Body Mass Index and Breast Size in Women: Same or Different Genes?
Abstract
The objective of the current study was to investigate the heritability of breast size and the degree to which this heritability is shared with BMI. In a sample of 1010 females twins (mean age 35 years; SD = 2.1; range 28-40), self-report data pertaining to bra cup size and body mass index (BMI) was collected in the context of self-report data and an interview relating to disordered eating respectively. In a sample of 348 complete twin pairs who completed data collection (226 MZ pairs and 122 DZ pairs and 360 incomplete pairs (170 MZ and 190 DZ)), we found that the heritability of bra cup size was 56%. Of this genetic variance, one third is in common with genes influencing body mass index, and two thirds (41% of total variance) is unique to breast size, with some directional evidence of non-additive genetic variation. The implications of these findings with respect to previous research linking breast size with reproductive potential are discussed.
450 Twin Research and Human Genetics Volume 13 Number 5 pp. 450–454
The objective of the current study was to investi-
gate the heritability of breast size and the degree
to which this heritability is shared with BMI. In a
sample of 1010 females twins (mean age 35 years;
SD =
2.1; range 28–40), self-report data pertaining to
bra cup size and body mass index (BMI) was col-
lected in the context of self-report data and an
interview relating to disordered eating respectively.
In a sample of 348 complete twin pairs who com-
pleted data collection (226 MZ pairs and 122 DZ pairs
and 360 incomplete pairs (170 MZ and 190 DZ)), we
found that the heritability of bra cup size was 56%.
Of this genetic variance, one third is in common with
genes influencing body mass index, and two thirds
(41% of total variance) is unique to breast size, with
some directional evidence of non-additive genetic
variation. The implications of these findings with
respect to previous research linking breast size with
reproductive potential are discussed.
Keywords: breast size, body mass index, heritability,
bivariate Cholesky
Individual differences in breast size are a conspicuous
feature of variation in human females and have been
variously associated with breast cancer risk (Thurfjell
et al., 1996), where a larger bra cup size is associated
with increased risk of cancer (Hsieh & Trichopoulos,
1991), and higher reproductive potential (Møller et
al., 1995). While more recent research shows that it is
the percentage of total radiologically dense breast
tissue area that is associated with a four- to six-fold
increase in breast cancer risk rather than breast size
(Boyd et al., 1998), the link with reproductive poten-
tial continues to be of interest.
There is a developing literature on the relationship
between female body weight, breast size and waist-to-
hip ratio (WHR) and male ratings of attractiveness.
Findings to date vary across studies, with recent work
indicating that British Caucasian males prefer a high
WHR black figure with small breasts and a high
WHR white figure with large breasts (Swami et al.,
2009). Ratings of attractiveness of such figures has
been significantly and positively associated with
ratings of health (Furnham et al., 2006), and women
with large breasts have been inferred to have higher
fecundity as assessed by measures of daily levels of 17-
β-oestradiol (E2) and progesterone (Jasien´ka et al.,
2004). Thus male preference for larger breast size
might be adaptive as it might contribute to higher
reproductive success.
While the heritability of WHR has been previously
examined (Nelson et al., 1999), where 48% of the
variance has been attributed to additive genetic effects,
no such examination of breast size has been con-
ducted. Earlier observations have attributed breast size
to energy intake early in life (Hsieh & Trichopoulos,
1991; Trichopoulos & Lipman, 1992) but it is likely
that genetic factors will have a major influence as it
does for WHR. Additionally, if breast size signals fer-
tility, whatever selective forces have been and are still
at work on breast morphology, they can only work on
genetic variation.
Hence the aim of the current investigation is to
examine the heritability of breast size. An obvious
covariate of breast size is overall size of the woman;
here we index this using body mass index (BMI). In
contrast to breast size, the heritability of BMI has been
extensively investigated, and twin studies suggest
between 50 to 90% of the variance in BMI is accounted
for by genetic factors (Maes et al., 1997; Schousboe et
al., 2003). We report results of a bivariate genetic
analysis of data on bra cup size and BMI in middle
aged MZ and DZ twins, collected in the context of a
study on eating habits and disorders, in order to investi-
gate the heritability of breast size and the degree to
which this heritability is shared with BMI.
Method
Participants
Participating twins were drawn from a cohort of 8536
twins (4268 pairs) born 1964–1971, who were regis-
tered as children with the Australian Twin Registry
(ATR) during 1980–1982, in response to media and
systematic appeals through schools. Female-female
twins who had participated in at least one of two waves
of data collection (Heath et al., 2001), one during
1989–1992 when the twins were aged 18–25 years, and
Body Mass Index and Breast Size
in Women: Same or Different Genes?
Tracey D. Wade,1 Gu Zhu,2and Nicholas G. Martin2
1School of Psychology, Flinders University, Australia
2Queensland Institute of Medical Research, University of Queensland, Australia
Received 16 June, 2010; accepted 29 July 2010.
Address for correspondence: Professor Tracey Wade, School of
Psychology, Flinders University, PO Box 2100, Adelaide, SA, 5001,
Australia. E-mail: tracey.wade@flinders.edu.au
451
Twin Research and Human Genetics October 2010
Body Mass Index and Breast Size in Women
the other during 1996–2000 when the median age of
the sample was 30 years, were approached during
2001–2003 to participate in a third wave of data collec-
tion; of 2,320, individual twins approached 1,083
(47%) consented to participate (Wade et al., 2006b).
1002 (43%) completed a semi-structured interview over
the telephone relating to current and lifetime eating and
1016 (44%) completed a mailed self-report question-
naire assessing various aspects of personality (Wade et
al., 2006a), with 962 women completing both (42%).
In all, 1056 females (46%) participated in at least one
of the data collection components.
The sample included 348 complete twin pairs who
completed Wave 3 data collection, comprising 226 MZ
pairs and 122 DZ pairs, and 360 incomplete pairs (170
MZ and 190 DZ), where only one twin of a pair partic-
ipated. Participation at the third wave of data collection
has previously been shown not to be predicted by the
number of eating problems at Wave 1 nor by any of the
16 individual eating problems making up this total,
including ever suffered from or been treated for eating
disorder, low body weight, binge eating, obesity, weight
loss, anorexia nervosa, bulimia (Wade et al., 2006c).
Neither did BMI at Wave 1 predict participation at
Wave 3 (OR = 0.99, 95% CI: 0.94–1.03, p= .56).
Zygosity was determined on the basis of responses to
standard questions about physical similarity and confu-
sion of twins by parents, teachers, and strangers,
methods that give better than 95% agreement with
genotyping (Eaves et al., 1989). The Flinders University
Clinical Research Ethics Committee approved the study
and written informed consent was obtained.
Measures
Self-reported body mass index (BMI) was calculated
using weight (kg)/height
2
(metres). Participants were
asked what bra cup size they normally wore: A, B, C, D,
E, F, G+, where A is the smallest size and G+ the largest.
These answers were converted to a scale of 1 to 7.
Statistical Analyses
For the purpose of the following analyses, all data were
treated as continuous. Using the statistical package Mx
(Neale, 1997), a full information maximum likelihood
(FIML) approach was employed with raw data, where
complete and incomplete pairs of twins are included in
the analyses. FIML can reduce the impact of any
respondent bias when the data are missing at random
(Little & Rubin, 1987). FIML estimation has been
found to be superior to the three ad hoc techniques
(listwise deletion, pairwise deletion, and mean imputa-
tion) in multiple regression models as FIML parameter
estimates had less bias and sampling variability than the
other three methods (Enders, 2001). Univariate and
bivariate genetic models were fitted using standard
methods (Neale & Cardon, 1992).
Results
The mean age of the women at the time of the data
collection was 35 years (SD = 2.11; range 28–40). The
mean BMI value was 24.09 (SD = 4.91) and ranged
from 14.20 to 63.98, where 10 people were classified
as morbidly obese with a 40 > BMI < 50, and 2 people
(MZ cotwins) had a BMI > 50. The BMI of 63.98 is
unlikely to be an error as the identical co-twin,
assessed at a different interview and by a different
interviewer, had a BMI of 54.56. For genetic analysis
four extreme values were reduced to 45.00 (4.5 SD)
before log transformation. Bra cup size ranged from 1
(A) to 7 (G+), with a mean of 2.67 (between B and C
cup) and a SD of 1.05. The distribution of bra cup
size in the total sample is shown in Figure 1.
BMI and bra size were significantly positively
skewed (respective zvalues 20.69 and 4.61).
Normality was best achieved by using a natural log
(ln) transformation for BMI (Kolmogorov-Smirnov Z
= 2.46, p < .001), the quadratic function (X0.25) for bra
size (Kolmogorov-Smirnov Z = 5.90, p < .001).
Logistic regression revealed no significant differences
between the monozygotic and dizygotic twins with
respect to mean values for BMI (OR=1.01, 95% CI:
0.98-1.03) and bra cup size (OR = 1.03, 95% CI:
0.92-1.17). Neither was cooperation bias detected as
there were no significant differences between complete
and incomplete pairs for mean values of BMI (OR =
1.00, 95% CI: 0.97-1.03) and bra cup size (OR=1.11,
95% CI: 0.98-1.26).
The number of complete pairs with data for BMI
was 204 (MZ) and 109 (DZ), the number of complete
pairs with data for bra cup size was 226 (MZ) and 122
(DZ), and the number of pairs with complete data for
both variables was 204 (MZ) and 109 (DZ). The FIML
correlations for BMI and bra cup size with age as fixed
effects are shown in Table 1. Given the narrow age
range of this sample, regression on age (and age2) was
negligible but was nevertheless retained for univariate
and bivariate model fitting.
Figure 1
Histogram of Bra cup size for all subjects (
N
= 1010).
452 Twin Research and Human Genetics October 2010
Tracey D. Wade, Gu Zhu, and Nicholas G. Martin
Univariate twin model fitting results are shown in
Table 2 and for both variables, the AE submodel was
the most parsimonious as judged by the Akaike
Information Criterion (AIC).
The results of bivariate model fitting are shown in
Table 3. Once again, the AE model gave the best fit and
the proportions of variance explained by the latent
sources of A and E are shown in Figure 2. It can be
seen that only 15% of the variance of bra cup size is
due to the additive genetic sources contributing to BMI
with a further 41% of genetic variance specific to BRA.
The correlation between genetic sources contributing to
BMI and bra cup was 0.52 and the correlation for non-
shared environmental sources was 0.44.
Discussion
Our main aim was to conduct a bivariate genetic
analysis of data on bra cup size and BMI in adult
female MZ and DZ twins. Doing so has allowed us to
answer two previously unanswered questions: (1)
what is the heritability of breast size, and (2) to what
degree are the genetic factors contributing to breast
size overlapping with BMI. In answer to this first
question, we found that the heritability of bra cup size
was 56%. Therefore while earlier research has focused
on environmental explanations for breast size such as
energy intake early in life (Hsieh & Trichopoulos,
1991; Trichopoulos & Lipman, 1992), our results
suggest that there is a substantial genetic contribution
Table 1
FIML Correlations (and 95% confidence intervals) for Transformed BMI and Bra Cup Size Between Twin 1 (t1) and Twin2 (t2) for Monozygotic (MZ)
(upper diagonal) and Dizygotic (DZ) twins (lower diagonal) (corrected for age)
Variables BMI t1 BRA t1 BMI t2 BRA t2
BMI t1 1 0.54 0.73 0.39
(0.45–0.61) (0.66–0.77) (0.23–0.43)
BRA t1 0.43 1 0.34 0.58
(0.31–0.54) (0.28–0.49) (0.48–0.65)
BMI t2 0.43 0.09 1 0.51
(0.25–0.56) (0.09–0.27) (0.41–0.58)
BRA t2 0.22 0.16 0.45 1
(0.05–0.37) (0.02–0.32) (0.32–0.55)
Note: twin correlations are shown in bold; Natural log (ln) transformation for BMI and the quadratic function (X0.25) for bra size.
Table 2
Results of Univariate Twin Model Fitting to Twin Data for Transformed of Body Mass Index (BMI) and Bra Cup Size (BRA) Corrected for Age
Variables Model tested Model # -2LL df AIC Compare χ2df Prob.
BMI ADE 1 2653.3 994 665.3
ACE 2 2652.5 994 664.5 2:3 0.8
1
3.9E-01
AE 3 2653.3 995 663.3 3:1 0.0
1
1.0E+00
CE 4 2669.5 995 679.5 4:2 17.0
1
3.7E-05
BRA ADE 1 2777.8 1006 765.8
ACE 2 2779.9 1006 767.9 2:3 0.0
1
1.0E+00
AE 3 2779.9 1007 765.9 3:1 2.2
1
1.4E-01
CE 4 2799.1 1007 785.1 4:2 19.2
1
1.2E-05
Note: Best-fitting model is bolded.
Table 3
Results of Bivariate Cholesky Decomposition for Transformed Body Mass Index and Bra Cup Size Corrected for Age
Model tested Model # -2LL df AIC Compare χ2df Probability
ADE 1 5178.2 1997 1184.2
ACE 2 5180.7 1997 1186.7 2:3 1.1
3
0.77
AE 3 5181.8 2000 1181.8 3:1 3.6
3
0.30
CE 4 5217.6 2000 1217.6 4:2 36.9
3
< .001
E 5 5434.6 2003 1428.6 5:2 254.0
6
< .001
Note: Best-fitting model is bolded.
453
Twin Research and Human Genetics October 2010
Body Mass Index and Breast Size in Women
explaining the phenotypic diversity of breast size. In
answer to our second question, of the genetic variance
contributing to breast size, one third was in common
with genes influencing BMI, and two thirds (41% of
total variance) was unique to breast size.
Given that larger breast size has been postulated to
be associated with greater reproductive success (Cant,
1981; Jasien´ ka et al., 2004), it is a puzzle that consider-
able phenotypic, and as shown here, genetic variance
remains in the population. It seems more likely that
breast size has been subject to stabilizing selection influ-
ences, perhaps affected by genetically programmed
differences between males in their breast size prefer-
ence. However, the picture is much more complex than
this. Other variables affecting the survival of young off-
spring must be considered. For example, the ability to
breastfeed must play a role, and larger breasts are not
necessarily the most functional at this stage, given that
babies of large breasted women have some difficulty in
latching on to the nipple because they have such a tiny
mouth in comparison to the areola. Thus the larger
picture that informs what the selective forces are for
breast size, and the direction in which they have acted,
remains unclear.
To our knowledge, there are few other studies of
bra cup size in an anthropometric setting and no others
from a quantitative genetic perspective. In a classic
study of clothing sizes in Dutch women, Vandenberg
(1968) included ‘chest girth’ among 15 body measure-
ments and found it loaded on the first factor of general
size, with little indication of a specific loading. This is
not surprising since chest girth confounds both breast
size and trunk girth, with variance specific to the
breast size being swamped by variance in overall size.
While we believe that our focus on bra cup size enables
us to ensure that these two factors are not confounded,
the imperfect nature of our measured phenotype needs
to be recognized, given that a 36DD bra may be the
same volume as a 34F or 32G. Further examination of
a better measure of the phenotype is required in order
to confirm the results of the current research.
There are a number of other limitations of the
current research in the context of which the results
should be interpreted. First, the data for all variables
are based on self-report. This may be particularly less
than satisfactory for BMI where has been estimated at
the kappa between self-reported and measured BMI is
0.705 for women (Craig & Adams, 2009). Second, we
had no information pertaining to child bearing or sur-
gical interventions that may impact on breast size.
Third, we have a moderate response rate (47%), com-
mensurate with another large Australian population
study where an initial response rate for mid-age
women was 54% (Brown et al., 1998). Previously no
response bias due to a past history of disordered
eating has been detected for this sample (Wade et al.,
2006c), or other samples of Australian twins (Wade et
al., 1997). We doubt, however, that any of these pro-
tective limitations would substantially alter our
conclusion that there is a substantial genetic contribu-
tion to breast size of which two-thirds of the variance
is relatively unique to this phenotype and not shared
with BMI.
Disclosure Statement
The authors declared no conflict of interest.
Acknowledgments
Grant 160009 from the National Health and Medical
Research Council (NHMRC) supported this work.
The author would like to thank the twins for their
participation in this research, and Ms Jacqueline
Bergin for coordinating the data collection.
Administrative support for data collection was
received from the Australian Twin Registry that is sup-
ported by an Enabling Grant (ID 310667) from the
NHMRC administered by the University of
Melbourne. We thank our reviewer for many helpful
comments that resulted in revisions of this manuscript.
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- CitationsCitations12
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- "The role of prominent female breasts is difficult to explain from an evolutionary perspective, as the human female is the only primate that has permanent, full-form breasts when not pregnant (Pawlowski, 1999). One of the explanations implies that women's breasts are signals of a woman's biological condition and/or fertility, as there is also a genetic contribution to breast size, not shared with body mass index (Wade et al., 2010). Male fetuses are subject to a stronger intrauterine negative selection than female ones (e.g., Boklage, 2005; Di Renzo et al., 2007). "
[Show abstract] [Hide abstract] ABSTRACT: Objectives Breast size and fluctuating asymmetry (FA) are related to women's biological condition, as size correlates positively with fecundity, whereas FA correlates negatively with biological quality. We tested if breast volume, FA, and their changes during pregnancy are related to a fetus's sex. Women with bigger, symmetrical breasts, with a greater increase in size during pregnancy, should be more likely to carry a more ecologically sensitive and energetically demanding male fetus.Methods Ninety-three women participated in a 3-stage longitudinal study. 3D breast scans were performed in the first, second, and third trimester of pregnancy. As there was a small variation in pregnancy week at each research stage between the participants, the expected breast volume and FA values for the 12th, 22nd, and 32nd pregnancy week were calculated, basing on the obtained measurements. Those values were compared between mothers who carried a boy and mothers who carried a girl.ResultsAlthough women who carried a boy had somewhat larger breasts at each trimester than women who carried a girl, the difference was not significant. ANOVA for repeated measurements revealed a greater breast size increase in women carrying a boy (P = 0.039). FA decreased during pregnancy, but was not related to a fetus's sex.Conclusion Pregnancy-induced breast volume increase is a better cue of a fetus's sex than breast asymmetry or breast size per se, i.e., the traits that are supposed to indicate a woman's biological condition. Women with a larger increase in breast size during pregnancy are more likely to carry to term a more ecologically vulnerable male fetus. Am. J. Hum. Biol., 2015. © 2015 Wiley Periodicals, Inc.- "The link between overweight, elevated serum testosterone concentration and pathologies such as the polycystic ovary syndrome could be underlying this relationship (Balen et al., 1995; Barber et al., 2006). Heritability of breast cup size has been estimated to be 56%, and one third of this variance was shared with body mass index (Wade et al., 2010). Some genetic variants associated with breast size also influence breast cancer risk (Eriksson et al., 2012). "
[Show abstract] [Hide abstract] ABSTRACT: Breastfeeding has been an important survival trait during human history, though it has long been recognized that individuals differ in their exact breastfeeding behavior. Here our aims were, first, to explore to what extent genetic and environmental influences contributed to the individual differences in breastfeeding behavior; second, to detect possible genetic variants related to breastfeeding; and lastly, to test if the genetic variants associated with breastfeeding have been previously found to be related with breast size. Data were collected from a large community-based cohort of Australian twins, with 3,364 women participating in the twin modelling analyses and 1,521 of them included in the genome-wide association study (GWAS). Monozygotic (MZ) twin correlations (r MZ = 0.52, 95% CI 0.46-0.57) were larger than dizygotic (DZ) twin correlations (r DZ = 0.35, 95% CI 0.25-0.43) and the best-fitting model was the one composed by additive genetics and unique environmental factors, explaining 53% and 47% of the variance in breastfeeding behavior, respectively. No breastfeeding-related genetic variants reached genome-wide significance. The polygenic risk score analyses showed no significant results, suggesting breast size does not influence breastfeeding. This study confers a replication of a previous one exploring the sources of variance of breastfeeding and, to our knowledge, is the first one to conduct a GWAS on breastfeeding and look at the overlap with variants for breast size.- "In addition, larger breasts tend to be more asymmetric [34], which is also a purported risk factor [35]. Breast size and breast cancer risk might also be genetically associated. Breast size heritability is about 56% [36] . Two of the seven single nucleotide polymorphisms (SNPs) associated with larger breast size have been shown to be in linkage disequilibrium with SNPs associated with greater breast cancer risk [37] . "
[Show abstract] [Hide abstract] ABSTRACT: The purpose of these analyses is to test prospectively whether post-diagnosis running and walking differ significantly in their association with breast cancer mortality. Cox proportional hazard analyses were used to compare breast cancer mortality to baseline exercise energy expenditure (METs, 1 MET-hour ~ 1 km run) in 272 runners and 714 walkers previously diagnosed with breast cancer from the National Runners' and Walkers' Health Studies when adjusted for age, race, menopause, family history, breastfeeding and oral contraceptive use. Diagnosis occurred (mean±SD) 7.9±7.3 years prior to baseline. Forty-six women (13 runners, 33 walkers) died from breast cancer during 9.1-year mortality surveillance. For the 986 runners and walkers combined, breast cancer mortality decreased an average of 23.9% MET-hours/d (95%CI: 7.9% to 38.3%; P=0.004). There was a significantly greater decrease in risk for running than walking (risk per MET-hours/d run vs. walked: P=0.03). For the 272 runners analyzed separately, breast cancer mortality decrease an average of 40.9% per MET-hours/d run (95%CI: 19.3% to 60.0%, P=0.0004). When analyzed by categories of running energy expenditure, breast cancer mortality was 87.4% lower for the 1.8 to 3.6 MET-hours/d category (95%CI: 41.3% to 98.2% lower, P=0.008) and 95.4% lower for the ≥3.6 MET-hours/d category (95%CI: 71.9% to 100% lower, P=0.0004) compared to the <1.07 MET-hours/d category. In contrast, the 714 walkers showed a non-significant 4.6% decrease in breast cancer mortality per MET-hours/d walked (95%CI: 27.3% decrease risk to 21.3% increased risk, P=0.71). These results suggest that post-diagnosis running is associated with significantly lower breast cancer mortality than post-diagnosis walking. © 2014 Wiley Periodicals, Inc.
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