Increasing Genetic Variance of Body Mass Index during
the Swedish Obesity Epidemic
Benjamin Rokholm
1
*, Karri Silventoinen
2
, Per Tynelius
3
, Michael Gamborg
1
, Thorkild I. A. Sørensen
1
,
Finn Rasmussen
3
1Institute of Preventive Medicine, Copenhagen University Hospital, Centre for Health and Society, Copenhagen, Denmark, 2Population Research Unit, Department of
Social Research, University of Helsinki, Helsinki, Finland, 3Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
Abstract
Background and Objectives:
There is no doubt that the dramatic worldwide increase in obesity prevalence is due to
changes in environmental factors. However, twin and family studies suggest that genetic differences are responsible for the
major part of the variation in adiposity within populations. Recent studies show that the genetic effects on body mass index
(BMI) may be stronger when combined with presumed risk factors for obesity. We tested the hypothesis that the genetic
variance of BMI has increased during the obesity epidemic.
Methods:
The data comprised height and weight measurements of 1,474,065 Swedish conscripts at age 18–19 y born
between 1951 and 1983. The data were linked to the Swedish Multi-Generation Register and the Swedish Twin Register
from which 264,796 full-brother pairs, 1,736 monozygotic (MZ) and 1,961 dizygotic (DZ) twin pairs were identified. The twin
pairs were analysed to identify the most parsimonious model for the genetic and environmental contribution to BMI
variance. The full-brother pairs were subsequently divided into subgroups by year of birth to investigate trends in the
genetic variance of BMI.
Results:
The twin analysis showed that BMI variation could be explained by additive genetic and environmental factors not
shared by co-twins. On the basis of the analyses of the full-siblings, the additive genetic variance of BMI increased from 4.3
[95% CI 4.04–4.53] to 7.9 [95% CI 7.28–8.54] within the study period, as did the unique environmental variance, which
increased from 1.4 [95% CI 1.32–1.48] to 2.0 [95% CI 1.89–2.22]. The BMI heritability increased from 75% to 78.8%.
Conclusion:
The results confirm the hypothesis that the additive genetic variance of BMI has increased strongly during the
obesity epidemic. This suggests that the obesogenic environment has enhanced the influence of adiposity related genes.
Citation: Rokholm B, Silventoinen K, Tynelius P, Gamborg M, Sørensen TIA, et al. (2011) Increasing Genetic Variance of Body Mass Index during the Swedish
Obesity Epidemic. PLoS ONE 6(11): e27135. doi:10.1371/journal.pone.0027135
Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America
Received August 8, 2011; Accepted October 11, 2011; Published November 7, 2011
Copyright: ß2011 Rokholm et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding was provided by the Danish Research Counsil (the Ministry of Science, Technology and Innovation; http://www.fi.dk/raad-og-udvalg/det-frie-
forskningsraad). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: br@ipm.regionh.dk
Introduction
The prevalence of obesity has increased strongly in Sweden
during the last decades, and 10% of all Swedish men and women
are now obese [1,2] In the USA the prevalence of obesity has
reached epidemic proportions and recent data show that more
than a third of all men and women are obese [3,4].
There is no doubt that environmental factors have initiated the
epidemic, since the gene pool in the population changes at a rate
that is much too slow to explain the observed pattern. Nevertheless
with heritability (the proportion of phenotypic variation attribut-
able to genetic differences among individuals) of body mass index
(BMI) in the range of 50–80%, twin studies suggest that genetic
differences account for the majority of variation in body fatness
within populations [5,6]. Furthermore, there is evidence for a
continuously high, and possibly increasing, heritability of BMI
despite the increasing effect of environmental factors, which gave
rise to the obesity epidemic [7]. This seems paradoxical since
environmental changes have resulted in an increase in the total
BMI variation [8]. An explanation could be that the genetic
influence on BMI has increased because of the influence from the
obesogenic environment, responsible for the increase in the
prevalence of obesity. Such an association suggests that the effect
of genes depends upon the environmental exposure, which implies
gene-environment interaction (GxE).
So far various presumed environmental risk factors for obesity
have been investigated as potential effect modifiers of genetic
effects on adiposity. Twin studies conducted in several populations
have found lower heritability of obesity in physically more active
individuals [9–11]. Similarly on a molecular genetic level, several
studies have recently found that physical activity attenuates the
effect of the fat mass and obesity associated (FTO) gene and other
genetic loci that are associated with body fatness [12–15]. In
addition, fat and carbohydrate intake has been found to interact
with the FTO gene on BMI [16].
These results all suggest that genetic effects on adiposity are
modifiable by environmental influences, which are related to the
obesogenic environment. However, the research is still at an early
PLoS ONE | www.plosone.org 1 November 2011 | Volume 6 | Issue 11 | e27135
stage and it is possible that the discovered interactions apply to
other genetic loci and to other environmental factors than physical
activity and fat and carbohydrate intake. On this background we
tested the more general hypothesis that the genetic variance of
BMI has increased during the obesity epidemic among Swedish
young men. If the hypothesis is confirmed it may imply that the
obesogenic environment reinforces the effect of genes related to
adiposity. Although a similar hypothesis has been investigated
previously on Danish twin data [17] the current study is the first to
investigate an actual secular trend in the genetic variance of BMI.
Methods
Ethics statement
This study has been ethically approved by the Swedish ethical
approval system (Centrala etikpro¨vningsna¨mnden, Vetenskapsra˚-
det i Stockholm). Informed consent was not considered necessary
by the Swedish ethical approval system since the study relies
purely on non-identifiable register-based data.
Study population
The data comprises height and weight measurements of all
Swedish men born between 1951 and 1983 who underwent
military conscription examination. Height was measured using a
wall-mounted stadiometer and weight using an analogue or digital
scale. BMI was calculated as weight (kg)/height
2
(m
2
). Extreme
BMI values (15 kg/m
2
.BMI.50 kg/m
2
) were excluded to
reduce the risk of misclassification due to measurement or data
entry errors. During the years covered by this study, conscription
examination was compulsory by law for all young men with
Swedish citizenship. Only men with severe diseases and disabilities
were exempted based on a certificate issued by a physician with
information on diagnosis.
Information on sibling status was acquired by linking the
conscript data with the Swedish Multi-Generation Register and
the Swedish Twin Register using the Swedish personal identifica-
tion number (ID) unique to each subject. The ID numbers of the
biological parents were included in the record of their offspring
and on this background 264,796 pairs of full-brothers and 1,736
MZ and 1,961 DZ twin pairs were identified among the
conscripts. Data on zygosity were obtained from the Swedish
Twin Register and from the Swedish Young Male Twins Study
[18,19]. Due to lack of information on zygosity 2,452 twin pairs
were excluded. To limit the possible change in childhood family
environment, brothers born more than 3 years apart were
excluded resulting in a total of 116,478 brother pairs for analysis
(Table 1).
Statistical analyses
The sibling pairs were analysed through quantitative genetic
modelling, in which the total phenotypic variance of a trait is
partitioned into environmental and genetic components. The
disentangling of genetic and environmental variance is made
possible through structural equation modelling of covariance
within pairs of different types of family relations. The relations
included in the modelling are genetically informative if they differ
in the degree of genetic or environmental similarity [20]. In the
case of MZ and DZ twin pairs, the genetic variance can be
estimated since MZ twins have the same gene sequence, while DZ
twins share, on average, 50% of their segregating genes. This,
however, rests on the assumptions that the common environmen-
tal influences are the same for MZ and DZ pairs. Furthermore,
this study design requires equal phenotype means and variances
for MZ and DZ pairs.
The genetic variance can further be decomposed into parts due
to additive genetic effects (A) and dominance genetic effects, i.e.
interaction between alleles on the same locus, (D) over all relevant
loci. Epistatic effects refer to interaction between different loci. If
the loci are unlinked, e.g. by being on different chromosomes,
these effects are modelled as part of dominance genetic effects. In
the case of linked loci, epistatic effects will be modelled as additive
genetic effects since the loci segregate together and form a unit.
The environmental variance can be divided into factors shared by
co-twins (C) and factors that are unique to each twin individual
including also any measurement error (E). Hence, the total
phenotypic variance can be decomposed into four components:
the additive genetic (A), dominant genetic (D), common environ-
mental (C), and unique environmental (E) variance component.
BMI was used as a measure of body fatness. Two sets of
statistical analyses were conducted to calculate the variance
components in BMI. The first is a classical twin analysis using
MZ and DZ twin pairs [20] from which we identified the most
parsimonious variance components model for BMI. The twin
analyses in the current study were conducted using the Mx
statistical software, version 1.7.03 [21]. Mx derives structural
equation models from twin and family data and calculates the
variance components through maximum likelihood estimation.
Since we only have data on twins reared together, the D and C
components could not be estimated simultaneously, and thus the
model fit of the ACE and ADE models were compared.
Subsequently, the significance of each variance component was
tested and the most parsimonious model was selected.
Assuming that the AE model adequately explains BMI variance,
the second set of analyses investigated the secular trends in the A
and E components using only full-brother pairs. The twin study
complements the full-brother pair analysis by testing whether this
assumption is reasonable. The advantage of using full brother pairs
in this second phase is that this data set is considerably larger
which improves the statistical power to detect possible secular
changes. The brother pairs were divided into birth cohorts defined
by the birth year of a randomly selected brother in a pair. The
BMI variance and brother pair covariance was then calculated for
each birth cohort. Assuming the absence of common environ-
mental and dominant genetic influences, or assuming that these
influences are very small, the covariance multiplied by two is an
estimate of the additive genetic variance in BMI since brothers
Table 1. Summary statistics for BMI in twins and full-brothers.
N Mean Variance Covariance [95%CI] Correlation [95%CI]
Full-brothers 116,478 21.69 7.88 2.96 [2.94–2.98] 0.37 [0.37–0.38]
DZ twins 1,961 21.05 5.65 2.33 [2.19–2.48] 0.43 [0.39–0.47]
MZ twins 1,736 21.04 5.61 4.72 [4.42–5.05] 0.83 [0.82–0.85]
doi:10.1371/journal.pone.0027135.t001
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share, on average, half of their segregating genes such as DZ
twins. The remaining variance can be attributed to the non-
shared environmental influences. A sensitivity analysis approach
was used to investigate whether the same results could be
obtained when normalising the BMI distribution with the
logarithmic transformation.
To compare the development in the variance components with
the general course of the Swedish obesity epidemic, we calculated
obesity prevalence for each birth year using the WHO defined cut-
point of BMI$30 [22]. Pearson correlation coefficients for the
relationship between prevalence of obesity and the A and E
variance components were calculated. The purpose was to explore
the hypothesis that changes in the variance components are
related to the influence of the obesogenic environment. For higher
accuracy of the estimates, we used the whole sample of conscripts
in the calculation of prevalence estimates.
Results
The prevalence of obesity increased more than five-fold from
0.8% to 4.4% from the 1951 to the 1983 birth cohort (Figure 1).
Less strong increase over time in prevalence of overweight and
even stronger increase in prevalence of severe obesity has been
reported previously [1]. Two phases can be identified from the
development. The first phase includes the birth cohorts from 1951
to around 1973 where the increase in prevalence was moderate. In
the second phase the increase became significantly stronger with
the birth cohorts from around 1973 and onward.
Table 1 shows summary statistics for full-brother pairs and MZ
and DZ twin pairs. The difference in BMI means and variances
between MZ and DZ twin pairs were not statistically significant,
thus the equal means and variances assumption was not violated.
It is noteworthy that both the BMI mean and variance was higher
for singletons than for twins. This discrepancy has also been found
for phenotypes such as body size and muscle strength and has
previously been thoroughly discussed [23].
When analysing the twin data, the ACE model provided at
marginally better fit of the data than the ADE model. The
standardized A, C and E components were estimated at 0.80 [95%
CI 0.73–0.84], 0.02 [95% CI 0.02–0.03] and 0.18 [95% CI 0.18–
0.19], respectively. The ACE model was compared to the AE
model. With a chi-square difference of 0.385 and a difference in
degrees of freedom of 1, the p-value was 0.535 indicating that the
ACE model did not fit the data better than the more parsimonious
AE model. Hence, evidence for the contribution of common
environmental influences to BMI variation was not found in the
current data. The standardized A and E components were
estimated at 0.82 [95% CI 0.82–0.84] and 0.18 [95% CI 0.18–
0.19], respectively, under the AE model. Since the AE model will
be used, we will, from now on, simply refer to the additive genetic
variance as genetic variance.
Among full-brothers the total BMI variance increased from 5.7
in the 1951 birth cohort to 9.9 in the 1983 birth cohort. The BMI
covariance increased in a similar proportion from 2.1 to 3.9 within
the period studied. The correlation coefficients for BMI within the
brother pairs increased slightly from around .375 [95% CI .340–
.410] to .394 [95% CI .345–441], which translates into an increase
in BMI heritability from 75% to 78.8%, although not statistically
significant.
Since we can assume an AE model the genetic variance is twice
the covariance within full-brother pairs. Hence, the genetic
variance increased from 4.3 [95% CI 4.04–4.53] to 7.9 [95% CI
7.28–8.54] over the period studied (Figure 2). The increase was
moderate from 1951–73 where after the genetic variance
increased strongly. The unique environmental variance is
responsible for the remainder of phenotypic variance and hence
increased from 1.4 [95% CI 1.32–1.48] to 2.0 [95% CI 1.89–2.22]
within the period (Figure 2).
In Figure 3 the prevalence of obesity is plotted against the
genetic and environmental variance for each birth cohort and
regression lines are shown for both sources of variance. The slopes
of both regressions were statistically significant (p,0.001 for both
Figure 1. The prevalence of obesity in the full sample of conscripts by year of birth with 95% CI.
doi:10.1371/journal.pone.0027135.g001
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genetic and environmental variance). The Pearson correlation
coefficient for the relationship between the genetic variance and
prevalence of obesity showed a very high positive correlation of
.921 [95% CI .844–.960], while the correlation between the
environmental variance and prevalence of obesity was .572 [95%
CI .285–.765].
Figure 2. The additive genetic variance (95% confidence intervals) and unique environmental variance of BMI plotted against year
of birth.
doi:10.1371/journal.pone.0027135.g002
Figure 3. The prevalence of obesity plotted against the additive genetic and the unique environmental variance of BMI for each
birth cohort. The regression lines are shown for the genetic and environmental variance vs. prevalence of obesity.
doi:10.1371/journal.pone.0027135.g003
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The analyses were also conducted with the logarithm of BMI to
test whether the same results would be obtained when the slight
right skewness of the BMI distribution were taken into account.
This transformation did not lead to different results and to ease the
interpretation only the untransformed results are presented.
Discussion
The results of structural equation modelling of MZ and DZ
twins showed that a model including additive genetic and non-
shared environmental factors adequately explains variance in
BMI. These findings are also consistent with results from most
adoption studies of non-biological siblings [24]. The analyses on
birth cohort strata of full-brothers showed a statistically significant
increase in BMI variance and covariance. The additive genetic
variance of BMI increased strongly from 4.3 to 7.9 while the
heritability of BMI increased slightly from 75% to 78.8%. The
unique environmental variance of BMI showed a moderate
increase from 1.4 to 2.0.
The results support the hypothesis that the genetic variance has
increased in Sweden between 1969 and 2001. In parallel with the
increasing prevalence of obesity, the increase in the genetic
variance was moderate from the first observations in birth cohorts
from 1951 until around 1973. From around birth cohort 1973 and
onward the increase became stronger. The trend in the genetic
variance was highly correlated (.921) with the trend for the
prevalence of obesity in the same population, supporting the
notion that it may be the obesogenic environment that drives the
upward trend in the genetic variance (Figure 3). The close
relationship points to a dose-response relationship between the
exposure to the obesogenic environment and the genetic variance.
Hence, the findings suggest GxE between the obesogenic
environment and adiposity related genes in the direction of
stronger genetic influences on adiposity in a more obesogenic
environment.
The results show that the strong increase in BMI variance
primarily is driven by the increase in genetic variance and only to
a small extent by an increase in the environmental variance. This
implies, that although the influence from the obesogenic
environment has increased during the last decades [25], it is
primarily the genetic background that creates the considerable
variation in body fatness within populations and as the force of the
obesogenic environment has become stronger the genetic
differences have become more pronounced.
The increase in the genetic variance can be decomposed on
basis of the mathematical formula for the contribution from one
genetic locus which equals p(1-p)a
2
, where ‘‘p’’ denotes the
population prevalence of an arbitrary obesity-related allele and
‘‘a’’ is the effect of that allele [26]. In the current study the
population strata was defined by year of birth. This will most likely
imply that the gene pool between strata is comparable, since,
except for possible changes in genetic variation due to immigra-
tion, the gene pool does not change noteworthy within a few
decades. This allows us to assume that ‘‘p’’ is constant across the
birth cohorts. Hence, there are two main explanations to the
increase in additive genetic variance in the more recent birth
cohorts. Either the effect of loci already contributing to adiposity
increases over time or new genes not previously expressed are
activated as the influence from the obesogenic environment
increases. Naturally, a combination of the two explanations is
possible as well. Thus, the results indicate that the obesogenic
environment modifies the expression of genes related to body
fatness in the direction of stronger genetic influences in a more
obesogenic environment.
The influence of the obesogenic environment on the genetic
variance of BMI is possibly the result of GxE, which could occur in
several ways. The genetic effects could be modified if the strength
of an association in a biological pathway from genotype to
phenotype is influenced by environmental circumstances. Fur-
thermore, it can be argued that the causal pathway from genotype
to phenotype goes beyond the limits of our physical organism. If a
behavioural pattern mediating the genetic influence on obesity is
either suppressed or facilitated under certain environmental
conditions, it is reasonable to assume that the strength of an
association between genotype and phenotype will change. Other
types of mediating phenotypes under genetic influence could play
a role, e.g. that an individual’s perceived level of stress is under
genetic influence and that, in turn, stress is a predictor of body
fatness. If the environment changes and becomes more stressful it
will have the strongest impact on individuals with a genetic
susceptibility to stress. The association between genotype and BMI
would then increase and thereby contribute to genetic variation in
BMI. Finally, the GxE may involve a combination of different
mechanisms.
This study is primarily exploratory and currently we have very
limited knowledge concerning which mechanisms and environ-
mental factors are actually underlying the findings. Considering
the magnitude of change in genetic variance in a more obesogenic
environment, the findings strongly encourage research in how the
environmental influences alter the genetic contribution to
differences in BMI, which would possibly allow a better targeting
of preventive efforts in the future.
The results align with a recent study using quintile regression of
genetic associations with BMI in children [27]. The authors
calculated a genetic score based on five previously confirmed
genetic markers of obesity. The genetic markers showed stronger
associations with BMI in the higher BMI quintiles. Although the
genetic profile may not be similar between the quintiles it
complements the current findings by showing stronger genetic
effects on adiposity among subgroups, which may be more
exposed to the obesogenic environment. These results may be a
consequence of GxE and/or gene-gene interaction, and the latter
may be the case if the gene pool differs between the quintiles.
Several studies have examined whether putative risk factors for
obesity modify the genetic variation in BMI. Although the
direction of causation between obesity and physical activity is
unclear, the level of physical activity may still be an indicator of
the exposure to the obesogenic environment. Hence, the consistent
evidence for an interaction between physical activity and the
additive genetic variance in BMI, where higher genetic variance
and stronger candidate gene effects are found in groups with a
lower physical activity, points in the same direction as the current
results [9–15]. The same argument applies to a recent study on
Danish twins where a lower genetic variance in BMI was found
with a higher level of education, which is known to be inversely
associated with obesity [28]. In addition, it is worth noting that
common variation in the CHRNA5-CHRNA3-CHRNB4 gene
region (chromosome 15q25) is associated with BMI in ever
smokers, but not in non-smokers, which implies that also smoking
modifies the genetic variation in BMI [29]. However, an
important strength in the current study is that the population
strata are defined by birth year, rendering the gene pool
comparable across strata. Hence, the changes in genetic variation
can be attributed to environmental modification of genetic effects
rather than differences in the gene pool between strata.
In addition to possible confounding from the C and D
components, assortative mating may play a role as well. The
assumption of equality of gene frequencies across birth cohorts
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relies on the assumption that mating occurs at random [20]. If, on
the other hand, mating occurs more frequently among individuals
with genetic similarity, the genotype frequency could change
across birth cohorts. This would undermine our interpretation that
the increase in genetic variance could only be the result of changes
in the genotypic effects. However, assortative mating is unlikely to
account for the strong increase in genetic variance. It occurs
primarily in the highest percentiles of the BMI distribution and
thereby presumably contributes only little to the general variation
in BMI [30]. Furthermore, assortative mating would increase the
similarity of DZ twins and thereby mimic common environmental
influences. Thus, the non-significant C component in the twin
study supports the assumption of no assortative mating.
An important strength of the current study is that all
participants were measured at approximately the same age, i.e.
around 18–19 y and through the use of standardized measure-
ment techniques. The genetic influence on adiposity may be age-
dependent and measurement error is modelled as unique
environmental variance, which attenuates the estimate of herita-
bility. Hence, keeping these factors nearly constant allows a more
accurate calculation of the genetic contribution to BMI.
Furthermore, the collection of data from mandatory conscription
examinations implies that the cohort is not self-selected, but is
highly representative of the general population.
Another strength is that this study utilizes the impressive size of
the Swedish conscript database. The classical twin design would
tend to suffer from statistical power problems in addressing the
secular trend in variance components. For example, the current
population of nearly 1.5 million conscripts included merely 3,699
twin pairs eligible for analysis. Since the AE model proves to be
the most parsimonious model for decomposing BMI variance, the
analysis of covariance in full-brother pairs is by far the most
statistically powerful approach to study secular changes in the
variance components of BMI.
The study is subject to various limitations. Using full-brothers
instead of twins comes at a price. While twin pairs are born at the
same time full-brother pairs can be years apart. Although we limit
our analyses to pairs with a maximum age difference of 3 years, the
obesogenic environment may still change slightly between the births
of two brothers. In periods with rapid secular changes in the
obesogenic environment, this may influence the correlation and
covariance estimates and hence bias the results. However, it seems
unlikely that such biases could alone produce the relatively uniform
secular trends and would probably rather tend to dilute the results.
Likewise the twin analyses may have produced biased estimates of
heritability. Perhaps the most critical assumption is the ‘equal
environments assumption’, implying that MZ twins do not share a
more similar environment than DZ twins [20]. This assumption
may however not always be valid, e.g. MZ twins may be treated
more similarly by their surroundings than DZ twins [31]. This could
potentially increase their phenotypic similarity later on and in turn
generate inflated estimates of heritability. Similarly, if MZ twins are
more similar than DZ twins in terms of epigenetic characteristics
[32] the heritability will also tend to be inflated. However, this
problem is not of serious concern in the current analyses since the
twin pairs are used primarily to test the significance of the common
environmental influence. Furthermore, our primary interest is not
in the absolute size of genetic influence but rather in investigating
secular changes herein. Another limitation is that some DZ twins
may falsely be classified as MZ twins and vice versa. However, with
a relatively low misclassification rate (less than 4%) studies have
confirmed that questionnaires are a valid method of zygosity
determination [33]. Hence, this issue should not bias our results
considerably. Finally it should be noted that the analyses only
include males. Corresponding analyses would have to be carried out
in female sibling pairs before we can determine whether the findings
are representative of the entire population.
The findings may have both theoretical and practical
perspectives. Firstly, they may elucidate the mechanisms of GxE
during the last decades and thereby improve our general
understanding of the role of adiposity-related genes in the obesity
epidemic. It may also nuance our understanding of genetic factors
as, rather than being fixed from conception, are quite flexible units
in terms of their influence on phenotypes. Secondly, the findings
may have a practical relevance if they are used instrumentally in
genome wide association studies (GWAS). Since the total genetic
variation is much higher in populations under a strong influence
from the obesogenic environment, the GWAS samples may be
selected to yield the highest genetic variance thereby improving
the chances of finding statistically significant genetic effects.
Furthermore, since the increase in genetic variance could be
explained both by an increase in the influence from genes as well
as by the activation of novel gene effects, this approach may also
allow the discovery of new adiposity related genes.
In summary, the twin analyses showed that a model including
additive genetic and unique environmental variance adequately
explains BMI variance. On basis of the AE model the variance and
covariance of BMI within Swedish brother pairs confirmed the
hypothesis that the genetic variance in BMI has increased strongly
throughout the obesity epidemic, suggesting GxE. The influence of
genes can potentially be modified by the environment in any part
in the causal pathway linking genotype to phenotype, including the
restriction of facilitation of behavioural patterns related to obesity.
The findings may have both theoretical and practical perspectives.
Firstly, they may be a step towards a better understanding of the
general role of genes during the obesity epidemic. Secondly, the
findings could be used instrumentally in GWAS studies. It is
plausible that if study population with the highest genetic variance
is selected the chance of finding new genetic variants is improved.
Thirdly, the results strongly encourage research in how the
environmental influences alter the genetic contribution to
differences in BMI, which would possibly facilitate development
of better targeted preventive efforts in the future.
Author Contributions
Conceived and designed the experiments: BR TIAS FR KS. Performed the
experiments: BR. Analyzed the data: BR. Contributed reagents/materials/
analysis tools: TIAS FR KS MG PT. Wrote the paper: BR. Reviewing and
editing of the text: BR KS PT MG TIAS FR.
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The Genetic Variance of Body Mass Index
PLoS ONE | www.plosone.org 7 November 2011 | Volume 6 | Issue 11 | e27135
