© 2000 Nature America Inc. • http://genetics.nature.com
commentary
The roads from phenotypic variation to
gene discovery: mutagenesis versus QTLs
Joseph H. Nadeau1 & Wayne N. Frankel2
© 2000 Nature America Inc. • http://genetics.nature.com
In model organisms, chemical mutagenesis provides a powerful alternative to natural, polygenic variation
(for example, quantitative trait loci (QTLs)) for identifying functional pathways and complex disease genes.
Despite recent progress in QTLs, we expect that mutagenesis will ultimately prove more effective because
the prospects of gene identification are high and every gene affecting a trait is potentially a target.
The landmark paper by Lander and Botstein1 stimulated enormous
interest in the genetic analysis of complex traits in mammals. It
drew to popular attention the idea that interval mapping based on
DNA markers could be used to genetically localize QTLs in natural
and experimental populations. These ideas led to mass production
of readily typed DNA polymorphisms2,3, development of analytical
methods for studying QTL traits4–8 and the chromosomal localization of numerous QTLs (ref. 9). The development of new QTL
mapping strategies and the successful identification of genes
responsible for many simple mendelian traits have reinvigorated
interest in QTLs. Despite these important developments, many
challenges remain. At the same time, there is renewed interest in
chemical mutagenesis of model organisms as means to obtain single-gene mutants affecting phenotypes of interest10,11. Here we
compare the merits of QTLs versus those of mutagenesis as ways to
discover the genes responsible for phenotypic variation in experimental populations. We argue that mutagenesis is generally a more
efficient way to discover the genetic basis of many complex developmental and physiological processes.
The long and bumpy road
Five generic steps, systematically applied, are intended to lead
from QTL discovery to gene identification. But each step has
shortcomings that make progress difficult. The first step is to
map QTLs to chromosome segments. QTL-mapping crosses typically involve hundreds of progeny that are typed for genetic
markers spaced every 15–20 cM and typically require 75–100
markers to survey the mouse genome. These progeny must also
be phenotyped with appropriate functional assays. Associations
between markers and traits are evaluated to calculate the likelihood that a QTL is near a marker locus. After hundreds of phenotype tests and tens of thousands of genotype tests, a QTL
remains poorly mapped with the confidence limits for its localization usually encompassing large chromosome segments.
The second step is to isolate genetically a single QTL from other
QTLs, usually in a congenic strain. This process converts a polygenic trait into a simpler, ideally single-gene, trait. (It is ironic that
QTLs that begin as polygenic must be converted into mendelian
traits for gene cloning.) For this strategy to be successful, the isolated QTL must have a measurable phenotypic effect when separated from other genes contributing to trait differences between the
parental strains. A limitation is that a single congenic strain does
not necessarily resolve closely linked QTLs that act in the same
direction, especially in the case of highly polygenic traits12,13. Not
knowing whether a QTL can be reduced to a measurable, singlegene trait makes the prospects of further progress questionable.
The third step is to map the QTL precisely with linkage crosses
involving a congenic strain and the host parental strain. Before
molecular studies can be undertaken for gene identification, the
critical region in which the QTL is located must be reduced to
several cM, preferably less. Adequate numbers of recombinants
and reliable phenotyping, which may require progeny testing in
particular individuals, remain important issues.
The fourth step is to identify and evaluate candidate genes.
Genome sequences, which are expected to be finished in the near
future, will provide complete lists of genes. But the number of
candidate genes in the critical region will usually remain too large
(a 1-cM interval contains more than 30 genes) to justify systematic functional studies in the absence of other evidence supporting a hypothesis about a particular candidate. Prioritizing
candidate genes for functional tests requires detailed knowledge
about the phenotypic effects of each QTL.
The final step involves tests to establish proof of identity of the
candidate gene, which is typically done with gene targeting or
gene-specific transgenesis. Because QTLs are usually ancient natural variants, perhaps the greatest challenge is how to distinguish
efficiently and definitively the mutation responsible for the trait
difference from the closely linked polymorphisms that differ
between the parental strains. These tests are complicated because
formal proof of identity requires that the allele causing the QTL
trait replace the alternative allele in the host strain, a technically
challenging task, particularly when the host strain is not a standard inbred strain, as is sometimes the case14–17.
Road repairs
Research in QTL analysis has led to powerful methods for
analysing gene interactions, multiple phenotypes and trait associations. New computational tools have provided more sensitive
QTL detection and somewhat better map resolution. Improved
genetic markers such as single-nucleotide polymorphisms
(SNPs) are being developed that are more readily typed at lower
cost. In a few instances, unequivocal evidence for a QTL candidate gene has been provided either by transgenics12 or very high
resolution mapping13. Major problems nevertheless remain. Het-
1Department of Genetics, Case Western Reserve University School of Medicine and Center for Human Genetics, University Hospitals of Cleveland, Cleveland, USA.
2The Jackson Laboratory, Bar Harbor, Maine, USA. Correspondence should be addressed to J.H.N. (e-mail: [email protected]) or W.N.F. (e-mail: [email protected]).
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erogeneous genetic backgrounds in conventional linkage crosses
and even in specialized mapping resources, such as recombinant
inbred and recombinant congenic strains, confound detecting
QTLs with weak effects. Not only do large confidence intervals
compromise precise localization of QTLs, but QTLs often fail to
replicate in congenic strains. Although these factors greatly complicate identifying the gene responsible for the QTL effect, several
new approaches improve the prospects for success.
Conventional QTL methods lose power rapidly as genetic complexity increases. The first problem is that the ability to detect weak
QTLs is a function of sample size. In general, analysis of several hundred individuals will typically detect QTLs that account for 10% or
more of the total variance. The second problem is that the simultaneous segregation of many QTLs complicates reliable detection of
individual QTLs. Conventional methods for dealing with genetic
complexity have relied on special mouse stocks, such as recombinant
congenic strains (RCS), which
make polygenic traits oligogenic14. These strains have on
average approximately oneeighth of the trait-controlling
QTLs from the donor parental
strain, with the remaining QTLs
distributed throughout the
genome. For example, if 100
polygenes differ between a pair
of parental strains, a typical RCS
derived from them would differ
by only 12 genes. Despite the
reduced genetic complexity, each
strain is genetically unique and
the remaining segregating QTLs
still make the isolation, characterization and identification of
individual QTLs complex.
A new approachchromosome substitution strains15 (CSSs)
was recently described whereby two parental strains are made to
differ by only one chromosome at a time. The first autosomal CSS
was used to identify linkage for genes controlling inherited susceptibility to testicular germ-cell tumours16. Because CSSs have a common and uniform genetic background, the authors obtained highly
significant evidence for linkage in fewer mice than was possible with
a much larger sample size in a segregating cross. Once constructed, a
panel of CSSs can be used to determine (without further genotyping
or genetic crosses) whether QTLs occur on a particular chromosome. Subsequent linkage crosses with the CSS can be used to localize the QTL on the substituted chromosome without the
confounding effects of other segregating QTLs. CSSs can be used to
make congenic strains in fewer generations than conventional methods. Crosses between a CSS and the donor strain can be used to map
residual QTLs without the confounding effects of the fixed QTL. If
complementary CSS panels are available for the two parental strains,
gene interactions can be readily detected and distinguished with
greater statistical power than other methods. Finally, CSSs are a
renewable resource for functional studies to characterize the phenotypic basis for each QTL.
Simplifying genetic complexity only partly resolves the problem
of map resolution. Depending on the method of QTL detection,
the typical confidence interval for a QTL is at least 10 cM. For most
traits, the corresponding number of candidate genes is an unrealistic number to evaluate genetically, molecularly and functionally.
For highly polygenic traits, another complicating factor is the
number of genes that may underlie a single QTL (ref. 17). The conventional remedy is to collect additional recombinants within the
Robin Lovell-Badge
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commentary
382
QTL interval and evaluate their associated phenotypes with either
progeny testing of recombinants or further strain construction.
When a QTL is itself genetically complex because several genes at
the locus affect the trait, however, the effect of each gene on phenotype becomes increasingly hard to measure. The task of collecting
adequate numbers of crossovers in short genetic intervals compounds this seemingly insurmountable difficulty.
An alternative approach uses linkage disequilibrium mapping in
heterogeneous stocks (HS) to take advantage of crossovers that have
accumulated over many generations of breeding. Thus, a parental
strain haplotype is reduced to a much smaller segment than can be
achieved after a limited number of linkage crosses. This principle
was applied to fine-structure map a mouse QTL to less than 1 cM
(ref. 18). The obvious advantage of this approach is that the QTL is
more likely to be due to a small number of genes, perhaps even a
single gene, that would require subsequent evaluation. The HS
approach has some disadvantages, including the often confounding effects of genetic
heterogeneity and the absence of
a genetically defined strain for
subsequent functional studies. A
related approach that exploits
cumulative crossovers with less
heterogeneity is advanced intercross (AI) lines19, which are typically derived from a single pair
of inbred strains followed
through many generations of
genetic and phenotypic analysis.
Both HS stocks and AI lines are
potentially powerful methods to
fine-structure map QTLs.
Many QTLs arose as ancient
spontaneous mutations before
the establishment of inbred strains. With inbreeding, these QTLs,
because of chance or selection, became fixed in some inbred
strains and not in others. Inbred strains are therefore a mapping
resource and may be exploited by studying patterns of linkage
disequilibrium. This approach, used in earlier studies on simpler
traits20, has been applied to QTLs. As with HSs, the many generations of mating before inbreeding allowed considerable recombination, reducing the length of retained ancestral segment
surrounding a QTL. In a recent study21, alleles at Pas1, a QTL
controlling susceptibility to lung tumours that had been mapped
in only a few inbred strains, were inferred in the majority of
strains by associating marker types with susceptibility. With these
inferences, haplotype analysis delimited the location of Pas1 to a
1.5-Mb region, reducing the number of candidate mutations that
required functional evaluation. Obviously, this type of approach
is valid only for trait alleles that existed before the inception of
common inbred strains and will have limited utility with genetically heterogeneous traits. Nevertheless, this strategy will be
greatly enhanced with the complete gene maps and high-density
SNP maps for genotyping common inbred strains.
Most approaches to gene identification rely on meiotic recombination mapping to refine the chromosomal location of a trait locus
so that candidate genes can be evaluated efficiently. The paucity of
published evidence documenting QTLs mapped to manageable
intervals suggests that such hopes may be overly optimistic. One
complementary approach is to use deletion breakpoints in the
vicinity of a previously mapped QTL to fine-structure map a QTL
(ref. 22), perhaps in combination with chemical mutagenesis to
generate new functional alleles23. Another study involves a more
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radical, brute-force alternative to recombination or deletion mapping24. The authors asked whether a QTL that had been mapped to
a broad region in humans could be mimicked by merely adding
human genetic material (in the form of YAC transgenes) to the
mouse. They found that the introduction of two human candidate
genes, IL4 and IL13, on YACs from a 1-Mb region caused the
downregulation of the endogenous mouse homologues, resulting
in an asthma-related phenotype. This ‘shotgun’ type of transgenic
approach may be generally applicable if we consider that many
QTLs may result from changes in gene expression rather than
amino acid substitution. Although it seems more appropriate to
use such trangenics to rescue natural variants in an allele-specific
manner, these results indicate that there may be more than one way
to find a QTL. Such brute-force approaches, which go directly to
the molecular level of the gene, are appealing, especially with the
complete sequences of human and mouse genomes anticipated in
the near future.
Seduced by the bright lights
QTL analysis is a beguiling means for studying the genetic basis of
complex traits in mammals, both for experts and for novices: one
need only discover trait variation within a species, after which the
methods are generic. Part of the attraction, and ultimately a
source of difficulty, is that the developmental or physiological
basis for the trait difference need not be known for mapping to be
successful. As a result, many QTLs are being mapped whose functional bases are ill defined. For example, consider a preliminary
evaluation of cardiovascular function based on treadmill tests of
the A/J and C57BL/6J inbred strains (B. Hoit and J.H.N., unpublished data). C57BL/6J readily perform as desired and spend considerable time running. A/J, by contrast, seem to ‘prefer’ to rest on
the stimulation plate rather than run. The trait difference is readily apparent and probably could be mapped. The more important
question concerns its basis. What is being mapped? Is it a motivational difference? Or is it a neurological, cardiovascular, metabolic
or physiological difference? With the availability of common
strains and the generic character of the methods it is relatively
easy, perhaps too easy, to map a responsible QTL and too tempting to jump straight ahead to candidate genes. Nevertheless, a
clearer understanding of the functional basis for the trait difference is critical not only for understanding what was mapped, but
also for evaluating candidate genes.
You can’t get there from here
The goal of many QTL studies is to identify genes and pathways that
underlie complex traits and that together elaborate the development
and function of biological systems. Given the difficulties of finding
‘QTL’ genes and the lack of prospects for ‘quick fixes’ in sight, it is
worth a closer look at why QTLs are considered valuable compared
with the alternatives. One issue is whether it is necessary that the
variant occurred naturally and that it exists as a complex trait among
many other segregating trait loci. For experimental disease models,
this is generally assumed to be of value because complex traits in
humans show this pattern. It is implied that the types of genes and
pathways should be similar in the corresponding model, but for
most models, compelling parallels are not usually demonstrated. On
the other hand, it seems likely that QTL-like analysis to identify
genetic modifiers of human disease genes in transgenic mice carrying a human mutation will be increasingly used.
A more critical issue is the proportion of genes surveyed relative
to the number of genes in a pathway. In QTL studies, investigators
typically survey a limited number of strains (often eight to ten) to
discover significant phenotypic differences. Crosses between these
strains are then evaluated to identify combinations that disclose
perhaps five major and several minor QTLs. Even with the recent
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commentary
progress in QTL detection and mapping (such as CSSs and HSs),
the number of naturally occurring allelic variants that can be evaluated in practice is a relatively small proportion of the total number of genes essential for pathway functions and systems biology.
Finally, although natural variants are intrinsically appealing, in
large part because they already exist, we should remember that
they are nevertheless accidents of history. Although many genes
may exist as QTLs for some pathways, in many other cases most
genes are invariant and are therefore not available for functional
studies, even though they may have critical roles in complex traits.
A major challenge is attributing functions to these invariant genes,
a problem that remains regardless of the success of QTL studies.
The road less travelled
Mutagenesis with chemicals such as ethylnitrosourea (ENU) is an
effective way to induce single-gene mutations affecting phenotypes of interest. The advantages of ENU are a high frequency of
induced mutations at particular genes (a high specific-locus
mutation rate), a substantial number of independent mutations
with phenotypic effects in each offspring of mutagenized individuals (multiple mutations in each mutagenized individual),
and simple molecular lesions, usually A:T to G:C transitions25.
Treatment of embryonic stem cells with a broader spectrum of
mutagens, such as EMS and ICR199, increases the power of the
approach26,27. The discovery of induced mutations affecting
medically important traits demonstrates the power of mutagenesis to induce relevant models of human diseases. These attributes
are the basis for the proliferation of mutagenesis programmes
designed to find new models of human diseases10,28,29.
How does mutagenesis compare with QTLs in the analysis of
complex traits? An important advantage of induced variation
is that potentially every gene in the genome that affects the
trait of interest is a target for mutagenesis. Otherwise, the same
phenotypic assays that are used to identify QTLs can be used to
survey mutagenized mice. If the assay is sufficiently sensitive to
detect a single QTL in a congenic strain, it should be adequate
to identify induced single-gene mutations affecting the same
trait. When considering mutagenesis for complex traits, as
opposed to its conventional use to obtain new alleles of a
gene30,31, it is important to note that pathways composed of
many critical genes are expected to yield more mutant mice
than surveys for mutations with phenotypic effects at a single
locus. As a result, the success rate for finding induced variants
is improved with increasing genetic complexity of developmental and physiological pathways, in contrast to more traditional complex trait analysis in which genetic complexity is
more of a nuisance than an advantage.
As with QTLs, it may in some cases be too easy to obtain
mutants of apparent interest without a refined understanding of
their functional basis. Therefore, the key to success in mutagenesis programmes is assembling a series of efficient, reliable and
meaningful phenotypic assays to not only survey large numbers
of mutagenized mice, but also to design follow-up tests to confirm that a phenotype is truly of interest. Thus, considerable
effort is being made to develop more rigorous and sensitive
assays (or to adapt existing ones) for use in high-throughput
screens and efficient follow-up tests.
Unlike QTLs, chemically induced allelic variants do not
already exist in a few common, easily obtained inbred strains, but
must often be generated in association with large mutagenesis
and screening centres and subsequently disseminated to the community. Therefore, further development of facile but robust
germplasm preservation and recovery technologies32–34 and
development of plans for community involvement with such
centres will be critical to the success of the mutagenesis approach.
383
commentary
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The continued development of mouse mutagenesis centres in
Europe and Japan and the imminent establishment of centres in
the United States will provide tests of this approach.
A more general need is to learn from experience the number
and types of genes that are the targets for induced variation in
these assay conditions. The many advantages of using mutagenesis in complex trait analysis outweigh its limitations. Some
induced mutants, like some QTLs, will escape detection. Some
induced mutants will be undetectable in linkage crosses because
of genetic background effects, just as some QTLs will not survive
transfer from a segregating background to congenic strains.
Overall, however, it is the successful identification of the genetic
lesions that give rise to mutant phenotypes, compared with the
ability to identify the variant that underlies naturally occurring
QTLs, which makes mutagenesis the road of choice.
© 2000 Nature America Inc. • http://genetics.nature.com
The end of the road
Some goals of QTL analysis include the generation of organisms
with improved traits such as plant yield, disease resistance, livestock fecundity and milk production. This is the modern extension of methods that have been used for many thousands of
years. Crop and livestock improvement is the goal; understanding the nature of genetic variation and gene identification is often
irrelevant. Other applications go a step further, towards understanding the relationship between environmental and genetic
determinants, but do not necessarily require gene identification.
By contrast, with the genomics era in transition from questions
of gene structure to problems of protein function, the bulk of
research in animal and plant sciences is aimed towards establishing precise relationships between gene structure and phenotypic
variation. Establishing the identity of each gene is essential for
this work to proceed.
Formal proof of gene identity is relatively easier to obtain for
induced mutants than for QTLs, especially if genetically defined
strains are used in the mutagenesis studies. Molecular analysis of
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candidate genes simply involves comparing the sequence of candidate genes or genomic sequence between the mutated and
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Induced mutants are discovered as single-gene traits, bypassing
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simpler path to gene discovery. It remains to be determined
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Acknowledgements
We thank G. Churchill, J. Naggert and J. Schimenti for comments on this
manuscript. This work was supported by NIH grants HL58982, CA75056
and RR12305 to J.H.N.; NS31348, DC03611, NS40246 to W.N.F.; by a
Cancer Center Support grant CA34196 to The Jackson Laboratory; by a grant
from the Keck Foundation to the Department of Genetics, Case Western
Reserve University; and by a Howard Hughes Medical Institute grant to the
Case Western Reserve University School of Medicine.
Received 17 February; accepted 20 May 2000.
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