Download CRITIQUE The character or the variation: the genetic analysis of the

Bulletin of Entomological Research (2000) 90, 3–7
3
CRITIQUE
The character or the variation: the genetic
analysis of the insecticide-resistance
phenotype
J.A. McKenzie*
Centre for Environmental Stress and Adaptation Research,
Department of Genetics, University of Melbourne,
Parkville 3010, Australia
Abstract
In this critique it is argued that the genetic basis of the evolution of resistance is
dependent on how the phenotypic, and underlying genotypic, variation is
channelled during a selective response. A polygenic response is preferentially
favoured if selection acts within the phenotypic distribution of susceptibles; a
monogenic response is predicted if selection screens rare mutations with
phenotypes outside that susceptible distribution. The relevance of this model to
the method of genetic analysis, the prediction of resistance mechanisms to novel
insecticides, the generation of resistant beneficial insects and the development of
the most effective resistance and integrated pest management programmes is
discussed.
Introduction
As biologists, we frequently concentrate our research on a
particular organism and a particular character or
developmental process of that organism. It is a method by
which expertise, and reputation, is gained. When a
particular phenotypic characteristic attracts the attention of
experts of many organisms and disciplines there is an
excellent opportunity, sometimes realized, for cross
fertilization of ideas. The evolution of insecticide resistance
has provided such an opportunity.
Resistance has occurred over a broad range of species
(Georghiou, 1986; Denholm et al., 1999) and has been
investigated by researchers with expertise in numerous
fields including applied entomology, behaviour,
biochemistry, ecology, genetics, molecular biology,
physiology, population biology and toxicology. The most
effective studies have utilized, or combined, this expertise
* Fax: (61 3) 9344 5139
E-mail: [email protected]
within an evolutionary framework (Roush & McKenzie,
1987; Mallet, 1989; McKenzie, 1996, for reviews). It is equally
true that resistance systems can contribute significantly to
the investigation of more general evolutionary phenomena
as the selective agent is known, the physiological/
biochemical/molecular basis of selection can be defined and
relative fitness differences between phenotypes are
sufficiently large to enable experimentation in the laboratory
or field on a tractable time scale (McKenzie, 1996).
Lenormand et al. (1999), in their analysis of the population
dynamics of the evolution of organophosphate resistance in
Culex pipiens Linnaeus (Diptera: Culicidae), provide a recent
elegant example of the contribution that can be made.
Resistance systems are therefore ideal to investigate the
process of adaptation. In this context, one area that has
generated ongoing debate in the evolutionary literature is
the relative importance of monogenic or polygenic
responses as the genetic basis of adaptive change (Lande,
1983; Orr & Coyne, 1992). The debate has been no less
fulsome in the resistance literature (Roush & McKenzie,
1987; Macnair, 1991; McKenzie & Batterham, 1994, 1998;
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J.A. McKenzie
Gressel, 1995; Groeters, 1995; Tabashnik, 1995; McKenzie,
1996).
It is my contention that much of the difference of opinion
concerning the genetic basis of resistance has occurred
because of a focus on the character, the resistance phenotype,
rather than on the variation associated with that character.
While conceding that an assessment of the quality of a
character, with respect to individual components of
resistance, may provide important clues to possible
mechanisms, I believe a philosophy which focuses on the
variation allows a flexibility in the prediction of a genetic
response during selection for resistance, interpretation of
data related to the resistance phenotype and in devising
strategies for the management of susceptibility.
Resistance as a phenotype
In the resistance literature it is not uncommon for
management decisions to be based on the assumption of
direct correspondence between resistance phenotype and
genotype. As any phenotype is influenced by the relative
contribution of genetic and environmental factors, and the
interaction between them, the assumption is not necessarily
correct (McKenzie, 1996). Typically, a scientific assessment of
resistance status has involved comparisons of concentrationmortality lines of the assessed strain, or population, against
a susceptible reference strain or population (Brown & Pal,
1971; Cahill et al., 1996). If a linear relationship results when
percentage mortality (in a probit scale) is plotted against log
insecticide concentration, it indicates that the resistance
phenotypes of the strain, or population, analysed fit a
normal distribution. This, of itself, tells us nothing of the
genetic basis of resistance or, indeed, of the level of genetic
variation in the strain or population. The concentrationmortality relationship of a particular strain provides
evidence of only a phenotypic distribution.
In this context it is worth noting that results based on the
gradient or the repeatability of concentration-mortality line
analyses should not be over interpreted. For example, the
gradient of the line reflects the level of phenotypic variation
in the population, a more shallow gradient reflecting greater
phenotypic variation. This is not necessarily indicative of
greater genetic variation as environmental influences during
the development of individuals, subsequently tested under
standard conditions, may influence the result. The difficulty
is compounded as concentration-mortality analysis is not
always successful in distinguishing between resistant and
susceptible phenotypes which limits the capacity to make
effective resistance management decisions (Roush & Miller,
1986). These very limitations now commonly result in a
simple compromise. Discriminating concentrations are used
to define resistance status. This approach, while less
laborious than concentration-mortality analysis, does not
necessarily improve the quality of the resistance
management response (McKenzie, 1996).
The dominance of resistance, with respect to inheritance,
is deduced by the distribution of the resistance phenotype of
progeny (F1) of a cross between resistant and susceptible
strains, relative to the parental distributions. Dominance
relationships of themselves tell us nothing of the genetic
basis of resistance, are influenced by the environment in
which the genotype is expressed (Bourguet et al., 1996) and
have the potential to evolve (Bourguet & Raymond, 1998).
The distinction between dominance with respect to
inheritance, and to fitness, is worthy of note. With respect to
inheritance, resistance may vary from recessive to
completely dominant but is frequently partially dominant
(McKenzie, 1996). With respect to fitness, resistance may be
rendered dominant, partly dominant or recessive depending
on the concentration to which resistant and susceptible
phenotypes are exposed (McKenzie, 1996; Bourguet &
Raymond, 1998). Thus, the distribution of F1 phenotypes,
relative to those of susceptible and resistant parents, defines
the dominance relationship with respect to inheritance,
while dominance with respect to fitness is concentrationdependent (McKenzie, 1996).
The genetic basis of resistance
The genetic basis of resistance depends on the genetic
variation available and how it is channelled during selection
of resistant phenotypes by an insecticide (McKenzie &
Batterham, 1994). Tabashnik et al. (1998) have argued that the
resistance profile of populations follows one of three
possible scenarios.
If mutations that generate a resistant phenotype to a
particular chemical are very rare, mutations at a single
genetic locus may result. Differences between populations
will result from allele frequency differences at that locus
(McKenzie, 1996) with the spread of the mutation
significantly influenced by migration, as demonstrated in a
study of the organophosphate resistance genes of C. pipiens
(Raymond et al., 1991). If the mutation rate is less restricted,
but the mechanism by which resistance can evolve is highly
constrained, parallel mutations may occur in a number of
species. This appears to be the case in the evolution of
resistance to dieldrin (ffrench-Constant, 1994). Finally, if
mutations causing resistance are neither uncommon nor
constrained, Tabashnik et al. (1998) predict that different
populations will have unique responses to selection. If such
variation is appropriately screened by natural or artificial
selection the response is likely to be polygenically based
(McKenzie, 1996), an outcome observed in a laboratory
selection programme for resistance to diazinon in the
Australian sheep blowfly Lucilia cuprina (Weidemann)
(Diptera: Calliphoridae) (McKenzie et al., 1992).
Theoretical genetic responses
The theoretical genetic responses expected given different
selection regimes have been discussed in detail elsewhere
(Whitten & McKenzie, 1982; McKenzie & Batterham, 1994,
1998). In summary, it is suggested that monogenic responses
for rare mutations will be preferentially favoured if selection
acts outside the phenotypic distribution of susceptible
phenotypes. That is above the LC100 of susceptibles.
However, a polygenic response is more likely if selection
acts within the phenotypic distribution of susceptibles, at
concentrations less than LC100, as genetic differences
between phenotypes of a continuous distribution are
expected to be polygenically based.
The response observed in the field depends on how the
variation is screened. It must be noted, however, that if a
resistant phenotype is primarily under monogenic control
this does not mean that polygenic variation for resistance
does not exist in a population. For example, selection with
diazinon within the resistance distributions of populations
of L. cuprina fixed for either a resistant (McKenzie et al., 1980)
The genetic analysis of insecticide resistance
or a susceptible allele (McKenzie et al., 1992) at the diazinon
resistance locus, Rop-1, resulted in a polygenically
determined response. As expected (McKenzie, 1996;
Tabashnik et al., 1998), the responses were population
specific (McKenzie et al., 1980, 1992).
A key question, on which few data are available, is to
analyse the response to selection when resistant mutations
are rare in a population subjected to selective concentrations
less than the LC100 of susceptibles. In some circumstances, a
monogenic response may result (Roush & Hoy, 1981;
Heather, 1986). However, a particular outcome may depend
on the balance between selection for and, because of
pleiotropic effects of the resistant allele (Roush & McKenzie,
1987; McKenzie, 1996), selection against a resistant
phenotype under monogenic control. At the point of
balance, chance events may be important in defining the
genetic basis of the response (Whitten & McKenzie, 1982;
Tabashnik, 1990). This is an area that I believe warrants
further work because it is of vital importance to the
determination of resistance management strategies (Gressel,
1995; McKenzie, 1996; Denholm et al., 1999) and, more
generally, to our understanding of the selective processes
that determine the genetic structure of populations (Lande,
1983; Macnair, 1991; Orr & Coyne, 1992). Equally important
is the capacity to genetically analyse the phenotypic
variation associated with resistance.
Methods of genetic analysis
Genetic analysis of the resistance phenotype has been
attempted in a number of ways. These include
concentration-mortality line analysis, quantitative genetic
analysis and classical genetic analysis using morphological
and/or molecular markers. The method of genetic analysis
is constrained by the ease with which the insect can be
reared, the capacity to generate pure breeding strains, the
ability to manipulate crosses for F2, backcross or testcross
analyses or for more complex quantitative analytical genetic
designs and by the availability of genetic markers for interand intra-chromosomal genetic mapping (McKenzie, 1996).
Concentration-mortality line analysis depends on the
presence or absence of inflection points in the lines of
segregant populations, that are exposed to a range of
concentrations, to determine the genetic basis of resistance.
In theory, inflection points at critical discriminating
concentrations allow single gene control to be distinguished
from polygenic control where a linear response, without
inflection, is expected over an appropriate concentration
range (McKenzie, 1996). In practice, analysis is more
difficult. For example, if parental and F1 concentrationmortality lines overlap, or if environmental effects are
significant, clearly defined inflection points may be difficult
to identify. The distinction between step function
(monogenic control) and continuous (polygenic control)
lines is obscure in these circumstances (Mallet, 1989; Roush
& Daly, 1990).
Classical quantitative genetic analysis has been restricted
to estimates of heritability (Firko & Hayes, 1990). An
estimate of heritability is strictly only applicable to the
particular population in which the estimate is made at the
time of that estimate. However, for a range of insects and
insecticides the estimates of heritability in field and
laboratory studies fall between 0.14 and 0.47 (McKenzie,
1996), comparable to the estimates of heritability for
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physiological characters in general (Mousseau & Roff,
1987).
Classical genetic analysis has traditionally been carried
out in organisms such as Drosophila melanogaster Meigen
(Diptera: Drosophilidae), Musca domestica Linnaeus (Diptera:
Muscidae), L. cuprina and some mosquito species where
morphological markers are available to distinguish
chromosomes and the regions within them (McKenzie,
1996). Essentially, to map resistance loci to particular
chromosomes, recombination within a chromosome must be
restricted in the segregant generation so that the association
between the marker and the chromosome it identifies is
complete. Intra-chromosomal mapping requires free
recombination to identify co-segregation of a marker with a
chromosomal region. The availability of molecular markers
has allowed this biphasic approach to be extended to
organisms with a limited availability of morphological
markers. In the case of resistance to Bacillus thuringiensis by
Plutella xylostella (Linnaeus) (Lepidoptera: Plutellidae), the
biphasic approach using molecular markers has been critical
in assessing the relative contribution of monogenic or
background polygenic influences in the evolution of
resistance (Tabashnik, 1994; Heckel et al., 1999). I believe the
approach used by Heckel and his colleagues has much to
offer in the future genetic analysis of resistance phenotypes,
providing a level of precision of mapping that has
previously been restricted to few insect species.
Future research
I have already alluded to some areas I believe warrant
more research effort. A consequence of considering the
variation, associated with resistance and the genetic
outcome of how that variation is selectively channelled
offers further opportunities. These potentially allow us to be
proactive rather than reactive in devising strategies to
maximize the impact of the insecticide while minimizing the
chance resistance will evolve.
Predicting resistance
If the selection regime screens for polygenic variation, the
response is likely to be population specific. Resistance
management programmes need to be structured accordingly.
If monogenic responses are favoured, insecticidal delivery
systems that generate rectangular decay curves, with the
initial concentration above the LC100 of resistant
heterozygotes, minimize the likelihood of resistance
evolving (McKenzie, 1987, 1996). Typically, the resistance
status and relative fitness of heterozygotes is unknown prior
to resistance evolving. However, mutagenesis of susceptible
laboratory populations followed by screening at
concentrations above the LC100 of susceptibles has
successfully generated monogenic resistant variants to a
number of different insecticides (Kikkawa, 1964; Wilson &
Fabian, 1986; McKenzie et al., 1992; Smyth et al., 1992;
Adcock et al., 1993; Yen et al., 1996). In the case of dieldrin
and diazinon resistance in L. cuprina, these variants are
genetically (McKenzie et al., 1992; Smyth et al., 1992) and
molecularly (McKenzie & Batterham, 1998) identical to those
that evolved in natural populations.
Further trials of the mutagenesis and selection approach
need to be conducted for a range of organisms and
chemicals to which they have evolved resistance to test if the
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J.A. McKenzie
apparent predictive power of the technique in L. cuprina can
be generalized. If laboratory screening continues to generate
similar genetic variants to those that have evolved in the
field, there would be considerable confidence in the capacity
of the approach to predict the genetic mechanism of
resistance to new insecticides.
There are obvious advantages in being able to predict
likely resistance mechanisms to a new insecticide before it is
released for pest control. Not least among these is being able
to assess possible cross-resistance and to be proactive in the
derivation of management practices to minimize the
evolution of resistance (McKenzie & Batterham, 1998). The
capacity to select for monogenic resistance may also have
important consequences in the selection of resistant
predators for use in integrated pest management (IPM).
Selecting resistant predators
Predator–prey interactions form an important foundation
of many IPM programmes. The availability of resistant
natural enemies potentially enables pesticide use against the
pest without adversely influencing the resistant predator.
Thus, the benefits of the predator–prey interactions are
maintained during periods of pesticide application.
Attempts to select resistant natural enemies in artificial
selection programmes have, with the exception of responses
in some phytoseiid mites, however, generally been
disappointing (Hoy, 1990).
Two explanations have been suggested for the relative
lack of response. It has been suggested that natural enemies
may have evolved so that they lack the capacity to detoxify
pesticides (Croft & Mullin, 1984) or that there is a lack of
genetic variability in laboratory populations on which
selection can act (Hoy, 1990). As responses have been
observed in several species of natural enemies (Hoy, 1990) it
seems unlikely that the first suggestion provides a general
explanation.
The second explanation is possible for particular
laboratory populations. During the establishment of these
populations, bottlenecks and the effects of genetic drift may
result in unrepresentative genotypic distributions and loss of
variation. It remains true, however, that selection
programmes have used concentrations of pesticide that
cause mortality to only a proportion of the population. That
is selection has occurred within the original phenotypic
distribution. Selection for such incremental change
preferentially selects for a polygenic response which may
not provide a sufficiently resistant phenotype to allow a
natural enemy to be effectively used in IPM programmes.
If monogenic variants are in the initial population they
may be screened by selection (Roush & Hoy, 1981). Such
events are very rare but produce a resistant phenotype
significantly different from the phenotype of the base
population. This has not been the typical response when
selection has occurred within the phenotypic distribution in
the absence of such variants as resistance levels have
typically been low (Hoy, 1990). By following a regime of
mutagenesis, to increase genetic variation, and selection
above the LC100 of susceptibles, to specifically screen for
monogenic variants in an appropriate field background, it
may be possible to select for natural enemies with high
levels of resistance. The mutagenesis/selection approach
does not guarantee that a resistant phenotype will be
selected but if the programme is successful the resistant
phenotypes, and genotypes, may be particularly suitable for
use in IPM programmes (McKenzie, 1996). It is an area of
research, based on a philosophy of channelling the variation
associated with the resistance phenotype in a manner to gain
a desired genetic response, that I am sure is worth
investigating.
Conclusion
Viewing resistance as a phenotype and relating an
adaptive response to the way selection acts on the
phenotypic variation to channel the underlying genetic
variation provides a template to predict whether the
response is more likely to be polygenically or monogenically
controlled. From this foundation, it is possible to consider
establishing the genetic basis of resistance to a new
insecticide before it evolves in natural populations and to
devise delivery and management systems that minimize the
probability that resistance will in fact evolve.
Much work is needed. However, imagine an IPM
programme in which a new insecticide is introduced via a
delivery system that minimizes selection for resistance in a
pest at concentrations that enable laboratory-generated
resistant natural enemies to survive. Such a programme
would provide a more stringent ecological and evolutionary
challenge to pests than has typically occurred and would
significantly enhance the management of agricultural and
horticultural ecosystems. It is a goal worth striving for.
Acknowledgements
Support from the Australian Research Council to enable
me to think about, and experiment in, the evolution of
insecticide resistance is gratefully acknowledged as is the
input of numerous colleagues with whom I have had many
helpful exchanges about the topics discussed in this paper.
Phil Batterham, David Heckel and Alan McCaffery are three
of those colleagues. They are thanked for comments on the
manuscript.
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(Accepted 1 January 2000)
© CAB International, 2000