A Newly published Study

Parental and Grandparental Ages in the Autistic
Spectrum Disorders: A Birth Cohort Study
Jean Golding*, Colin Steer, Marcus Pembrey
Centre for Child and Adolescent Health, Department of Community Based Medicine, University of Bristol, Bristol, United Kingdom
Abstract
Background: A number of studies have assessed ages of parents of children with autistic spectrum disorders (ASD), and
reported both maternal and paternal age effects. Here we assess relationships with grandparental ages.
Methods and Findings: We compared the parental and grandparental ages of children in the population-based Avon
Longitudinal Study of Parents and Children (ALSPAC), according to their scores in regard to 4 autistic trait measures and
whether they had been given a diagnosis of ASD. Mean maternal and paternal ages of ASD cases were raised, but this
appears to be secondary to a maternal grandmother age effect (P = 0.006): OR = 1.66[95%CI 1.16, 2.37] for each 10-year
increase in the grandmother’s age at the birth of the mother. Trait measures also revealed an association between the
maternal grandmother’s age and the major autistic trait–the Coherence Scale (regression coefficient b = 0.142,
[95%CI = 0.057, 0.228]P = 0.001). After allowing for confounders the effect size increased to b = 0.217[95%CI 0.125,
0.308](P,0.001) for each 10 year increase in age.
Conclusions: Although the relationship between maternal grandmother’s age and ASD and a major autistic trait was
unexpected, there is some biological plausibility, for the maternal side at least, given that the timing of female meiosis I
permits direct effects on the grandchild’s genome during the grandmother’s pregnancy. An alternative explanation is the
meiotic mismatch methylation (3 M) hypothesis, presented here for the first time. Nevertheless the findings should be
treated as hypothesis generating pending corroborative results from other studies.
Citation: Golding J, Steer C, Pembrey M (2010) Parental and Grandparental Ages in the Autistic Spectrum Disorders: A Birth Cohort Study. PLoS ONE 5(4): e9939.
doi:10.1371/journal.pone.0009939
Editor: Chenxi Wang, University of Louisville, United States of America
Received November 12, 2009; Accepted February 24, 2010; Published April 1, 2010
Copyright: 2010 Golding et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The UK Medical Research Council (MRC), the Wellcome Trust and the University of Bristol currently provide core support for ALSPAC. The statistical
analyses for this paper were specifically funded by the MRC. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: jean.golding@bristol.ac.uk
Introduction
There is evidence from many parts of the developed world that
the prevalence of diagnosed autistic spectrum disorder (ASD) has
been rising dramatically. Although evidence from twin studies
suggests a strong level of heritability, it is clear that there must also
be other factors at play. Parental ages have received some
attention - there have been a few case control studies comparing
ages of mothers of children with autistic spectrum disorder (ASD)
with controls and showing that ASD mothers tended to be older
than controls, but these studies either had selected non-population
based cases, or had inadequate controls or numbers too small for
adequate conclusions. In recent years, however, there have been a
number of large population based studies of cases of ASD
compared either with all births born over the same period or with
a set of controls randomly selected from the population at risk.
Their conclusions have varied. For example, there have been three
studies from Scandinavia, taking advantage of their birth registries
and their facility to link these with case registries. That from
Sweden, compared 408 cases with 2040 controls and reported no
association with advanced maternal age [1]; the two studies from
Denmark covering births from 1984–98 [2] and 1973–98 [3]
overlapped considerably, yet they come to different conclusions in
regard to parental age. One [2] states that maternal age was
significantly associated with autism but that this was secondary to a
paternal age effect, whereas the other reports that neither were
significant on adjusting for one another [3]. The latter study fell
into the trap of failing to take account of collinearity - thus if you
have two factors closely correlated such as the ages of each parent,
then taking account of both simultaneously will automatically
result in neither showing an association with the outcome under
consideration. In Western Australia, this problem was circumvented
by using a step-wise procedure and offering both maternal
and paternal ages [4]. Although both factors were univariably
highly associated with ASD (P,0.001), it was only maternal age
that entered the equation with an almost 3-fold increase in risk to
children of mothers aged 35+ compared with those ,25.
Conversely a study of Israeli conscripts found increased paternal
but not maternal ages [5], but two major studies of births in the
USA in 1994 showed independent relationships with both
maternal and paternal ages [6,7].
Thus there is a lack of clarity as to whether it is the age of the
mother, or of the father, or both that are related to ASD. In
reviewing the literature two publications [8,9] concluded that both
older maternal and older paternal ages played a role. Because of
the lack of agreement, we decided to address the topic more
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broadly. We take advantage of a population based study to assess
the parental ages at the birth of the study children with ASD,
compared with those of the rest of the population, and also assess
whether ages of the preceding generation may be important. We
consider not only the children who have been diagnosed, but also
the various traits that contribute to the autistic spectrum disorders.
Materials and Methods
The Avon Longitudinal Study of Parents and Children
(ALSPAC) started in September 1990 and aimed to enrol all
pregnant women resident in the geographic area of Avon, in southwest
England, who had an expected date of delivery in the period
April 1st 1991 to December 31st 1992 inclusive. The aim of the study
was to assess the contribution of the environment (broadly defined
to include both the physical and psychosocial influences) on the
health, development and wellbeing of children from the earliest ages
[10]. The study also aims to look at the way in which genetic
variation influences a variety of outcomes and how these influences
may be modified by the environment. In all, 14075 children were
born to 13881 mothers, an estimated 80%of the eligible population.
A total of 13971 children survived to age 7 years. Ethical approval
for the study was obtained from the ALSPAC Law and Ethics
Committee and the Local Research Ethics Committees.
A dual approach was made to identify the children from the
cohort with ASD. Both the health service records, where a
multidisciplinary team had reached this diagnosis, and the
education system, where ASD had been given as a reason for
special educational needs, were used [11]. A total of 86 children
were identified in this way.
In parallel we have looked at traits associated with ASD in the
ALSPAC study, and shown that 4 traits are particularly predictive
of ASD [12]: the coherence scale of the Children’s Communication
Checklist (CCC) at about 9 y [13]; the Social and
Communication Disorders Checklist (SCDC) at about 8 y [14];
the sociability score from the EAS temperament scale at 38 m
[15], and a scale of repetitive behaviour at 69 m derived for the
ALSPAC Study. For each scale the higher the results, the more
autistic the behaviour. All four traits were highly associated with
the ASD diagnosis explaining individually between 10% (sociability)
and 46% (coherence) of the log-likelihood.
Here we look at the ages of the parents and the grandparents at
the birth of the study child and study parents, respectively,
comparing children with and without ASD and the worst 10% of
scores for the autistic traits. We used stepwise logistic regression for
these dichotomous outcomes. We also analysed the traits as
continuous variables using multiple regression.
Results
Unadjusted associations with autistic measures
Figure 1 demonstrates the variation in the rate of ASD
according to the maternal and paternal ages and shows that there
is a lower prevalence of children with an ASD if the parents are
young (,25), and increased rates at ages 30–34. The rates of ASD
when a parent is 35 or more are similar to the rates at ages 25–29
in this study.
However for the maternal grandparents the rates of ASD in
their grandchildren are highest if they were aged 35 or more at the
time of birth of the study mother (Figure 1). There is also evidence
among the paternal grandparents of an increase in risk when the
parents were aged 30 or more.
Comparisons of the mean ages show similar patterns–the mean
ages of the parents and grandparents of the children with ASD
were higher than found for those without ASD (Table 1). The
greatest differences were demonstrated for the grandmothers, with
mean differences of 1.90 and 1.97 years for the maternal and
paternal grandmothers respectively. Linear regression demonstrates
the increase in risk of ASD with each 10 year increase in
age, and shows that similar effect sizes are found for ages of
mothers and grandmothers (Table 2).
In Tables 3a and b, relationships with the ages are shown for the
4 autistic traits considered. Maternal age was associated with the
SCDC trait, such that the younger the mother the more autistic
the trait. Coherence showed a similar but less marked younger
mother pattern. There were no significant associations with
paternal age. There were, however, significant trends for older
maternal grandmothers to be associated with worse levels on the
Coherence Scale whether analysed according to the worst decile of
the scale (P =0.025, Table 3), or using the continuous scale
(P= 0.007, Table 4). There were no other traits showing
statistically significant associations with grandparental age.
Correlations between ages
There is, however, a strong correlation between ages of spouses.
Table 5 shows that there is also a strong correlation between the
parent’s age at the birth of the study child and the age of each
grandparent at the birth of the study parent. Although the
correlations between the ages of the maternal and the paternal
grandparents are low (0.05–0.08), those between the parents and
grandparents are higher but still modest (r = 0.17 to 0.26). The
correlation between the ages of spouses is higher for grandparents
than for parents.
Adjusted associations
ASD. To assess which of the highly correlated ages were most
important in regard to ASD, forward step-wise multiple regression
was used; on offering all the 6 age variables only one entered the
model–the maternal grandmother’s age: OR 1.66, [95% CI 1.16,
2.37] per 10 year increase in age (P= 0.006). In the presence of
this factor the mother’s age showed an odds ratio (OR) of 1.39
(95% CI 0.86, 2.24; P =0.177). The paternal grandmother’s age in
the presence of the maternal grandmother’s age, however, did
exhibit an effect that bordered on statistical significance (OR 1.54,
95%CI 0.97, 2.45; P= 0.069).
In order to ensure that the analysis using the ages as continuous
variables were not hiding important non-linear effects, we used the
ages as 4 categorical variables as shown in Figure 1, and used
stepwise logistic regression to determine which factors would enter.
Again it was only the maternal grandmother’s age that entered the
model (P= 0.004); once again, however, the paternal grandmother’s
age was of borderline significance in the presence of the
maternal grandmother’s age (P= 0.066) and the mother’s age
given that of her own mother was not statistically significant
(P= 0.169). Numbers were too small for detailed multivariable
analysis, but the only unadjusted socio-demographic or psychosocial
factor that was significantly associated with ASD was paternal
social class. Taking this into account the maternal grandmother
effect size did not change substantially (OR =1.69, 95%CI 1.15,
2.49, P= 0.007); n= 9957.
Analyses were also repeated taking into account the nonindependence
of multiple births. Both maternal and paternal
grandmother effects were strengthened and exerted independent
effects–OR = 2.04 [1.26, 3.29] and 1.06 [1.01, 1.11] respectively.
But in all other respects, the conclusions were not affected by these
adjustments.
Autistic traits. In regard to the coherence trait, only the age
of the maternal grandmother entered the logistic regression,
Grandparental Age and ASD
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Grandparental Age and ASD
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indicating that the child was 1.17 times more likely to be in the
worst decile of this trait for each 10 year increase in grandmother’s
age [95% CI 1.02, 1.34], P= 0.025. When the scale was used
linearly, both the grandmother’s and the mother’s ages entered–
the grandmother showing an adverse effect with increasing age
(b = 0.14, 95%CI 0.06, 0.23, P= 0.001), whereas maternal age
independently showed the reverse association (b =20.14, 95% CI
20.25, 20.03, P = 0.011). The only other trait that showed any
independent association with an age measure was maternal age
which entered the SCDC model with OR= 0.80 [95% CI 0.68,
0.93] for the worse decile and b=20.30 [95%CI 20.48, 20.13]
for linear trend (P= 0.005 and 0.001 respectively): i.e. the SCDC
trait score increased as the mother’s age decreased.
Analysis to assess whether the relationship of the maternal
grandmother’s age with the ‘Coherence’ score was an artefact first
investigated over 100 environmental, behavioural or psychological
measures that might be related to this trait. On assessment of the
unadjusted associations we selected 35 which were highly
significant (P,0.001). Each of these were then examined to
determine their effect on the regression coefficient for the ages.
Only maternal personality at 18 w gestation (29.8%) and parity
(22.1%) attenuated the age effect. In all, 7 variables had an effect
between 10 and 20% (maternal social class, paternal education,
paternal grandmother’s education, repetitive behaviour at 30 m,
maternal depression scale at 32 weeks gestation and 8 months
postnatally and a Family Adversity Scale covering the first 2 years
of the study child’s life). There remained 6 factors with an effect
.20%. (maternal education level, housing tenure, child’s passive
smoke exposure at 15 m, whether the TV was on in the afternoons
at 18 m, and the Family Adversity Level in pregnancy and in the
3rd–4th year of the child’s life). Maternal age was also kept in the
analysis. The regression coefficients for maternal age and for
maternal grandmother’s ages on inclusion of the 6 factors were
b=20.046 [95% CI 20.173, + 0.080] (P =0.474) and b =0.217,
[95% CI 0.125, 0.308] (P,0.001) respectively per 10 year increase
in age (n= 5989). Further analysis taking account of the child’s IQ
resulted in b= 0.207 [95% 0.115, 0.299] (P,0.005) per 10 year
increase in maternal grandmother’s age (n= 4770).
Adjustments for multiple births did not alter the conclusions.
Missing data. As Table 1 shows, varying amounts of missing
data existed for different ages varying from zero for maternal age
(data obtained from registration details) to 45% for paternal
grandfather age (data obtained from a partner questionnaire
administered during pregnancy). Complete data were available for
5851 (42%) children. Analyses using this sample suggested a
similar dominant effect for maternal grandmother age as reported
in Table 2 and in Adjusted associations above. Athough this age effect
was somewhat higher for the restricted sample OR= 2.17 [95%
1.32, 3.55], this effect was not statistically different from the result
of OR=1.65 obtained for 11075 children (interaction p =0.127).
This result may imply the transition from observed to the total
sample of 13971 children will also have a minimal impact on the
associations reported.
Confounding. The associations of outcomes and predictors
with socio-demographic variables are shown in Table 6. While
strong associations existed, in general, there was little evidence of
confounding. For instance, with gender, associations were only
present for outcomes but not for predictors. For others, such as
social class, education and housing, the direction of associations
between outcomes and predictors tended to occur in opposite
directions. This may explain the result above whereby adjustment
tended to strengthen the associations.
Discussion
There have been a number of population studies showing that
children with ASD are more likely to be born to older parents. In
this study, for the first time to our knowledge, we have assessed
possible relationships with the ages of the parents’ own parents at
the time of their birth. We had no prior hypotheses as to whether
we would find relationships through the male or female line, but
found that the ages of the grandmothers were higher than
expected, and that the relationship with the maternal grandmother
was statistically significant (P= 0.006).
There is increasing recognition that trying to find a biological
basis for syndromes such as ASD is probably best served by study
Figure 1. Rates per 1000 of a child having ASD are shown according to ages of parents at child’s birth and of grandparents at
parents’ birth with 95% CIs.
doi:10.1371/journal.pone.0009939.g001
Table 1. Comparison of mean ages of parentsa and
grandparentsb of children with and without ASD.
ASD Non- ASD
Mean
difference
Age of n
Mean
(SD) n
Mean
(SD) [95% CI]
Mother 86 29.24
(4.97)
13885 27.99
(4.96)
1.26 [0.20, 2.31]c
Father 71 31.55
(5.30)
11599 30.71
(5.74)
0.84 [20.50,
2.17]
Maternal
grandmother
76 28.68
(6.20)
11001 26.79
(5.85)
1.90 [0.58, 3.22]d
Maternal
grandfather
73 31.04
(6.01)
10164 29.88
(6.72)
1.16 [20.39,
2.71]
Paternal
grandmother
51 29.27
(6.43)
7977 27.30
(5.95)
1.97 [0.33, 3.61]e
Paternal
grandfather
50 31.86
(7.09)
7594 30.40
(6.84)
1.46 [20.44,
3.37]
aage of parent at birth of study child.
bage of grandparent at birth of the parent.
cP = 0.019.
dP = 0.005.
eP = 0.018.
doi:10.1371/journal.pone.0009939.t001
Table 2. Increase in risk [95% confidence interval] of child
having ASD for each 10 year increase in age of parents and
grandparents adjusted for gender.
Age of OR [95% CI] P
Mother 1.60 [1.05, 2.44] 0.029
Father 1.24 [0.84, 1.82] 0.278
Maternal grandmother 1.66 [1.16, 2.37] 0.006
Maternal grandfather 1.27 [0.92, 1.75] 0.140
Paternal grandmother 1.65 [1.07, 2.54] 0.023
Paternal grandfather 1.29 [0.89, 1.87] 0.176
doi:10.1371/journal.pone.0009939.t002
Grandparental Age and ASD
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of the component traits [16,17]. Elsewhere we have investigated
within ALSPAC the associations between ASD and 90 different
trait scales. Of these, 4 were identified as independently associated
with ASD, thus providing a compromise between parsimony and
explanatory power [12]; these were coherence, social communication,
sociability and repetitive behaviour; together they accounted
for 54% of the variance. Of the 4 traits the coherence scale
showed the strongest relationship with ASD. Therefore in this
study, as well as looking at study subjects which have an ASD
diagnosis, we have assessed relationships with these 4 traits. We
showed that the coherence trait, whether treated as a continuous
scale or dichotomously studying the worst decile of the
distribution, was significantly associated with the maternal
grandmother’s age.
In regard to whether the grandmother’s age effects might be
explained by older women being more likely to have daughters (i.e.
study mothers) with autistic traits, we examined the information
that had been collected from the study parents during pregnancy
and later. For ASD there was no hint of any associations with
maternal history of child guidance or speech therapy or of current
unusual personality traits (data not shown). The parents had
similar social networks and education levels to the rest of the
population, but there were positive associations with paternal
social class such that the children of fathers in non-manual as
opposed to manual occupations were at increased risk of ASD
[11]. Taking this into account the maternal grandmother effect
remained strongly associated.
Although analyses of data on the ASD cases suffer from lack of
statistical power, this was not true of the trait measures. The
Coherence trait which has been shown to be closely related to a
diagnosis of ASD in our data, had sufficient power for a number of
factors to be taken into account. Unadjusted analyses concerning
over 100 potential confounders were examined and 6 showing a
change of at least 20% in the effect size were selected for
multivariable analysis. The result was an increase in the regression
coefficient from 0.142 [95% CI 0.057, 0.228] to 0.217 [95% CI
0.125, 0.308] per 10 year increase in maternal grandmother’s age.
In a further analysis we took the child’s IQ into account in order to
ensure that it was the Coherence trait we were assessing rather
than some effect of the child’s intellectual ability, but again this
made little difference to the relationship with maternal grandmother’s
age.
This maternal grandmother’s age effect, found for both ASD
and for one of the major autistic traits, was unexpected and will
need replication, but it is biologically plausible because of the
timing of meiosis in females. As Figure 2 illustrates, the paired (and
recombining) grandparental chromosomes that will be transmitted
from the mother to her offspring are already there in the fetal
Table 3. Increase in risk [95% confidence interval] of child being in the worst decile of each autistic trait for each 10 year increase
in age of parents and grandparents adjusted for gender.
UNADJUSTED OR [95% CI]
Age of Coherence SCDCa RBb Sociability
Motherc 0.92 [0.79, 1.09] (P = 0.337) 0.80 [0.68, 0.93] (P = 0.005) 0.94 [0.79, 1.13] (P = 0.520) 0.96 [0.84, 1.09] (P = 0.529)
Fatherc 1.13 [0.98, 1.29] (P = 0.088) 0.91 [0.79, 1.05] (P = 0.197) 0.95 [0.82, 1.11] (P = 0.557) 0.99 [0.89, 1.12] (P = 0.928)
Maternal grandmotherd 1.17 [1.02, 1.34] (P = 0.025) 0.99 [0.86, 1.13] (P = 0.839) 0.99 [0.85, 1.16] (P = 0.921) 0.95 [0.84, 1.06] (P = 0.341)
Maternal grandfatherd 1.13 [1.00, 1.28] (P = 0.052) 0.97 [0.86, 1.10] (P = 0.663) 1.03 [0.89, 1.19] (P = 0.684) 1.00 [0.90, 1.11] (P = 0.936)
Paternal grandmotherd 1.00 [0.86, 1.17] (P = 0.980) 1.15 [0.98, 1.34] (P = 0.084) 1.04 [0.87, 1.24] (P = 0.691) 1.11 [0.97, 1.26] (P = 0.125)
Paternal grandfatherd 0.98 [0.85, 1.12] (P = 0.751) 1.07 [0.94, 1.23] (P = 0.316) 1.09 [0.94, 1.28] (P = 0.245) 1.07 [0.95, 1.20] (P = 0.273)
aSocial and Communication Disorders Checklist.
bRepetitive behaviour score.
cage at birth of study child.
dage of grandparent at birth of the parent.
doi:10.1371/journal.pone.0009939.t003
Table 4. Linear regression of parental and grandparental ages on autistic traits; results per 10 years increase in age, high scores
indicate more autistic like traits adjusted for gender.
UNADJUSTED REGRESSION COEFFICIENT [95% CI]
Age of Coherence SCDCa RBb Sociability
Motherc 20.13 [20.23, 20.03] (P = 0.009) 20.30 [20.48, 20.13] (P = 0.001) 20.01 [20.03, +0.01] (P = 0.509) +0.03 [20.10. +0.16] (P = 0.676)
Fatherc +0.02 [20.06, +0.11] (P = 0.594) 20.12 [20.27, +0.03] (P = 0.110) +0.00 [20.02, +0.02] (P = 0.794) 20.02 [20.13, +0.10] (P = 0.779)
Maternal grandmotherd +0.12 [+0.03, +0.20] (P = 0.007) 20.11 [20.25, +0.04] (P = 0.152) +0.00 [20.01, +0.02] (P = 0.645) 20.01 [20.13, +0.10] (P = 0.799)
Maternal grandfatherd +0.07 [20.01, +0.14] (P = 0.084) 20.08 [20.21, +0.05] (P = 0.233) +0.01 [20.01, +0.02] (P = 0.410) +0.00 [20.10, +0.10] (P = 0.994)
Paternal grandmotherd 20.01 [20.10, +0.09] (P = 0.914) +0.09 [20.08, +0.25] (P = 0.301) +0.00 [20.02, +0.02] (P = 0.919) +0.09 [20.03, +0.22] (P = 0.150)
Paternal grandfatherd 20.05 [20.14, +0.03] (P = 0.209) +0.03 [20.12, +0.18] (P = 0.684) +0.01 [20.01, +0.03] (P = 0.290) +0.04 [20.07, +0.15] (P = 0.469)
aSocial and Communication Disorders Checklist.
bRepetitive behaviour score.
cage at birth of study child.
dage of grandparent at birth of the parent.
doi:10.1371/journal.pone.0009939.t004
Grandparental Age and ASD
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ovary from the second trimester of the grandmother’s pregnancy.
This permits a direct grand-maternal effect on the germ line that
will be passed to the grandchild.
Although in this study maternal and paternal ages were raised,
our analysis indicates that the primary association with ASD risk is
the maternal grandmother’s age. If this is confirmed in other
studies, it suggests three broad possibilities–
a) Women with older mothers are more likely to push for a
diagnosis for their child. Although there is no literature on
this it is known that older mothers tend to recognise signs of
ASD earlier [18], and it is likely that older grandmothers
would do the same. However this does not explain the
association between maternal grandmother’s age and the
Coherence trait, since the scale involved was completed by a
large population of mothers, and did not depend on any
diagnosis.
b) There is something about early stages of meiosis I in the fetal
ovaries that is particularly sensitive to maternal age effects,
with the (genomic) malfunction being played out in the
grandchild.
c) An inherited risk factor is amplified in some way by passage
across at least one generation, i.e. the maternal age effect
increases the ASD risk in the daughter to a lesser degree than
in the grandchild. This could happen in two ways:
enrichment for those germ cells that happen to carry an
increased ASD risk or some progressive change to the
genome. Candidates for the former include an age-related
loss of selection against oogonia or oocytes with de novo genetic
damage or indeed a proliferative advantage of cells with an
ASD risk genotype. Candidates for progressive change to the
genome include a dynamic triplet repeat mutation, as in
fragile X, but where older maternal age is associated with de
novo premutations, or some epigenetic spreading of the
genetic malfunction during transmission to the next generation,
e.g. at meiosis.
The last two scenarios imply a transmitted change in the
genome or other heritable material. Twin [19] and sibling risk
studies [2] show that ASD is highly heritable with monozygotic
twins having 92% concordance compared to 10% in dizygotic
twins. It is usually assumed that this heritability is genetic, although
Table 5. Correlations between parental and grandparental ages–all study families.
Age of M F MGM MGF PGM PGF
Mother (M) 1.000
Father (F) 0.661 1.000
Maternal grandmother (MGM) 0.255 0.195 1.000
Maternal grandfather (MGF) 0.230 0.205 0.809 1.000
Paternal grandmother (PGM) 0.190 0.189 0.076 0.058 1.000
Paternal grandfather (PGF) 0.172 0.177 0.068 0.050 0.790 1.000
[ages are those of parents at the birth of the study child, and of grandparents at the birth of the study parent].
All correlations were statistically significant p,0.001.
doi:10.1371/journal.pone.0009939.t005
Table 6. Association of ASD, traits and age variables with socio-demographic variables.
Gender Birth order Social class Education Housing
ASD 0.152 (1.403)*** 1.525 (1.280) 0.803 (1.104)* 0.984 (1.094) 0.898 (1.196)
Traits
Coherence 20.461 (0.046)*** 0.164 (0.048)*** 0.055 (0.020)** 20.129 (0.020)*** 0.122 (0.039)**
SCDC 20.856 (0.082)*** 20.161 (0.084) 0.089 (0.034)** 20.119 (0.034)*** 0.287 (0.067)***
RB 20.055 (0.010)*** 0.002 (0.010) 20.000 (0.004) 20.006 (0.004) 0.033 (0.008)***
Sociability 20.331 (0.062)*** 0.688 (0.063)*** 0.070 (0.026)** 20.191 (0.025)*** 20.011 (0.049)
Ages
M 20.155 (0.087) 2.656 (0.086)*** 21.061 (0.034)*** 1.050 (0.033)*** 22.068 (0.061)***
F 20.245 (0.108)* 2.409 (0.108)*** 20.954 (0.043)*** 0.877 (0.043)*** 21.689 (0.081)***
MGM 20.078 (0.113) 0.201 (0.116) 20.503 (0.047)*** 0.685 (0.045)*** 20.888 (0.086)***
MGF 20.010 (0.135) 0.271 (0.139) 20.529 (0.056)*** 0.744 (0.054)*** 20.729 (0.105)***
PGM 20.165 (0.136) 0.453 (0.137)*** 20.536 (0.056)*** 0.553 (0.055)*** 20.796 (0.105)***
PGF 20.357 (0.159)* 0.545 (0.161)*** 20.524 (0.066)*** 0.562 (0.065)*** 20.782 (0.125)***
Gender effect is for females. Birth order coded as 0 (no older siblings) and 1 (one or more older siblings). Paternal social class coded as I, II, III non-manual, III manual and
IV+V combined. Maternal educational qualifications coded as none or CSE, vocational, O level, A level or degree. Housing coded as mortgaged/owned, local authority
housing and other. Traits are coded so that low scores reflect a more favourable response. Traits are labelled as in Table 3. Age variables are labelled as given in Table 4.
Reported effect sizes relate to a linear trend. Effect sizes are ORs for ASD and regression coefficients for other outcomes. Standard errors (or exp(SE) for ASD) are given in
parentheses.
doi:10.1371/journal.pone.0009939.t006
Grandparental Age and ASD
PLoS ONE | www.plosone.org 6 April 2010 | Volume 5 | Issue 4 | e9939
transmission of molecular information other than through DNA
sequence (e.g. microRNAs) cannot be ruled out [20]. Recently,
specific genetic risk factors for ASD have been reported, but they
are very heterogeneous. To date one common single nucleotide
polymorphism (SNP) variant associated with ASD risk has been
reported [21], but there have been several studies showing an
excess of rare microdeletions and in some cases microduplications
in ASD compared with controls [22–25]. These so-called copy
number variants (CNVs) may be inherited from a parent or arise
de novo, i.e. new mutations. Some studies [24] deliberately excluded
inherited CNVs in an attempt to enrich for causal variants, whilst
a recent study of smaller deletions and duplications involving the
coding regions (exons) of genes demonstrates that possible
susceptibility CNVs are often inherited [25]. However when
inherited in multiplex families (i.e. with several affected family
members) there is imperfect segregation with ASD with affected
siblings often not inheriting the exonic deletion [25]. This result is
less easy to explain in a multiplex family than some asymptomatic
family members carrying the exonic deletion, if indeed the
inherited deletion is contributing to the familial ASD.
There are few published data on the possible mediators of
(grand)maternal age effects that help distinguish between scenarios
b and c above. A recent study of reproductive and epigenetic
outcomes with aging mouse oocytes found morphological
abnormalities (increased trophoblast giant cells) in the resulting
placentae [26], which raises the possibility of impaired transpla-
Figure 2. Three generations of genotypes are illustrated (A): that involving the grandmother’s pregnancy, her female fetus and the
fetal ovary that contains the emerging genotype of the grandchild. The grandmother has two normal, wild type genes (++). The fetus has a
deletion of the gene inherited from grandfather (n) which confers some susceptibility to autistic spectrum disorder. The hypothesised mispairing of
the grandparental chromosomes at the site of the gene deletion (n) in the fetal oocytes is shown (B). The chromosome containing the wild type
gene loops out at meiotic pairing and this gene becomes liable to be silenced by DNA methylation. This results in no grandchild receiving a
functional gene.
doi:10.1371/journal.pone.0009939.g002
Grandparental Age and ASD
PLoS ONE | www.plosone.org 7 April 2010 | Volume 5 | Issue 4 | e9939
cental transfer of nutrients or metabolic signals to the germ line in
the fetal ovaries. Despite early work suggesting a reduction in
DNA methyltransferases with age, the authors found no
impairment of DNA methylation at imprinted genes and more
widely across the genome.
Given the ongoing generation of de novo CNVs, deterioration
with maternal age in the ‘surveillance’ mechanisms for eliminating
cells with genetic imbalance is a possibility. Less selection against
oogonia with a high CNV load in the fetal ovaries would increase
the ASD risk. Recent work in relation to Down syndrome indicates
that ovarian trisomy 21 mosaicism is common and the maternal
age effect is likely to be, in part, a change in oocyte selection [27].
DNA fragmentation is increased in oocytes from older mice [28]
as is mitochondrial dysfunction [29] which in turn may
compromise the cell quality surveillance role of mitochondria
through apoptosis [30]. However, human research on the
development of oocytes and influences on female meiosis is
necessarily limited, so family based genetic and epigenetic studies
might prove more productive.
One possibility, prompted by our data suggesting a maternal
grandmother age effect in ASD, would be to test what we have
called the meiotic mismatch methylation (3 M) hypothesis (Fig. 2b).
Mispairing of a chromosome bearing an ASD risk deletion with
the normal homologous chromosome during early meiosis I (in the
ovaries of the mother as a fetus) might lead to methylation and
silencing of the normal gene. If this were the case then all the
mother’s children would inherit a risk allele, either the deletion or
a gene silenced by DNA methylation. All other ASD risk factors
being equal, such a meiotic mismatch methylation (beginning
during the grandmother’s pregnancy) would lead to a higher risk
of ASD in the grandchildren than in the mother. It is known that
meiotic mismatches are accommodated in various ways and the
looping out illustrated in Fig. 2b has been known as one such
mechanism for a long time [31].
Beyond the inherited ASD risk deletion (or duplication) itself,
are there any distinctive features of genomic regions harbouring
CNVs that might predispose to meiotic mismatch and DNA
methylation? Such genomic regions tend to contain repeated DNA
sequences (that are implicated in the generation of the CNV in the
first place [32]), and such sequences can lead to other forms of
mismatch at meiosis such as non-allelic-homologous-pairing. The
key question is whether these perturbations of meiotic pairing
trigger spread of DNA methylation to silence the otherwise
functional normal gene (wild type allele). DNA methylation is
believed to have first evolved as a genome defence system to
silence transposons and the like and such sequences are
preferentially methylated [33], so the same genomic architecture
that predisposes to CNVs might also attract DNA methylation.
The 3 M hypothesis is testable in family studies such as reported
by Bucan et al [25] where a specific ASD risk CNV is segregating.
It would predict that those affected members not carrying the CNV
would have DNA methylation silencing of the wild type allele.
Acknowledgments
We are extremely grateful to all the families who took part in this study, the
midwives for their help in recruiting them, and the whole ALSPAC team,
which includes interviewers, computer and laboratory technicians, clerical
workers, research scientists, volunteers, managers, receptionists and nurses.
This publication is the work of the authors who will serve as guarantors for
the contents of this paper.
Author Contributions
Conceived and designed the experiments: JG CDS MP. Performed the
experiments: JG MP. Analyzed the data: CDS. Wrote the paper: JG CDS
MP.
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