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Genetic Redundancy
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David C Krakauer, Santa Fe Institute, Santa Fe, New Mexico, USA
. The Organizational Levels of Genetic Redundancy
Genetic redundancy typically relates to the duplication of an open reading frame within
a genome. Genetic redundancy is often inferred when the modification or deletion of
a portion of genetic material in a duplicated genome results in minimal changes in trait
or organismal phenotype in reference to the nonduplicated wildtype. This invariance
has been attributed to buffering mechanisms promoted by duplicates and to a number
of compensatory pathways independent of the duplicate. Most duplicates are rapidly
lost from genomes by mutation and drift.
. Modifiers and Networks
. Mechanism versus Function of Genetic Redundancy
. Two Functionalist Perspectives
. Mechanisms Allowing the Evolutionary Preservation of
Redundancy
. Incidence of Genetic Redundancy
. Genetic Redundancy and its Close Conceptual Cognates
. Open Questions and the Future of Redundancy
Online posting date: 15th July 2008
The Organizational Levels of Genetic
Redundancy
Biologists frequently describe genetic redundancy, also referred to as functional redundancy, in relation to the
knockout of one or more genetic transcripts. Subsequent to
the elimination of a transcript, the absence of a scoreable
change in phenotype, is the marker for genetic redundancy.
It is assumed that a second genetic locus, or group of genetic loci, can compensate for the loss. Geneticists also
speak of redundancy in the genetic code. This refers to the
many to one mapping of codons to amino acids, allowing
for the possibility of silent or synonymous nucleotide substitutions in a sequence. Redundancy in the genetic code is
typically thought of as involving separate mechanisms to
those producing redundancy at the level of the gene, and
hence these concepts are kept separate. However, redundancy can also be present at levels above that of the
gene. Whole sets of co-localized genes (syntenic blocks),
or more frequently, whole chromosomes (polyploidy) can
be redundant. In each case, the mechanisms buffering the
phenotype from change are likely to be different.
Modifiers and Networks
In an effort to understand the underlying mechanics of
redundancy, biologists have explored several working
models. For all models, redundancy first emerges through
a stochastic process of duplication of a locus, either by
unequal crossing over, or by transcription and ribonucleic
acid (RNA) splicing. By far most duplicates are rapidly
silenced and lost by degenerative mutation (Lynch and
Conery, 2000). The role of models of redundancy is to
ELS subject area: Evolution and Diversity of Life
How to cite:
Krakauer, David C (July 2008) Genetic Redundancy. In: Encyclopedia of
Life Sciences (ELS). John Wiley & Sons, Ltd: Chichester.
DOI: 10.1002/9780470015902.a0006116.pub2
account for how redundancy can be preserved for long
enough to be observed. In one set of models, genetic redundancy arises as a consequence of a secondary locus or
set of secondary loci, whose role is to substitute or compensate for damage to a primary locus or loci. Frequently,
this involves duplicate sets of a reading frame expressing
identical proteins. In a second set of models, redundancy
arises out of the network structure of genetic regulatory
pathways, or metabolic circuits. In the first model, redundancy is the product of transcripts related in structure and
function or through direct interactions among duplicates;
we call these loci modifiers of functional redundancy. In the
second model, nominally, unrelated genes interact through
complex pathways to produce a stable phenotype; we call
these functionally redundant networks (see Figure 1 for a toy
model of network redundancy).
An example of redundancy through modifiers that interact directly with damaged proteins but differ in structure and
function is the role played by heat shock proteins in suppressing phenotypic variation. The heat shock protein
Hsp90 is not required for the maturation or maintenance
of most proteins in vivo. The targets of Hsp90 are predominantly signal transduction proteins, with which it interacts
repeatedly, identifying protein structures indicative of misfolding, and stabilising the wild-type conformation. At low
temperatures, or when there are no mutations to its target
proteins, Hsp90 is functionally redundant. However,
at high temperatures producing developmental noise, or
when there is extensive polymorphism among signal transduction proteins, Hsp90 plays an essential buffering role.
Homozygous mutations to Hsp90 are lethal. Rutherford
and Lindquist (1998) explored three hypotheses to explain
the role of Hsp90: (1) loss of Hsp90 increases susceptibility
to developmental noise, (2) loss of Hsp90 increases the rate
of mutation and (3) loss of Hsp90 exposes cryptic variation
buffered in the wildtype. A series of cross breeding experiments established that the third, followed by the first hypotheses were most consistent with the data. Rutherford
and Lindquist were able to selectively breed for defective
flies, by crossing flies heterozygous for mutations to Hsp83
(a related heat shock protein) starting from a small
population. It is unlikely that new mutations arose during
the breeding experiment, it is more likely that formerly
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Genetic Redundancy
A
B
C
Figure 1 Convergent activation and nonspecific negative feedback can lead
to functional redundancy in a gene regulatory network. Proteins A and B are
transcription factors produced at an equal rate and capable of activating the
expression of gene C. Gene A activates C at a rate ka and gene B activates C at a
rate kb. Gene C can be thought to work according to three different negative
feedback mechanisms: (1) encode a protein capable of inhibiting the
production of A and B, (2) encode a protein inhibiting the activation of C by A
and B or (3) increase the decay of A and B mRNA. In all three cases the
inhibitory influence of C is given by the rate constant kc. All three proteins are
degraded at an equal rate d. We are interested in the steady state
concentration of C following the knockout of A in comparison with the steady
state concentration of C in the wildtype (in which A and B are both present).
The ratio of mutant to wild-type concentration
of C assuming feedback
pffiffiffiffiffipffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
mechanisms (1) and (2) is given by kb ka þ kb . If we assume that ka = kb,
then the reduction in steady state concentration of C is about 30%. The steady
state ratio assuming feedback mechanism (3) depends on the rate of protein
decay d. When the decay rate is very small (d ! 0), then the ratio is equal to 1.
In other words, the circuit is completely redundant with respect to the loss of
A. When the decay rate is very fast (d ! 1) then for ka = kb, the ratio of mutant
to wildtype is 0.5. For realistic rates of decay the ratio remains approximately
equal to 1. This toy model shows that for a range of negative feedback
mechanisms, this network structure produces a degree of functional
redundancy greater than the additive contribution of individual genes.
buffered mutations were masked by functioning Hsp83, and
revealed in its absence. Hsp90 is highly pleiotropic and interacts with many kinds of misfolded protein. At any one
time it is likely that Hsp90 will be redundant with respect to
one of its targets, and will play an essential role with another.
Thus, Hsp90 is a modifier of genetic redundancy that works
through direct interaction with the perturbed protein.
Examples of redundancy through modifiers that are
similar in structure and function but do not interact directly
with damaged protein, are the members of the Cyclin
D/INK4/pRB/E2F signal transduction cascade and the
a-actin proteins.
The genetic analysis of human tumours has revealed that
many of the genes damaged in cancer cells are involved in
the G1/S transition of the cell cycle. Around 80% of all
human cancers have damage to the Cyclin D/INK4/pRB/
E2F signal transduction cascade. for a cell to progress from
G1 to S phase of the cell cycle, the retinoblastoma proteins
(pRB) must be inactivated (phosphorylated) by the D-type
cyclin-dependent kinases: Cdk4 and Cdk6. In Drosophila
and in mice ablation of the Cdk4 locus results in a minimal
change in phenotype, even though the cyclin-dependent
kinases play an essential role in cell cycle (Ortega et al.,
2002). It is suggested that Cdk6 can compensate for the loss
of Cdk4. Similar functional redundancy is observed upon
selective mutation to the INK4 cell cycle inhibitors
(ink4a,b,c,d), and the D-type cyclins (D1, D2 and D3).
Thus, each component of the pathway demonstrates a degree of functional redundancy that arises through a shared
structure and function.
2
The a-actins are actin-binding proteins forming a major
structural component of the Z-lines in myofilaments. Early
studies implicated the loss of ACTN3 (a-actinin-3) in muscular dystrophy, and other dystrophic, myopathic and
neurogenic pathologies. Subsequent testing revealed that
normal individuals very often shared the defective genotype, homozygous for a nonsense mutation to ACTN3.
The data suggest that ACTN3 is functionally redundant in
humans and its loss is compensated for by the closely related ACTN2 (North et al., 1999).
An example of network redundancy, as opposed to modifer redundancy, is the negative regulation of RTK signalling
by Src42A in Drosophila (Lu and Li, 1999). The receptor
tyrosine kinases (RTK) respond to extracellular stimulation
from a large number of different ligands, and transmit information about these ligands, into cells through autophosphorylation of tyrosine residues contained in their tails.
These phosphotyrosine residues are then bounded by SH2
domains of Grb2 adaptor proteins. This leads to the activation of Sos, which in turn catalyses the formation of
Ras-GTP (guanosine triphosphate) which binds to RAF
initiating three protein kinase phosphorylation cascades. In
parallel, the RTK recruit another set of SH2 domain proteins: members of the Src family of non-RTK. Extensive
knockout experiments have revealed that many of these Src
proteins (including Src42A) are functionally redundant. Lu
and Li (1999) have found that the role of Src42A is to repress
RTK signalling and function in a Ras-independent manner.
The logic of the parallel pathway and the nature of Src42A
redundancy are thought to operate as follows. Upon stimulation of RTK, with say growth factors, the primary stream
of excitatory or activating signal transduction flows from Sos
to RAF to mitogen activated protein kinase (MAPK). In
parallel an inhibitory signal flows through the Src42A pathway. Under normal conditions, the influence of this inhibitory pathway is not felt, such that mutations to Src42A and
stimulation of the RTK lead to a wild-type response. However, when the Ras/Mapk cascade is compromised, the removal of Src42A has severe phenotypic consequences. The
Src42A is redundant in the wildtype but plays an important
inhibitory role in the mutant. Redundancy is not achieved by
backing up members of the Ras/Mapk cascade (as in the
actin and cyclin examples) or through direct interactions
among proteins (as in the heat shock example), but through
properties of the parallel network structure. The inhibitory
Src42A pathway acts as a sensitive indicator and suppressor
of perturbations to the Ras/Mapk pathway. The many connections in the Ras/Mapk pathway make it particularly
vulnerable to mutation and developmental noise. The high
frequency of potential damage should be sufficient to maintain the redundant network.
Mechanism versus Function of Genetic
Redundancy
When biologists ask why a given trait is genetically
redundant, there are two possible interpretations to this
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Genetic Redundancy
question. The first question relates to the underlying mechanisms that buffer the effects of genetic modification (modifiers versus networks). The second question relates to the
evolutionary origins and functions of those mechanisms
promoting genetic redundancy. In other words, how does a
given mechanism preserve the wild-type phenotype (mechanism), and what selective advantage is there for mechanisms that preserve the wild-type phenotype and how
might they evolve (function). Studies on genetic redundancy can be classified according to which of these two
approaches is adopted. In the previous section mechanistic
studies were emphasized.
A good example of the functional study of redundancy
are Wagner’s 2000a, 2000b and Gu et al. (2003) statistical
analyses of the relationship between sequence identity,
gene expression pattern and phenotypic effects in yeast. In
the first study, Wagner sought to determine whether redundancy is best explained by assuming that functional
redundancy arises through the presence of multiple closely
related genes (modifier model) that can compensate for one
another, or whether alternatively, redundancy is better
thought of as a network property of the genome involving
multiple unrelated genes (network model). The analysis has
three components. In the first, it was determined whether
there was a positive correlation between the fitness effects
of mutations to single genes within one of the six duplicated
blocks on chromosome V, and sequence similarity to the 44
alternative duplicated genes within the blocks. In the second, sequence similarity among genes outside the duplicated blocks was correlated with fitness effects. And in the
third, all genes in the study were categorized according to
whether mutation leads to undetectable fitness effects, or
severe fitness reductions. It was established how far these
two groups of genes differed in mean similarity to other
genes in the genome, and in similarity in gene expression
pattern to other genes of the genome. In none of the three
cases was there a significant association between the genetic
similarity among genes, the expression profiles among
genes and their ability to buffer the effects of mutation. The
study concludes by suggesting that redundancy is most
likely to have evolved as a network property of genetic
circuits, rather than through duplication of existing genes.
However, in the subsequent study by Gu et al., which was
conducted with a larger sample of 1147 genetic loci, the
results were the opposite from those of Wagner, and genetic
similarity proved to be a strong predictor of robustness.
These studies illustrate the heterogeneous nature of the underlying robustness mechanisms, and the requirement for
large sample sizes in redundancy studies.
Two Functionalist Perspectives
There are at least two perspectives on the evolution of redundancy. Some hypothesize that redundancy is only apparent, and reflects ‘genetic uncertainty’ arising from
detection constraints in artificial laboratory populations
(Tautz, 1992, 2000). Population genetics tells us that a
gene’s selective value need only exceed the reciprocal of
population size to become fixed (Kimura, 1983). In other
words, for a gene to become ubiquitous, it needs to have a
better than random probability of surviving from one
generation to the next. This suggests that the selective value
of a gene expressed in an organism evolving in a very
large population, can be very small and yet remain functional and nonredundant. Experimental or technological
limitations in detection could feasibly be below than that
achievable by natural selection and give rise to ‘effective
redundancy’. Moreover, to obtain statistical significance
for differences in phenotype following knockout even when
they are detectable, the laboratory populations might have
to approach the size of natural populations. Thus, ‘effective
redundancy’ arises through weak selection in large
populations.
The alternative perspective is to accept that undetectable
changes in phenotype or fitness are the result of true redundancy in redundancy promoting modifiers, and/or network
properties of the genomic system that have been selected.
The lack of a scoreable phenotype is not the result of very
subtle changes in phenotype, but the outcome of effective
mechanisms for buffering perturbations. In this case, the
detection of redundancy does not require very large populations but the correct choice of perturbation. Consider the
example of Hsp90. Mutations to the signal transduction
proteins are very subtle, suggestive of redundant functions.
In a suitably sensitive experiment, these subtle differences
might have been detected. It would have been concluded
that the redundancy was only apparent. However, the
knockout of Hsp90 reveals that the redundancy is not in the
signal transduction genes, but in the Hsp90 modifier of redundancy. This modifier, unlike the proteins with which it
interacts, is likely to come under rather strong selection, as it
interacts with a broad class of proteins.
Further important evolutionary questions include
whether redundancy has come under direct selection for
its error-buffering benefit, or whether redundancy is expected to arise and persist as a side effect (epiphenomenon)
of constraints and mutational processes acting within genetic sequences, protein structures or genetic circuits. Some
degree of pleiotropy, arising through unanticipated overlap in protein structure (in a transcription factor or a receptor) or in genetic sequence (in a promoter or transfer
RNA, tRNA) might lead to epiphenomenal redundancy.
There is also the question of redundancy in hierarchically
organized systems. Organisms are multilevelled, comprising genes, cells, tissues and organ systems. When we speak
of mechanisms of redundancy they are likely to differ at
each of these levels. Krakauer and Plotkin (2002) have
shown using evolutionary models that often redundancy at
a more inclusive level is achieved though it is inverse at a
lower level. Thus, tissues can remain robust against mutations by eliminating defective cells. If the population of
cells is sufficiently large, such a strategy is highly effective,
as it prevents defective cell lineages from fixing within the
tissue. This amplification of damage at more populous levels of organization has been called antiredundancy.
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Genetic Redundancy
Mechanisms Allowing the
Evolutionary Preservation of
Redundancy
Once a degree of redundancy has arisen and is detectable,
there remains the problem of how it is maintained. This is a
problem because random inactivation of one or more genes
contributing to an overlapping function, will by definition,
have minimal phenotypic effects. Selection does not act
directly on the redundant function, but against those without it (Wagner, 2000a, 2000b). This implies that selection
for redundancy is indirect and consequently rather weak. If
a redundant gene is truly redundant, selection will be unable to prevent it from accumulating disabling mutations.
In other words ‘Use it or Lose it!’. The difficulty of preserving redundant functions makes the epiphenomal view
questionable. Different mechanisms for preserving redundancy have been hypothesized (Krakauer and Nowak,
1999). These include a cumulative benefit from multiple
identical genes (doseage effects), robustness through
overlapping functions subject to high rates of error (error
buffering), genes that are pleiotropic with respect to an
overlapping function and maintained by stabilising selection on their unique functions; shared theories and by
temporal and spatial subfunctionalization.
(1) Cumulative benefit theories: When increasing the
quantity of a gene product increases fitness, genetic
redundancy is only apparent, and reflects the genetic
uncertainty of the laboratory (Thomas, 1993). All
eukaryotic cells contain multiple copies of the mitochondrial genome, promoting efficient metabolism,
and multiple copies of tRNA and messenger RNA
(mRNA) genes for rapid translation. Eliminating copies reduces organismal fitness in large populations,
hence redundancy is maintained by weak stabilizing
selection acting on an ensemble of more or less identical
transcripts.
(2) Mutational error-buffering theories: Genetic error buffering is defined as any mechanism that reduces mutational load. Assuming that there are two identical genes
contributing to a shared function with different mutation rates, the gene with the higher mutation rate will be
lost from the population in due course. If mutation rates
are equal, one of two genes will eventually become silenced by random drift. By allowing an asymmetry in
how well each gene performs a shared function, then
redundancy can be maintained whenever the more able
gene has the higher rate of effective mutation (Nowak
et al., 1997). This does not require that one gene mutate
more frequently, but that the protein product of the
more able gene is more sensitive to mutation.
(3) Developmental error-buffering theories: Developmental
error buffering is defined as any mechanism that reduces
the deleterious effects of nonheritable perturbations of
the phenotype during ontogeny. Developmental noise
selects for genetic redundancy by increasing the effective
4
number genes damaged during a life time. Individuals
carrying multiple redundant copies of a gene will be less
compromised by the deleterious effects of noise than individuals carrying a single copy of a gene.
(4) Pleiotropic theories: Pleiotropy describes instances
where a single gene experiences selection in more than
one context. Consider two genes with two independent
specialist functions. Further assume that one of these
genes can in addition to its own specialist function,
perform as a generalist, the function of the other gene
but less efficiently. Some mutations to the pleiotropic
gene will eliminate its specialist function and others will
eliminate its generalist function. Redundancy will be
maintained whenever the rate of elimination of the
generalist function is lower than the rate of elimination
of the two specialist functions (Nowak et al., 1997).
Once again the rate of elimination need not involve
differences in rates of mutation but in rates of
phenotypic change. In small populations pleiotropy
has a very considerable influence on the preservation of
partially redundant genes (Wagner, 2000a, 2000b).
(5) Genetic regulatory element theories: There is redundancy not only among coding regions, but also among
regulatory elements associated with structural genes. Indeed, redundancy is more prevalent in regulatory genes
than structural genes. In part this can be the result of
differences in specificity. During a duplication event
structural genes and regulatory elements may be duplicated. In some cases each structural gene shares the same
regulatory genes with its paralogue, and in other cases,
the paralogous genes might acquire different regulatory
genes from the initial set (Averof and Akam, 1995). In
such a scenario there are two sources of redundancy.
Redundancy in both the regulatory gene and structural
gene, as both copies of the structural gene possess the
same set of regulatory genes. And redundancy in the
structural gene, as each structural gene can be accompanied by a different regulatory gene. Partial redundancy can be maintained in both cases if the regulatory
genes respond to different spatio-temporal stimuli. Furthermore, redundancy in regulatory genes might persist
for longer as a consequence of their small mutation target size. This process has been explored in some detail as
a model of subfunction fission (Force et al., 1999).
(6) Subfunctions and complementary degenerative mutation. The imperfect duplication of a genetic locus might
prove to be essential to its maintenance. If we start with
a transcript encoding a protein with multiple, modular
subfunctions, and each copy degenerates a different
subfunction upon duplication, then selection maintains two-loci where formally it required only a single
locus (Force et al., 1999).
Incidence of Genetic Redundancy
The taxonomic incidence of genetic redundancy reflects a
double sampling bias: firstly, genetic redundancy is best
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Genetic Redundancy
documented in those organisms favoured by experimental
researchers in molecular genetics: Drosophila, Xenopus,
mouse, Caenorhabditis elegans, Arabidopsis and Saccharomyces cerevisiae. Secondly, redundancy tends to be
reported in studies involving development. This has led to a
situation in which genetic redundancy has a patchy taxonomic distribution concentrated largely in developmental
genes. This makes it difficult to identify true phylogenetic
patterns in the incidence of genetic redundancy and to determine the sorts of biological function most likely to require genetic redundancy. Even with these constraints
genetic redundancy has been reported in a very diverse
group of genes including transcription factors, transcription enhancers, protein kinases, cytokines, cell adhesion
proteins, receptors, growth factors, trafficking proteins,
chaperones, neuropeptides, oncoproteins and promoters
(see Table 1 for a few examples). It has been observed –
subject to the inadequacy of the data – that redundancy is
more common in regulatory genes than in structural genes.
The explanation provided for this asymmetry is that regulatory genes tend to interact with a larger number of other
genes than structural genes. This might suggest that, on
average, more than one regulatory gene interacts with the
same target (Cooke et al., 1997). Thus, pleiotropic interactions are more common among regulatory genes and this
allows for greater redundancy.
Table 1 A table of genes for which one or more duplicates
exist classified according to primary gene function and species
of provenance
Gene function
Examples
Organisms
Transcription
factor
Egr-1
SPT10
Hox genes
Sgs-4
CD4 silencer
TATA box
HTF9
ILR
TCR
RAR
MTMR2
Mouse
S. cerevisiae
Mouse
D. melanogaster
Transcription
regulator
Promoter
Receptor
Protein
phosphatase
Protein kinase
Cytokine
Cell cycle gene
Enzyme
Cell adhesion gene
Genetic Redundancy and its Close
Conceptual Cognates
Genetic redundancy is closely related to the ideas of canalization neutrality and neutral networks. The principle
of canalization was introduced by Waddington (1942) as a
phenomenological means of explaining the constancy of
tissues and organ types during development. Canalization
refers to those mechanisms that have evolved to suppress
phenotypic variation during development and thereby reduce the cumulative cost of deviations from a locally optimal trajectory (Gibson and Wagner, 2000). Waddington
thought of deviations as the result of mutations or environmental insults. They are also likely to arise from the
cascade of nonsynchronous network activities following
cell stimulation. Genetic redundancy provides one set of
mechanisms that could produce canalization of the phenotype, checkpoints in the cell cycle, another.
The principle of neutrality is best known to biologists in
relation to the selective neutrality of alleles in populations.
The neutral theory (Kimura, 1983) rose to prominence as a
means of explaining the higher than expected level of variation in electrophoretic data (Lewontin, 1974–1975).
Neutrality refers to the selective equivalence of different
phenotypes. The idea does not require that mutations to a
wild-type sequence leave the phenotype unchanged (although phenotypic neutrality is possible), but that the
phenotypic differences are beyond the detection limit for
selection.
Apoptotic gene
Growth factor
Chaperone
Oncogene
Cell structure gene
Cell trafficking
gene
Vesicle fusion
gene
tRNA
rRNA
Cyclin-D
Wee1, Mik1
Src-family
IL15
IFN-gamma
Cyclin A/B
Cathepsin S
Acetyltransferase
Cysteine
proteinase
Desmoplakin
Pecam-1
Bcl-2
Bcl-xl
IGFBP-2
CAF1
Human
Mouse
C. elegans
Mouse
Mouse
Mouse
Mouse
S. cerevisiae
S. cerevisiae
Human
Mouse
D. melanogaster
Mouse
Mouse
Leishmania
mexicana
Mouse
Mouse
Mouse
E1A
ACTN2, ACTN3
Sec24p
Mouse
Dictyostelium
discoideum
Adenovirus
Human
S. cerevisiae
DNSF1, DNSF2
D. melanogaster
S. cerevisiae
D. melanogaster
Neutrality, unlike canalization, is not assumed to be an
adaptive means of suppressing variability. Nor is neutrality
concerned with the development of phenotypes. Neutrality
is simply a measure of the selective equivalence of phenotypes, and is primarily concerned with finite population
effects. Canalization through genetic redundancy or checkpoint mechanisms can produce neutrality. More recently,
interest has turned to the discussion of neutral networks
(Fontana and Shuster, 1998; Huynen et al., 1996). These
networks are sets of selectively or structurally equivalent
genotypes, connected via single mutational steps. Neutral
networks span large volumes of genotype space and provide a natural buffering mechanism. As a result of mutation, sequences randomly walk through sequence space
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Genetic Redundancy
and provide potential variation for evolutionary change.
Work on neutral networks highlights the restrictions that
genetic redundancy can place on evolvability by limiting
phenotypic variance.
Open Questions and the Future of
Redundancy
1. As more knockout studies have been conducted, facilitated by increasingly efficient quantitative assays like
high-throughput genetic interaction maps (see Ihmels
et al., 2007), the data seem to become more ambiguous
rather than more clear. The data suggest that selection
for robustness can only account directly for some small
percentage of duplicate loci (around 25%). Furthermore,
duplicates rarely preserve the identical networks of interaction partners as their singleton ancestors, suggesting
that even when transcripts are conserved their distributed
functions need not be. Does this suggest that sequence
similarity and functional role can be decoupled?
2. Theoretical models suggest that selection for phenotypic robustness against nonheritable errors in development, impose a stronger selection pressure than
robustness to heritable mutations (de Visser et al., 2003)
and it could be that these more diverse, phenotypic
sources of noise are favouring redundancy. Perhaps, we
should be looking to cellular stochasticity as an important factor preserving genetic redundancy.
3. Over the next few years, as molecular biology and genetics establish more efficient functional assays, and
move away from an emphasis on sequence analysis,
more nuanced positions on both redundancy and robustness are likely to emerge. It is likely that there will
be a shift away from single locus redundancy to the
redundancy of comparable network structures, whose
single components might or might not be conserved.
This raises questions about how we go about studying
redundancy when we do not know which level we
should be analysing.
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Further Reading
Ihmels J, Collins SR, Schuldiner M, Krogan NJ and Weissman JS
(2007) Backup without redundancy: genetic interactions reveal
the cost of duplicated genes. Molecular System Biology 3: 1–11.
ENCYCLOPEDIA OF LIFE SCIENCES & 2008, John Wiley & Sons, Ltd. www.els.net