Copyright ? 2007 by the Genetics Society of America
Age Specificity of Inbreeding Load in Drosophila melanogaster and
Implications For the Evolution of Late-Life Mortality Plateaus
Rose M. Reynolds,*,1Sara Temiyasathit,†Melissa M. Reedy,†Elizabeth A. Ruedi,*
Jenny M. Drnevich,†Jeff Leips‡and Kimberly A. Hughes*,†
*Program in Ecology and Evolutionary Biology and†Department of Animal Biology, University of Illinois, Urbana-Champaign,
Illinois 61801 and‡Department of Biological Sciences, University of Maryland, Baltimore, Maryland 21250
Manuscript received December 20, 2006
Accepted for publication July 4, 2007
Current evolutionary theories explain the origin of aging as a byproduct of the decline in the force of
natural selection with age. These theories seem inconsistent with the well-documented occurrence of late-
life mortality plateaus, since under traditional evolutionary models mortality rates should increase mono-
tonically after sexual maturity. However, the equilibrium frequencies of deleterious alleles affecting late life
are lower than predicted under traditional models, and thus evolutionary models can accommodate
mortality plateaus if deleterious alleles are allowed to have effects spanning a range of neighboring age
classes. Here we test the degree of age specificity of segregating alleles affecting fitness in Drosophila
melanogaster. We assessed age specificity by measuring the homozygous fitness effects of segregating alleles
across the adult life span and calculated genetic correlations of these effects across age classes. For both
males and females, we found that allelic effects are age specific with effects extending over 1–2 weeks across
all age classes, consistent with modified mutation-accumulation theory. These results indicate that a mod-
1996). The evolutionary explanation for senescence is
that natural selection is very efficient in eliminating ge-
netic variants (alleles) that have detrimental effects early
rimental effects that are confined to late ages (Medawar
1994). Alleles with deleterious effects confined to late
ages therefore accumulate in populations and so cause
age-related increases in morbidity and mortality. Math-
ematically, the decline in the strength of selection with
age is inevitable in age-structured populations, at least
insects, and other organisms with determinate growth
(Charlesworth 1994; Vaupel et al. 2004). However,
assumption that populations harbor genetic variants
(alleles) with deleterious effects that are confined to
specific age classes. Specifically, the antagonistic pleio-
tropy (AP) model assumes that mutations contributing
at early ages but harmful at late ages. In contrast, the
ENESCENCE is an age-related decline in individual
performance and fitness that is a nearly universal
mutation-accumulation(MA)model assumesthat muta-
tions causing senescence have only harmful effects and
of the general evolutionary model require age-specific
effects of alleles, and both predict that the fertility and
survival of individuals will decrease with advancing age
(organisms will senesce).
Despite substantial empirical evidence supporting
and Reynolds 2005; Partridge and Gems 2006), the
Curtsinger et al. 1992), in which cohort mortality rates
appear to plateau or even decrease at late ages, chal-
lenged the validity of evolutionary explanations of aging.
Under traditional MA and AP models, the effects of age-
specific alleles are confined toa singleage class, and age-
specific mortality should increase monotonically with
Pletcher and Curtsinger 1998; Baudisch 2005),
producing a ‘‘wall of death’’ coincident with or slightly
prior to (Steinsaltz et al. 2005) the cessation of re-
production. In contrast to this prediction, mortality ap-
pears to plateau at ?20% per day in common and
Mediterranean fruit flies(Carey et al. 1992; Curtsinger
et al. 1992). Qualitatively similar results are found for
yeast (Pohley 1987), nematodes (Brooks et al. 1994;
Vaupel et al. 1998; Johnson et al. 2001), beetles (Tatar
et al. 1993), and humans (Wilkin 1982).
1Corresponding author: Institute for Genomic Biology, 1206 W. Gregory
Dr., 2414K, Urbana, IL 61801. E-mail: firstname.lastname@example.org
Genetics 177: 587–595 (September 2007)
Although traditional MA and AP theories do not
predict mortality plateaus, subsequent studies have
suggested that the phenomenon could result from the
decline in the force of natural selection. Rose et al.
(2002) and Rauser et al. (2006) reported that manip-
ulating the age at which the force of natural selection
reaches zero caused a coincident change in the timing
of the onset of plateaus in late-life mortality and
fecundity. Indeed, it has been proposed that both MA
and AP processes can lead to mortality plateaus, if the
assumption of strict age specificity is relaxed. Specifi-
cally, alleles with late-life effects may be under weak but
they have some pleiotropic effects on prereproductive
ages (Abrams and Ludwig 1995; Mueller and Rose
1996; Charlesworth and Partridge 1997; Pletcher
2001; Steinsaltz et al. 2005).
Recently, Charlesworth (2001) presented a modi-
to occur at late ages if alleles affecting fitness do so for
more than one age class (moderate age specificity). Un-
der this model, late-life mortality plateaus were pre-
dicted whether deleterious alleles affected fitness during
a ‘‘window’’ of just a few age classes or affected fitness
cumulatively at all ages after a given age. Computer
simulations testing this model of age specificity were
consistent with existing data on late-life mortality
Whether, onaverage, alleles affectingfitnessshow the
strict age specificity assumed under the original MA
theory, or are more moderately age specific as modeled
byCharlesworth, canbeassessed bymeasuresofcorrela-
as assumed under traditional MA theory, we should ob-
adjacent ages will have higher genetic correlations than
will nonadjacent ages; the correlations should fall off
with time between age classes. Finally, it is possible that
the majority of alleles affecting fitness are age indepen-
Under this scenario, there is little scope for the
evolution of senescence under current models.
mortality plateaus, we must examine evolutionarily rele-
vant alleles: those affecting fitness. We used Drosophila
melanogaster as a model system in an assay of the average
age specificity of segregating alleles on chromosome III
(cIII) sampled from a natural population in Raleigh,
North Carolina. We first measured the age-specific in-
population. We then assessed genetic correlations in in-
breeding load among age classes. Our results indicate
that cIII alleles affecting fitness in this population are
theory and the Charlesworth (2001) model of mod-
erately age-specific effects.
MATERIALS AND METHODS
Experimental organisms: For this experiment we used nine
cIII extraction lines created in the laboratory of T.F.C. Mackay
at North Carolina State University. These lines were derived
from a natural population in Raleigh, North Carolina. Chro-
mosome III was extracted from each isofemale line and sub-
stituted into a common genetic background (the Samarkand
inbred strain; De Luca et al. 2003) using balancer chromo-
somesandstandard Drosophilacrossing schemes (Greenspan
1997). Each isogenic line thus contained naturally occurring
variation at cIII, which accounts for ?40% of the genome;
all lines were genetically identical at all other loci. Ebony
flies used as competitors in the fitness assay were derived
from a spontaneous mutation that arose in an inbred Ives
population stock in 1990 (provided by D. Houle). The ebony
mutation results in a dark body color and has since been in-
trogressed into a large randomly mating Ives population to
retain the body-color mutation while eliminating effects of
Fitness assays: The modified MA model depends on the
assumption of moderate age specificity of fitness effects of
as a metric for measuring age-specific fitness effects, rather
2001 theory makes predictions about the pattern of mortality
expected in late life, the mathematical model is based upon
the correlated effects of any allele affecting fitness, not just
those alleles whose effects are solely on viability or longevity
(Charlesworth 2001). Second, measures of mortality rate
suffer from being biased estimates whenever mortality is low
relative to the inverse of sample size, yielding a downward bias
in estimates of genetic variance in mortality for those age
classes (Shaw et al. 1999). Alternately, measures of reproduc-
tive fitness do not suffer from the measurement bias inherent
in age-specific mortality.
We also chose to use measures of inbreeding load rather
than estimates of genetic variance in fitness measures (typi-
cally used for calculating genetic correlations). Estimates of
genetic variance are often associated with large standard er-
rors (Lynch and Walsh 1998), making it difficult to dis-
tinguish among different correlation patterns and thus to
evaluate the age specificity of alleles. Calculations of inbreed-
than estimates of additive genetic variance. Additionally, in-
breeding load itself has great relevance to fitness since, on
average, segregating deleterious alleles have partially domi-
nant effects (h ¼ 0.2) (Simmons and Crow 1977; Hughes
and Caballero 2000), and measures of inbreeding load will
reflect the effects of alleles contributing to variation in fitness
components in natural populations (Hughes 1995; Santos
1997; Kelly 2003). Inbreeding load is thus an excellent metric
by which to determine the age specificity of alleles affecting
fitness in model organisms and for studying the evolution of
aging in general. Here we assume that patterns of correlations
among ages for inbreeding load will be similar to patterns of
correlation of the heterozygous effects of segregating alleles.
To calculate age-specific inbreeding load, we produced
of nine isogenic lines. Each diallel consisted of all possible
crosses among each of the nine genotypes, using each line as
both sire and dam (a complete diallel with reciprocals). We
588 R. M. Reynolds et al.
performed the diallel cross three times, in independent rep-
licates using different parental flies in each replicate. Within
each replicate, we produced the parents for the diallel by
crossing 12 males and females of each isogenic line and sup-
plied them with 10 ml of standard corn–soy media. We re-
moved adults after 7 days and collected adult offspring 3 days
gence and housed separately until 3–6 days posteclosion. To
produce the 81 genotypes of the complete diallel, we crossed
25 males and females of the appropriate genotypes in 175-ml
plastic bottles supplied with agar–molasses substrate and fresh
larvae were made from each laying bottle, all within a single
until emergence. Adults were collected within 4 hr of emer-
To obtain the ebony competitor males and females for the
fitness assays, we placed seven ebony males and seven ebony
females in each vial with standard media and allowed them to
mate and lay eggs for 7 days, at which time adults were dis-
carded. Virgin ebony male and female offspring were collected
from these vials within 4 hr of emergence and housed sepa-
rately until 3 days of age, at which time they were used in a
competitive mating assay with one of the 81 focal genotypes.
To calculate age-specific inbreeding load we assayed an
aspect of fitness, male and female competitive reproductive
success. Reproductive success was assayed separately for each
of the three sets of flies per genotype produced within a given
replicate of the diallel cross. For each genotype, all three sets
of flies within a replicate shared the same parents. For each
assay of age-specific male fitness, 3 virgin 3-day-old males of a
female 3-day-old virgin ebony flies. Flies were transferred to
fresh vials without anesthesia every 7 days until all wild-type
flies in a vial had died. At each transfer dead flies were re-
moved and counted, and all ebony flies were replaced with
virgin, 3-day-old ebony flies. To minimize any possible compe-
tition bias due to ebony source vial, these ebony flies were col-
lected from many different source vials, mixed, and randomly
assigned to experimental vials in which competitors were
needed. We maintained levels of competition within each vial
by adjusting the number of competitor flies for the number of
remaining focal flies; for example, if 1 focal male died, leaving
2 live focal males, then 2 male ebony (competitor) flies and 2
was retained for 10 days after flies were removed, and all
emerging offspring were counted. Because the ebony mutation
is a recessive body color mutation, the parentage of offspring
from competitive trials was easily determined by body color.
Age-specific male fitness was measured in two ways: (1) the
and (2) the average number of wild-type offspring produced
per individual within a particular vial. Male fitness was mea-
sured for ?730 adult males, producing a total of 212,992
Female age-specific fitness was assayed in a similar manner,
by placing 3, 3-day-old virgin females of a focal genotype
together with 3 virgin ebony 3-day-old females and three virgin
as the totalnumber of wild-type offspring producedfrom each
vial and the average number of wild-type offspring produced
per individual within a vial, as for males. Female fitness was
offspring. All replicates of male and female assays were com-
pleted between June and October of 2003.
Inbreeding load: Forbothmaleandfemalefitnessassays,we
calculated two measures of age-specific inbreeding load to
reflect the age-specific deleterious effects of segregating
load, standardized inbreeding depression (SID), which is the
difference in fitness between inbred and noninbred individ-
uals, standardized by the mean noninbred fitness (Hartl and
(wo,x? wi,x)/wo,x, where wo,xis the mean trait value at age x for
all the heterozygous (outbred cIII) genotypes that had that
particular inbred genotype as a parent, and wi,xis the mean
value of male or female competitive reproductive success at
age x for an inbred cIII genotype (one of the nine genotypes
produced by crossing males and females from the same cIII
isogenic line). This standard measure of inbreeding load is
valid assuming that factors affecting fitness are multiplicative
on a linear scale. If independent factors affecting fitness are
multiplicative on a log scale rather than a linear scale, then as
outbred fitness decreases with age, age-specific inbreeding
depression (ID) will be biased toward higher values at later
ages (Snoke and Promislow 2003). Thus, we used an addi-
tional, unstandardized measure of load (ID), assuming mul-
tiplicative effects on a log scale, calculated simply as the
at each age: IDx¼ (wo,x? wi,x) (e.g., Snoke and Promislow
2003; Moorad and Wade 2005).
In both male and female assays of competitive reproductive
success, .70% of all genotypes were present for the first 4
weeks, after which less than half of the genotypes were rep-
resented. Therefore, we restricted our analysis to weeks 1–4
of the reproductive success data. Untransformed values of
SID and ID are reported in the figures and tables; however,
statistical tests were conducted on transformed values to meet
the assumptions of statistical analysis by mixed linear models.
To meet these assumptions, the values of wo,xand wi,xwere
transformed before statistical analysis by taking the square
root, as appropriate for count data, and SIDxvalues were
arcsine square-root transformed as appropriate for propor-
tions (Zar 1999).
Statistical analysis: Within each replicate, reproductive
success was assayed separately for each of the three sets of
flies per genotype produced within a given replicate of the
diallel cross. For each genotype, all three sets of flies within a
analysis with the three measures derived from each genotype
within a replicate being nested within replicate (Ott and
term in the analysis to account for time-of-year effect that
could have differed among replicates.
conduct a repeated-measures mixed linear model analysis to
test for changes in SID and ID with age and to determine the
pattern of covariance and correlation of SID andID across age
classes (Littell et al. 2002). For both measures of inbreeding
load, we fit the model yikðjÞ¼ m1ai1gkðjÞ1eikðjÞ, where yik(j)
is SID (or ID) of genotype k at age i from replicate j, m is the
grand mean, aiis the fixed effect of age i, gk(j)is the random
effect associated with genotype k nested within replicate j, and
replicate j at at age i. SID or ID was treated as the repeated
measure. There was no effect of experimental block (time of
year the replicate was performed), nor were there effects of
adirectionalchange(nonzero slope)inSIDorIDwithage. To
Table 1. We used the finite-population corrected Akaike’s in-
formation criterion (AICC) (Burnham and Anderson 1998)
to determine the best-fitting covariance structure for the data.
Age Specificity of Inbreeding Load589
If alleles are age specific on the scale of ,1 week (consistent
with traditional MA theory), the data should best fit the
independent covariance model. If alleles are moderately age
specific, data will fit a model of maximum correlation for
neighboring age classes and decreasing correlation with in-
creasing distance in time. Models consistent with this pattern
include the autoregressive, antedependence, Toeplitz, or un-
structured covariance models. If alleles are age independent
on average (pleiotropic across the life span), data will best fit
either independent or compound symmetry models.
If the data fit a model indicating moderate age specificity of
alleles,thewidthofaverageallelic effectswillbedefinedas the
amount of time during which both covariances and correla-
tions between age classes are significantly different from zero.
Covariances of age-specific inbreeding load were considered
significantly different from zero if maximum-likelihood-ratio
tests produced chi-square statistics with P-values ,0.05. To
model while holding all covariance values constant at the
values under the best-fit model. Next, we fit a reduced model
setting the covariance of interest to zero. Finally, we calculated
the difference between the ?2 residual log-likelihood values
and assigned the difference a P-value based upon a chi-square
distribution with 1 d.f. Correlations across age classes were
computed using the rcorr option in PROC MIXED. Correla-
tions were considered to be significantly different from zero at
a ¼ 0.05 if the 95% confidence interval of correlation values
did not overlap zero, where confidence intervals were de-
termined by jackknife resampling (Efron 1979).
Differences between males and females in age-specific
patterns of inbreeding load were tested using a maximum-
dels. We used the model yikðjÞl¼ m1ai1gkðjÞ1ll1ðalÞil1
eikðjÞl, where yik(j)lis SID or ID of an individual of sex l in
genotype k from replicate j at week i, m is the grand mean of
SID or ID, aiis the fixed effect of age i, gk(j)is the random
effect of genotype k nested within replicate j (subject term), ll
is the fixed effect of sex l, al is the possible interaction be-
tween sex l and age i, and eik(j)lis the random error associated
with sex l in the kth genotype from replicate j at age i. The
effect of block was not significant; therefore, we did not
include block in our final model. We performed this repeated
measures analysis of variance using the best-fit covariance
model as determined above. A significant result for the inter-
action among sex and age terms would indicate that the
change in SID or ID with age depends upon sex.
In this experiment, the mean adult life span for
reproductive, noninbred males was 17.3 days and for
females was 11.0 days (?27.3 and 21.0 days from egg,
respectively). Results using individual-level fitness pro-
duced the same best-fit covariance models and signifi-
fitness (results not shown). Vial-level fitness measures
were used to produce the following results. For both
sexes, competitive reproductive success declined with
age in both inbred and noninbred genotypes (Table 2).
SID increased significantly with age for both males and
females (Figure 1, Table 3). In contrast, ID declined sig-
nificantly with age in both sexes, due to a dramatic de-
cline with age in outbred fitness combined with an
Distinctions among major covariance structures
Covariance structure Description
Compound symmetry (Cs)
Assumes there are no correlations among age classes for the dependent variable.
Assumes that measurements at each age are correlated to all other measurements to the
same degree, e.g., there is only one correlation value for all pairs of age classes.
Assumes that correlations are high for neighboring age classes and fall off as time between age
Correlations decrease as a specific, linear function of time between age classes until
correlations reach zero.
Assumes equal variances among ages.
h option allows unequal variances (Ar1h).
Assumes that correlations will decrease by some linear function determined by the amount of
time between the two ages being correlated. Incorporates the possibility that because repeated
measures were taken on the same individuals over time, no two measurements are completely
independent, regardless of the separation between them, and therefore correlations never
Assumes equal variances among ages.
h option allows unequal variances (Ar1Rh).
Assumes decreasing correlation with time between ages.
Does not require that these correlations decrease by a definable function.
h option allows unequal variances (Toeph).
Assumes that each age class may have a unique correlation with any other age class.
Allows for unequal variances, covariances among age classes.
Running PROC MIXED using the unstructured covariance model allows viewing the raw
covariance and correlation values for each pair of age classes, without assuming an underlying
pattern of correlations.
random effects (Ar1R)
590 R. M. Reynolds et al.
absorbing boundary of zero for inbred fitness (Figure 2,
Table 3). There were no significant differences between
the sexes for either measure of inbreeding load or for
the change in SID with age. However, the change in ID
with age is dependent upon sex, as indicated by a
significant sex-by-age interaction (Table 4, Figure 2).
Covariances in inbreeding load across age classes are
reported for both sexes in Table 5. For males, age-
specific SID was best described by the first-order autor-
correlations among neighboring age classes are the
maximum present correlations and decrease by a linear
function as time between age classes increases. Age-
specific ID was also best described by a first-order
autoregressive model, but with unequal variances in ID
across age classes (Table 6, Arh1). Best-fit covariance
structures for both SID and ID indicate that alleles
affecting inbreeding load in males have moderate age
specificity, with small but significant correlations be-
tween age classes 1 week apart. Correlations for age
classes separated by $2 weeks tend toward zero as the
time between classes increases (Figure 3a).
For females, age-specific patterns of both SID and ID
best fit the autoregressive 1 covariance model allowing
for heterogeneity of variances among ages (Table 6). As
in males, there are significant positive correlations for
age classes .1 week apart (Table 5, Figure 3b). These
patterns indicate that segregating alleles have a window
of significant deleterious effects spanning at least 1, but
We have shown that alleles affecting inbreeding load
have effects that are significantly positively correlated
over 40% of mean life span for males and 64% of mean
life span for females. Average allelic effects are appar-
ently neither pleiotropic for the entire life span nor ex-
These data are therefore most consistent with modified
MA theory assuming a moderate age specificity of allelic
If the alleles affecting inbreeding depression are
strictly deleterious in nature, then the inbreeding load
should be inversely related to the sensitivity of fitness
with age: inbreeding load should increase with cohort
age (Charlesworth and Hughes 1996). We found
that SID increased dramatically with age in both sexes
(Figure 1), as has been shown in other experiments
Figure 1.—Change in mean SID with age. Males are repre-
sented by squares, and females are represented by circles. Er-
ror bars are standard deviation from the mean.
Mean competitive reproductive success by week
Mean no. of offspring
Treatment groupWk 1 Wk 2Wk 3 Wk 4
Males, inbred crosses
Males, heterotypic crosses
Females, inbred crosses
Females, heterotypic crosses
Effect of age on inbreeding load
d.f. F value Pr . F
43.1 353.30 ,0.0001
45.4 131.88 ,0.0001
Num., numerator; Den., denominator.
Figure 2.—Change in mean ID with age. Dashed lines rep-
homozygous reproductive success, and the solid line indicates
ID. Error bars are standard deviation from the mean.
Age Specificity of Inbreeding Load591
(Hughes et al. 2002; Snoke and Promislow 2003).
Snoke and Promislow (2003) also showed an increase
in ID with age. In contrast, ID values in this experiment
decreased significantly with age. In this case, it is likely
that the decrease in ID with age is due to a decrease in
both inbred and outbred fitness, combined with an ab-
sorbing boundary of zero for inbred fitness: of compe-
tition vials that produced offspring at 14 days, 40% of
inbred male competition vials produced zero wild-type
offspring, and 59% of inbred female competition vials
produced zero wild type (these vials did produce ebony
flies). Results on the direction of change in inbreeding
load with age should be interpreted with caution: the
validity of the prediction could be sensitive to violation
of its underlying assumptions. One assumption implicit
in the prediction that inbreeding load should increase
with age is that the effects of alleles are independent
across ages. Our data show that the effects of alleles
are not completely independent among neighboring
age classes. Future work is necessary to determine how
these developments in our understanding of the na-
ture of alleles contributing to inbreeding load do or do
not change predictions concerning the trajectory of
ID with age.
Our results indicate that female ID decreases faster
than does male ID with age (Table 3, Figure 2). Alleles
affecting male and female fitness traits need not be the
same. Indeed, there is considerable evidence for sex-
specific alleliceffects from quantitative trait locus (QTL)
experiments investigating life span and age-specific
survival in D. melanogaster (Nuzhdin et al. 1997, 2005;
Leips and Mackay 2000; but see Vieira et al. 2000;
Reiwitch and Nuzhdin 2002) and sex-specific effects
of inbreeding in general (Saccheri et al. 2005). Even
different temporal patterns of age specificity in males
and females (Nuzhdin et al. 2005) or different magni-
tudes of effects in the sexes (e.g., Clancy et al. 2001;
Flurkey et al. 2002; Holzenberger et al. 2003; Kapahi
et al. 2004). Despite evidence for ubiquitous sex speci-
ficity of allelic effects, our results indicate that alleles de-
termining inbreeding depression have similar duration
of effect in both sexes: ?1 week (Figure 3).
Previous mutation-accumulation and quantitative ge-
netic experiments have been used to estimate genetic
vious study (Tatar et al. 1996) measured the age spec-
ificity of naturally segregating alleles affecting female
fecundity in a population of laboratory-adapted D.
melanogaster. In that study, additive genetic correlations
in age-specific fecundityat 3daysofage (the earliest age
class measured) and later ages were not significantly dif-
ferent from zero. Correlations among all other age
classes were significantly positive. The focus of the Tatar
et al. study was to determine the general sign of corre-
lations across ages; although the study showed that
correlations were generally positive for most age com-
parisons, statistical tests comparing additive genetic
Test for age-by-sex interaction
Metric of inbreeding loadCovariance structure EffectNum. d.f. Den d.f.
F value Pr . F
Age by sex
Age by sex
Num., numerator; Den., denominator.
Age-specific variances and covariances in inbreeding load
Age classes compared (wk x, wk y)
Data set1, 1 1, 21, 3 1, 4 2, 2 2, 32, 4 3, 33, 4 4, 4
Variances and covariances in inbreeding load are shown as calculated by repeated measures analysis of var-
iance (SAS) using the best-fit covariance structure. Covariances are listed under the age classes being compared;
e.g., ‘‘1, 1’’ is the variance of week 1, while 1, 2 is the covariance in inbreeding load between weeks 1 and 2.
Asterisks indicate that a value is significantly different from zero at a ¼ 0.05.
592 R. M. Reynolds et al.
correlations for neighboring vs. distant age classes were
In a study of D. melanogaster homozygous lines that
had been accumulating spontaneous mutations for 47
generations, Pletcher et al. (1998, 1999) found that
mutations affecting both male and female mortality at
generation 19 were significantly positively correlated
across age classes separated by 1–3 weeks: 26–78% of
average adult life span. In that experiment, correlations
of mutation accumulation, but large estimates of stan-
dard errors made statistical significance unclear.
Life-span QTL identified in a cross between lines
selected for divergent longevity had significantly posi-
tively correlated effects across ages separated by 7–35
days (61–100% of mean adult life span; Curtsinger
and Khazaeli 2002). Leips et al. (2006) identified QTL
age, with no genetic correlation in fecundity between
the two ages, while the genetic variance among homo-
zygous lines increased with age.
Overall, these studies indicate that new mutations
(Pletcher et al. 1999) and segregating alleles (this
study; Curtsinger and Khazaeli 2002; Leips et al.
2006) have moderately age-specific effects, on average.
All estimates, except for those derived from the QTL
study, reflect the average correlations of allelic effects
across particularchromosomes,or wholegenomes.Our
findings do not rule out a small to moderate number of
alleles that act in an antagonistically pleiotropic man-
ner. There is no a priori expectation that MA and AP are
mutually exclusive; in fact, there is a substantial body of
in D. melanogaster (e.g., Chippindale et al. 1994; Rose
et al. 2002; Leips et al. 2006).
Although we have shown that alleles affecting fitness
are consistent with a modified MA model and are mod-
erately age specific on average, further investigations
common for these alleles. The Charlesworth (2001)
modified MA model covers a continuum of alleles with
moderately age-specific effects. On one end of the con-
tinuum, allelesmayhaveawindow ofage-specificeffects
such that there are an age of onset and an age at which
the effects are no longer seen. At the other end of the
continuum, alleles may have ‘‘cumulative’’ effects: an
age of onset and pleiotropic effects throughout the rest
of the life span. These effects could remain the same
with age, e.g., Alzheimer’s disease. It is not possible to
distinguish what proportion of the alleles that we de-
tected have typical window-style vs. cumulative effects
(or something in between) using our data. Although
considerable variation in age specificity may exist, the
empirical results support theoretical models that pre-
dict mortality plateaus when the assumptions of strict
age specificity are relaxed.
In conclusion, we have shown a moderate age spec-
ificity of allelic effects on inbreeding depression. We
Corrected Akaike information criterion scores for likely covariance structures
Data set InCs Ar(1)Arh(1)Ar(1)R Arh(1)RToepToephUn
aBest-fit covariance structure.
bAnalysis stopped because of too many likelihood evaluations.
Figure 3.—Correlations in inbreeding load among age
classes. Each bar represents the correlation between two
age classes; e.g., the first bar is the correlation in inbreeding
load for age classes 1 week apart. Shaded bars represent cor-
relations among age classes using SID values. Open bars rep-
resent correlations among age classes using ID values. Error
bars are the standard deviation of the jackknifed data set.
Age Specificity of Inbreeding Load593
provide evidence that the evolutionary theory of late
life, based on the decline in the efficacy of natural se-
lection with age, explains—indeed, predicts—mortality
plateaus. This conclusion is consistent with studies in
which the timing of the decline in the force of natural
selection was altered and the timing of mortality and
fecundity plateaus changed in the predicted direction
(Rose et al. 2002; Rauser et al. 2006). Our results do not
imply that selection on moderately age-specific alleles is
the only or even the predominant cause of observed
mortality plateaus. For example, nongenetic heteroge-
tality plateaus for purely demographic reasons (Vaupel
et al. 1979; Service 2000), as can age-related changes
in individual behavior (Kowald and Kirkwood 1993;
Sgro and Partridge 1999) or in environmental con-
Khazaeli et al. 1996; Partridge 1997). Resolution of
evolved characteristic of populations or are a statistical
consequence of demographic variation will require ex-
periments that manipulate both potential causes and
possible interactions between them.
We thank Amy Schwartz and Adam Devore for laboratory assistance
and Fernando Miguez, Jacob Moorad, and Bruce Walsh for statistical
from Carla Ca ´ceres, Brian Charlesworth, Gene Robinson, Silvia
Remolina, Amy Toth, Ashley Johnson, Katelyn Michelini, and two
anonymous reviewers. We thank the National Institutes of Health
National Institute on Aging (AG022824), the National Science
Foundation (DEB 0296177), Sigma Xi grants in aid of research, and
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