Heritability of somatotype components: a multivariate analysis



To study the genetic and environmental determination of variation in Heath–Carter somatotype (ST) components (endomorphy, mesomorphy and ectomorphy).


Multivariate path analysis on twin data.


Eight hundred and three members of 424 adult Flemish twin pairs (18–34 years of age).


The results indicate the significance of sex differences and the significance of the covariation between the three ST components. After age-regression, variation of the population in ST components and their covariation is explained by additive genetic sources of variance (A), shared (familial) environment (C) and unique environment (E). In men, additive genetic sources of variance explain 28.0% (CI 8.7–50.8%), 86.3% (71.6–90.2%) and 66.5% (37.4–85.1%) for endomorphy, mesomorphy and ectomorphy, respectively. For women, corresponding values are 32.3% (8.9–55.6%), 82.0% (67.7–87.7%) and 70.1% (48.9–81.8%). For all components in men and women, more than 70% of the total variation was explained by sources of variance shared between the three components, emphasising the importance of analysing the ST in a multivariate way.


The findings suggest that the high heritabilities for mesomorphy and ectomorphy reported in earlier twin studies in adolescence are maintained in adulthood. For endomorphy, which represents a relative measure of subcutaneous adipose tissue, however, the results suggest heritability may be considerably lower than most values reported in earlier studies on adolescent twins. The heritability is also lower than values reported for, for example, body mass index (BMI), which next to the weight of organs and adipose tissue also includes muscle and bone tissue. Considering the differences in heritability between musculoskeletal robustness (mesomorphy) and subcutaneous adipose tissue (endomorphy) it may be questioned whether studying the genetics of BMI will eventually lead to a better understanding of the genetics of fatness, obesity and overweight.


The somatotype (ST) is a method for describing the human physique as it refers to an individuals body form as a whole.1 The endomorphy component describes the relative degree of fatness of the body; the mesomorphy component is characterized by the predominance of muscle, bone and connective tissue; and ectomorphy by linearity and slenderness of built. The ST and its components are related to disease risk factors and cardiovascular disease.2, 3, 4, 5, 6 It is also related to different levels of performance in different athletic disciplines.7 In this modern era of advanced imaging techniques, the somatotyping method remains an interesting health-related phenotype as it is a fairly straightforward and inexpensive method for gaining insight into the body composition and body type in large-scale epidemiological studies,8 where it may not be feasible to use more expensive imaging techniques.

At present the focus of most studies on heritability, association and linkage of obesity and overweight is on body mass index (BMI) as an indicator of overweight and obesity. As BMI, apart from the weight of organs, not only comprises adipose tissue but also muscle and bone tissue it can be questioned if this approach will eventually lead to a better understanding of the genetics of fatness, obesity and overweight. ST has long been used as an overall indicator of human physique; and in the Heath–Carter ST system, three components are calculated from anthropometric measurements. These components are relative measurements of subcutaneous adipose tissue, musculoskeletal robustness and relative slenderness or linearity.1, 7

Family studies and twin studies agree that the considerable variation in ST components in the general population is both environmentally and genetically mediated. In general, heritability estimates derived from twin studies, which are mainly based on adolescent samples, are above 0.70 for all three ST components,9, 10, 11, 12, 13 although lower estimates were reported for the ST components in the small adult twin sample of Osborne and De George.11, 14 In family studies, these estimates in general cluster around 0.40 and 0.5515, 16, 17, 18 with the exception of the value of 0.68 for mesomorphy in the study of Katzmarzyk et al.17 In a longitudinal study on Flemish twins followed from 10 to 18 years, it was found that sex difference in the heritability of ST components become significant between 13 and 14 years of age, which corresponds to the timing of the adolescent growth spurt in boys.13 The study also demonstrated that variation in the ST components is to a large extent determined by genetic and environmental factors that are shared between components, and hence demonstrates the need to analyse the ST in a multivariate way as was suggested earlier by Song et al.,19 rather than analysing each component separately. To date there are, however, no studies on the heritability of ST components in adults which fully take into account this multivariate nature and study the underlying causes of the well-known covariation between Heath–Carter ST components.

The aim of the present report is therefore to establish the heritabilities of ST components in adults, taking into account the multivariate nature of the phenotype by applying multivariate path-analysis to a large sample of twin data.

Materials and methods

Study participants

The study sample of the Prenatal Programming Twin Study consists of 424 twin pairs between 18 and 34 years of age.20 Twins were selected randomly from the East Flanders Prospective Twin Survey (EFPTS), a Belgian population-based register.21 Eventually, anthropometric data were obtained from 803 twins derived from 424 twin pairs. Table 1 shows the number of subjects for each zygosity group. A detailed description of the zygosity determination can be found in Loos et al.20 The twins gave informed consent and the project was approved by the Local Committee of Medical Ethics.

Table 1 Number of subjects and pairs per zygosity group

Determination of the ST

ST components – endomorphy, mesomorphy and ectomorphy – were determined according to the anthropometric Heath–Carter technique7 using the ST prediction equations. The anthropometric measurements used for calculating the three components are stature, body mass, four skinfolds (triceps, subscapular, supraspinal and medial calf), humerus and femur breadths, and biceps and calf girths. Stature was measured with the subjects on bare feet with an Harpenden stadiometer (Holtain Instruments, Holtain Ltd., Crymych, Wales), to the nearest millimetre. Body mass was measured to the nearest 0.1 kg using a beam balance scale (Seca, Seca gmbh & co. kg., Hamburg, Germany). Skinfolds at the triceps, subscapular, supraspinal and medial calf sites were measured with a Harpenden calliper to the nearest 0.1 mm. Bicondylar and biepicondylar breadths were measured with a small sliding calliper (Siber Hegner, Siber Hegner Maschinen AG, Zürich, Switzerland). Flexed arm circumference and maximal calf circumference were measured to the nearest millimetre with a Holtain flexible fibreglass tape. All bilateral measurements were taken on the left side of the body by two trained anthropometrists. The measurements were performed as described by Claessens et al.1

Statistical analysis

To reduce skewness, the endomorphy data were log-transformed for the analysis, except for the descriptive statistics. No transformation was performed on the other two ST components.

In order to determine the relative contribution of genetic and environmental factors to variation in ST components and to simultaneously take into account the covariation between the components, multivariate path models were fitted to the data.22 First the assumptions for these models were tested, including a test for normal distribution (Shapiro–Wilk test) and a significance test for differences in means (t-test) and variances (F-test) in birth order and zygosity. The raw data were used as input, allowing a maximal use of the available data. Mx, a structural equation modelling (SEM) package,23 was used to compute the goodness of fit of the models and maximum likelihood parameter estimates.

In SEM, the structural linear equations can be visually represented in path diagrams (Figure 1). In these diagrams or models, the latent variables are enclosed in circles. In the classical twin studies, with data of monozygotic (MZ) and dizygotic (DZ) twins reared together, these latent variables can be additive genetic (A), unique (non-shared, non-familial) environmental (E) and common (shared, familial) environmental (C) or dominant genetic (D) factors, although C and D cannot be modelled together in the same model in the classical twin study.22 A, E, C and D are unmeasured variables and are the putative causes of variation in the observed variables (e.g. variation in ST components) that are represented by squares. The causal paths between the latent variables and observed variables are specified and are depicted as single-headed arrows. The correlational paths between the latent variables are depicted by two-headed arrows. As MZ twins are genetically identical, the correlation between their A-factors is 1.0. The correlation between their D-factors also equals 1.0. For DZ twins, which like other sibs share on average 50% of their genetic material, the corresponding values are 0.5 for A-factors and 0.25 for D-factors. The correlation between the C-factors, which cause the members of the pair to be more alike, by definition is 1.0 for MZ and DZ twins. The correlation between E-factors, on the other hand, by definition is zero as they represent an environmental influence unique to an individual. In multivariate models, these latent variables can be further decomposed into ‘common’ factors (Ac, Ec and Cc or Dc), which are shared by, for example, the three ST components and thus contribute to variation in all three components and account for the covariation between the components. Variable-specific latent factors (As, Es and Cs or Ds) explain the remaining variance in the individual ST components, which cannot be accounted for by the pleiotropic effect of the common factors. They are unique for every ST component and therefore do not cause covariation between the components.21 As ST components are correlated with age, the age of the twins was incorporated in the model as described by Neale et al.23 (p 124), that is, an age regression is entered into the model and variance and covariance between ST components explained by age can be separated from variance explained by the genetic and environmental sources of variance (A, C, D and E). In the present analyses, the fit of the tested models is compared with that of the ‘saturated’ model, which provides a baseline fit. The fit of these models is evaluated by the likelihood ratio test (LRT) and by Akaike's Information Criterion. The best-fitting and most parsimonious model was retained to compute heritabilities and maximum-likelihood-based 95% confidence intervals (CIs) of the percentages of variance explained by all sources of variance.

Figure 1

Best fitting model, including age regression. This is the model for DZ opposite-sex twin pairs including sex-specific parameters. Latent variables are represented in circles. Squares represent the observed variables (variances). One-headed arrows are causal paths (1–37). Double-headed arrows represent correlational paths. T1, first-born twin; T2, second-born twin; endo: endomorphy; meso: mesomorphy; ecto: ectomorphy; A: additive genetic source of variance, C: common environmental source of variance, E: specific environmental source of variance. As, Cs and Es: variable-specific sources of variance; Ac, Cc and Ec, common (shared) sources of variance. Note that in MZ twins, correlations between A sources of variance are 1.0 as opposed to a correlation of 0.5 in DZ twins.


In Table 2, descriptive statistics for men and women are represented alongside Heath–Carter ST components of other Flemish samples. Mean age of men in the present study was 25.7 (±4.7) years and 25.5 (±4.7) years of age for women. Taking into account the small age differences between the samples, the data of the present study are comparable with those from the previous studies. Therefore, the assumption that the present twin sample is representative for the general Flemish population and the results of the present study may be generalized towards the Flemish population is tenable.

Table 2 Descriptive statistics of Heath–Carter ST components in Flemish men and women

There were no differences in means and variances for neither birth order nor zygosity in men or women.

The best-fitting and most parsimonious model (Figure 1) (LRT: P=0.30) included additive genetic sources of variance shared by all three ST components (Ac), shared and unique environmental sources of variance shared by the three components (Cc and Ec) and component-specific additive genes (As) and unique environment (Es). Furthermore, a significant age-regression on all components was found. This model is a specific scalar model, which implies that all parameter estimates are sex-specific. Although the same genes and environmental factors explain variation in men and women, their relative and absolute contribution to explaining the phenotypic variation is significantly different for men and women. Models including genetic dominance (D) instead of common environment (C) did not fit the data well (LRT: P<0.05), nor did models omitting sex differences or dropping either C or A.

In men, age explained 14.1, 12.5 and 16.5% of the variation of endomorphy, mesomorphy and ectomorphy, respectively. In women, corresponding values were 10.4, 5.9 and 5.5% for the three respective components. Age also explained 16.4–26.4% and 6.5–13.1% of the covariance between the three components in men and women, respectively. In Figure 2, the percentages of variance, explained by the Ac, Cc, Ec, As and Es, excluding variance and covariance explained by age, are represented visually. For men, heritabilities (Ac+As) are 28% (CI 8.7–50.8%), 86.3% (71.6–90.2%) and 66.5% (37.4–85.1%) for endomorphy, mesomorphy and ectomorphy, respectively. For women corresponding values are 32.3% (8.9–55.6%), 82.0% (67.7–87.7%) and 70.1% (48.9–81.8%). For all three components in men and women, more than 70% of the total variation was explained by sources of variance shared between the components (Ac, Cc and Ec).

Figure 2

Percentages of variance in ST components explained under the best fitting model after excluding variance explained by age. Endo: endomorphy; meso: mesomorphy; ecto: ectomorphy; A: additive genetic source of variance, C: common environmental source of variance, E: specific environmental source of variance. As, Cs and Es: variable-specific sources of variance; Ac, Cc and Ec: common (shared) sources of variance. Error bars represent 95% maximum-likelihood CIs on the total heritability (Ac+As).


This is the first study on the heritability of Heath–Carter ST components in adult twins using a multivariate path analysis approach. It has a considerably larger sample size than any other twin study on ST components reported in the literature. Our results demonstrate the importance of both genes and environment in variation of ST components, the significance of sex differences, and the importance of genetic and environmental covariation between ST components in young adults. Heritability estimates for the relative degree of fatness (endomorphy) were considerably lower than reported for example for BMI, which is the most commonly used indicator of overweight and obesity.

The best-fitting model for the present data confirms that the model reported earlier in the Leuven Longitudinal Twin Study (LLTS)– in which 105 twin pairs were followed between 10 and 18 years– 13 can be extended into adulthood. The twins from the LLTS were also recruited from the EFPTS and in fact 91 individuals from 46 twin pairs included in the present study (803 subjects from 424 twin pairs), also participated in the LLTS. Considering that both twin studies were drawn from the same population-based twin register and partially overlap, the present findings can be considered with some caution as an extension of the LLTS and mostly confirm the findings at 18 years of age with a larger sample size, and hence more statistical power. As all twins included in the LLTS were same-aged twin pairs, no age regression was included in the earlier analyses.

Comparing the present findings to those reported earlier for 18-year-old twins in the LLTS shows comparable heritability estimates for endomorphy (LLTS: 20.9%, present study: 28.0%) and mesomorphy (72.0 and 84.6%) in men; however, for ectomorphy the results of the present study are about twice as high as those found in the LLTS (LLTS: 31.5 vs 66.5%). In women, heritability estimates are markedly lower for endomorphy in the present study (LLTS: 75.7 vs 32.3%) and higher for mesomorphy (LLTS: 51.2 vs 82.0%), and comparable for ectomorphy (LLTS: 72.0 and 70.1%). It is not entirely clear why these differences occur and it should also be noted that because of the fairly small sample size of 105 twin pairs in the LLTS,13 leading to very broad CIs, the CIs of both studies do overlap. Some differences may occur between young adulthood and the later measurements of the present study, as twins may tend to go their separate ways as they grow older which may increase non-shared environmental variation. However, if this effect were present, it would probably be related to age, which was regressed out in the present analyses. Furthermore, even though twins may no longer live in the same household once they reach adulthood, it seems fairly likely that they remain in a relatively similar socio-economic environment or continue to have fairly similar nutritional habits, which may have been acquired when they were raised together. If the latter assumption is valid, this may explain why a considerable amount of variance is explained by shared environment in endomorphy, even though the twins are probably no longer living in the same household. Another explanation for the differences may be the substantially larger sample size in the present study that reduces the potential of random sampling error.

The ST was devised a ‘Gestalt’, a ‘three-dimensional’ index of physique. At this point, both the LLTS and the current analyses strongly agree; more than 70% of the total variance of each of the ST components is attributable to genes and environmental factors that are shared between the three components (Ac, Cc and Ec). This observation seems to question the approach of regressing out the covariance and hence the correlations between the ST components, as was performed in most of the more recent studies on the familial aggregation or the heritability of the ST components,17, 19, 24, 25, 26 as the majority of the phenotypic variance (>70%) is then statistically removed from the analyses. This approach would be comparable to calculating heritabilities for the separate ST components based solely on the estimates of the component-specific sources of variance (i.e. As and Es) in the current study. Thus, in a justified attempt to control for the covariation between the components, as opposed to treating the components as separate unrelated phenotypes, this approach thus may also distort the picture of the true underlying causes of variation. If the covariation with other components is ignored in the present analyses, the considerable contribution of shared environment (Cc) to endomorphy and to a lesser extent to ectomorphy would probably go undetected. Katzmarzyk et al.17 summarized the ST in one principal component, explaining more than 80% of the total variance and report a maximal heritability of 0.64 for this principal component. This approach recognizes the importance of the covariation between components; however, it ignores the component-specific variation of each component and does not allow to gain insight into component-specific heritabilities which may differ substantially.

Owing to the different analyses techniques and regressing out covariance between components, or treating the components as univariate traits, regressing out age and sex effects, it is not straightforward to compare the present results to those reported earlier in twin and family studies. In general, the high heritabilities for mesomorphy and ectomorphy correspond to the values reported in earlier twin studies, which, however, mainly cover the adolescent period.9, 10, 11, 12, 13 For endomorphy, heritabilities are lower and more closely resemble results from family studies.15, 17, 18 The values are even somewhat lower in the present study, which may again be related to the removal of the large proportion of Cc for endomorphy as a consequence of regressing out the covariation with the two other components. In many of the studies, irrespective of the methodology that is used, mesomorphy tends to be the most heritable of the three components or it has the highest familial correlations, although there are exceptions. This trend is confirmed in the present study and also parallels the high (upper-limit) heritabilities for several muscle and muscle–bone measures reported in young Flemish men by Huygens et al.27, 28 The lower heritability of the endomorphy component found in the present study intuitively makes sense as fat tissue is relatively prone to environmental variation in, for example, nutrition and physical activity. Huygens et al.,27 however, reported upper-limit heritability estimates for adiposity indicators in young adult Flemish brothers which were situated between 50 and 70%. This seems higher than those reported for endomorphy in the present analyses, yet the estimates of Huygens et al.27 are upper-limit heritabilities based on sibling correlations, which may include an unknown amount of familial environment. When Cc is added to the heritability in men in the present study, the combined effect of all familial components also explains 74.0% of the variation in endomorphy, which is similar to what was reported by Huygens et al.27 To the extent that socio-economic status (SES) remains similar in adult twins and hence can be interpreted as shared (familial) environment, these results also agree with the SES effect reported by Bouchard et al.15 which was largest for endomorphy, somewhat smaller for ectomorphy and mesomorphy, although for the latter component in the present study the contribution of Cc was rather marginal. Rebato et al.,24 on the contrary, found that controlling for socio-familial variables lowered familial correlations for mesomorphy and not for the other two components. Sanchez-Andres25 on the other hand found no variance explained by socio-economic variables (occupation and education level of parents) after age and sex were regressed out. Song et al.26 found no effect of controlling for activity level and energy intake on familial correlations, although one can expect these to have a more direct influence on, for example, endomorphy. Again this may be a consequence of controlling for the other two components and thus potentially removing the variance explained by Cc as was found in the present analyses.

The phenotype most commonly used to estimate the heritability of obesity is the BMI. Heritabilities for this phenotype in general range between 0.50 and 0.90 in twin studies,29 which is considerably higher than the 0.28 (men) and 0.32 (women) for the subcutaneous adipose-tissue component (endomorphy) in the present study. This apparent discrepancy may be caused by the fact that BMI may not be the most accurate measure of obesity as it includes both fat body mass and lean body mass. Hence the heritability of BMI represents in part the heritability of fat mass as well as the heritability of muscle- and bone mass. As the heritability of muscle- and bone mass, as represented in the mesomorphy component, is considerably higher than for endomorphy in the present study, using BMI as the obesity phenotype might lead to an overestimation of the genetic contribution to fatness. As endomorphy is calculated based on a sum of skinfolds (SSF), with some caution, it is interesting to consider studies reporting heritabilities on both BMI and the SSF. The results of Rice et al.30 in the Québec family study seem to confirm that the heritability of SSF is considerably lower than for BMI, providing support for the findings and interpretation for the present study. However, Schousboe et al.31 (twins) and Katzmarzyk et al.32 (family study) reported similar heritabilities clustering around 0.60 for both BMI and SSF. However, having a similar degree of genetic determination does not necessarily imply that both phenotypes are under control of the same genes and environmental factors or that they represent the same concept. None of these studies include both phenotypes in a bivariate analysis that could provide information on the underlying genetic and environmental covariation between BMI and SSF.

It should also be noted that both BMI and ectomorphy are calculated based on stature and weight. BMI is basically a measure of weight for height with a high BMI indicating high weight for stature (i.e. kg/m2). Ectomorphy is calculated based on height for weight (i.e. height (cm) divided by the cube root of mass (kg)), with a high ectomorphy score indicating low weight for height and therefore conceptually almost the inverse of BMI. Thus, both concepts, in fact, seem to represent opposite ends of a continuum. The heritability of ectomorphy in the present study (men: 0.67 and women 0.70) in fact falls well within the range of values reported for BMI in previous twin studies. Again a multivariate genetic analyses including both BMI and the three ST components could shed light on the extent of their covariation and its underlying causes.

The heritabilities for mesomorphy and ectomorphy are very high compared with that of endomorphy in the present study, which may imply that searching for genes for muscularity and leanness might be a more successful route than searching for genes for fatness.

In summary, the present study on the heritability of ST components in young Flemish adults demonstrates the importance of allowing for the covariation between ST components when studying the genetic and environmental determination of this phenotype. It confirms that the high heritabilities for mesomorphy and ectomorphy reported in earlier twin studies in adolescence are maintained in adulthood. For endomorphy, however, the results suggest heritability may be considerably lower than most values reported earlier during adolescence.


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The Prenatal Programming Twin Study was funded by a grant (no. 3.0269.97) from the National Fund for Scientific Research, Flanders (Belgium). Since its origin, the East Flanders Prospective Twin Survey has been partly supported by grants from the Fund of Scientific Research, Flanders (Belgium) and by the Association for Scientific Research in Multiple Births (Belgium). MWP was supported by Research Fund KU Leuven (PDM/05/260).

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Peeters, M., Thomis, M., Loos, R. et al. Heritability of somatotype components: a multivariate analysis. Int J Obes 31, 1295–1301 (2007). https://doi.org/10.1038/sj.ijo.0803575

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  • somatotype
  • heritability
  • twins
  • adults
  • path analysis

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