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Significance Our finding of a significant gene-by-birth-cohort interaction adds a previously unidentified dimension to gene-by-environment interaction research, suggesting that global changes in the environment over time can modify the penetrance of genetic risk factors for diverse phenotypes. This result also suggests that presence (or absence) of a genotype–phenotype correlation may depend on the period of time study subjects were born in, or the historical moment researchers conduct their investigations.
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Cohort of birth modifies the association between FTO
genotype and BMI
James Niels Rosenquist
, Steven F. Lehrer
, A. James OMalley
, Alan M. Zaslavsky
, Jordan W. Smoller
and Nicholas A. Christakis
Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114;
School of Policy Studies and Department of Economics, Queens University,
Kingston, Ontario, Canada K7L 3N6;
National Bureau of Economic Research USA, Cambridge, MA 02138;
The Dartmouth Institute for Health Policy
and Clinical Practice, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755;
Department of Health Care Policy, Harvard Medical School,
Boston, MA 02115;
Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
Department of Sociology, Yale University, New Haven, CT 06520;
Department of Medicine, Yale University, New Haven, CT 06520;
Department of
Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520; and
Yale Institute for Network Science, Yale University, New Haven, CT 06520
Edited by Kenneth W. Wachter, University of California, Berkeley, CA, and approved November 11, 2014 (received for review June 25, 2014)
A substantial body of research has explored the relative roles of
genetic and environmental factors on phenotype expression in
humans. Recent research has also sought to identify geneenvi-
ronment (or g-by-e) interactions, with mixed success. One poten-
tial reason for these mixed results may relate to the fact that
genetic effects might be modified by changes in the environment
over time. For example, the noted rise of obesity in the United
States in the latter part of the 20th century might reflect an in-
teraction between genetic variation and changing environmental
conditions that together affect the penetrance of genetic influences.
To evaluate this hypothesis, we use longitudinal data from the
Framingham Heart Study collected over 30 y from a geographically
relatively localized sample to test whether the well-documented
association between the rs993609 variant of the FTO (fat mass and
obesity associated) gene and body mass index (BMI) varies across
birth cohorts, time period, and the lifecycle. Such cohort and pe-
riod effects integrate many potential environmental factors, and
this gene-by-environment analysis examines interactions with
both time-varying contemporaneous and historical environmental
influences. Using constrained linear ageperiodcohort models
that include family controls, we find that there is a robust relation-
ship between birth cohort and the genotypephenotype correla-
tion between the FTO risk allele and BMI, with an observed
inflection point for those born after 1942. These results suggest
genetic influences on complex traits like obesity can vary over
time, presumably because of global environmental changes that
modify allelic penetrance.
population genetics
birth cohort
The rise in obesity in the United States and other Western
countries is a major public health concern, and obesity is
known to have both genetic and environmental determinants (1
3). Changes in the population distribution of body mass index
(BMI), a common measure of obesity, have attracted the at-
tention of researchers from disciplines across the health and
social sciences. Social scientists have attributed changes in obesity
to macroenvironmental developments, such as urban design, oc-
cupational shifts, dietary modifications, and social effects (410).
Many of these arguments are plausible and hold considerable in-
tuitive appeal. In parallel, research in the health sciences provides
significant evidence to suggest that genetic factors, notably the FTO
gene, play an important role in BMI over the lifespan (1114).
Although these research studies were typically not designed to
assess interactions between genetic variants and environmental
factors, it is likely that environmental effects are modulated by
genetic pathways, causing some individuals or population groups to
be differentially affected by changes in the environment (7).
To date, geneenvironment interaction studies have primarily
examined within-birth-cohort differences among individuals with
varying environmental exposures in a narrow time period (3).
The foregoing research design uses a cross-sectional approach to
sample environmental variation and focuses on whether the effects
of a single specific environmental variable (e.g., childhood mal-
treatment) with respect to some outcome (e.g., adult depression)
depend on a specific genetic polymorphism (15). This empirical
strategy has prompted some debate regarding its ability to detect
g-by-e effects (16, 17).
On the other hand, using between-birth-cohort differences is
different, allowing for the testing of hypotheses related to time-
varying changes in the whole of the environment affecting a
population. To our knowledge there have been no longitudinal pop-
ulation studies that seek to determine whether there are between-
birth-cohort differences in genotypephenotype associations. Dis-
entangling the extent to which historical versus contemporaneous
environmental factors interact with genetic features, and how these
in turn differ from simple aging, can shed light on the mechanisms
underlying the rise in obesity (and similar phenomena).
Here, we extend the statistical approach used for decades by
epidemiologists and social scientists to understand temporal
trends in health outcomes. This approach, known as age
periodcohort analysis(18), presumes that the patterns of obesity
rates across people of different ages at one point in time do not
solely reflect the physiological effects associated with aging but
also the accumulation of varied experiences over the lifecycle.
These experiences include external factors (such as technological
innovations or cultural changes) that influence multiple birth
cohorts simultaneously (albeit at different moments in their
lives)known as period effects”—but that also, in addition,
differentially affect specific groups of individuals born within the
same eraknown as cohort effects. This distinction is impor-
tant because, for example, younger cohorts might be more likely
to either embrace new technologies and their corresponding
Our finding of a significant gene-by-birth-cohort interaction
adds a previously unidentified dimension to gene-by-environ-
ment interaction research, suggesting that global changes in
the environment over time can modify the penetrance of ge-
netic risk factors for diverse phenotypes. This result also sug-
gests that presence (or absence) of a genotypephenotype
correlation may depend on the period of time study subjects
were born in, or the historical moment researchers conduct
their investigations.
Author contributions: J.N.R., S.F.L., and N.A.C. designed research; J.N.R., S.F.L., and A.J.O.
analyzed data; and J.N.R., S.F.L., A.J.O., A.M.Z., J.W.S., and N.A.C. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
To whom correspondence should be addressed. Email:
This article contains supporting information online at
January 13, 2015
vol. 112
no. 2
modes of work and leisure or be exposed to a sophisticated
marketing campaign at more impressionable ages.
Our approach allows for differential responses to age, period,
and cohort factors depending on the genetic markers one carries,
thereby providing insights into the source of geneenvironment
interactions. In addition, we use an estimation strategy that sta-
tistically determines the optimal breakpoint (if any) at which the
effects of the explanatory variables differ by genetic variant (13).
This allows us to directly examine the hypothesis that genetic
effects on a phenotype vary meaningfully according to the era of
birth of an individual (i.e., the specific cohort to which people
belong). Specifically, using a unique dataset, we test the hypoth-
esis that a particular genetic variant with an established associa-
tion with BMI may have differential influence on the phenotype
of BMI depending on when, exactly, an individual was born,
suggesting a gene-by-birth cohort (g-by-c) interaction.
To quantify the separate effects of age, period, and cohort
(APC) and their interactions with genetic variation, we analyze
longitudinal data from the Offspring Cohort of the Framingham
Heart Study (FHS) collected between 1971 and 2008 (www. To eval-
uate statistically which environmental or demographic factors
interact with rs9939609 to affect BMI, we estimate augmented
versions of ageperiodcohort models. These models partition
the time-related variation in obesity to the three distinct sources.
Intuitively, age effects represent the influence of a persons cur-
rent age on obesity, thereby reflecting biological and social
processes of maturation and aging internal to individuals. Pe-
riod effects represent temporal variations in obesity rates over
time affecting all age groups simultaneously and subsume
a complex set of historical events and environmental factors. In
our case, period is quantified as the subintervals of time captured
by the eight waves of data collection from 1971 through 2008.
Cohort effects represent differences in obesity across groups of
individuals born in different eras, implying that members of
a given group encounter the same historical and social events at
the same ages. Thus, to argue for a g-by-c interaction (the idea
that the genotypephenotype relationship varies by era of birth),
it becomes necessary to show, through results and reasoned
arguments, that one of the other interactions is not confounding
our results. In this case, we argue that g-by-p (gene-by-period)
effects are minimal using empirical and circumstantial evidence.
Our main analyses (described in Materials and Methods)begin
with a simple descriptive analysis and then postulate a linear model
for associations between BMI of person iin family fat time twith
a particular age, period (i.e., wave), and cohort (YOB). That is,
BMIift =β0+β1ageift +β2waveit +β3YOBi+β4genei+β5Xift +μift;
where age and wave are a series of indicators for an individuals
age in 5-y intervals when the measurement occurred, respec-
tively, and YOB is the year of birth. We also include a genetic
main effect for each genetic variant being investigated (gene),
controls for relevant covariates including sex (X), and μ
, which
is a random error term with a mean of zero. This model makes
an assumption of stationarity by assuming the parameters βare
constant across APC. To address the research question posed
above, we first augment Eq. 1by interacting each of the key
variables with indicators for genotype (gene
), thus allowing for
differential coefficients by genotype. A nonzero interaction of
age, period, or cohort with the genetic factors would indicate
differential effects for individuals at a given age, in a different
period, or in a different cohort group, identification of which is
described in detail in Supporting Information, though it is impor-
tant to note that our identification is inherently constrained as in
any APC model due to collinearity. We used a previously de-
veloped estimator (19) to identify whether there is a change
point in the parameters that represents a discontinuity in the
genotypephenotype relationship. By allowing the parameters
for YOB to undergo a structural shift in an unspecified year, this
allows us to test for a structural break of unknown timing. Our
approach assumes that birth cohort effects, as well any of their
interactions with genetic markers, are homogenous before and
after the year of the identified structural break, but allows the
effects to vary between the pre- and postbreak periods.
The main advantage of this approach is that we can conduct
specification tests to determine whether future research should
focus on genetic interactions with specific historical influences
(cohort effects) and/or contemporaneous influences (period
effects), and/or exposure accumulation (age effects). Our ap-
proach requires restrictions to be placed on two parameters of
the model because it is well known that no statistical model can
simultaneously estimate all of the linear APC effect parameters
in Eq. 1, given their collinearity (i.e., cohort =period age). Thus,
we followed earlier research relating to identification of these
effects (detailed in Supporting Information) and used graphical data
describing the obesity trends by period, age, and cohort to establish
the choice of constraints for this model; and we investigated
whether the results were sensitive to the chosen constraints. Our
preferred estimates are obtained by selecting the first age and
period groups as the reference categories and also restricting any
linear birth cohort effect to be zero, allowing only for a nonlinear
effect of cohort. We argue that it is natural in our setting to set the
linear cohort effect to zero because, in a model with separable age
and time effects and only a linear cohort effect, we would only
observe parallel shifts of the cross-sectional age profiles over time.
This is unlikely to be the case for BMI, and we wish to observe how
these responses varied across genetic markers using the most
common genotype (TT) at rs9939609 as the reference category in
the underlying regression specifications.
By restricting the first age and period groups as well as the
most common genotype to be reference categories, we can
identify unique parameter estimates. The choice of which
restrictions that constrain any two specific APC variables to serve
as reference categories does affect the estimated coefficient
values and SEs. Unfortunately, there is no empirical method of
differentiating between alternative variables whose effects are
constrained because, irrespective of the restrictions, all esti-
mated models yield identical fits of the data. Thus, to investigate
the sensitivity of our estimated g-by-c effects, we conducted
numerous robustness exercises including (i) fixing alternative age
or period effects to be zero allowing for only a nonlinear cohort
effect, (ii) treating birth year as a continuous variable so that the
function of the cohort variable does not have a perfect linear
relationship with the discrete age and period effects we condition
upon, and (iii) constraining a set of parameters (i.e., the effect of
two age effects) to be equal. In general, these alternative models
placed different constraints that were also chosen using external
information on obesity prevalence over time. However, these
alternative models placed restrictions that were more difficult to
justify in our setting based on a graphical examination of our data
that showed rising rates of obesity both across time and age. That
said, our analyses led to identical findings of a significant g-by-c
interaction irrespective of the constraints and restrictions imposed.
We first undertook a primarily descriptive analysis by reviewing
the average BMI in cells of a two-way table presented in Table 1.
Each cell denotes the ageperiod combination where the rows
represent categories of subject age and the columns define cate-
gories of year when the measurement was taken. The diagonal of
Table 1 (going from upper left to lower right) defines the patterns
of mean BMI for successive cohorts of the FHS Offspring sample
who were born together and hence age together. Looking across
rows, columns, and the diagonal, we generally see increased values
for BMI. For example, moving down each column, we document
Rosenquist et al. PNAS
January 13, 2015
vol. 112
no. 2
the well-established age profile that generally reflects rising BMI
over the lifecycle. The trajectories observed across waves and
lifecycle documented in Table 1 also justify setting the first age
and period categories as reference groups; and, looking across the
diagonal, there does not appear to be a linear relationship be-
tween BMI and cohort. This suggests that restricting the first age
and period groups to be reference categories is acceptable. Cau-
tion should be exercised in reaching any further conclusions from
this table, however, because it simply provides a general qualita-
tive impression about APC rate patterns and does not decompose
their separate effects. To more rigorously assess these effects we
use the methods described below.
Modeling birth year as a continuous variable, we find evidence
from estimates of the augmented version of Eq. 1of a significant
change in the relationship between FTO genetic variants and BMI
in the early 1940s (Table S1). That is, we use an estimation ap-
proach (Supporting Information) that finds the point at which the
genotypes have the greatest overall difference in their effect on
BMI between subgroups of the population born before and after
this threshold. The change points supported by estimating various
models ranged from 1942 to 1945. We chose 1942 as the change
point in further models that treated the YOB as a discrete variable,
but results were insensitive to alternative values from 1942 to 1945.
As shown in Fig. 1, mean BMI evolves over the lifecycle for
individuals with the same genotype, comparing the pre- and post-
1942 birth cohorts in the full dataset. However, mean BMI differs
across the three genotypes in the later birth cohort compared with
the pre-1942 cohort. The between-birth-cohort differences in
mean BMI are statistically significant (P<0.017) for individuals
with one or two of the risk (A)FTO allele, particularly during
early middle age. This difference (and the lack of difference be-
tween cohorts without the risk allele) suggests that differences
between BMI growth curves from different birth cohorts are
more pronounced among individuals carrying A alleles.
Table 2 presents estimates from our preferred specification of
the ageperiodcohort regression models, allowing for differential
relationships between the genetic effects and BMI on the basis of
sex and APC variables (for details, see Materials and Methods).
Tests of the joint significance of regression parameter estimates
indicate a highly significant cohort-gene interaction [Fstatistic for
joint effects, F(2, 19,617) =17.51, P=2.54 ×10
] controlling for
agegene and periodgene interactions. This suggests that the
effect of FTO varies across cohorts or eras. More specifically, we
find a highly significant interaction between the post-1942 birth
cohort indicator and genotype, with the more efficient random
effects estimator (Supporting Information)showinginteractions
with both AA and AT genotypes compared with the TT genotype.
The results indicate that, among individuals in the cohort born
after 1942, the AA and AT genotypes are associated with an ad-
ditional average gain in BMI of 1.04 units [95% confidence in-
terval (CI) 0.152.03, P=0.023] and 1.14 units (95% CI 0.501.77,
P=0.0005), respectively, relative to individuals with the same
genotype born before 1942 (Table S2). Our results provide evi-
dence that only AA homozygosity is associated with a statistically
significant BMI difference for both cohorts born before and after
1942. Further, our estimates indicate that the AT genotype is
characterized by different rates of increase in BMI between
cohorts; and, for homozygous TT subjects, there was little change
in BMI across cohorts. Several of the periodgenetic variant in-
teractions are individually statistically significant at conventional
levels, but they are jointly insignificant (F=0.59, P=0.69), sug-
gesting that these effects are likely to be artifacts of multiple testing.
In Figs. 24, we demonstrate that the age gradient in BMI
does not significantly differ for individuals with the TT genotype
across birth cohorts (Fig. 4). In contrast, we not only observe
a significantly different FTOBMI relationship across ages for those
with the AT genotype, but the age gradient documented in Fig. 3
becomes steeper in the post-1942 cohort. Last, whereas the estimates
in Table 2 showed that individuals with the AA polymorphism had
significantly higher BMI in both the pre- and post-1942 cohorts, we
did not find a significant difference in the BMI age gradient between
cohorts (Fig. 2), although this may be due to low power resulting
from the smaller sample size. Taken together, the set of Figs. 24
illustrate that there is an age gradient across all genotypes, but it does
not point to an overall steepening of the age gradient. The results
continue to point out differences in the estimated relationships be-
tween those born before and after 1942, and, given our sample size, it
would not be surprising if, with additional data, we would see the
observed difference in the BMI age gradient for the AA genotype
become statistically significant. Last, we note that the statistically
significant differences in BMI between and within birth cohorts on
the basis of genotype do not arise due to the specification of our
linear model and are also observed when simply comparing the
unconditional sample means of BMI across genetic variant, birth
cohorts, and 5-y age intervals (as reported in Table S3).
We conducted several robustness exercises that exploit the fa-
milial structure of the FHS data by estimating a further augmented
ageperiodcohort model that incorporates family-specific un-
observed heterogeneity through random effects, as suggested in ref.
20 (Tables S1,S2,andS4). This allows us to control for family
effects shared by siblings, including childhood diet and other
Table 1. Average BMI by subject age and period measured in the full sample used in our estimation
Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7 Wave 8
Age, years 30 Aug 1971 26 Jan 1975 20 Dec 1983 22 Apr 1987 23 Jan 1991 26 Jan 1995 11 Sep 1998 10 Mar 2005
2729.99 24.36 24.37 24.57 25.12 30.38
3034.99 24.74 24.26 25.08 26.05 26.53 26.80 22.41
3539.99 25.44 25.07 25.19 25.41 26.64 28.13 28.99
4044.99 25.83 25.68 25.86 26.31 26.39 27.80 28.79 29.97
4549.99 26.09 26.05 26.55 26.90 27.13 27.40 27.81 29.19
5054.99 26.27 26.50 26.52 27.48 27.71 27.98 27.72 28.65
5559.99 26.38 26.28 26.82 27.16 27.79 28.55 28.59 28.41
6063 28.13 26.45 26.67 27.15 27.77 28.00 28.73 28.60
Each cell contains the average BMI of individuals measured in the age and period denoted by the row and column and for the sample denoted by the panel.
35-40 40-45 50-55 55-60
Fig. 1. BMI over the ages of 3560 by birth cohort for AA, TT, and AT/TA
genotypes by general birth cohort (born before or during/after 1942).
| Rosenquist et al.
aspects of physical and social environment as well as similarities
of genetic endowment other than the target gene. In addition, in
Table S2, we consider alternative estimators for our preferred
model, and, in Table S4, we explore sex differences in the magni-
tude and statistical significance of the interaction of birth cohort
and genotype with BMI by testing sex differences in sample means
and in coefficients of sex-stratified regression models. Consistent
with previous studies, our longitudinal family fixed-effect model
(Table S1) finds a significant main effect for rs9939609 both for
AA and AT genotypes indicating an average increase of 0.88 (95%
CI 0.261.50, P=0.006) and 0.49 (95% CI 0.0750.93, P=0.017)
units of BMI, respectively, relative to those with the TT genotype.
Our results suggest that the well-documented rise in BMI in the
United States over the past 40 y may have been disproportion-
ately driven by individuals for whom genetic factors interacted
with environmental changes encountered in their development
due to their era of birthin this case, being born later. Although
our approach, by its nature, cannot ever rule out a g-by-p in-
teraction, tests of joint significance of these interactions (F=
0.59, Pvalue =0.69) are fairly suggestive of a minimal g-by-p
contribution, holding all else constant. Furthermore, the lack of
any g-by-p findings over the time period studied, and the fact that
our study focused on adults (who, according to previous research,
have already incorporated differential genetic contributions
to BMI) (1, 2125), all provide strong suggestive evidence of
limited g-by-p influence on our results.
Our results also help to disentangle the impact(s) of FTO ge-
notype, age, and generational environment on BMI. As discussed
above, previous GWAS (genome-wide association studies) and
g-by-e work has generally examined interactions of genotype with
a specific environmental change or attributed all changes in phe-
notype to changes in environment, assuming that genotype effects
did not change in the period studied. However, such analyses do
not make it possible to distinguish effects of contemporaneous and
lifetime environmental shocks as well as maturation effects, a
limitation of single birth cohort and cross sectional studies.
More generally, these findings raise the possibility that genetic
associations may differ across birth cohorts due to variation in
prevailing environmental contexts. If so, a genetic association
detected by a gene-by-environment (g-by-e) study performed to-
day might not be detectable in future generations. Conversely,
effects not seen at this time may appear as environmental changes
occur that affect entire populations. This general point could
certainly extend beyond the particular case of FTO and obe-
sity; and although the odds that a gene discovery effort would be
successful increase with larger sample sizes, the results of such
studies (and even their ability to detect a genotypephenotype
relationship) may be influenced by the within-sample birth co-
hort distribution or the time when such research was undertaken
(26). The fact that allelic penetrance could vary across over time
(e.g., across birth cohorts) may have implications for the in-
terpretation of genetic risk data. This idea, that genetic effects
could vary by geographic or temporal context is somewhat self-
evident, yet has been relatively unexplored and raises the question
of whether some association results and genetic risk estimates may
be less stable than we might hope.
The concept of time-dependent genetic penetrance has been
raised in the past. The so-called thrifty-gene hypothesis suggested
that genetic variants selected for energy conservation have con-
tributed to increased obesity prevalence in modern environments
where food has become more plentiful, although recent empirical
tests of the hypothesis have not supported it (27, 28). This work
raises the question of whether broad environmental changes
might have differential impacts on the BMI of individuals based
on genotype. Many hypothesized environmental influences on the
rise in obesity did indeed occur after the early 1940s, including
technological advances reducing energy expenditure at work as
well as increases in the caloric content of processed foods (4),
whose effect may be experienced most strongly by individuals
whose tastes and habits would be influenced at a young age (1).
Although our work shows a general g-by-c effect, we do not
attempt to identify the particular environmental factor(s) whose
change(s) might be driving these results. Understanding which
specific historical influences alter the penetrance of genetic
variants across cohorts is beyond the scope here, but is an im-
portant avenue of research that is worth additional comment.
Because many of the environmental changes between birth
cohorts hypothesized to be responsible for the rise in obesity are
correlated over both time and geographic space, well-powered
studies will be required. Although other research designs, such as
natural experiments, can in principle help identify the particular
environmental factors that might interact with specific genotypes,
they require that the specific geneenvironment interaction being
investigated not be confounded with other potential gene and
environment interactions (2931). Implementing such an approach
would be challenging: spatial variation in the price of calories may
Table 2. Random effect estimates of factors influencing BMI
from a specification using discrete variables to indicate birth
cohort differences and their interactions with genetic factors
Explanatory variables Random effects estimates
Subject is male 1.641*** (0.146)
Age 3034.99 0.477*** (0.174)
Age 3539.99 0.608*** (0.174)
Age 4044.99 1.011*** (0.188)
Age 4549.99 1.199*** (0.212)
Age 5054.99 1.231*** (0.238)
Age 5559.99 1.272*** (0.269)
Age 6063 1.229*** (0.300)
Subject was born after 1942 1.360*** (0.280)
AA genotype 0.708* (0.398)
AT genotype 0.412 (0.282)
Born after 1942 by AA genotype 1.041** (0.459)
Born after 1942 by AT genotype 1.135*** (0.326)
Constant 24.01*** (0.250)
Observations 19,617
No. of individuals 3,720
Presented are the estimates of the ageperiodcohort model where the co-
hort variable is treated as discrete. Each entry refers to the effect of the variable
listed in the first column on BMI holding all other factors constant. SEs are
presented in parentheses. Specifications also include gene-by-age (g-by-a)
interactions and the estimates of all other factors included in this model as well
as other estimators are presented in Table S2.SeeTable S6 for the calendar
time corresponding to examinations in each wave. Note that our main results
of birth cohort and genotype interactions are not sensitive to the method by
which the model was estimated. The following indicate statistical significance
of each explanatory variable: ***P<0.01, **P<0.05, and *P<0.1.
35-40 40-45 45-50 50-55 55-60
Fig. 2. BMI over the ages of 3560 by birth cohort for the AAFTO genotype.
Rosenquist et al. PNAS
January 13, 2015
vol. 112
no. 2
be correlated with spatial variation in the rate of change in seden-
tary lifestyles or other environmental changes that have been hy-
pothesized to be linked with obesity. In addition, the large number
of potential g-by-e hypotheses creates a large number of testable
hypotheses, thereby reducing the statistical power of the study and
increasing the multiple-testing burden.
To overcome these challenges, we propose that future research
into these effects could estimate ageperiodcohort models with
samples defined on the basis of geographic regions. Regional
environmental changes that track with regional differences in the
timing of breakpoints would be candidate mediators of g-by-c
effects. This approach would be well suited for other large-scale
longitudinal databases that are now beginning to genotype
subjects (32).
There are some notable limitations to our study. First, given
the unique nature of the FHS, it is not yet possible to find an
appropriate replication sample for the time period of birth years
studied and our genetic variant of interest, both of which would
be required to test the specific FTOvariantbirth-cohort in-
teraction results (3337). The special circumstances of the FHS
with localized, longitudinal data over a large birth cohort range,
means that it would be hard to perform a traditional replication
study (16, 17). However, with the advent of more studies that
include genetic data in longitudinal samples, the conceptual
approach we are proposing, if not this particular finding, will
likely be testable in additional settings soon (32).
A second limitation of our study is that all of the observations
in our analyses were of adults; hence, we cannot examine critical
periods of growth and development where many environmental
factors particular to given birth cohorts may have been influential.
Because most evidence suggests that the genetic influences on
BMI heterogeneity are first seen in childhood and may relate to
food intake levels in that developmental period (1, 3841), studies
of younger subjects may elucidate which particular environmental
influences might be interacting with genetic factors. Third, our
observation that the 95% confidence bands for those with the AA
genotype overlap between the two cohorts in Fig. 2 may reflect
limited power to detect an effect and/or the stronger relative im-
pact of birth-cohort-associated-factors on heterozygotes. However,
in addition to sample size differences, nonlinearity in the effects of
the A allele on BMI is also a possibility (42). Fourth, there re-
mains the possibility of sample selection bias arising from subjects
in the older cohort dying before the time when they would have
been genotyped, particularly if those who died were dispropor-
tionately heavier or of a certain genotype, although we saw no
evidence of this in measured attributes.
In sum, we have outlined what we believe to be a useful ap-
plication of ageperiodcohort modeling to improve population
genetic research. Our findings are suggestive of a previously
unidentified factor to consider when assessing time trends in
obesity, as well as the interpretation of genetic association find-
ings more broadly. The phenotypic expression of individual-level
genetic variation and our ability to detect it may depend on
historical contingencies.
Materials and Methods
The FHS was initiated in 1948 when 5,209 people were enrolled in the original
cohort; since then, the study has come to be composed of four separate but
related populations. The Framingham Offspring Study began in 1971, consist-
ing of 5,124 individuals who represented the children of the original cohort
population and their spouses. Participants in the offspring study were given
physical examinations and detailed questionnaires at regular intervals starting
in 1972, with a total of eight waves completed through 2008. BMI was calcu-
lated from measured height and weight. Notably, the offspring cohort was
born over a 40-y period, with participants ranging in age from their teens to
their late 50s at the time of study onset in 1971. In addition to providing survey
and examination data, a large fraction of participants (73.0%, 3,742 individuals)
had their DNA genotyped using the 100KAffymetrix array (43). Genotypes at the
rs9939609 allele were extracted using PLINK (44) from data contained in the
Framingham SHARe database accessed through the dbgap system (www.
For simplicity, we elected to focus attention on the rs9939609 polymorphism
although a large number of variants have been associated with BMI across large-
scale genome-wide studies (and/or been in strong linkage disequilibrium with
other FTO variants) (6). For example, in the large GIANT (Genome-wide In-
vestigation of Anthropomophic Traits) consortium (n=249,794), the less com-
mon A allele rs1558902 (in strong linkage disequilibrium with rs9939609 r
0.901) on the FTO gene was strongly associated with BMI (P=4.8 ×10
a per-allele change associated with an increase in BMI of 0.39 (7).
To minimize the possibility that the g-by-c effects would be capturing dif-
ferences in age ranges of the participants across cohorts, we focus our analyses on
observations between the ages of 27 and 63. That is, by excluding observations
collected during examinations when subjects were at younger and older ages,
we ensure that individuals who areunique to the earliest and latestcohorts (for
who we cannotuse as self-controls) respectively are removed fromthe analyses,
thereby mitigating potential bias from model misspecification (26). These
restrictions ensured that age is balanced between cohorts and brought the
sample size to 19,617 phenotypic observations regarding 3,720 individuals.
Summary statistics for the variables used in the regression analysis reported in
the main text and SI Materials and Methods and Tables S1S5,S7,andS8 are
shown in Table S6. Although only 3,724 of 5,124 individuals in the FHS Offspring
sample were genotyped and not every subject attended each medical exami-
nation, χ
tests of differences in proportions indicate that neither specific
genotypes nor birth cohort were associated with missing data from our sample,
(2) =2.91 and P[X >Χ
(2) =0.23], reducing concerns about nonresponse.
We also compare the distribution of genetic variants for those born before
and after the identified structural breakpoint (of 1942) in the relationship
with BMI. Specifically, at the base of Table S5, we present evidence that
the differences in genetic variant association with BMI across cohorts
were not due to differences in sample characteristics before and after
1942 (26) (P=0.1550).
In motivating our specification of a modified ageperiodcohort model, we
initially hypothesized that the significance of the association between the FTO
genetic variant and BMI may be significantly stronger for individuals born in
later years due to environmental changes in the United States following World
War II that influenced food availability, the overall levels of physical activity, and
other factors that could affect bodily metabolism, all previously noted in
a number of studies as potential modifiers of FTO expression (42, 45, 46).
Table S3 presents some descriptive evidence supporting a g-by-c effect. Each
entry corresponds to 5-y age-intervals of a persons life and presents the
sample means of BMI across genetic variants and birth cohorts. Thus, partic-
ipants born in 1940 would have belonged to the 3034.99 age group in 1974
35-40 40-45 50-55 55-60
Fig. 3. BMI over the ages of 3560 by birth cohort for AT/TAFTO genotype.
35-40 40-45 50-55 55-60
Fig. 4. BMI over the ages of 3560 by birth cohort for the TTFTO genotype.
| Rosenquist et al.
and the 4044.99 age group in 1984. Within these g-by-a bins, we conducted
simple hypothesis tests to assess whether there were differences in BMI be-
tween the pre- and post-World War II cohorts. Table S3 presents evidence
that, unconditionally, there are statistically significant differences in BMI
between and within birth cohorts on the basis of genotype, particularly for
those with the risk allele.
Although tests of differences in means can be used to look at broad trends
over time, the participants age or commonly shared environmental changes
(such as the invention of television or a price shock in food) might also
trigger interactions if their impacts are modified by specific genetic variants.
The full specification of our modified ageperiodcohort models, and
methods used to identify the separate effects where the cohort variable is
treated as either linear or continuous, is detailed in Supporting Information.
Our modified version of Eq. 1includes a full set of interactions with genetic
variants where the TT genotype is the reference category; this full set
of interactions is not considered in earlier, distinct age, period, or cohort
analyses of the evolution of obesity prevalence, although we have made
similar assumptions as those in prior studies (47). To reduce additional
concerns that we were restricting the relationship between the explanatory
variables (including age and period) and BMI to be linear, we converted all
of our data, including age, period of examination, era of birth, and genetic
variants, to indicator variables, coding responses as 1if the characteristic
of the individual observation fell in that category, and 0otherwise. By
generating the indicator variables in this way, we are reducing functional
form assumptions. We also used YOB as a continuous cohort variable with
a single linear term in some specifications.
Finally, allCIs and significance tests reported here accounted for correlations
over time due to repeated observations of the same individualor family group,
using a standard clustered robust variance estimator (48), and the errors are
assumed to be independently distributed across clusters and correlated within
clusters. Throughout, we did not impose any distributional assumptions
on μ
and we note that whereas the weighted least-squares estimates of
the random effects estimator were virtually identical to a maximum likelihood
estimator that imposes more structure on the data, both the ordinary least
squares and family fixed-effect estimates are identical to maximum likelihood
estimates where μ
is assumed to be normally distributed.
ACKNOWLEDGMENTS. We thank David Cutler, Eliana Hechter, Heidi Williams,
and two anonymous reviewers for helpful comments. We also thank Peter
Treut and Emily Hau for assistance with data visualizations. This work was
supported by Grant P01-AG031093 from the National Institute on Aging and
the Social Sciences and Humanities Research Council (to S.F.L.). Funding for
SHARe Affymetrix genotyping was provided by National Heart, Lung, and
Blood Institute (NHLBI) Contract N02-HL-64278. The Framingham Heart Study
is conducted and supported by the NHLBI in collaboration with Boston Uni-
versity (Contract N01-HC-25195). Data were downloaded from NIH dbGap,
Project 780, with accession phs000153.SocialNetwork.v6.p5.c1.GRU and gen-
eral research use phs000153.SocialNetwork.v6.p5.c2.NPU.
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January 13, 2015
vol. 112
no. 2
Supporting Information
Rosenquist et al. 10.1073/pnas.1411893111
SI Materials and Methods
Our main results are obtained by estimating ageperiodcohort
models, one of the key models used by epidemiologists and social
scientists in the quantitative analysis of social change. A large
literature going back to the 1970s has examined the problem of
identification in these models (13) because it is well known that
age (years since birth), period (current year), and cohort (YOB)
are collinear with each other because age =period cohort.
Intuitively, it would be impossible to observe two individuals at
the same point in time that have the same age but were born at
different dates. In our analysis, we treated the cohort variable as
both continuous and discrete, and we discuss how we achieve
identification in both of these specifications of the model below.
Models Treating Birth Year as Continuous. We begin by estimating
multivariate regression models using the estimator proposed in
ref. 4 that extends the threshold regression to a static panel data
structure. The threshold regression determines if there is a
unique breakpoint at which there is a permanent structural
change in the relationship between the specific genotypes of the
FTO gene (rs9939609) and BMI. That is, these models can be
used to determine the set of threshold YOBs at which there are
important changes in the relationships between BMI and FTO
genotypes. This threshold is chosen based on the minimization of
the concentrated sum of squared errors, and we impose the
constraint that there must be at least 5% of observations lying on
both sides of the breakpoint. Ignoring this constraint did not
change our main results identifying the main breakpoint at 1942,
but it offers substantial computational advantages by reducing
the search over all possible breakpoints. Intuitively, the threshold
regression model with a single breakpoint can be viewed as se-
lecting the regression that provided the best fit to the data from
the set of all regressions which only differ by the selection of
birth year as the breakpoint. That is, we define YOB_Threshold
as the birth year that is selected as the breakpoint and estimate
the following equation:
BMIift =β0+β1ageift +β2wavet+β3YOBi+β4genei
+β5Xift +β6ðgenei×sexiÞ+β7genei×ageift
is the BMI of person iin family fat time t;
YOB is the year-of-birth indicator variable if the individual
was born during or after the year in which a structural break is
determined, henceforth referred to as the threshold year;
1{YOB YOB_Threshold} is an indicator for whether the
individual was born following the threshold year.
wave is a series of indicators for when the measurement oc-
curred (eight waves);
age is a series of indicators for an individuals age in 5-y
gene can represent a vector of discrete indicators for poly-
morphisms of the gene being investigated (although in this
case we are looking at only the FTO rs9939609 SNP);
X is a vector of exogenous attributes including sex; and
is random error term with a mean of zero.
This model is run repeatedly because each time the threshold
YOB changes, so does 1{YOB YOB_Threshold}. Because the
birth year in the FHS data contains day and month, we use this
information for a subset of observations and do not treat birth
year as integer valued for all observations in the FHS. This strategy
of running a separate regression for each potential breakpoint
would have been computationally challenging. The estimator de-
veloped in ref. 4 uses grid search techniques to choose the
threshold year at which the relationship between the FTO ge-
notype and BMI is significantly modified for individuals born
before and after 1942. The threshold year is chosen as the value
that minimizes the sum of squared errors. Once the threshold
year is identified, OLS is run on Eq. S1 to obtain the estimates of
βs. Note, that although conventional SEs on the coefficients in
Eq. S1, which treat YOB_Threshold as the true value of the
threshold, are asymptotically valid, one needs to be careful in
testing the statistical significance of whether there is a non-
linearity in the estimated relationship between cohorts. Standard
tests using the Wald statistic have poor finite sample behavior
since the asymptotic sampling distribution depends on an un-
known parameter (YOB_Threshold) that is not identified under
the null hypotheses. We thus adopt the bootstrap Ftest proposed
in ref. 4 when testing if there is a significant threshold effect.
Although this estimator has the advantage of accurately
identifying the point at which there are significant changes in the
impact of the genotypes based on YOB, it imposes restrictions on
how the YOB affects BMI. Although we could add higher-order
terms to increase the flexibility, these terms make it more difficult
for the test statistics to exhibit dramatic changes as such tests will
have no power in many settings. Using different sets of control
variables in these models, we consistently identified breakpoints
between the years of 1942 and 1945 with decidedly nonlinear
changes in the magnitude of the parameter estimates after that
time. Estimates of the preferred specification from the breakpoint
model are depicted in Fig. 1, where we consider only a single
break at 1942, although various models after that time period
yield consistent results.
To identify age, period, and cohort (APC) effects in Eq. S1,we
exploit the fact that we used categorical variable age, irregular
period (year of observation) dummies, and mixed continuous
categorical cohort (year born +birth era) in these linear and ad-
ditive APC models. This empirical strategy has been used widely in
the social sciences (5). An alternative approach to identifying the
separate effects of APC variables would be to consider nonlinear
relationships of a subset of these effects in the specification of the
model. To examine the robustness of our results, we followed this
strategy and first used small-order polynomials in the YOB to
identify and estimate cohort effects. Second, we conducted ro-
bustness exercises that estimated specifications allowing for poly-
nomials in period effects. Our main results were robust to these
alternative nonlinear treatments of cohort and period effects.
Models Treating Birth Cohort as Discrete. Our preferred method of
analysis does not include a continuous birth-year variable for the
reasons described above. Instead, we use the 1942 cutoff identified
as a breakpoint in our continuous model as a way to compare pre-
and postbirth cohorts. By treating the APC variables as dummy
variables, identification can be easily achieved by dropping a small
number of these variables. Our preferred strategy was to restrict
the indicator for individuals under the age of 30 and the indicator
for the first medical visit to be equal to zero. Intuitively, we hy-
pothesized that BMI was increasing both over time and as indi-
viduals age. Thus, we anticipate that these restrictions would impose
the weakest assumption on the model because the reference groups
Rosenquist et al. 1of10
include the youngest individuals and the earliest time period. Be-
cause the selection of which age and period indicators to drop is ad
hoc and because prior research (6) demonstrated that the results
obtained from APC models can be quite sensitive to which pa-
rameter restrictions are made, we investigated the sensitivity of our
results to dropping nine different age or period indicators. In each
of these nine cases, our main results showed a significant inter-
action between FTO genotype and cohort.
By using indicator variables, we are relaxing the assumptions
made on the form and pattern of the relationship between BMI
and the explanatory variables, relative to the analysis where birth
cohort was modeled as a continuous variable. Estimates of the
preferred specification of this model, using discrete birth cohort
variables with the earliest age and time period effect restricted to
be zero, are presented in Table 1.
In Table S3, we list sample means for BMI within subsamples
defined by their rs9939609 genotype and age at examination, with
age measured in 5-y intervals. In the bottom two rows of the table
for each genotype, we present results from ttests of differences in
means across cohorts. These results show that, without control-
ling for other factors, there are numerous significant differences
in BMI between those born before and after 1942. Although there
is no significant difference in BMI between those born pre-/post-
1942 for any age cell for the rs9939609 TT polymorphism, nearly
every age cell for the AT polymorphism indicates that BMI is
significantly greater for those born after 1942. Similarly, among
the sample for those born post-1942 and either aged 3540 or 45
50, we observe significantly higher BMI among the later cohort.
To more formally examine the importance of birth cohort
interactions with genotype, we initially estimated models that
allowed for other sources of heterogeneity, shown in Table S3.
Specifically we decomposed the error term (μ
) from Eq. S1 into
two components and estimate
BMIift =β0+β1ageift +β2wavet+β3post42i+β4genei
+β5Xift +β6ðgenei×sexiÞ+β7genei×ageift
post42 is an indicator variable if the individual was born dur-
ing or after 1942;
is a term that controls for family-specific unobserved het-
erogeneity; and
is random error term with a mean of zero.
This model allows for contemporaneous impacts as measured
by period of interview, cohort effects, and age effects as well as
their interactions with genetic factors. Again, note that family
fixed-effect models implicitly include shared genotype as part of
shared familial environment. To identify all of these factors, in the
main test we imposed restrictions and removed indicators for the
first wave, first age interval (2730), and the TT polymorphism
(and their interactions) to ensure there was no multicollinearity.
To evaluate the individual importance of including genetic
interactions with sex and APC indicators, we considered specifi-
cation tests that compared estimates of the unrestricted model in
Eq. S2 to a series of nested models in which only one of these sets
of interactions was restricted to be zero. These Ftests test the joint
significance of the set of indicators and help us to identify the
regression model that best fits the population from which the data
were sampled. Tests of joint significance individually reject both
the period interactions (β
=0, F=0.5891, P>F=0.6912) and
the sex interactions (β
=0, F=1.12, P>F=0.3494) but not the
cohort interactions at significance levels below 0.01 (β
0, F=
17.51, P>F=2.1 ×10
). Thus, our preferred specification
excludes these two sets of interactions and we focus on the fol-
lowing model:
BMIift =α0+α1ageift +α2wavet+α3post42i+α4genei+α5Xift
Note we use different notation for both the coefficients and error
term in Eqs. S2 and S3 because they may differ due to the omis-
sion of the genetic interactions with both sex and wave. We esti-
mate Eq. S3 using three different estimators that each impose
a different assumption regarding v
. OLS estimates are obtained
by assuming v
=0. The family fixed-effects estimator assumes
that v
is sibling-invariant family-specific unobserved heterogene-
ity that may be correlated with the explanatory variables. A ran-
dom-effects estimator assumes that v
is sibling-invariant family-
specific unobserved heterogeneity that is uncorrelated with the
explanatory variables. Because these fixed-effect and random-
effect models account for family-specific unobserved heterogene-
ity, more reliable estimates are likely obtained because they adjust
for the effects of shared unobserved influences on BMI between
biological siblings. The random-effect model yields more precise
estimates when part of the effect of genetic factors operates at the
level of the family (e.g., there is an independent effect of the
extent to which a genotype is present within a family and the mean
BMI in the family). However, the family fixed-effects model
blocks both genetic factors and parental characteristics/behaviors
that are common to family members (e.g., siblings), including un-
measured factors; therefore, from the perspective of confounding,
the fixed-effect specification is preferred.
As first noted in ref. 7, estimates of the impacts of genetic
factors on outcomes that ignore family fixed effects may also
capture dynastic effects because both genetic markers and many
phenotypes are transmitted from one generation to the next.
OLS and random-effect estimates of Eq. S3 may not isolate the
unique contribution of ones genotype from those arising from
intergenerational transmission of genetic and behavioral char-
acteristics. That is, the random-effects model (as with the tradi-
tional linear regression estimator) assumes that the family-
specific term is uncorrelated with the explanatory variables but
makes use of the structure of the error term (μ
) to provide more
reliable and precise estimates. On the other hand, using a family
fixed-effects estimator that controls for these unobserved family-
specific effects assuming their effects are constant between sib-
lings, allows for correlations with explanatory variables thereby
removing a potential source of bias in the resulting estimates,
and can (more importantly) isolate the specific contribution of
ones genotype.
More generally, we suggest that presenting estimation results
that are made with different estimators that each impose different
assumptions on how v
relates to the discrete cohort variables
serves as an additional robustness check on the main findings.
The results for these three estimators are presented in Table S2.
Notice that, irrespective of the estimation method, the inter-
action term of birth cohort and genotype is significant for AT
and AA in the random-effects specification. Because in many
age groups BMI was higher for those born before 1942 than after
1942 for those with the TT polymorphism, the negative sign on
post42 was expected. Finally, the last two columns of Table S2
indicate the robustness of the main results to different methods
of accounting for family unobserved heterogeneity, increasing
our confidence in the main findings. Repeated models run on
males and females separately further support our findings, as the
interactions between genetic polymorphism and being born after
1942 are positive for both sexes and statistically significant,
particularly in the random-effects specifications for which the
most efficient estimates are obtained.
Rosenquist et al. 2of10
A final point related to the identification of APC models is that
many of the explanatory variables will be highly correlated. For
example, in later waves, older individuals will be by definition
born in the later cohort. The correlation between the explanatory
variables will not bias our estimates but will lead to larger SEs,
assuming the model is specified correctly. As such, it is not a
surprise that many of the estimated coefficients in our models
have wide CIs. Intuitively, large SEs imply that the effects of
different variables are highly uncertain, and, when independent
variables are highly correlated, high uncertainty is what should be
reported. The only solution to reduce the width of CIs would be to
collect more data to gain more independent variation to identify
the separate effects. Chapter 23 of ref. 8 provides a more detailed
discussion of how highly correlated explanatory variables will
lead to unbiased estimates but may influence the interpretation
of results from linear regression models.
Lastly, in Tables S7 and S8, respectively, we considered esti-
mating models that either ignore both age and cohort effects (as
well as their interactions) and models that only ignores cohort
effects. Table S7 can be viewed as a model that allows for main
genetic effects and contemporaneous g-by-p relationships. Not
surprisingly, we find that interaction effects in later waves are
larger in magnitude. This is in part capturing the effect of having
a larger percentage of older individuals in later time periods and
having more people born in the second cohort being interviewed
in later time periods. In other words, the g-by-p variable is likely
positively correlated with g-by-a and g-by-c variables that cor-
respond to both older individuals and those born in later cohorts.
Thus, by omitting both age and cohort effects when estimating
a variant of Eq. S3, the estimate of the g-by-p effect is biased
upwards because it is also capturing part of the effects of these
omitted variables that, as described, are correlated with the g-by-p
variable. Table S8 shows that many of these biased estimates
become smaller once we also allow for age effects. That is, by
including age indicators, the coefficients on the g-by-p effect on
average become smaller in magnitude, though they continue to
exceed the estimates presented in Table 1. The decline in the
magnitude of many of the g-by-p effects reinforces the bias from
simply omitting relevant information on how genetic factors in-
fluence human development over the lifecycle.
However, the estimates in Tables S7 and S8 also omit relevant
information on how genetic effects differ across eras in which an
individual grows up and, thus, it is not surprising that they differ
markedly from those presented in both Table 1 and Tables S1, S2,
and S4. In particular, omitting this relevant information allows
one to erroneously conclude that several of the g-by-p and g-by-a
interactions have a statistically significant impact. Many of these
effects become statistically insignificant once we allow for g-by-c
effects. Because the specifications presented in Tables S7 and S8
are restricted versions of our more general APC model presented
in Eq. S2, we conducted a series of model specification tests to
examine the validity of these restrictions. Irrespective of the
estimator used, the test results reject these restrictions rein-
forcing that researchers working with the FHS data should both
allow for both main cohort effects and g-by-c interactions. This
finding has implications for the interpretation of estimates from
many g-by-e studies which only use interactions between gene
and contemporaneous periodswhich, primarily due to data
limitations, have collected data on individuals for shorter durations
and fewer cohorts. This also reinforces the utility of genotyping
large-scale longitudinal databases thereby allowing researchers to
examine whether specific g-by-e effects are sensitive to APC effects.
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sues in cohort analysis of archival data. Am Sociol Rev 38(2):242258.
4. Hansen BE (1999) Threshold effects in non-dynamic panels: Estimation, testing, and
inference. J Econom 93(2):345368.
5. Card D, Lemieux T (2001) Can falling supply explain the rising return to college for
younger men? A cohort-based analysis. Q J Econ 116(2):705746.
6. Glenn N-D (1976) Cohort analystsfutile quest: Statistical attempts to separate age,
period and cohort effects. Am Sociol Rev 41(5):900904.
7. Ding W, Lehrer S-F, Rosenquist J-N, Audrain-McGovern J (2009) The impact of poor
health on academic performance: New evidence using genetic markers. J Health Econ
8. Goldberger AS (1991) A Course in Econometrics (Harvard Univ Press, Cambridge, MA).
Rosenquist et al. 3of10
Table S1. Model estimates of factors influencing BMI, where birth year is treated as continuous variables
Models that exclude genetic interactions with
both age and birth cohort variables
Models that include genetic interactions with
both age and birth cohort variables
Linear regression
with controls
for family fixed
Linear regression
with controls
for family fixed
Subject is male 1.805*** (0.152) 1.635*** (0.146) 1.855*** (0.169) 1.804*** (0.152) 1.633*** (0.146) 1.855*** (0.169)
Age 3034.99 0.195 (0.204) 0.413*** (0.106) 0.0802 (0.165) 0.525* (0.315) 0.437** (0.175) 0.191 (0.263)
Age 3539.99 0.504* (0.266) 0.648*** (0.126) 0.329* (0.193) 0.497 (0.310) 0.515*** (0.181) 0.247 (0.257)
Age 4044.99 0.838** (0.375) 1.014*** (0.160) 0.568** (0.266) 0.968** (0.407) 0.860*** (0.204) 0.445 (0.316)
Age 4549.99 1.023** (0.500) 1.175*** (0.201) 0.564 (0.345) 1.091** (0.525) 0.992*** (0.237) 0.433 (0.395)
Age 5054.99 1.113* (0.620) 1.193*** (0.243) 0.522 (0.425) 1.105* (0.640) 0.968*** (0.274) 0.26 (0.468)
Age 5559.99 1.122 (0.743) 1.138*** (0.289) 0.349 (0.505) 1.104 (0.756) 0.954*** (0.316) 0.162 (0.541)
Age 6063 0.902 (0.858) 0.998*** (0.330) 0.142 (0.589) 0.876 (0.871) 0.859** (0.355) 0.00354 (0.623)
Birth year 0.039 (0.0265) 0.0439*** (0.0121) 0.0733*** (0.0219) 0.0835*** (0.0288) 0.0793*** (0.0161) 0.107*** (0.0266)
Wave 2 0.132 (0.205) 0.259*** (0.0893) 0.367** (0.145) 0.132 (0.205) 0.259*** (0.0893) 0.369** (0.145)
Wave 3 0.608* (0.315) 0.752*** (0.126) 0.974*** (0.221) 0.609* (0.315) 0.753*** (0.126) 0.979*** (0.220)
Wave 4 1.250*** (0.400) 1.338*** (0.156) 1.652*** (0.278) 1.250*** (0.400) 1.339*** (0.156) 1.657*** (0.277)
Wave 5 1.854*** (0.494) 2.007*** (0.190) 2.391*** (0.346) 1.855*** (0.493) 2.008*** (0.190) 2.396*** (0.345)
Wave 6 2.521*** (0.598) 2.667*** (0.228) 3.143*** (0.414) 2.520*** (0.597) 2.667*** (0.228) 3.149*** (0.413)
Wave 7 2.836*** (0.668) 3.054*** (0.256) 3.558*** (0.463) 2.834*** (0.667) 3.053*** (0.256) 3.563*** (0.462)
Wave 8 3.307*** (0.847) 3.713*** (0.318) 4.321*** (0.583) 3.313*** (0.846) 3.718*** (0.318) 4.337*** (0.582)
AA genotype 1.060*** (0.247) 1.035*** (0.226) 0.881*** (0.318) 0.767 (1.267) 0.953 (0.958) 0.312 (1.407)
AT genotype 0.421** (0.167) 0.379** (0.161) 0.490** (0.217) 2.546*** (0.841) 2.021*** (0.699) 2.027** (0.943)
Born after 1942
by AA Genotype
0.0632** (0.0286) 0.0538** (0.023) 0.0432 (0.0329)
Born after 1942
by AT Genotype
0.0699*** (0.0187) 0.0537*** (0.0168) 0.0519** (0.0221)
3034.99 by AA 1.293** (0.644) 0.492 (0.311) 1.142** (0.485)
3539.99 by AA 0.775 (0.513) 0.230 (0.290) 0.746* (0.389)
4044.99 by AA 0.991* (0.524) 0.136 (0.279) 0.680* (0.404)
4549.99 by AA 0.876 (0.544) 0.167 (0.280) 0.682 (0.429)
5054.99 by AA 0.672 (0.549) 0.0483 (0.279) 0.368 (0.43)
5555.99 by AA 0.6 (0.571) 0.0159 (0.283) 0.431 (0.457)
6063 by AA 0.378 (0.564) 0.0663 (0.294) 0.251 (0.454)
3034.99 by AT 0.271 (0.393) 0.0883 (0.220) 0.11 (0.338)
3539.99 by AT 0.227 (0.309) 0.319 (0.204) 0.365 (0.278)
4044.99 by AT 0.0216 (0.319) 0.334* (0.199) 0.43 (0.296)
4549.99 by AT 0.114 (0.337) 0.400** (0.199) 0.443 (0.306)
5054.99 by AT 0.218 (0.336) 0.422** (0.198) 0.612* (0.313)
5559.99 by AT 0.202 (0.355) 0.356* (0.202) 0.473 (0.325)
6063 by AT 0.163 (0.344) 0.245 (0.208) 0.326 (0.328)
Constant 24.98*** (1.227) 25.07*** (0.551) 26.42*** (0.987) 26.74*** (1.312) 26.59*** (0.696) 27.89*** (1.172)
Observations 19,617 19,617 19,617 19,617 19,617 19,617
No. of family
fixed effects
Not applicable Not applicable 1,414 Not applicable Not applicable 1,414
0.095 0.098 0.479 0.098 0.103 0.48
Presented are estimates of the ageperiodcohort model where the cohort variable is treated as continuous. Each entry refers to the effect of the variable
listed in the first column on BMI holding all other factors constant. Robust SEs are presented in parentheses. The columns in this table differ based on what
factors are accounted for and the method used to estimate the statistical model. See Table S6 for the calendar time corresponding to examinations in each
wave. Note that our main results of birth cohort and genotype interactions are not sensitive to the method by which the model was estimated. Estimates from
the fifth column were used to generate Fig. 1. The following indicate the statistical significance of an explanatory variable on BMI: *** P<0.01, **P<0.05, and
Rosenquist et al. 4of10
Table S2. Model estimates of factors influencing BMI, where birth year is treated as a discrete variable for pre-/post-1942 as birth year
Estimator explanatory variables Linear regression Random effects
Linear regression with controls for
family fixed effects
Subject is male 1.812*** (0.152) 1.641*** (0.146) 1.835*** (0.154)
Age 3034.99 0.532* (0.306) 0.477*** (0.174) 0.542** (0.254)
Age 3539.99 0.608** (0.255) 0.608*** (0.174) 0.710*** (0.243)
Age 4044.99 1.245*** (0.291) 1.011*** (0.188) 1.285*** (0.298)
Age 4549.99 1.498*** (0.340) 1.199*** (0.212) 1.498*** (0.362)
Age 5054.99 1.640*** (0.383) 1.231*** (0.238) 1.540*** (0.425)
Age 5559.99 1.765*** (0.453) 1.272*** (0.269) 1.688*** (0.497)
Age 6063 1.658*** (0.503) 1.229*** (0.300) 1.635*** (0.567)
Subject was born after 1942 1.086*** (0.326) 1.360*** (0.280) 1.020*** (0.353)
Wave 2 0.0447 (0.113) 0.173** (0.0774) 0.0239 (0.127)
Wave 3 0.321* (0.166) 0.617*** (0.105) 0.389** (0.192)
Wave 4 0.874*** (0.206) 1.163*** (0.128) 0.888*** (0.241)
Wave 5 1.385*** (0.252) 1.791*** (0.155) 1.434*** (0.299)
Wave 6 1.958*** (0.310) 2.406*** (0.185) 1.984*** (0.365)
Wave 7 2.216*** (0.356) 2.760*** (0.207) 2.223*** (0.416)
Wave 8 2.576*** (0.453) 3.356*** (0.258) 2.703*** (0.526)
AA genotype 1.385** (0.599) 0.708* (0.398) 1.622*** (0.570)
AT genotype 0.359 (0.389) 0.412 (0.282) 0.412 (0.383)
Born after 1942 by AA genotype 0.956* (0.509) 1.041** (0.459) 0.689 (0.563)
Born after 1942 by AT genotype 1.255*** (0.348) 1.135*** (0.326) 1.129*** (0.386)
Age 3034.99 by AA 1.236* (0.637) 0.488 (0.311) 1.596*** (0.527)
Age 3539.99 by AA 0.723 (0.507) 0.227 (0.290) 1.241*** (0.404)
Age 4044.99 by AA 0.999* (0.517) 0.135 (0.279) 1.246*** (0.442)
Age 4549.99 by AA 0.921* (0.539) 0.168 (0.280) 1.141** (0.460)
Age 5054.99 by AA 0.751 (0.543) 0.0459 (0.279) 0.968** (0.470)
Age 5559.99 by AA 0.708 (0.582) 0.0173 (0.283) 0.911* (0.493)
Age 6063 by AA 0.539 (0.568) 0.0613 (0.294) 0.710 (0.497)
Age 3034.99 by AT 0.171 (0.392) 0.0928 (0.220) 0.199 (0.332)
Age 3539.99 by AT 0.343 (0.308) 0.324 (0.204) 0.279 (0.273)
Age 4044.99 by AT 0.0780 (0.321) 0.338* (0.199) 0.183 (0.293)
Age 4549.99 by AT 0.155 (0.340) 0.402** (0.199) 0.237 (0.306)
Age 5054.99 by AT 0.240 (0.338) 0.423** (0.198) 0.455 (0.310)
Age 5559.99 by AT 0.208 (0.367) 0.357* (0.202) 0.418 (0.326)
Age 6063 by AT 0.147 (0.360) 0.244 (0.208) 0.272 (0.333)
Constant 23.80*** (0.348) 24.01*** (0.250) 23.75*** (0.335)
Observations 19,617 19,617 19,617
0.099 0.106 0.397
No. of Individuals 3,720 3,720 3,720
No. of family fixed effects 1,414 1,414 1,414
Presented are estimates of the ageperiodcohort model where the cohort variable is treated as discrete as indicated in Eq. S3. Each entry refers to the effect
of the variable listed in the first column on BMI holding all other factors constant. Robust SEs are presented in parentheses. The columns in this table differ
based on what factors are accounted for and the method used to estimate the statistical model. See Table S6 for the calendar time corresponding to
examinations in each wave. Note that our main results of birth cohort and genotype interactions are not sensitive to the method by which the model was
estimate. The following indicate the statistical significance of each explanatory variable: ***P<0.01, **P<0.05, and *P<0.1.
Rosenquist et al. 5of10
Table S3. Descriptive statistics of BMI by genotype based on age at examination and between birth cohorts (1942 change point)
AA genotype
Age group 3034.99 3539.99 4044.99 4549.99 5054.99 5559.99
Pre-1942 24.679 (0.569) 25.605 (0.370) 26.234 (0.405) 26.526 (0.271) 27.747 (0.253) 28.194 (0.249)
95% CI 23.52625.832 24.86926.341 25.43327.035 25.99127.060 27.24828.246 27.70428.685
Post-1942 25.51027 (0.412) 26.368 (0.391) 27.047 (0.332) 28.155 (0.359) 28.480 (0.385) 29.089 (0.455)
95% CI 24.69726.323 25.59727.141 26.39527.700 27.44828.861 27.72129.239 28.19229.986
Observations by birth cohort
38 (pre) 82 (pre) 131 (pre) 222 (pre) 292 (pre) 345 (pre)
154 (post) 201 (post) 279 (post) 267 (post) 227 (post) 189 (post)
ttest of difference in means
between cohorts
0.948 1.162 1.457 3.504 1.649 1.879
Pvalue of two-sided ttest above
0.172 0.123 0.073 0.0002 0.050 0.030
AT genotype
Age group 3034.99 3539.99 4044.99 4549.99 5054.99 5559.99
Pre-1942 25.134 (0.372) 25.413 (0.233) 25.803 (0.178) 26.269 (0.159) 26.692 (0.135) 27.116 (0.135)
95% CI 24.39625.872 24.95425.871 25.45326.153 25.95726.581 26.42726.957 26.85127.382
Post-1942 24.903 (0.189) 25.822 (0.177) 26.573 (0.180) 27.465 (0.182) 28.283 (0.190) 28.899 (0.244)
95% CI 24.53125.275 25.47426.170 26.22026.925 27.10927.822 27.91028.655 28.42029.377
Observations by birth cohort
115 (pre) 290 (pre) 477 (pre) 748 (pre) 1,003 (Pre) 1,116 (pre)
587 (post) 775 (post) 926 (post) 901 (post) 841 (post) 580 (post)
ttest of difference in means
between cohorts
0.504 1.268 2.739 4.855 6.981 6.933
Pvalue of two-sided ttest
above P(T<t)
0.693 0.103 0.003 P<0.001 P<0.001 P<0.001
TT genotype
Age group 3034.99 3539.99 4044.99 4549.99 5054.99 5559.99
Pre-1942 24.824 (0.486) 25.392 (0.285) 25.807 (0.245) 26.277 (0.197) 26.878 (0.178) 27.346 (0.170)
95% CI 23.85725.791 24.82925.954 25.32526.288 25.89026.663 26.52827.228 27.01227.680
Post-1942 24.314 (0.233) 24.568 (0.181) 25.687 (0.194) 26.470 (0.191) 27.082 (0.211) 27.611 (0.257)
95% CI 23.85524.773 24.21124.924 25.30526.069 26.09626.845 26.66727.497 27.10628.117
Observations by birth cohort
86 (pre) 212 (pre) 373 (pre) 565 (pre) 731 (pre) 876 (pre)
377 (post) 485 (post) 591 (post) 579 (post) 552 (post) 370 (post)
ttest of difference in means
between cohorts
0.943 2.475 0.383 0.708 0.741 0.855
Pvalue of two-sided ttest
above P(T<t)
0.827 0.993 0.649 0.240 0.230 0.761
The means and SDs are shown in parentheses of BMI for individuals with a specific FTO allele type and age range at time of examination. ttests test that
there are no differences in average BMI conditional on age and FTO allele type across the birth cohorts with the 1942 breakpoint are calculated. ***P<0.01,
**P<0.05, and *P<0.1. Observation numbers are pre-1942 cohort +post-1942 cohort. The table clearly indicates that there are statistically significant
differences for those with the AA and AT genotypes by birth cohort but there are no age ranges for those with the TT genotype where a statistically significant
difference in BMI exists between cohorts.
Rosenquist et al. 6of10
Table S4. Model estimates by sex of factors influencing BMI, where birth year is treated as a discrete variable for pre-/post-1942 as
birth year
Females Males
Linear regression
with controls for
family fixed effects
Linear regression
with controls for
family fixed effects
Age 3539.99 0.125 (0.290) 0.0874 (0.171) 0.205 (0.298) 0.364 (0.222) 0.438*** (0.150) 0.438* (0.254)
Age 4044.99 0.872*** (0.336) 0.676*** (0.191) 0.903*** (0.294) 0.853*** (0.268) 0.617*** (0.166) 0.545** (0.252)
Age 4549.99 1.117** (0.446) 0.752*** (0.232) 0.973*** (0.315) 1.097*** (0.317) 0.886*** (0.195) 0.714*** (0.266)
Age 5054.99 1.179** (0.519) 0.780*** (0.279) 1.065*** (0.347) 1.335*** (0.396) 0.895*** (0.230) 0.693** (0.287)
Age 5559.99 1.387** (0.630) 0.920*** (0.330) 1.264*** (0.387) 1.349*** (0.470) 0.783*** (0.272) 0.539* (0.320)
Age 6063 1.193 (0.738) 0.793** (0.382) 1.202*** (0.437) 1.373** (0.548) 0.856*** (0.314) 0.566 (0.358)
Subject was
born after
1.764*** (0.485) 2.067*** (0.427) 1.89*** (0.227) 0.313 (0.421) 0.612* (0.342) 0.229 (0.204)
Wave 2 0.148 (0.179) 0.427*** (0.116) 0.217 (0.163) 0.223 (0.136) 0.0561 (0.0952) 0.00904 (0.132)
Wave 3 0.525** (0.263) 0.926*** (0.157) 0.658*** (0.192) 0.126 (0.198) 0.346*** (0.128) 0.466*** (0.153)
Wave 4 1.147*** (0.329) 1.568*** (0.192) 1.264*** (0.217) 0.592** (0.246) 0.798*** (0.156) 0.980*** (0.172)
Wave 5 1.676*** (0.400) 2.266*** (0.232) 1.869*** (0.249) 1.079*** (0.304) 1.359*** (0.189) 1.577*** (0.198)
Wave 6 2.355*** (0.493) 3.013*** (0.277) 2.591*** (0.289) 1.532*** (0.371) 1.845*** (0.225) 2.140*** (0.230)
Wave 7 2.648*** (0.568) 3.421*** (0.310) 2.913*** (0.320) 1.746*** (0.423) 2.146*** (0.252) 2.478*** (0.255)
Wave8 3.240*** (0.723) 4.164*** (0.384) 3.687*** (0.399) 1.805*** (0.529) 2.574*** (0.315) 2.931*** (0.322)
AA genotype 0.908 (0.838) 0.270 (0.584) 0.982 (0.677) 1.265* (0.744) 0.618 (0.468) 0.982* (0.593)
AT genotype 1.389*** (0.532) 1.501*** (0.397) 0.832* (0.437) 0.0426 (0.432) 0.0210 (0.310) 0.148 (0.354)
Born after 1942 by AA
0.950 (0.782) 0.917 (0.717) 0.689* (0.361) 0.925 (0.630) 1.146** (0.552) 1.410*** (0.320)
Born after 1942 by AT
2.043*** (0.524) 1.729*** (0.503) 1.505*** (0.248) 0.362 (0.441) 0.443 (0.398) 0.0421 (0.219)
Age 3034.99 by AA 1.109 (0.741) 0.0415 (0.383) 0.823 (0.690) 0.413 (0.782) 0.0732 (0.339) 0.246 (0.599)
Age 3539.99 by AA 0.409 (0.684) 0.305 (0.386) 0.316 (0.708) 0.418 (0.634) 0.230 (0.346) 0.421 (0.617)
Age 4044.99 by AA 0.515 (0.707) 0.294 (0.367) 0.405 (0.676) 0.876 (0.633) 0.0224 (0.332) 0.213 (0.593)
Age 4549.99 by AA 0.580 (0.723) 0.483 (0.367) 0.400 (0.672) 0.632 (0.685) 0.268 (0.331) 0.398 (0.589)
Age 5054.99 by AA 0.118 (0.744) 0.744** (0.368) 0.0716 (0.674) 0.747 (0.685) 0.109 (0.331) 0.183 (0.589)
Age 5559.99 by AA 0.185 (0.793) 0.474 (0.373) 0.0693 (0.681) 0.567 (0.734) 0.0982 (0.335) 0.0631 (0.594)
Age 6063 by AA 0.0671 (0.788) 0.467 (0.391) 0.256 (0.718) 0.403 (0.724) 0.196 (0.352) 0.0746 (0.626)
Age 3034.99 by AT 0.166 (0.370) 0.838*** (0.214) 0.437 (0.383) 0.614* (0.324) 0.349** (0.176) 0.327 (0.303)
Age 3539.99 by AT 0.706* (0.397) 1.100*** (0.251) 0.791* (0.462) 0.726** (0.325) 0.230 (0.212) 0.384 (0.374)
Age 4044.99 by AT 0.550 (0.393) 1.114*** (0.241) 0.767* (0.443) 0.366 (0.343) 0.235 (0.204) 0.315 (0.360)
Age 4549.99 by AT 0.775* (0.452) 1.349*** (0.238) 1.039** (0.437) 0.296 (0.346) 0.127 (0.203) 0.162 (0.356)
Age 5054.99 by AT 0.972** (0.437) 1.347*** (0.237) 1.187*** (0.436) 0.248 (0.374) 0.181 (0.201) 0.233 (0.354)
Age 5559.99 by AT 1.077** (0.484) 1.288*** (0.243) 1.091** (0.444) 0.0747 (0.387) 0.123 (0.206) 0.0603 (0.362)
Age 6063 by AT 1.130** (0.492) 1.195*** (0.256) 1.086** (0.470) 0.104 (0.417) 0.0281 (0.216) 0.106 (0.380)
Constant 24.29*** (0.420) 24.34*** (0.302) 24.16*** (0.269) 25.85*** (0.335) 26.04*** (0.244) 25.87*** (0.233)
Observations 1,957 1,957 1,957 1,763 1,763 1,763
0.080 0.87 0.569 0.062 0.073 0.531
No. of
10,404 10,404 10,404 9,213 9,213 9,213
No. of family
fixed effects
Not applicable Not applicable 983 Not applicable Not applicable 888
Presented are estimates of the ageperiodcohort model where the cohort variable is treated as discrete as indicated in Eq. S3. Each entry refers to the effect of
the variable listed in the first column on BMI holding all other factors constant. Robust SEs are presented in parentheses. The columns in this table differ based on
the sex subsample as indicated row 1 and the method used to estimate the statistical model indicated in row 2. See Table S6 for the calendar time corresponding to
examinations in each wave. The following indicate the statistical significance of each explanatory variable: ***P<0.01, **P<0.05, and *P<0.1.
Rosenquist et al. 7of10
Table S5. Descriptive statistics on genetic characteristics across
birth cohorts
Genotype at
Individuals born
Individuals born
TT 787 (36.52%) 517 (33.04%)
AT 1,049 (48.68%) 812 (51.77%)
AA 319 (14.85%) 236 (15.08%)
No. of people 2,155 1,565
Presented is the distribution of genetic risk alleles of individuals in the
Framingham Offspring Study born pre- and post-1942. A Pearsonsχ
for the
hypothesis that the rows and columns in a two-way table are independent
accounting for correlations within families yields P>Χ
=0.1550, χ
(2) =
1.88. This indicates that the distributions of genetic risk factors do not differ
between cohorts born pre- and post-1942.
Table S6. Descriptive statistics
No. of unique subjects 3,720
Total no. of observations in estimation sample 19,617
Subjects that are male, % 52.61
Mean age of subject at data collection (SD) 48.1763 (9.4095)
No. of individuals born before 1920 83
No. of Individuals born between 1920 and 1925 323
No. of Individuals born between 1925 and 1930 481
No. of Individuals born between 1930 and 1935 561
No. of Individuals born between 1935 and 1940 575
No. of Individuals born between 1940 and 1945 715
No. of Individuals born between 1945 and 1950 537
No. of individuals born after 1950 351
Observations collected in wave 1 beginning 30 Aug 1971 3,720
Observations collected in wave 2 beginning 26 Jan 1995 3,581
Observations collected in wave 3 beginning 20 Dec 1983 3,326
Observations collected in wave 4 beginning 22 Apr 1987 2,955
Observations collected in wave 5 beginning 23 Jan 1991 2,488
Observations collected in wave 6 beginning 26 Jan 1995 1,916
Observations collected in wave 7 beginning 11 Sep 1998 1,310
Observations collected in wave 8 beginning 10 Mar 2005 321
No. of Individuals with FTOAA, % 555 (14.56)
No. of Individuals with FTOAT, % 1,861 (50.03)
No. of Individuals with FTOTT, % 1,304 (35.05)
BMI 26.869 (5.013)
Provided are the summary statistics for the measures used in the multivariate regression analysis. We only list
the date of the first interview for each wave in the description above because the examinations in each wave
were held over several years and the exact time could be inferred by taking the difference between age at
examination and YOB.
Rosenquist et al. 8of10
Table S7. Model estimates of factors influencing BMI, where we ignore cohort effects and interactions of genetic factors with age and
birth cohort indicators
Estimator Linear regression Random effects
Linear regression with controls for
family fixed effects
Wave 2 0.252** (0.101) 0.0764 (0.0943) 0.203 (0.169)
Wave 3 0.0669 (0.132) 0.491*** (0.109) 0.146 (0.176)
Wave 4 0.538*** (0.158) 1.023*** (0.122) 0.573*** (0.182)
Wave 5 0.928*** (0.184) 1.530*** (0.139) 0.961*** (0.194)
Wave 6 1.304*** (0.235) 2.146*** (0.161) 1.387*** (0.212)
Wave 7 1.338*** (0.277) 2.337*** (0.178) 1.458*** (0.227)
Wave 8 1.275*** (0.374) 2.831*** (0.220) 1.657*** (0.277)
AA genotype 0.574 (0.357) 0.854*** (0.328) 0.123 (0.254)
AT genotype 0.131 (0.237) 0.336 (0.231) 0.523*** (0.179)
Age 3539.99 0.595*** (0.101) 0.489*** (0.0718) 0.681*** (0.125)
Age 4044.99 1.111*** (0.116) 0.954*** (0.0859) 1.244*** (0.126)
Age 4549.99 1.485*** (0.146) 1.214*** (0.107) 1.570*** (0.134)
Age 5054.99 1.762*** (0.176) 1.337*** (0.131) 1.856*** (0.147)
Age 5559.99 1.960*** (0.217) 1.381*** (0.157) 2.015*** (0.163)
Age 6063 1.912*** (0.251) 1.341*** (0.183) 2.117*** (0.182)
Wave 2 by AA 0.279 (0.182) 0.0714 (0.156) 0.165 (0.305)
Wave 3 by AA 0.225 (0.222) 0.139 (0.157) 0.176 (0.307)
Wave 4 by AA 0.500** (0.235) 0.224 (0.159) 0.396 (0.309)
Wave 5 by AA 0.395 (0.273) 0.254 (0.165) 0.394 (0.319)
Wave 6 by AA 0.620* (0.338) 0.289* (0.175) 0.530 (0.338)
Wave 7 by AA 1.024*** (0.382) 0.553*** (0.182) 0.762** (0.350)
Wave 8 by AA 0.910 (0.590) 0.237 (0.229) 0.561 (0.436)
Wave 2 by AT 0.135 (0.120) 0.0521 (0.110) 0.0905 (0.215)
Wave 3 by AT 0.135 (0.141) 0.00837 (0.111) 0.0159 (0.216)
Wave 4 by AT 0.138 (0.155) 0.0530 (0.111) 0.00228 (0.217)
Wave 5 by AT 0.314* (0.182) 0.108 (0.116) 0.209 (0.225)
Wave 6 by AT 0.517** (0.230) 0.0139 (0.124) 0.281 (0.239)
Wave 7 by AT 0.724*** (0.267) 0.201 (0.130) 0.502** (0.250)
Wave 8 by AT 1.325*** (0.384) 0.371** (0.160) 0.831*** (0.304)
Constant 23.65*** (0.187) 23.60*** (0.181) 23.41*** (0.150)
Observations 19,617 19,617 19,617
0.096 0.097 0.480
No. of individuals 3,720 3,720 3,720
No. of family fixed effects Not applicable Not applicable 1,414
Presented are estimates of the ageperiod model where the cohort variable is not included and the only genetic interactions included are those with period
effects allowing solely for contemporaneous geneenvironment interactions. The age and period variables are treated as discrete as indicated in Eq. S3.Each
entry refers to the effect of the variable listed in the first column on BMI holding all other factors constant. Robust SEs are presented in parentheses.The
columns in this table differ based on what factors are accounted for and the method used to estimate the statistical model. See Table S6 for the calendar time
corresponding to examinations in each wave. Note that our main results of birth cohort and genotype interactions are not sensitive to the method by which the
model was estimate. The following indicate the statistical significance of each explanatory variable: ***P<0.01, **P<0.05, and *P<0.1.
Rosenquist et al. 9of10
Table S8. Model estimates of factors influencing BMI, where we ignore cohort effects and interactions of genetic factors with birth
cohort indicators
Estimator Linear regression Random effects
Linear regression with controls for
family fixed effects
Wave 2 0.411*** (0.121) 0.0757 (0.112) 0.337* (0.174)
Wave 3 0.197 (0.172) 0.244* (0.144) 0.0737 (0.188)
Wave 4 0.204 (0.215) 0.700*** (0.172) 0.288 (0.200)
Wave 5 0.528** (0.252) 1.124*** (0.204) 0.609*** (0.218)
Wave 6 0.835*** 0.319) 1.646*** 0.242) 0.960*** 0.243)
Wave 7 0.820** (0.378) 1.764*** (0.270) 0.973*** (0.262)
Wave 8 0.654 (0.494) 2.093*** (0.335) 1.032*** (0.319)
AA genotype 1.489** (0.607) 0.954** (0.401) 0.860* (0.465)
AT genotype 0.172 (0.309) 0.0794 (0.269) 0.213 (0.291)
Age 3539.99 0.593*** (0.181) 0.498*** (0.123) 0.700*** (0.213)
Age 4044.99 1.434*** (0.202) 1.068*** (0.146) 1.363*** (0.210)
Age 4549.99 1.950*** (0.259) 1.438*** (0.183) 1.847*** (0.220)
Age 5054.99 2.356*** (0.307) 1.657*** (0.224) 2.163*** (0.237)
Age 5559.99 2.764*** (0.379) 1.886*** (0.269) 2.568*** (0.260)
Age 6063 2.904*** (0.433) 2.026*** (0.312) 2.870*** (0.288)
Age 3034.99 by AA 0.707 (0.570) 0.00615 (0.267) 0.759 (0.499)
Age 3539.99 by AA 0.758 (0.523) 0.127 (0.304) 0.782 (0.527)
Age 4044.99 by AA 1.255** (0.585) 0.216 (0.345) 0.942* (0.521)
Age 4549.99 by AA 1.422** (0.648) 0.434 (0.408) 1.183** (0.533)
Age 5054.99 by AA 1.511** (0.748) 0.412 (0.478) 1.091** (0.555)
Age 5559.99 by AA 1.710* (0.880) 0.630 (0.557) 1.358** (0.585)
Age 6063 by AA 1.760* (0.966) 0.737 (0.632) 1.378** (0.624)
Age 3034.99 by AT 0.331 (0.242) 0.577*** (0.142) 0.505* (0.264)
Age 3539.99 by AT 0.304 (0.277) 0.464** (0.193) 0.393 (0.331)
Age 4044.99 by AT 0.186 (0.301) 0.325 (0.224) 0.253 (0.328)
Age 4549.99 by AT 0.414 (0.375) 0.215 (0.271) 0.0164 (0.336)
Age 5054.99 by AT 0.639 (0.427) 0.0510 (0.322) 0.0647 (0.351)
Age 5559.99 by AT 1.009* (0.516) 0.208 (0.379) 0.473 (0.374)
Age 6063 by AT 1.367** (0.577) 0.506 (0.434) 0.862** (0.401)
Wave 2 by AA 0.547** (0.253) 0.246 (0.209) 0.384 (0.321)
Wave 3 by AA 0.641* (0.354) 0.420 (0.268) 0.506 (0.343)
Wave 4 by AA 0.998** (0.414) 0.589* (0.316) 0.797** (0.361)
Wave 5 by AA 0.967* (0.502) 0.704* (0.377) 0.865** (0.390)
Wave 6 by AA 1.261** (0.629) 0.828* (0.445) 1.073** (0.430)
Wave 7 by AA 1.705** (0.724) 1.159** (0.497) 1.351*** (0.461)
Wave 8 by AA 1.674* (0.991) 1.003 (0.618) 1.276** (0.565)
Wave 2 by AT 0.357** (0.160) 0.221 (0.147) 0.269 (0.226)
Wave 3 by AT 0.522** (0.225) 0.300 (0.189) 0.328 (0.240)
Wave 4 by AT 0.640** (0.278) 0.342 (0.225) 0.419* (0.253)
Wave 5 by AT 0.925*** (0.332) 0.613** (0.268) 0.732*** (0.273)
Wave 6 by AT 1.247*** (0.417) 0.651** (0.318) 0.930*** (0.302)
Wave 7 by AT 1.539*** (0.486) 0.937*** (0.354) 1.249*** (0.323)
Wave 8 by AT 2.338*** (0.645) 1.340*** (0.439) 1.829*** (0.393)
Constant 23.45*** (0.204) 23.58*** (0.190) 23.36*** (0.175)
Observations 19,617 19,617 19,617
0.098 0.097 0.481
No. of individuals 3,720 3,720 3,720
No. of family fixed effects Not applicable Not applicable 1,414
Presented are estimates of an ageperiod model where the cohort variable and all interactions are not included in the specification. All age and period
variables are treated as discrete as indicated in Eq. S3. Each entry refers to the effect of the variable listed in the first column on BMI holding all other factors
constant. Robust SEs are presented in parentheses. The columns in this table differ based on what factors are accounted for and the method used to estimate
the statistical model. See Table S6 for the calendar time corresponding to examinations in each wave. Note that our main results of birth cohort and genotype
interactions are not sensitive to the method by which the model was estimated. The following indicate the statistical significance of each explanatory variable:
***P<0.01, **P<0.05, and *P<0.1.
Rosenquist et al. 10 of 10
... Genotype-birth cohort interactions for the debute of alcohol consumption or frequent alcohol use in early age were also found with other functional gene variants such as VMAT1 (rs1390938), NRG1 (rs6994992) and OXTR (rs53576) (see for review). Birth cohort can modify even the associations between genotype and somatic measures such as body mass index (Rosenquist et al., 2015). Given that NPY is related to anxiety regulation and social behaviour, we hypothesised that functional variants of NPY may interact with the birth cohort in shaping sociability-related traits. ...
... The fact that NPY genotypes are not directly associated with personality traits but interact with birth cohort to predict Agreeableness and its facets suggests that these genetic associations relate to the variation in environmental contexts. It is likely that the impact of environmental effects is modulated by genetic pathways, causing some individuals or population groups to be differentially affected by composite changes in the environment leading to birth cohort effects (Rosenquist et al., 2015). Possibly, the NPY gene variants might have an effect either on coping styles with stress through personality-dependent choices or through modifying the interpretation of stressful events. ...
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Objective: Neuropeptide Y (NPY) is a powerful regulator of anxious states, including social anxiety, but evidence from human genetic studies is limited. Associations of common gene variants with behaviour have been described as subject to birth cohort effects especially if the behaviour is socially motivated. This study aimed to examine the association of NPY rs16147 and rs5574 with personality traits in highly representative samples of two birth cohorts of young adults, the samples having been formed during a period of rapid societal transition. Methods: Both birth cohorts (original n= 1238) of the Estonian Children Personality Behaviour and Health Study (ECPBHS) self-reported personality traits of the five-factor model at 25 years of age. Results: A significant interaction effect of the NPY rs16147 and rs5574 and birth cohort on Agreeableness was found. The T/T genotype of NPY rs16147 resulted in low Agreeableness in the older cohort (born 1983) and in high Agreeableness in the younger cohort (born 1989). The C/C genotype of NPY rs5574 was associated with higher Agreeableness in the younger but not in the older cohort. In the NPY rs16147 T/T homozygotes, the deviations from average in Agreeableness within the birth cohort were dependent on the serotonin transporter promoter polymorphism. Conclusions: The association between the NPY gene variants and a personality domain reflecting social desirability is subject to change qualitatively in times of rapid societal changes, serving as an example of the relationship between the plasticity genes and environment. The underlying mechanism may involve the development of the serotonergic system.
... [6][7][8]15 Two previous studies in FHS had sought to examine geneby-birth cohort interactions on BMI. 15,16 One of the studies using the Offspring cohort (N=3720) detected an interaction between a FTO SNP (rs9939609, linkage disequilibrium: r 2 ≥0.9 with rs9922708 in our study) and birth cohort on BMI. 16 Another study on ≈5000 unrelated FHS participants described a gene by historical period interaction whereby genetic effects on BMI were larger after 1985 compared with before 1985. ...
... 15,16 One of the studies using the Offspring cohort (N=3720) detected an interaction between a FTO SNP (rs9939609, linkage disequilibrium: r 2 ≥0.9 with rs9922708 in our study) and birth cohort on BMI. 16 Another study on ≈5000 unrelated FHS participants described a gene by historical period interaction whereby genetic effects on BMI were larger after 1985 compared with before 1985. The authors further concluded that this genetic influence weakened over the life course. ...
Background Whether genetics contribute to the rising prevalence of obesity or its cardiovascular consequences in today’s obesogenic environment remains unclear. We sought to determine whether the effects of a higher aggregate genetic burden of obesity risk on body mass index (BMI) or cardiovascular disease (CVD) differed by birth year. Methods We split the FHS (Framingham Heart Study) into 4 equally sized birth cohorts (birth year before 1932, 1932 to 1946, 1947 to 1959, and after 1960). We modeled a genetic predisposition to obesity using an additive genetic risk score (GRS) of 941 BMI-associated variants and tested for GRS–birth year interaction on log-BMI (outcome) when participants were around 50 years old (N=7693). We repeated the analysis using a GRS of 109 BMI-associated variants that increased CVD risk factors (type 2 diabetes, blood pressure, total cholesterol, and high-density lipoprotein) in addition to BMI. We then evaluated whether the effects of the BMI GRSs on CVD risk differed by birth cohort when participants were around 60 years old (N=5493). Results Compared with participants born before 1932 (mean age, 50.8 yrs [2.4]), those born after 1960 (mean age, 43.3 years [4.5]) had higher BMI (median, 25.4 [23.3–28.0] kg/m ² versus 26.9 [interquartile range, 23.7–30.6] kg/m ² ). The effect of the 941-variant BMI GRS on BMI and CVD risk was stronger in people who were born in later years (GRS–birth year interaction: P =0.0007 and P =0.04 respectively). Conclusions The significant GRS–birth year interactions indicate that common genetic variants have larger effects on middle-age BMI and CVD risk in people born more recently. These findings suggest that the increasingly obesogenic environment may amplify the impact of genetics on the risk of obesity and possibly its cardiovascular consequences.
... However, the pattern of increasing obesogenic genes does support the microevolutionary hypothesis by itself. For example Rosenquist et al. (33) reported the gene pool frequency of the well known obesity promoting allele FTO pre and post 1942. Comparing the frequency of the obesity prone homozygote (AA) and heterozygote (AT), with the frequency of the obesity protective homozygote (TT) in the birth cohorts born before 1942 finds the pre 1942 frequency of the AA/AT was 63.1% compared with the post 1942 frequency of 66.7%. ...
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The obesity epidemic represents potentially the largest phenotypic change in Homo sapiens since the origin of the species. Despite obesity’s high heritability, a change in the gene pool has not generally been presumed as a potential cause of the obesity epidemic. Here we advance the hypothesis that a rapid change in the obesogenic gene pool has occurred second to the introduction of modern obstetrics dramatically altering evolutionary pressures on obesity - the microevolutionary hypothesis of the obesity epidemic. Obesity is known to increase childbirth related mortality several fold. Prior to modern obstetrics, childbirth related mortality occurred in over 10% of women. After modern obstetrics, this mortality reduced to a fraction of a percent, thereby lifting a strong negative selection pressure. Regression analysis of data for ∼ 190 countries was carried out to examine associations between 1990 maternal death rates (MDR) and current obesity rates. Multivariate regression showed MDR correlated more strongly with national obesity rates than GDP, calorie intake and physical inactivity. Analyses controlling for confounders via partial correlation show that MDR explains approximately 11% of the variability of obesity rate between nations. For nations with MDR above the median (>0.45%), MDR explains over 20% of obesity variance, while calorie intake, and physical inactivity show no association with obesity in these nations. The microevolutionary hypothesis offers a parsimonious explanation of the global nature of the obesity epidemic. Significance Statement Humans underwent a rapid increase in obesity in the 20 th century, and existing explanations for this trend are unsatisfactory. Here we present evidence that increases in obesity may be in large part attributable to microevolutionary changes brought about by dramatic reduction of childbirth mortality with the introduction of modern obstetrics. Given the higher relative risk of childbirth in women with obesity, obstetrics removed a strong negative selection pressure against obesity. This alteration would result in a rapid population-wide rise in obesity-promoting alleles. A cross-country analysis of earlier maternal death rates and obesity rate today found strong evidence supporting this hypothesis. These findings suggest recent medical intervention influenced the course of human evolution more profoundly than previously realized.
... Birth cohort has been used as a proxy for exposure to obesogenic environments. A recent study demonstrated that birth cohort modified the association between FTO and BMI, suggesting that genotype-phenotype (outcome) correlations are likely highly dependent on the time period or birth cohort of individuals (Rosenquist et al. 2015). Using a polygenic score for BMI, another study found that the magnitude of associations of the polygenic score for BMI were larger for more recent cohorts (Walter et al. 2016). ...
... Work in this area has demonstrated how effects that appear conceptually distinct can be difficult to distinguish when specified in models . Changes in genetic effects modeled in terms of cohorts (e.g., Rosenquist et al., 2015;Sanz-de-Galdeano, Terskaya, & Upegui, 2020), for example, might equivalently be framed in terms of periods (e.g., war, socioeconomic conditions, or policy eras) which are often implicitly the focus of explanation anyway. Other research suggests that apparently simple sociological concepts might require relatively complex model features to capture when considered simultaneously against the backdrop of development . ...
Uchiyama et al. rightly consider how cultural variation may influence estimates of heritability by contributing to environmental sources of variation. We disagree, however, with the idea that generalisable estimates of heritability are ever a plausible aim. Heritability estimates are always context-specific, and to suggest otherwise is to misunderstand what heritability can and cannot tell us.
... Work in this area has demonstrated how effects that appear conceptually distinct can be difficult to distinguish when specified in models . Changes in genetic effects modeled in terms of cohorts (e.g., Rosenquist et al., 2015;Sanz-de-Galdeano, Terskaya, & Upegui, 2020), for example, might equivalently be framed in terms of periods (e.g., war, socioeconomic conditions, or policy eras) which are often implicitly the focus of explanation anyway. Other research suggests that apparently simple sociological concepts might require relatively complex model features to capture when considered simultaneously against the backdrop of development . ...
Epigenetics impacts gene–culture coevolution by amplifying phenotypic variation, including clustering, and bridging the difference in timescales between genetic and cultural evolution. The dual inheritance model described by Uchiyama et al. could be modified to provide greater explanatory power by incorporating epigenetic effects.
Much of the increase in the prevalence of overweight and obesity has been in developing countries with a history of famines and malnutrition. This paper is the first to examine overweight among adult grandsons of grandfathers exposed to starvation during developmental ages. I study grandsons born to grandfathers who served in the Union Army during the US Civil War (1861-5) where some grandfathers experienced severe net malnutrition because they suffered a harsh POW experience. I find that male-line but not female-line grandsons of grandfathers who survived a severe captivity during their growing years faced a 21% increase in mean overweight and a 2% increase in mean BMI compared to grandsons of non-POWs. Male-line grandsons descended from grandfathers who experienced a harsh captivity faced a 22%-28% greater risk of dying every year after age 45 relative to grandsons descended from non-POWs, with overweight accounting for 9%-14% of the excess risk.
In this article, we highlight the contributions of passive experiments that address important exercise-related questions in integrative physiology and medicine. Passive experiments differ from active experiments in that passive experiments involve limited or no active intervention to generate observations and test hypotheses. Experiments of nature and natural experiments are two types of passive experiments. Experiments of nature include research participants with rare genetic or acquired conditions that facilitate exploration of specific physiological mechanisms. In this way, experiments of nature are parallel to classical "knockout" animal models among human research participants. Natural experiments are gleaned from data sets that allow population-based questions to be addressed. An advantage of both types of passive experiments is that more extreme and/or prolonged exposures to physiological and behavioral stimuli are possible in humans. In this article, we discuss a number of key passive experiments that have generated foundational medical knowledge or mechanistic physiological insights related to exercise. Both natural experiments and experiments of nature will be essential to generate and test hypotheses about the limits of human adaptability to stressors like exercise. © 2023 American Physiological Society. Compr Physiol 13:4879-4907, 2023.
Une préoccupation persistante au sujet des personnes âgées est leur capacité à maintenir leur bienêtre économique après leur retraite. Dans une large mesure, ce sont les décisions d’épargne prises pendant les années précédant la retraite qui financent la consommation pendant les années de retraite. Les disparités entre les sexes en ce qui concerne les gains sur le marché du travail et les régimes de retraite financés par l’employeur, de même que la longévité plus grande des femmes, donnent à penser que les ressources dont disposent les Canadiennes à la retraite sont probablement différentes de celles des Canadiens. En étudiant sur 38 ans les données administratives, représentatives à l’échelle nationale, d’une banque de données construite à partir des déclarations d’impôt sur le revenu des particuliers, nous examinons l’évolution des tendances de l’épargne-retraite par rapport à la répartition du revenu selon l’âge et la cohorte de naissance, pour les femmes et pour les hommes. Nous constatons que même si les femmes sont plus susceptibles d’épargner à tous les âges, sous réserve de leur participation, elles épargnent moins et ont tendance à être attirées par des placements qui, en moyenne, offrent un taux de rendement inférieur. Par conséquent, il est possible que les politiques centrées sur la marge d’épargne extensive (les décisions de participation) ne réduisent pas les différences de bienêtre économique entre les femmes et les hommes à la retraite, de sorte que les politiques devraient également viser la marge intensive (le montant de la contribution). Enfin, sur l’ensemble de la répartition des revenus, nous constatons une hétérogénéité substantielle des différences significatives entre les sexes quant aux effets de l’âge, de la cohorte et de la période, et ce, dans les deux marges d’épargne-retraite.
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Background: Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age, therefore meta-analyzing GWAS of age-diverse samples could be misleading. Meta-regression allows adjustment for study specific characteristics and models heterogeneity between studies. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. Methods: With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Results Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both the main genetic effects and the age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. This is similar to the association that has been previously reported by a study that used individual participant data. Conclusions: GWAS using summary statistics from age-diverse samples should consider using meta-regression to explore age-varying genetic effects.
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Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions. The book makes two essential contributions to quantitative studies of time-related change. Through the introduction of the GLMM framework, it shows how innovative estimation methods and new model specifications can be used to tackle the "model identification problem" that has hampered the development and empirical application of APC analysis. The book also addresses the major criticism against APC analysis by explaining the use of new models within the GLMM framework to uncover mechanisms underlying age patterns and temporal trends. Encompassing both methodological expositions and empirical studies, this book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. It compares new and existing models and methods and provides useful guidelines on how to conduct APC analysis. For empirical illustrations, the text incorporates examples from a variety of disciplines, such as sociology, demography, and epidemiology. Along with details on empirical analyses, software and programs to estimate the models are available on the book’s web page.
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Genome-wide association studies have revealed that SNPs in the first intron of FTO (Fat mass and Obesity related) are robustly associated with body mass index and obesity. Subsequently, it has become clear that this association with body weight, and increasingly food intake, is replicable across multiple populations and different age groups. However, to date, no conclusive link has been made between the risk alleles and FTO expression or its physiological role. FTO deficiency leads to a complex phenotype including postnatal mortality and growth retardation, pointing to some fundamental developmental role. Yet, the weight of evidence from a number of animal models where FTO expression has been perturbed indicates some role for FTO in energy homoeostasis. In addition, emerging data points to a role for FTO in the sensing of nutrients. In this review, we explore the in vivo and in vitro evidence detailing FTO's different faces and discuss how these might link to the regulation of body weight.
Context The prevalence of overweight among children in the United States increased between 1976-1980 and 1988-1994, but estimates for the current decade are unknown. Objective To determine the prevalence of overweight in US children using the most recent national data with measured weights and heights and to examine trends in overweight prevalence. Design, Setting, and Participants Survey of 4722 children from birth through 19 years of age with weight and height measurements obtained in 1999-2000 as part of the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, stratified, multistage probability sample of the US population. Main Outcome Measure Prevalence of overweight among US children by sex, age group, and race/ethnicity. Overweight among those aged 2 through 19 years was defined as at or above the 95th percentile of the sex-specific body mass index (BMI) for age growth charts. Results The prevalence of overweight was 15.5% among 12- through 19-year-olds, 15.3% among 6- through 11-year-olds, and 10.4% among 2- through 5-year-olds, compared with 10.5%, 11.3%, and 7.2%, respectively, in 1988-1994 (NHANES III). The prevalence of overweight among non-Hispanic black and Mexican-American adolescents increased more than 10 percentage points between 1988-1994 and 1999-2000. Conclusion The prevalence of overweight among children in the United States is continuing to increase, especially among Mexican-American and non-Hispanic black adolescents.
The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.
We have investigated the evidence for positive selection in samples of African, European, and East Asian ancestry at 65 loci associated with susceptibility to type 2 diabetes (T2D) previously identified through genome-wide association studies. Selection early in human evolutionary history is predicted to lead to ancestral risk alleles shared between populations, whereas late selection would result in population-specific signals at derived risk alleles. By using a wide variety of tests based on the site frequency spectrum, haplotype structure, and population differentiation, we found no global signal of enrichment for positive selection when we considered all T2D risk loci collectively. However, in a locus-by-locus analysis, we found nominal evidence for positive selection at 14 of the loci. Selection favored the protective and risk alleles in similar proportions, rather than the risk alleles specifically as predicted by the thrifty gene hypothesis, and may not be related to influence on diabetes. Overall, we conclude that past positive selection has not been a powerful influence driving the prevalence of T2D risk alleles.
Candidate gene × environment (G × E) interaction research tests the hypothesis that the effects of some environmental variable (e.g., childhood maltreatment) on some outcome measure (e.g., depression) depend on a particular genetic polymorphism. Because this research is inherently nonexperimental, investigators have been rightly concerned that detected interactions could be driven by confounders (e.g., ethnicity, gender, age, socioeconomic status) rather than by the specified genetic or environmental variables per se. In an attempt to eliminate such alternative explanations for detected G × E interactions, investigators routinely enter the potential confounders as covariates in general linear models. However, this practice does not control for the effects these variables might have on the G × E interaction. Rather, to properly control for confounders, researchers need to enter the covariate × environment and the covariate × gene interaction terms in the same model that tests the G × E term. In this manuscript, I demonstrate this point analytically and show that the practice of improperly controlling for covariates is the norm in the G × E interaction literature to date. Thus, many alternative explanations for G × E findings that investigators had thought were eliminated have not been.