genome) is necessary to firmly establish the causal link
between gene, phenotype and fitness. This is clearly
seen in the case of LDH-B in Fundulus, in which the
effects of LDH-B genotype vary widely depending on
the genetic background in which the alleles are tested.
Performing experiments without controlling for genetic
background can reduce the power to infer causal relationships between genotype and phenotype, or even
result in false conclusions (discussed by Dean & Thornton 2007). Thus, in the section below, we explore some
of the available methods for controlling for background
genetic variation in studies attempting to link genetic
variation at a candidate gene to effects on phenotypes,
performance and fitness.
Controlling for background genetic variation
Genetic variation at candidate genes can be feasibly
tested in controlled genetic backgrounds using (i) carefully selected naturally occurring genetic variants,
including the use of clonal or asexual lines when possible, (ii) forward genetics or controlled cross approaches,
and (iii) reverse genetics. The most appropriate method
of controlling for background genetic variation (using
naturally occurring variants; forward genetics; or
reverse genetics) will ultimately depend upon the characteristics of the species being examined, the available
genetic resources, and the question being addressed.
Where possible, a combination of approaches will be
extremely fruitful (e.g. Kammenga et al. 2007). In addition, because forward and reverse genetic methods of
controlling for background genetic variation might
underestimate the complexity found in natural populations (see Jensen et al. 2007; Ungerer et al. 2008), it is
advisable to complement these approaches with a wide
sampling from naturally occurring populations to
achieve the best estimate of the effects of epistatic interactions.
Controlling for background genetic variation:
naturally occurring variants
To control for background genetic variation using naturally occurring genetic variants, one must identify populations in which the alleles of interest are segregating
within a largely homogenous genetic background. One
possible approach is to take advantage of naturally
occurring hybrid zones, as individuals at the centre of
the hybrid zone may be segregating for the multiple
alleles at the candidate gene of interest (e.g. LDH-B in
Fundulus heteroclitus; Powers et al. 1991). However,
strong linkage disequilibrium at the centres of hybrid
zones can limit this approach as the variant of interest
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MECHANISTIC BIOLOGY AND MOLECULAR ECOLOGY 5007
may be linked to many other loci also differing among
populations. Collecting individuals at the edges of
hybrid zones where a particular allele has introgressed
into an otherwise pure genetic background might be an
alternate strategy, but the steep clines expected for
selected genes might also make this approach difficult
(Barton & Gale 1993).
In other cases, it may be possible to identify populations in which all alleles of interest are segregating independent of a hybrid zone. For example, if the genetic
variant of interest in a locally adapted population was
selected from standing genetic variation, then this variant
may still be segregating in the ancestral population as
well. If so, alternate alleles can be tested in the genetic
background of the ancestral and ⁄ or novel population.
One study that used this strategy investigated the gene
underlying defensive bony lateral plates in the threespine
stickleback (Barrett et al. 2008). The threespine stickleback is a small fish that colonized and has adapted to
hundreds of freshwater lakes from an ancestral marine
environment. This transition into a freshwater environment is associated with a reduction in lateral plates and
up to 70% of the phenotypic variation in plates is
explained by an allele at the Ectodysplasin-A (Eda) locus
(Colosimo et al. 2005). The ‘low’-plated allele at Eda is a
standing genetic variant found at low levels in the ancestral marine population amidst the more frequent ‘full’
allele (Colosimo et al. 2005). Using a marine population,
Barrett et al. (2008) were able to isolate the ‘low’- and
‘complete’-plated Eda alleles by collecting phenotypically
partially armoured marine fish, predicted to be heterozygotes. Using these fish helped to account for background genetic variation because heterozygous marine
fish were genetically ancestral with the exception of their
Eda genotype and tightly linked loci (Barrett et al. 2008).
Although effective, this strategy can be quite time-consuming; Barrett et al. (2008) examined over 35 000 fish to
find 354 that were partially plated. Of these 354 fish, only
182 were confirmed Eda heterozygotes. This discrepancy
between phenotype and predicted genotype highlights
the importance of modifier loci (i.e. loci which alter the
effects of the candidate locus). Clearly, the utility of using
phenotypes to screen the large numbers of individuals
from wild populations needed for these studies will be
dependent on the percentage of phenotypic variation
explained by the candidate gene, and how much modifier loci and interactions with other genes or the environment alter phenotypic expression of the trait of interest.
Thus, studies of the impacts of variation at a candidate
gene such as the one described above are likely to be
most successful when examining semi-dominant alleles
of large effect, because low-frequency alleles can be
found in the heterozygous state by screening phenotypes
instead of genotypes. De novo mutations, which are also
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an important sources of ecologically relevant variation
(e.g. Linnen et al. 2009) can also be studied in a controlled genetic background as long as the ancestral alleles
at the candidate loci remain present in the population
containing the new alleles, there is sufficient migration
among populations, or crosses can be made between
these populations.
An alternative strategy to control for background
genetic variation when testing the effects of variation at
candidate gene(s) is to compare naturally occurring
clonal lines of plants or animals (e.g. Collier & Rogstad
2004). The primary challenge of comparing clonal
lineages is the possibility that additional mutations
(other than those in the candidate gene of interest) may
be present between the lines. Extensive genomic surveys
are thus required to rule out this possibility. In some
species, it may also be possible to take advantage of existing information on relationships among individuals (e.g.
pedigree information) to statistically test the effects of a
candidate allelic variant alleles in variety of genetic backgrounds or test the effects of alternate versions of an
allele among family members (e.g. Gratten et al. 2007).
Controlling for background genetic variation:
forward genetics
The second approach to controlling for background
genetic variation when testing associations between
genotype and phenotype involves the use of forward
genetics, or controlled crosses. Depending on the type
of crossing design, the gene of interest can be tested in
either a randomized [e.g. F2 and advanced generation
intercrosses, recombinant inbred lines (RILs), or identical-by-descent lines (IBD)] or uniform [e.g. near isogenic
lines (NILs)] genetic background (see supporting information Fig. S1 for the crossing designs used to make
these types of lines). For model organisms, such as Arabidopsis, mice, maize, Drosophila and Caenorhabditis elegans, RILs are readily available from a number of
parental populations (reviewed by Kammenga et al.
2008; Shindo et al. 2007; Peters et al. 2007) and have
been used to test the effects of ecologically relevant variation (e.g. Callahan et al. 2005; Kammenga et al. 2007).
Identical By Descent (IBD) lines (Fig. S1b) are used to
break down linkage disequilibrium surronding a gene
of interest and are another option for testing a gene in a
randomized genetic background. Successful examples
of this approach include studies of haemoglobin alleles
in low- and high-altitude populations of deer mice
(Chappell & Snyder 1984) and PGI variants in sulphur
butterflies (e.g. Watt 1977). Introgression lines, which
include NILs (Fig. S1c) and chromosome substitution
lines, can be used to test the effects of a focal gene
against a uniform genetic background. This strategy
5008 A . C . D A L Z I E L , S . M . R O G E R S and P . M . S C H U L T E
was successfully used by Bradshaw & Schemske (2003)
to assess the effects of the yellow upper locus on flower
colouration and pollinator preference, in Mimulus lewisii
and Minnulus cardinalis (Bradshaw & Schemske 2003).
The decision on whether to test a candidate gene’s
function against a randomized (e.g. RILs) or uniform
(e.g. NILs) genetic background depends the expected
number of genes involved and their predicted effect
size. For example, NILs are preferable to RILs when
epistatic effects are not expected to be important (e.g.
Bradshaw & Schemske 2003). NILs are also preferable
when the candidate gene is thought to have a small
effect on phenotype because its effects are less likely to
be masked when another major QTL is also present as
might occur in RILs (Keurentjes et al. 2007). On the
other hand, RILs are preferable to NILs when epistatic
interactions are expected to be important, because RILs
maintain many combinations of alleles from both source
populations (Keurentjes et al. 2007). Both RILs and NILs
can be used to detect genotype by environment (G · E)
interaction because each line is essentially clonal (e.g.
Ungerer et al. 2003; Callahan et al. 2005).
However, most forward genetic approaches are limited
in their ability to capture the genetic variation present in
wild populations, as even most cutting edge designs are
limited to a few sets of outbred parents (reviewed by
Cavanagh et al. 2008). The time needed for many generations required to establish RILs and NILs will be a major
limiting factor in the application of these approaches for
most systems. Thus, for species with longer generation
times, F2 crosses (although possibly less informative than
RILs or NILs) are likely to be more tractable. Of course,
all forward genetic techniques have the limitation that
they can only be applied to organisms that can be easily
bred and raised in the laboratory.
Controlling for background genetic variation:
reverse genetics
Reverse genetics is a collective term for methods in
which a gene’s sequence or function is altered by the
investigator either by altering the DNA sequence of the
candidate gene or by manipulating its expressed product. These approaches hold exceptional promise for isolating the effects of a single gene, but as with forward
genetic approaches, reverse genetics are largely limited
to organisms that can be bred, or at least raised, in the
laboratory. There are two main approaches to directly
altering the DNA sequence of a candidate gene: untargeted and targeted. The classic untargeted approach is
random mutagenesis, but the resulting artificial mutants
may not mimic the naturally occurring variants of a
gene. Thus, mutagenesis can provide information on
the link from genotype to phenotype, but this informa-
tion may be limited in its ecological relevance. Targeted
approaches for directly altering the sequence of a candidate gene include complete ablation of an allele (i.e.
knockouts) or targeted gene replacement. Knockout
approaches may suffer from problems similar to those
of untargeted mutagenesis; knockouts may not mimic
naturally occurring phenotypes. On the other hand, targeted gene replacement allows the investigator to alter
DNA sequence at the candidate gene from one ecologically relevant allele to another, allowing these alleles to
be tested in a constant genetic background. As such,
targeted gene replacement provides an exceptionally
clear picture of the relationship between genotype and
phenotype with the potential to test variants equivalent
to those observed in natural populations. The ability to
perform targeted gene replacement is currently limited
to a few model species, but techniques are developing
rapidly (e.g. Choi et al. 2009; Shukla et al. 2009; Townsend et al. 2009; Yan et al. 2009).
An alternative to directly altering the sequence of a
candidate gene of interest is to manipulate the amount
of functional gene product that is present. RNA interference (RNAi) or morpholinos are commonly used
techniques for knocking down RNA levels (e.g. Moczek
& Rose 2009). Increasing the expression of a candidate
gene is most often accomplished by inserting an extra
copy of a gene into a cell (e.g. Feder et al. 1996; Abzhanov et al. 2004; Colosimo et al. 2005). Both knockouts
and insertions are needed for transgenic complementation experiments, during which an allele of interest is
inserted into the genetic background of a knockout
(often heterologous) to see if a candidate gene restores
the phenotype (e.g. Maloof et al. 2001; Zufall & Rausher
2003; Reeves et al. 2007).
Conceptually similar approaches to reverse genetics
techniques such as RNAi and gene insertion involve the
use of pharmacological agents to increase or decrease the
amount of functional protein that is present (reviewed by
Skromne & Prince 2008). Using drugs to alter the amount
or function of gene product is often the easiest way to
look at the links from genotype to phenotype because it
is simple, inexpensive, and can be used in most organisms and at most life history stages. However, pharmacological agents have the potential to affect genes other
than those targeted and vary widely in their specificity.
Thus, only highly specific agents should be used when
attempting to make a strong connection between a particular candidate gene and a phenotype.
Controlling for background genetic variation:
combined approaches
The most convincing evidence for the effects of genetic
variation on phenotype, performance and fitness come
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MECHANISTIC BIOLOGY AND MOLECULAR ECOLOGY 5009
from studies that have combined different strategies to
control for background genetic variation (Hoballah
et al. 2007; Kammenga et al. 2007; Chiang et al. 2009).
For example, Chiang et al. (2009) used all three methods of controlling for background genetic variation to
investigate potential pleiotropic effects of FLC, a gene
known to be involved in flowering time, on temperature-dependent germination in Arabidopsis. Naturally
occurring FLC variants, overexpression of FLC in transgenic plants and NILs were examined in parallel and
provided strong evidence for FLC’s effects on germination time (Chiang et al. 2009). In addition, Chiang et al.
(2009) incorporated mechanistic knowledge of the hormonal regulation of both flowering and germination to
design experiments to examine the mechanisms underlying the pleiotropic effects of FLC. Studies such these
demonstrate the power of combining a mechanistic perspective with population genomics and quantitative
genetics when attempting to understand the phenotypic
consequences of naturally occurring genetic variation.
A roadmap for future research
In this section, we provide a roadmap for research programs aiming to test the importance of an identified
candidate gene. Such a research program is likely to
include three major components, that span the levels of
biological organization depicted in Fig. 1: (i) a molecular biology component that can characterize the alternate alleles of a candidate gene in vitro (i.e.
genotype fi biochemical phenotype); (ii) a wholeorganism component that can determine the impacts of
candidate genes on cellular function, morphology,
behaviour, physiology and performance in vivo (i.e.
genotype fi performance); and (iii) a component that
assesses the fitness consequences of genetic variants
either directly, with experimental tests in relevant environments, or indirectly by using comparative phylogenetic approaches and analyses looking for molecular
signatures of selection (i.e. genotype fi fitness).
Genotype fi biochemical phenotype (in vitro tests of
function)
The first step of most biochemical studies is to fully
sequence the alternate alleles of a candidate gene. If the
gene of interest is a protein, it can be expressed, purified
and characterized in vitro (e.g. Watt et al. 1977; Place &
Powers 1979; Maloof et al. 2001). Alternatively, it is possible to express the protein in a suitable cellular system
and examine its function(s) within the cell (e.g. Protas
et al. 2006). If a genetic variant is in a regulatory element, in vitro tests can also be used to determine
whether this variant has functional consequences for
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gene expression (e.g. Schulte et al. 1997, 2000; Tung
et al. 2009). Hoekstra et al. (2006) provide an example of
the utility of linking genotype to biochemical phenotype
as part of a research programme aimed at understanding the genetic basis of adaptation. Beach mice found
along the light sandy dunes of Florida’s gulf coast have
a lighter coat colour than do inland populations of this
species and are thus better camouflaged when on these
dunes – presumably as a result of predation pressure
(see Table S1). Hoekstra et al. (2006) used quantitative
genetics to demonstrate that alternate alleles at a candidate protein coding gene, the melanocortin-1 receptor
gene (Mc1r), were responsible for up to 35% of variation
in coat colour. As Mc1r is an important part of the signal
transduction pathway regulating the production of melanin (which causes dark coat colour), they predicted that
the allele associated with light coat colour would have
reduced function, thus reducing the activity of the signal
transduction pathway and resulting in less melanin formation. They tested this prediction by expressing the
alternate Mc1r alleles in cultured cells and recording
melanocortin receptor activity (Hoekstra et al. 2006). As
predicted, the light coat colour allele of Mc1r generated
less of the signal that induces melanin production.
Unfortunately, mechanistic knowledge is not available for all genes, making approaches that work ‘up’
through the hierarchy shown in Fig. 1 from gene to
phenotype difficult in some cases. If necessary, an
alternate approach that works in the opposite direction
may be productive. For example, if a genome scan
reveals the signature of strong selection in a gene with
no known function, it may be possible to generate
hypotheses about the impacts of this genetic variation
based on correlated ecological and environmental factors and their relationship to organismal performance,
which can then suggest physiological processes or morphological traits that might be of importance. In fact,
exploration of the function of adaptively significant
genetic variation in natural populations holds the
promise of contributing to mechanistic biology by
revealing potentially subtle functions of poorly understood genes, pathways and networks (Landry & Aubin-Horth 2007; Benfey & Mitchell-Olds 2008; Rockman
2008).
If there are multiple SNPs or other types of genetic
variation in a candidate gene, each can be tested separately by constructing the alternate alleles by site-directed mutagenesis to determine which site represents the
functional variant, or funSNP (e.g. Newcomb et al.
1997; Lozovsky et al. 2009). Site-directed mutagenesis
can also be used to reconstruct ‘extinct’ alleles to
examine the function of ancestral alleles that are no
longer segregating in the population (reviewed by
Thornton 2004).
5010 A . C . D A L Z I E L , S . M . R O G E R S and P . M . S C H U L T E
Genotype fi whole-organism performance (in vivo
tests of function)
The specific experiments that must be performed to test
the functional consequences of a genetic variant at higher
levels of biological organization largely depend upon the
type of gene to be tested and the species in which it is
tested, but in all cases incorporating a priori knowledge
about gene function will aid in hypothesis formulation
and the design of relevant experiments. Ideally, experiments will also be informed by prior in vitro assessment
of the alleles of interest (e.g. Genotype fi biochemical
phenotype’) and should be conducted under ecologically
relevant environmental conditions.
Examining functions at these higher levels adds the
complexity of potential epistatic interactions, which,
while ecologically important, render testing mechanistic
predictions difficult. It is thus imperative to control for
background genetic variation, using the approaches outlined above, when first assessing the consequences of
genetic variation in a candidate gene. However, once
the effects of a genetic variant are established, it is then
necessary to determine whether the candidate gene’s
effects are context specific and dependent on epistatic
interactions that may alter the links between genotype,
phenotype and fitness.
Genotype fi fitness
The links between genotype, phenotype and fitness can
be tested directly, through laboratory- and field-based
experiments, or indirectly with phylogenetic comparative methods (reviewed by Garland et al. 2005; Freckleton 2009) and methods that identify molecular signals
of natural selection (reviewed by Nielsen 2005; Jensen
et al. 2007). Indirect methods to test for evidence of
selection can be used in systems where direct tests of fitness are not feasible, as a precursor to subsequent direct
tests of fitness, or to provide a complementary line of
evidence for the adaptive significance of genetic variation. Many studies have successfully integrated these
indirect methods to provide evidence of selection in the
context of a mechanistic research paradigm (e.g. Schulte
et al. 1997; Wheat et al. 2006; Whitehead & Crawford
2006; Miller et al. 2007; Flowers et al. 2009). Laboratorybased experiments can isolate and control for environmental variables that may be correlated in the field and
thus can help clarify the selective pressures acting upon
a locus. Laboratory-based studies of the fitness consequences of genetic variation have been performed in a
range of multicellular eukaryotes (e.g. Arabidopsis FRI
(Callahan et al. 2005) and resistance genes (Korves &
Bergelson 2004); Drosophila heat shock proteins
(reviewed by Hoffmann 2008)]. However, laboratory-
based measures of phenotypes, performance and fitness
may not translate directly to field measures of fitness
(e.g. Irschick 2003; Irschick et al. 2005), so they should,
whenever possible, be complemented with experiments
performed under natural or semi-natural conditions.
Although measuring fitness in the wild is a difficult
task, it is one for which evolutionary ecologists have
laid excellent groundwork (examples and techniques
reviewed by Arnold 1983; Brodie et al. 1995; Ellegren &
Sheldon 2008; Endler 1986; Hoekstra et al. 2001; Irschick
et al. 2007; Irschick 2003; Kingsolver et al. 2001; Mitchell-Olds & Schmitt 2006; Pigliucci 2003; Primack & Hyesoon 1989; Schluter 2000). For example, common garden
and reciprocal transplant experiments can be used to
test for differences in fitness among populations. The
fitness of different ecotypes in these experiments is
assessed through the quantification of fitness components, measuring population growth or directly competing individuals from the different ecotypes and
measuring the contribution of each ecotype to following
generations (see Kawecki & Ebert 2004). To link genotype to fitness, these classic techniques need only be
modified such that the genotypes of individuals used in
fitness experiments are known for the candidate gene of
interest, and variation at other loci is controlled.
As yet, few studies have controlled for background
genetic variation when experimentally testing the effects
of genotype on fitness in the wild, but some studies have
successfully used reverse genetics [e.g. Arabidopsis (Tian
et al. 2003; Frenkel et al. 2008) and Drosophila (Sorensen
et al. 2009)] or forward genetic approaches [e.g. Helianthus (Lexer et al. 2003), Arabidopsis (Ungerer & Rieseberg
2003), Avena barbata (Latta 2009) and barley (Verhoeven
et al. 2004)]. Note that most experiments using forward
genetics have looked at the effects of a large genomic
region and not a single gene (often by mapping fitness
components and phenotypes of interest in combination),
but once the genes underlying the traits of interest are
identified in these systems it will be possible to make
direct linkages between genotype and fitness. Naturally
occurring variation has also been used to test the links
from genotype to fitness components (e.g. Watt et al.
1983; Korves et al. 2007), but to our knowledge, only one
study has succeeded in directly measuring the fitness of
alleles associated with a candidate gene in the wild
while controlling for background genetic variation: a
field experiment measuring selection on the alleles at the
Eda gene in stickleback (Barrett et al. 2008).
Conclusions
Understanding the functional, ecological and evolutionary consequences of genetic variation requires adopting
an integrative approach that combines molecular
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MECHANISTIC BIOLOGY AND MOLECULAR ECOLOGY 5011
biology, physiology, quantitative genetics, ecology and
evolutionary biology and is informed by a mechanistic
perspective. To effectively establish the links between
genetic variants and in vivo phenotypes, whole-organism performance and fitness, it is key that experimenters control for background genetic variation. Only then
can clear causal relationships be established. Although
many of the key experimental methods to control for
genetic background are most easily applied in certain
systems (e.g. those with short generation times and that
are easily bred in the laboratory), the development of
controlled crosses (including RILs and NILs) and transgenic technologies in well chosen, ecologically informative species will pay enormous dividends (reviewed by
Abzhanov et al. 2008; Ellegren & Sheldon 2008; Ungerer
et al. 2008). Most studies to date have examined the
effects of a single gene, but many ecologically relevant
traits are expected to be influenced by multiple genes
and their interactions. In this context, a mechanistic perspective may be even more illuminating, because it can
provide information about the potential interactions
among genes.
We predict that first identifying phenotypes of interest (ideally under field conditions) and then working
down through the levels of biological organization to
genotype and up to fitness may be a particularly fruitful approach to forging the links between genotypes,
phenotypes and fitness. In particular, combining this
mechanistically informed perspective with population
genomic and quantitative genetic approaches (e.g. Rogers & Bernatchez 2007) should enhance the chances of
successfully identifying and understanding the consequences of ecologically relevant genetic variation. In
addition, examining whole-organism performance or
morphological traits for which the underlying mechanistic basis is well understood (e.g. growth, metabolism,
locomotion, flowering time, colouration) will aid in the
generation of clear predictions about the physiological
systems, morphological traits, metabolic networks, pathways and genes involved in the phenotype of interest.
This approach facilitates the identification of ecologically relevant genetic variation and helps to generate
hypotheses about the potential pleiotropic effects (e.g.
Chiang et al. 2009) of variation at the identified candidate gene and potential epistatic interactors (e.g. Caicedo et al. 2004).
By testing hypotheses about the links between genotype, phenotype and fitness under ecologically relevant
conditions while controlling for background genetic
variation, we can take into account the potential ‘frailties of adaptive hypotheses’ (Lynch 2007) and the pitfalls of the adaptationist programme (Gould &
Lewontin 1979). Although the effort required to apply
this approach is substantial, the potential benefits of
! 2009 Blackwell Publishing Ltd
further integrating mechanistic and quantitative genetic
approaches into molecular ecology are enormous.
Acknowledgements
This work was financially supported by the Natural Sciences
and Engineering Research Council of Canada (NSERC) through
discovery grants to P.M.S and S.M. R. and a Canada Graduate
Scholarship to A. C. D. A. C. D was also supported by a UBC
University Graduate pre-doctoral fellowship. Special thanks to
R. D. Barrett, L. Bernatchez, H. Collin, A. C. Gerstein, D. Irschick, T. H. Vines and two anonymous reviewers for their perceptive comments on earlier drafts of this manuscript that
substantially improved this review. We thank E. D. Brodie III
and Ward B. Watt for providing us with the photographs used
in Fig. 2. We apologize to colleagues working on relevant projects that we could not cite because of space limitations.
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ACD is completing her PhD thesis in PMS’s laboratory. Her
dissertation focuses on the genetic, physiological, and morphological underpinnings of intra-specific variation in exercise performance. SMR’s laboratory studies molecular evolutionary
mechanisms for coping with environmental change by integrating ecological genomics and quantitative genetics with field
studies of natural selection. In her research, PMS takes an integrative approach to understand the evolution and mechanistic
basis of inter- and intra-specific variation in physiological traits.
Supporting information
Additional supporting information may be found in the online
version of this article.
Fig. S1 Forward genetic crossing designs that may be used to
control for background genetic variation.
Table S1 Studies using mechanistic knowledge to test the
impacts of genetic variation found in wild populations
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