Download Heterogeneous Stocks and Selective Breeding in Aging Research

Heterogeneous Stocks and Selective Breeding in Aging Research
Gerald E. McClearn and Roger J. McCarter
Abstract
There are advantages and limitations to using genetically
heterogeneous stocks and selective breeding procedures in
gerontological and healthspan research. Animal models
that address complex systems of aging involve constraint or
manipulation of genetic diversity. They derive from levels of
genetic analysis ranging from molecular to quantitative and
are relevant to levels of causal hierarchy from base sequences
to complex multivariate phenotypes. For some research purposes control of the genotypic source of phenotypic variability by fixation is desirable; others involve establishing
genetic diversity or deliberately manipulating identified genes
or anonymous gene complexes to specification. Genetic heterogeneity is essential or advantageous for multivariate description of complex phenomena, the examination of associations
among variables, or manipulation of polygenic systems. This
review concentrates on these latter quantitative requirements.
Space limitations preclude a comprehensive review, but relevant sample references and some hints of historical perspectives are provided, with apologies to the many relevant
authors not cited.
Key Words: aging; animal model; complexity; genetic variability; genotype; heritability; heterogeneous stock; phenotypic selection; selective breeding
Genetic Perspectives on Health and Aging:
Genes as Variables
K
nowledge of the nature of aging owes much to a very
substantial body of data derived from the study of
animal model systems. Because of convenience, economy, and the wealth of available biological information about
them, there has been a concentration of research on rats and
mice. Although a strong case can be made for the importance
of broadening the phyletic base of gerontological knowledge
(Austad 1993), it is likely that these species will remain an
Gerald E. McClearn, PhD, is Evan Pugh Professor of Health and Human
Development and Biobehavioral Health, and Roger J. McCarter, PhD, is
Professor of Biobehavioral Health, both in the College of Health and Human
Development at the Pennsylvania State University in University Park.
Address correspondence and reprint requests to Dr. Gerald E.
McClearn, College of Health and Human Development, The Pennsylvania
State University, 315 Health and Human Development East, University
Park, PA 16802 or email [email protected].
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important source of knowledge for the foreseeable future.
Broadly described, these models variously involve stable, homogeneous genotypes, genetically heterogeneous stocks, and
directionally manipulated genotypes. In the context of experimental design, the genes may serve as controlled variables,
randomized variables, or independent, manipulated variables.
Inbred Strains
Perhaps the most extensively used control procedure has been
the use of inbred strains. (We provide a brief summary as a
basis for comparative evaluation of models generated by various mating regimens; for an in-depth discussion, we refer the
reader to Yuan et al. 2011 in this issue.)
Inbreeding involves the mating of relatives—in laboratory
animals, usually siblings. The effect of mating a female with
a brother is to increase the genetic similarity of the offspring
relative to that of animals derived from random mating. Over
successive generations, the level of homozygosity in likeallelic configuration at all loci asymptotically approaches 100%
(Falconer and Mackay 1996). Another strain similarly derived will also be “isogenic” but for a different set of alleles.
With respect to genotype, inbreeding is not deliberately
directional. It is important to stress, however, the natural selection of inbreeding depression: reproductive fitness generally decreases as homozygosity increases, and a substantial
proportion of nascent inbred strains terminate through early
death, infertility, or impaired rearing, among other reasons.
The roster of standard inbred strains must be regarded as
select survivors of the inbreeding process and can hardly be
considered representative of the species as a whole.
Furthermore, the attainment of near-complete homozygosity is no assurance of reduced phenotypic variance. Indeed,
an active research area explores the increase in sensitivity of
inbred strains to environmental effects due to their elevated
level of homozygosity (see Petkov et al. 2005 for a description of constraints on distributions of alleles in functionally
related genes).
In quantitative genetic analysis, individual differences
among animals of a strain are attributed to environmental
sources, which may be shared or nonshared among the subjects (despite rigorous experimental controls, animals can be
differentially exposed to environmental subtleties: e.g., number
of siblings in the cage, intrauterine implantation site, height
of cage rack shelf, time of day of phenotype measurement,
exposure to olfactory stimuli, handling trauma). Moreover,
as discussed below, the effects of a particular environmental
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influence may vary depending on the genotype of the individual. Measurement error is also usually attributed to environmental interindividual variance.
The inbreeding process in its simplest application is
unconcerned with any specific phenotype (except fitnessassociated traits); it concerns relatedness of mates only. Thus,
after the requisite number of consecutive generations of inbreeding, the genotype approaches uniformity, but of an unknown configuration (males differ from females in having
a Y chromosome, of course, but, like other chromosomes, it
will be genetically the same in all males in the strain).
The power of inbreeding is remarkable: it generates a
(relatively) fixed configuration of a fundamental source of
organismal individuality—the genotype—without the requirement of identifying, characterizing, or measuring any element
of that genotype. The resulting uniformity has made possible
a high degree of standardization with respect to genetics,
both across laboratories and within laboratories across time.
Of course, this model of stability is subject to effects of
mutation. Falconer and Mackay (1996) and Roberts (1967)
provide details of quantitative genetics in general and of the
process of inbreeding specifically; Silver (1995) presents nomenclature and descriptions of major contemporary strains
as well as the history of strain origins. Strains have for decades played a leading role in basic genetic research as well
as in extensive biomedical and agricultural applications.
The undeniable advantages of standardized genetic control offered by an inbred strain are accompanied by a restricted
range of generalization. Yet conclusions from a single inbred
strain are sometimes uncritically interpreted as characterizing
all members of its species. As far as genetically influenced
variation and covariation are concerned, however, the situation is comparable to that of generalizing to a human population from a sample of one. Indeed, Roberts (1967, 248) noted
that “an inbred strain can be considered as a representative
of one gamete only from the base population.… Any conclusions from such work should not be deemed to apply to the
species at large without supplementary evidence.”
Multiple Strains
One tactic to obtain such supplementary evidence is the examination of other inbred strains. A consistent result across
a panel of different inbred strains will build confidence in the
generality of a particular outcome; an inconsistent one will
prevent premature sweeping conclusions. Such a research
strategy capitalizes both on the genetic uniformity within
strains and on the genotypic differences among them, even if
there is no specific evidence about the number, chromosomal
location, or molecular identity of any of the involved genes.
Multiple strain comparisons offer vivid examples of the
potentially powerful influence noted above of the strain “background” or “residual” genotype on the effect of any particular
gene or environmental factor. An exemplar case of influence
of such background on the effects of a single “disease” gene
is that of Leiter and colleagues (1981), who examined the
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diabetic symptom pattern displayed by mice homozygous for
a mutation that occurred in a particular inbred strain. The
authors established the mutant allele (recessive, identified as
db) in homozygous condition in seven other inbred strains and
compared these animals to “normal” animals (homozygotes
Db/Db and heterozygotes Db/db) within and across strains
with respect to body weight, plasma insulin, blood sugar
level, and mortality. The results were dramatic and classical:
there were huge differences between strains in the overall
values of control animals for all of the phenotypes, and the
difference within strains between the “normal” controls and the
homozygous recessive animals was enormous. Thus a single
“disease” gene may or may not be accompanied by the symptoms of the disease depending on the genetic context.
Likewise, an environmental influence may be a function
of the genotype of the recipient organism. In the context of
research suggesting aluminum exposure to be a risk factor in
the etiology of Alzheimer’s disease, Fosmire and colleagues
(1993) examined the effects of a high-aluminum diet in five
inbred mouse strains. Relative to matched strain mates on
control diets, the high-aluminum diet resulted in an elevated
brain level of aluminum in only one strain. If the research
question concerned whether dietary aluminum can affect brain
levels and only this particular strain had been selected for
the research, the correct answer would ensue. If the question
was whether dietary aluminum does (always) affect brain
level, the same choice of strain would have given an incorrect conclusion. A disparity of results as in this case not only
recommends modesty in claiming broad generality of results
but also can indicate promising avenues for further exploration; for example, the availability of both susceptible and
nonsusceptible strains invites study of blood-brain barriers
or other mechanisms that might affect susceptibility.
Another striking result concerns the influence of caloric
restriction on longevity. The life-extending effect of this
treatment has long been regarded as highly reliable and dependable. In an examination of 41 recombinant inbred strains
of mice (more on such strains below), Liao and colleagues
(2010) observed that in some strains caloric restriction produced life extension, in about the same number the influence
was life-shortening, and in others there was no appreciable
effect.
Recently, of course, molecular genetic methods have made
possible the detailed identification of specific loci or of the
whole genome of inbred strains as well as procedures such
as the introduction of transgenes and targeted mutagenesis
(Yuan et al. 2011). Thus, for example, the comparison of
standard control animals of an inbred strain to those of the
same strain but with a single locus manipulation is likely to
become more popular, particularly in reductionist exploration
of the paths from genes to phenomena higher in the causal
hierarchy (“reverse” genetics). This merger of classical and
molecular genetics is full of promise. The exquisite control
provided by these molecular methods does not, however,
eliminate the restraint on generalization of single-strain results.
Any finding on a single inbred background simply establishes
that outcome as a possible one. It is not at all informative
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about the generality of the result, but may reveal new research
opportunities.
F1 Hybrids
Matings between different strains produce F1 (first “filial”
generation) hybrid offspring, which have a greater allelic
diversity than that of their inbred parents by virtue of being
heterozygous for each autosomal locus for which the parent
strains differ. F1s are also uniform with respect to their autosomal genes, but the males can differ with respect to their
Y chromosome, depending on the strain of their male parent.
(They may also display effects due to maternal mitochondrial genes and to other “parent of origin” characteristics
identified in recent epigenetics research.) A frequent research
design involving F1 animals compares them to their parent
strains. This design permits evaluation of effects of dominant
inheritance, including the phenomena of inbreeding depression and of the “hybrid vigor” associated with differences in
heterozygosity. Phelan and Austad (1994) have observed
that F1 hybrids are often more biologically uniform than
their inbred parents.
Genetic Heterogeneity
For much research using fixed genotypes, the outcome is
describable in terms of group mean differences, where the
within-group variance is the source of the error term. As noted
earlier, this variance is of environmental (or error) origin only
and is thus limited in any investigation in which phenotypic
variation or covariation is of central interest. For these latter
purposes, groups that segregate at many genetic loci, display
the effects of diverse genes, and allow both intralocus effects
of dominance and interlocus interaction effects offer many
strong advantages. Genetically heterogeneous stocks (HS1)
that are systematically derived and maintained provide many
opportunities in agricultural and biomedical research—for use
as normative groups, for parent-offspring heritability analyses, for quantitative trait locus (QTL1) analyses, for any analyses involving hypotheses about correlations among phenotypes
or the generation of multivariate constructs, and for phenotypic or genotypic selective breeding (as foundation stocks).
Although the merits of such heterogeneous stocks have
long been recognized in many research domains, they have
been underexploited in healthspan and gerontological research.
A number of papers (McClearn and Hofer 1999; McClearn
and Meredith 1964; Miller et al. 1999b; Phelan and Austad
1994) have restated the arguments (including some dissent;
Festing 1999) for using appropriate genetically heterogeneous
stocks in the study of aging. Because of a growing appreciation of the “complex system” attributes of developmental processes, and the consequent necessity of multivariate analytic
procedures, these considerations will certainly make the use
1Abbreviations
used in this article: HS, heterogeneous stock(s); QTL,
quantitative trait locus
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of heterogeneous stocks increasingly frequent in gerontological
research.
There are some genetically heterogeneous stocks for
which the founding “recipe” is not known but that may have
many of the advantages of heterogeneity just described. Many
have become standard features of research in different research domains and have yielded valuable amounts of reference and normative data. Chia and colleagues (2005) provide
a detailed and critical review of the origins and uses of such
mouse outbred stocks. However, it seems likely that the possibility of reconstituting the foundation stock from intermating of known standard inbred strains will be of special merit
as new tools are developed and exploited.
F2 Crosses
Although all animals in an F1 have near-identical genotypes,
they do not afford stability across generations. In the mating
of a female and male from the same F1, each may provide
the allele from either parent. Thus, there are three possible
genotypes in the offspring of F1 × F1 matings for each locus
for which the parent inbred strains had a different allele.
Each member of the resulting offspring, the F2, will be genetically unique. If there are recurring needs for research
animals of the F1 genetic configuration, they must be generated anew by appropriate matings of the inbred strains. Thus
F2s present neither genetic uniformity nor constancy across
generations. However, relative to inbred strains, they offer
enhanced genetic diversity within a generation and can provide relative constancy across generations, not of individual
genotypes but of the group’s allelic frequencies. For many
purposes, this variability is no shortcoming but a strong virtue: the allelic diversity in a group of F2 animals makes them
a better platform for generalization of research results than
any single group of homogeneous genotype.
Recombinant Inbred Strains
Some of the merits of F2s and of inbred strains have recently
been merged in the generation of recombinant inbred (RI)
strains, which are produced by inbreeding a number of lines
from a common genetically heterogeneous parent population.
For example, numerous RI strains have been inbred from an
F2 population derived from the C57BL/6 and DBA/2 inbred
strains, whose allelic diversity is reshuffled into different configurations in the RI offspring. RI strains differ from each other
within the limits provided by the initial gene pool diversity, but
provide for relative genetic uniformity within strain and stability across laboratories and across time. Thus each strain has
the same advantage of genetic uniformity and the same limitation of genetic uniqueness as all inbred strains, but collectively
they present an array of standardized unique configurations of
the genetic diversity of the two initial parent strains.
RI strains have recently featured in the identification of
QTLs. The molecular mapping of the mouse genome has made
possible the identification of chromosomal regions that are
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associated with variability in a phenotype of interest. These
regions vary in size and may contain one or more as yet unidentified genes affecting the trait. The nomination of QTLs
is often an early step in exploring the causal chain back to
the molecular level of the gene.
In a landmark application to aging, Vieira and colleagues
(2000) explored the association of QTLs to longevity in Drosophila under differing environmental conditions of temperature or feeding. The result was an archetypical demonstration
of gene-environment interaction, with many effective QTLs
pertinent to only one environment and not others, in one sex
but not the other, in different directions in the sexes, or in
both sexes but in only one environment. In mice, Gelman
and associates (1988) identified six chromosomal regions associated with age at death in a sample of RI strains derived
from C7BL/6 and DBA/2 inbred strains, and a recent project
examined animals of another sampling of this RI panel as
well as F2s from the same gene pool in a longitudinal study
of diverse age-related variables (Lang et al. 2010). In other
studies researchers have identified QTLs for blood pressure
(Blizard et al. 2009), body length, weight, adipose mass, serum alkaline phosphatase, and structural and material properties of the femur and tibia (Lang et al. 2005); and the weight
of soleus, gastrocnemius, tibialis anterior, and extensor digitorum longus muscles (Lionikas et al. 2006).
The search for QTLs from RIs derived from an F2 can reveal the chromosome regions containing effective genes only if
the original inbred parent strains of the F2 possessed different
alleles for those genes. Clearly, restrictions pertain to generalization from single panels, so several panels of F2-derived RI
lines are in use, with different F2 origins, making empirical
confirmation feasible. Furthermore, several projects have established the utility of seeking QTLs from the more heterogeneous
gene pools of 4-way-cross mice (Miller et al. 1998; Turri et al.
2001) and rats (Fernandez-Teruel et al. 2002) as well as in other
heterogeneous stock mice (Valdar et al. 2003).
Increases in the use of RI strains for addressing complex
genetic systems are promised by the Complex Trait Consortium (Churchill et al. 2004; Threadgill et al. 2011). The general availability of these strains for biomedical research should
have a major influence in the near future on gerontological
science (Chesler et al. 2008).
Heterogeneous Stocks
The demonstrated broad utility of F2 animals suggests that
offspring with four rather than two grandparental strains
would be even more satisfactory for many purposes. Recently,
such 4-way-cross animals have seen increasing application
in gerontological research (e.g., Miller et al. 1998, 2002;
Swindell et al. 2008; female parents of these 4-way animals
were F1 offspring of a cross between BALB/c and C57BL/6
mice, and the males had C3H and DBA/2 parents). The versatility of the approach is illustrated by the fact that these authors addressed relationships between longevity and causal
agency at the molecular genetic level (marker loci) and between
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longevity and biomarkers in putative causal pathways of gene
influence (body weight, and IGF-1, serum leptin, and thyroid
levels). In a further example, Harrison and colleagues (2009)
used this 4-way-cross group for evaluating the life-extending
effects of rapamycin.
Greater heterogeneity is provided by eight-way-cross
groups, which have been extensively used in agricultural research. Two such stocks have been useful in studies of aging,
health, and cognitive functioning. The (Boulder) HS (McClearn
et al. 1970) has been used in longitudinal quantitative genetic
analyses and multivariate analyses of a variety of biomarkers
of aging from domains of behavior, immunology, physiology,
and pathology (Heller et al. 1998; Strauss et al. 1992), and
Galsworthy and colleagues (2002, 2005) used this stock to
explore psychometric and multivariate aspects of cognitive
functioning.
The (Northport) HS (Talbot et al. 2003) were experimental
models (Valdar et al. 2006a) in projects on fine mapping of
QTLs pertinent to human disease and to a variety of immunological, biochemical, and hematological phenotypes. Huang
and colleagues (2009) used the same HS to investigate transcript abundance, illustrating the potential for combining quantitative and molecular genetics.
The merit of heterogeneous stocks is clearly a function
of the number of foundation strains. Also relevant is the number
of generations since founding. Initially, there is advantage in
an increasing number because the problem of spurious association due to linkage declines as the number of crossing-over
events increases, providing enhanced precision in chromosomal location. Later, due to the pragmatically necessary
limits of population size, the cumulative effects of inbreeding will have reduced allelic diversity, but this may be offset
by the accumulation of normative data over generations.
Phenotypic Selective Breeding
A particularly valuable use of genetically heterogeneous stocks
is as foundation stock for selective breeding programs. Methodologically speaking, much of quantitative genetics depends
on associative research procedures in which strong theory
permits the interpretation of correlations among relatives in
terms of statistical behavior of hypothesized but anonymous
groups of genes. Molecular genetic techniques permit the
introduction, alteration, or elimination of the influence of specific identified genetic loci. Selective breeding provides the
researcher with the capability of directional manipulation of
large genotypic complexes, even though the individual loci
in the complexes may be entirely unknown.
Early Selective Breeding
Early humans used pragmatic selective breeding for food
species with stupendous success. In terms of scientific research, selective breeding was critical for testing Darwin’s
theory of natural selection (Falconer 1992). For more than a
century it has been a major method in the application of
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quantitative genetic theory in agriculture, behavioral genetics,
pharmacogenetics, biomedical genetics and many other domains, where it has generated research models of enormous
power (Falconer and Mackay 1996; for an overview of principles and applications of selective breeding to pharmacogenetic research, McClearn et al. 1981). Harrison and Roderick
(1997) described opportunities awaiting selective breeding
for aging research in mice; we address some of these areas in
the next section.
Selective breeding assigns mates on the basis of similar
values of a particular phenotype. In the simplest case, the
objective may be to increase the level in the population of
some desirable trait. At a rate dependent on the heritability
and the extent to which the selected animals differ from their
population average (the “selection differential”), the mean level
of the phenotype will change over repeated generations.
Many research purposes are best served by bidirectional selection, creating high and low lines for comparison with each
other and with controls.
The resulting model systems are comprehensive, in the
sense that all of the gene loci, structural or regulatory, segregating in the foundation population and capable of influencing
the phenotype (within the limitations of sampling and inbreeding due to finite sample size) are distributed in alternative
allelic form in the upward and downward selected lines. The
phenotypic difference between the lines is due to differences
in all of the many systems through which the genes operate
and for which gene-influenced differences exist in the foundation population. Selectively bred lines are thus powerful
research tools for testing hypotheses about the mechanistic
routes from the level of the genes upward in the causal hierarchy through the proteome and metabolome to the gerontological phenotype of interest.2 As just one example of the
utility range of such a tool, Williams and colleagues (2004)
identified some 400 studies exploring the biochemical, biophysical, pharmacological, and anatomical attributes of two
lines of mice selectively bred (from the Boulder HS) for high
and low sensitivity to hypnotic doses of alcohol (McClearn
and Kakihana 1981) and from their derivative inbred lines.
Uses of Selective Breeding in Aging Research
An exemplar of the power of selective breeding to produce
model systems pertinent to aging and health is that of the
panel of senescence-accelerated mouse (SAM) strains (Takeda
2009): senescence-acceleration prone (SAMP) and senescenceacceleration resistant (SAMR) strains, developed by selection
and inbreeding, have contributed substantially to understanding of early-onset aging processes. An unorthodox feature of
these strains is that the foundation stock had been designated
“inbred.” The individual differences in early senescence in
this strain were so pronounced that the selection regime was
initiated, with dramatic results. Clearly, the foundation strain
2Indeed, the potency of selective breeding is captured by the synonyms
“experimental evolution” and “laboratory evolution” offered by Rose and
colleagues (2006; Burke and Rose 2009).
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was genetically heterogeneous from an unknown source and
to an unknown extent.
The merit of selected lines is not restricted to analyses of
associated traits. For example, they also constitute valuable
test vehicles for assessing the efficacy of proposed interventions to elevate or reduce the level of the primary selected
phenotype or any of its covariates. A particularly engaging
form of phenotypic selective breeding that addresses issues
of environmental influence is that of genetic assimilation. An
environmental manipulation is applied to a genetically heterogeneous population, and individuals with resulting like-extreme
phenotypes are mated. Then the intensity of the manipulation is reduced and the procedure repeated. The outcome is
the derivation of two lines, one with heightened and one with
lessened sensitivity to the environmental influence. In the classical genetic assimilation studies on Drosophila by Waddington
(1956), the sensitive line eventually required no environmental manipulation for the phenotype to be present. Genetic
assimilation has been closely examined in the context of
evolutionary theory (Kaneko 2009; Minelli and Fusco 2010)
and may have significant potential for studies of environmental risk factors or therapeutic or preventive interventions
in aging processes.
Another variant of phenotypic selection exerts selection
pressure in the usual way but with the effect of a hypothesized
component of the mechanistic network controlled. Thus,
mates may be selected to produce high and low offspring with
respect to an age-related biomarker but with the requirement
that there be no mean difference in the two lines with respect
to (for example) oxidative stress. Failure to achieve separation between the lines in the primary phenotype would suggest a crucial (necessary?) mechanistic role for the controlled
variable. Success would indicate that the network of the primary variable is neither sufficient nor necessary and would
provide a model system for the study of the aging determinants other than those associated with the controlled variable.
Significant attributes of complex systems can rarely be
captured by single variables but require composite multivariate measures. For example, Galsworthy and colleagues (2002)
described a first principal component of variables from six
cognitive tasks to characterize general learning ability in HS
mice. Other multivariate indices with attractive prospects include canonical correlations that provide the composite of
measures with the highest stability across longitudinal measurements, discriminant functions that maximally distinguish
between specific genotypes, and cluster analyses assigning individuals to groupings based on several phenotypic measures.
Genotypic Selective Breeding
Genotypic selective breeding involves the mating of animals
with specific alleles of loci known or hypothesized to affect
the phenotype to produce research models according to genotypic specification. In contrast to models generated by phenotypic selection, which involve the entire complex network of
mechanisms in contrasting configurations, genotypic selection
generates groups that are distinguished by only those aspects
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of the mechanistic network that are downstream from the
specific selected locus or loci, thus enabling focused evaluation of a subsection of that network.
Genotypic selection has long been used in the development of congenic strains, through the placement of a particular
gene or chromosomal region (or combinations of such genes)
by successive backcrossing on differing inbred strain backgrounds. Advances in molecular genetics now provide an
unprecedented roster of candidates for this procedure and
QTL methods enhance this capability by encompassing the
loci of a known chromosomal location but unknown molecular
identity. This method was promoted by Harrison and colleagues (1995) under the concept of “allele capture by selection for flanking markers,” and Bennett and Johnson (1998)
used the application to assess QTLs for hypnotic sensitivity
to ethanol to generate “speed congenics.”
Genotypic selection is also effective for placing specific
genetic material on a heterogeneous background. Examples
of this usage include the confirmation or rejection of QTLs
nominated for influence on alcohol-related processes in mice
(Geraghty 1998; Tarantino et al. 1998).
Genotypic selection also offers a powerful capability to
assemble panels of genes and/or QTLs in any specifiable combination to study the interactions of the constituent elements
of a complex determinative system. Advantages of genotypic
selection over phenotypic selection in this context are the
availability of results (for one or a few genes or QTLs) in a
single generation and the ability to reconstitute the lines at any
time. The lines require maintenance by consecutive-generation
breeding only as long as necessary for a particular research
purpose. If the need reappears after termination of the lines,
or in another laboratory, the lines can be assembled in their
specifiable configuration within a few generations. In contrast,
with conventional selection a terminated line is gone forever.
Models generated by genotypic selection will not replace
conventional phenotypically selected lines but will be a powerful complement to them. It is therefore easy to predict that
genotypic selection will become a primary method for the
tailoring of animal models for gerontological research.
characters at an early age and relating them to the late-life
measures of interest. For example, Luckinbill and Clare (1985)
and Rose and Charlesworth (1981) generated extensively
studied Drosophila lines by selecting for late fecundity. A
key to success with this strategy is accurate identification of
biomarker variables of youth that have predictive value for
late-life phenomena. Numerous measures have been thus
identified, but developmental considerations suggest that such
long-term effects cannot be assumed. An approach with multivariate indices might provide maximum predictive value
from early-life measurements. One generation of animals
could provide from a panel of early life measurements the
multivariate index that maximally predicts lifespan, which
could then be the selected phenotype.
Selective Breeding and Lifespan Variables
References
The research strategy in generating tools to specification by
phenotypic selective breeding is to breed for line differences
in the target phenotype, and compare the resulting lines for
“correlated characters” hypothesized as mediating, intervening, and mechanistic variables in the genome, proteome, or
metabolome. But there is a challenge to the use of this approach in gerontological and healthspan research: many of
the most relevant phenotypes are measurable only in late life
or at the end of life, past the possibility of reproduction of
the selected animals. Harrison and Roderick (1997) suggested one solution: a form of family selection, mating individuals on the basis of the lifespans of their grandparents.
Temporal constraints suggest the utility of selecting directly for one or more correlated (and presumptive causal)
Austad SN. 1993. The comparative perspective and choice of animal models in aging research. Aging Clin Exp Res 5:259-267.
Bennett B, Johnson TE. 1998. Development of congenics for hypnotic sensitivity to ethanol by QTL-marker-assisted counter selection. Mamm
Genome 9:969-974.
Blizard DA, Lionikas A, Vandenbergh DJ, Vasilopoulos T, Gerhard GS,
Klein LC, Stout JT, Mack HA, Lakoski JM, Larsson L, Spicer JM,
Vogler GP, McClearn GE. 2009. Blood pressure and heart rate QTL in
mice of the B6/D2 lineage: Sex differences and environmental influences. Physiol Genomics 36:158-166.
Burke MK, Rose MR. 2009. Experimental evolution with Drosophila. Am
J Physiol Regul Integr Comp Physiol 296:R1847-R1854.
Chesler EJ, Miller RA, Branstetter LR, Galloway LD, Jackson BL, Philip VM,
Voy BH, Culiat CT, Threadgill DW, Williams RW, Churchill GA,
Johnson DK, Manly KF. 2008. The collaborative cross at Oak Ridge
National Laboratory: Developing a powerful resource for systems
genetics. Mamm Genome 19:382-389.
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Summary
It is clear that there is no one “best” model (Sprott 1997) and
that the optimal model or combination of models depends on
the circumstances. The diversity and efficacy of the research
tools provided by the genetic sciences offer underexploited
and powerful opportunities. Heterogeneous stocks and selective breeding permit the assembly of model systems that:
(1) scramble alleles (i.e., those that are polymorphic in the
founding parent strains) in order to provide normative
groups, vehicles for assessment of complex correlational relationships, and foundation stocks for selective
breeding;
(2) concentrate alleles that are relevant to a particular phenotype into contrasting groups of animals—one with
all of the increasing alleles and one with all of the decreasing alleles—in order to provide material for the
investigation of the total mechanistic network through
which the phenotypic differential is accomplished; and
(3) concentrate configurations of alleles of specific loci (or
QTLs) in order to permit examination of the dynamics
of a subset of the mechanism that is causally downstream from those loci.
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Chia R, Achilli F, Festing MFW, Fisher EMC. 2005. The origins and uses of
mouse outbred stocks. Nat Genet 37:1181-1186.
Chrisp CE, Turke P, Luciano S, Swalwell S, Peterson J, Miller RA. 1996.
Lifespan and lesions in genetically heterogeneous (four-way cross)
mice: A new model for aging research. Vet Pathol 33:735-743.
Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J, Beavis
WD, Belknap JK, Bennett B, Berrettini W, Bleich A, Bogue M, Broman
KW, Buck KJ, Buckler E, Burmeister M, Chesler EJ, Cheverud JM,
Clapcote S, Cook MN, Cox RD, Crabbe JC, Crusio WE, Darvasi A,
Deschepper CF, Doerge RW, Farber CR, Forejt J, Gaile D, Garlow SJ,
Geiger H, Gershenfeld H, Gordon T, Gu J, Gu W, de Haan G, Hayes NL,
Heller C, Himmelbauer H, Hitzemann R, Hunter K, Hsu HC, Iraqi FA,
Ivandic B, Jacob HJ, Jansen RC, Jepsen KJ, Johnson DK, Johnson TE,
Kempermann G, Kendziorski C, Kotb M, Kooy RF, Llamas B, Lammert
F, Lasalle JM, Lowenstein PR, Lu L, Lusis A, Manly KF, Macucio R,
Matthews D, Medrano JF, Miller DR, Mittleman G, Mock BA, Mogil JS,
Montagutelli X, Morahan G, Mott R, Nadeau JH, Nagase H, Nowakowski
RS, O’Hara BF, Osadchuk AV, Page GP, Paigen K, Palmer AA, Pan HJ,
Petonen-Palotie L, Peirce J, Pomp D, Pravenec M, Prows DR, Qi Z,
Reeves RH, Roder J, Rosen GD, Schadt EE, Schalkwyk LC, Seltzer Z,
Shimomura K, Shou S, Sillanpaa MJ, Siracusa LD, Snoeck HW,
Spearow JL, Svenson K, Tarantino LM, Threadgill DW, Toth LA, Valdar
WS, deVillena FP, Warden C, Whatley S, Williams RW, Wiltshire T, Yi
N, Zhang D, Zhang M, Zou F. 2004. The collaborative cross, a community resource for the genetic analysis of complex traits. Nat Genet
36:1133-1137.
Falconer DS. 1992. Early selection experiments. Annu Rev Genetics 26:1-14.
Falconer DS, Mackay TF. 1996. Introduction to Quantitative Genetics, 4th
ed. Essex: Longman Group.
Fernandez-Teruel A, Escorihuela RM, Gray JA, Aguilar R, Gimenez-Llort L,
Tobena A, Bhomra A, Nicod A, Mott R. 2002. A quantitative trait locus
influencing anxiety in the laboratory rat. Genome Res 12:618-626.
Festing MFW. 1999. Warning: The use of heterogeneous mice may seriously damage your research. Neurobiol Aging 20:237-244.
Fosmire GJ, Focht SJ, McClearn GE. 1993. Genetic influences on tissue
deposition of aluminum in mice. Biolog Trace Element Res 37:115-121.
Galsworthy MJ, Paya-Cano JL, Monleon S, Plomin R. 2002. Evidence for
general cognitive ability (g) in heterogeneous stock mice and an analysis of potential confounds. Genes Brain Behav 1:88-95.
Galsworthy MJ, Paya-Cano JL, Liu L, Monleon S, Gregoryan G, Fernandes
C, Schalkwyk LC, Plomin R. 2005. Assessing reliability, heritability
and general cognitive ability in a battery of cognitive tasks for laboratory mice. Behav Genet 35:675-692.
Gelman R, Watson A, Bronson R, Yunis E. 1988. Murine chromosomal regions correlated with longevity. Genetics 118:693-704.
Geraghty JT. 1998. A review of the genetics of alcoholism and a confirmatory study of an acute alcohol withdrawal quantitative trait locus in
mice. In: Intercollege Graduate Program in Genetics: Master of Science.
University Park: Pennsylvania State University.
Harrison DE, Roderick TH. 1997. Selection for maximum longevity in
mice. Exp Gerontol 32:65-78.
Harrison DE, Roderick TH, Paigen K. 1995. Allele capture by selection for
flanking markers: A new method for analyzing multigenic traits. Growth
Dev Aging 59:73-76.
Harrison DE, Strong R, Sharp ZD, Nelson JF, Astle CM, Flurkey K, Nadon
NL, Wilkinson E, Frenkel K, Carter CS, Pahor M, Javors MA, Fernandez
E, Miller RA. 2009. Rapamycin fed late in life extends lifespan in
genetically heterogeneous mice. Nature 460:392-395.
Heller DA, Ahern FM, Stout JT, McClearn GE. 1998. Mortality and biomarkers of aging in heterogeneous stock (HS) mice. J Gerontol 53A:B217B230.
Huang G-J, Shifman S, Valdar WS, Johannesson M, Yalcin B, Taylor MS,
Taylor JM, Mott R, Flint J. 2009. High resolution mapping of expression QTLs in heterogeneous stock mice in multiple tissues. Genome
Res 19:1133-1140.
Kaneko K. 2009. Relationship among phenotypic plasticity, phenotypic
fluctuations, robustness, and evolvability: Waddington’s legacy revisited
under the spirit of Einstein. J Biosci 34:529-542.
22
Lang DH, Sharkey NA, Mack HA, Vogler GP, Vandenbergh DJ, Blizard DA,
Stout JT, McClearn GE. 2005. Quantitative trait loci analysis of structural and material skeletal phenotypes in C57BL/6J and DBA/2 secondgeneration and recombinant inbred mice. J Bone Miner Res 20:88-99.
Lang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard
DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci
(QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin Exp Res 22:8-19.
Leiter EH, Coleman DL, Hummel KP. 1981. The influence of genetic background on the expression of mutations at the diabetes locus in the mouse.
III. Effect of H-2 haplotype and sex. Diabetes 30:1029-1034.
Liao CY, Rikke BA, Johnson TE, Diaz V, Nelson JF. 2010. Genetic variation
in the murine lifespan response to dietary restriction: From life extension to life shortening. Aging Cell 9:92-95.
Lionikas A, Blizard DA, Vandenbergh DJ, Stout JT, Vogler GP, McClearn
GE, Larsson L. 2006. Genetic determinants of weight of fast- and slowtwitch skeletal muscles in old mice. Mamm Genome 17:689-699.
Luckinbill LS, Clare MJ. 1985. Selection for life span in Drosophila melanogaster. Heredity 55:9-18.
McClearn GE, Hofer SM. 1999. Genes as gerontological variables: Genetically heterogeneous stocks and complex systems. Neurobiol Aging
20:147-156.
McClearn GE, Kakihana R. 1981. Selective breeding for ethanol sensitivity:
SS and LS mice. In: McClearn GE, Deitrich RA, Erwin VG, eds. Development of Animal Models as Pharmacogenetic Tools. DHEW Publication No. [ADM] 81-1133. Washington: US Government Printing Office.
p 147-159.
McClearn GE, Meredith W. 1964. Dimensional analysis of activity and
elimination in a genetically heterogeneous group of mice (Mus musculus).
Anim Behav 12:1-10.
McClearn GE, Wilson JR, Meredith W. 1970. The use of isogenic and heterogenic mouse stocks in behavioral research. In: Lindzey G, Thiessen
DD, eds. Contributions to Behavior-Genetic Analysis: The Mouse as
Prototype. New York: Appleton-Century-Crofts. p 3-22.
McClearn GE, Deitrich RA, Erwin VG. 1981. Development of Animal
Models as Pharmacogenetic Tools. Research Monograph No. 6, DHEW
Publication No. [ADM] 81-1133. Washington: US Government Printing
Office.
Miller RA, Chrisp C, Jackson AU, Burke D. 1998. Marker loci associated
with life span in genetically heterogeneous mice. J Gerontol 53A:M257M263.
Miller RA, Austad SN, Burke D, Chrisp C, Dysko R, Galecki A, Jackson
AU, Monnier V. 1999a. Exotic mice as models for aging research: Polemic and prospectus. Neurobiol Aging 20:217-231.
Miller RA, Burke D, Nadon NL. 1999b. Four-way cross mouse stocks: A
new, genetically heterogeneous resource for aging research. J Gerontol
54A:B358-B360.
Miller RA, Harper JM, Galecki A, Burke D. 2002. Big mice die young:
Early life body weight predicts longevity in genetically heterogeneous
mice. Aging Cell 1:22-29.
Minelli A, Fusco G. 2010. Developmental plasticity and the evolution of
animal complex life. Philos Trans R Soc Lond B Biol Sci 365:631-640.
Petkov PM, Graber JH, Churchill GA, DiPetrillo K, King BL, Paigen K.
2005. Evidence of a large-scale functional organization of mammalian
chromosomes. PLoS Genetics 1:e33.
Phelan JP, Austad SN. 1994. Selecting animal models of human aging: Inbred strains often exhibit less biological uniformity than F1 hybrids. J
Gerontol 49:B1-B11.
Roberts RC. 1967. Some concepts and methods in quantitative genetics. In:
Hirsch J, ed. Behavior-Genetic Analysis. New York: McGraw-Hill. p
214-257.
Rose MR, Charlesworth B. 1981. Genetics of life history in Drosophila
melanogaster. II. Exploratory selection experiments. Genetics 97:187196.
Rose MR, Rauser CL, Mueller LD, Benford G. 2006. A revolution for aging
research. Biogerontology 7:269-277.
Silver LM. 1995. Mouse Genetics: Concepts and Applications. New York:
Oxford University Press.
ILAR Journal
Sprott RL. 1997. Mouse and rat genotype choices. Exp Gerontol 32:79-86.
Strauss JA, Ballough GP, Anthony A, Mitchell RB. 1992. Changes in HS
mouse erythrocyte mass and area with respect to donor age. J Gerontol
47:B13-B17.
Swindell WR, Harper JM, Miller RA. 2008. How long will my mouse live?
Machine learning approaches for prediction of mouse life span. J Gerontol 63A:895-906.
Takeda T. 2009. Senescence-accelerated mouse (SAM) with special references to neurodegeneration models, SAMP8 and SAMP10 mice. Neurochem Res 34: 639-659.
Talbot CJ, Radcliffe RA, Fullerton J, Hitzemann R, Wehner JM, Flint J.
2003. Fine scale mapping of a genetic locus for conditioned fear. Mamm
Genome 14:223-230.
Tarantino LM, McClearn GE, Rodriguez LA, Plomin R. 1998. Confirmation of quantitative trait loci for alcohol preference in mice. Alcohol
Clin Exp Res 22:1099-1105.
Threadgill D, Miller D, Churchill GA, Pardo-Manuel de Villena F. 2011.
The Collaborative Cross: Recombinant inbred lines in the systems genetic era. ILAR J 52:24-31.
Turri MG, Henderson ND, DeFries JC, Flint J. 2001. Quantitative trait locus
mapping in laboratory mice derived from a replicated selection experiment for open-field activity. Genetics 158:1217-1226.
Volume 52, Number 1
2011
Valdar WS, Flint J, Mott R. 2003. QTL fine-mapping with recombinantinbred heterogeneous stocks and in vitro heterogeneous stocks. Mamm
Genome 14:830-838.
Valdar WS, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson
WO, Taylor MS, Rawlins JN, Mott R, Flint J. 2006a. Genome-wide
genetic association of complex traits in heterogeneous stock mice. Nat
Genet 38:879-887.
Valdar WS, Solberg LC, Gauguier D, Cookson WO, Rawlins JN, Mott R,
Flint J. 2006b. Genetic and environmental effects on complex traits in
mice. Genetics 174:959-984.
Vieira C, Pasyukova EG, Zeng ZB, Hackett JB, Lyman RF, Mackay TF.
2000. Genotype-environment interaction for quantitative trait loci
affecting life span in Drosophila melanogaster. Genetics 154:213227.
Waddington CH. 1956. Genetic assimilation of the bithorax phenotype.
Evolution 10:1-13.
Williams RW, Bennett B, Lu L, Gu J, DeFries JC, Carosone-Link PJ, Rikke
BA, Belknap JK, Johnson TE. 2004. Genetic structure of the LXS panel
of recombinant inbred mouse strains: A powerful resource for complex
trait analysis. Mamm Genome 15:637-647.
Yuan R, Peters LL, Paigen B. 2011. Mice as a mammalian model for research on the genetics of aging. ILAR J 52:4-15.
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