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Am J Physiol Endocrinol Metab 292: E765-E770, 2007. First published November 7, 2006; doi:10.1152/ajpendo.00321.2006
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Genetic and environmental interrelations between measurements of thyroid function in a healthy Danish twin population

Pia Skov Hansen,1,2 Thomas Heiberg Brix,1 Ivan Iachine,3 Thorkild I. A. Sørensen,4 Kirsten Ohm Kyvik,2 and Laszlo Hegedüs1

1Department of Endocrinology and Metabolism, Odense University Hospital; 2The Danish Twin Registry, Epidemiology, Institute of Public Health, University of Southern Denmark; 3Department of Statistics, University of Southern Denmark, Odense; and 4Danish Epidemiology Science Centre, Institute of Preventive Medicine, Copenhagen University Hospital, Centre for Health and Society, Copenhagen, Denmark

Submitted 3 July 2006 ; accepted in final form 6 November 2006


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Serum thyrotropin (TSH), free thyroxine (T4), and free triiodothyronine (T3) levels illustrate the thyroid function set point, but the interrelations between these have never been characterized in detail. The aim of this study was to examine the associations between TSH and thyroid hormone levels in healthy euthyroid twins and to determine the extent to which the same genes influence more than one of these biochemical traits; 1,380 healthy euthyroid Danish twins (284 monozygotic, 286 dizygotic, 120 opposite-sex twin pairs) were recruited. Genetic and environmental associations between thyroid function measurements were examined using quantitative genetic modeling. In bivariate genetic models, the phenotypic relation between two measurements was divided into genetic and environmental correlations. Free T4 and free T3 levels were positively correlated (r = 0.32, P < 0.0001). The genetic correlation between serum free T4 and free T3 levels was rg = 0.25 (95% CI 0.14–0.35), suggesting that a set of common genes affect both phenotypes (pleiotropy). The correlation between the environmental effects was re = 0.41 (0.32–0.50). From this we calculated that the proportion of the correlation between free T4 and free T3 levels mediated by common genetic factors was 48%. Only 7% of the genetic component of serum free T3 levels is shared with serum free T4. Serum TSH and thyroid hormone levels did not share any genetic influences. In conclusion, thyroid hormone levels are partly genetically correlated genes that affect free T4 levels and exert pleiotropic effects on free T3 levels, although most of the genetic variance for these measurements is trait specific.

thyroid-stimulating hormone; thyroid hormones; twin study; bivariate analyses

THE SERUM THYROTROPIN (TSH), free thyroxine (T4), and free triiodothyronine (T3) levels represent measurements of the thyroid function set point, reflected from different angles. Twin and family studies have shown that genetic factors exert considerable influence on the levels of each of these biochemical measurements (6, 12, 14, 20). We (6) have previously demonstrated that ~0.65 of the variation in serum TSH level was explained by genetic influences. Almost identical heritability estimates were found for serum free T4 (0.65) and free T3 (0.64) levels (6). In other studies, the heritability estimates range from 0.32 to 0.67, lowest for serum TSH levels and highest for serum T3 (12, 14, 20). Many genes are involved in the regulation of these measurements, and several polymorphisms located in thyroid hormone pathway genes, such as the TSH receptor gene as well as the deiodinase genes, have been shown to be associated with TSH levels as well as thyroid hormone levels (18, 19).

It seems plausible that serum TSH, free T4, and free T3 levels show a certain degree of interrelatedness. However, the extent to which these biochemical measurements are genetically distinct from one another is unknown. Extension of the classic twin study to involve multiple variables allowed us to assess the degree to which the associations between serum TSH, free T4, and free T3 levels are explained by shared genetic or environmental influences. Furthermore, our study population allowed for the evaluation of sex-related differences of the heritability estimates. Apart from interesting physiological information, our study contributes useful information in the understanding of the etiology of thyroid dysfunction. The diagnosis of clinically overt thyroid disease is based on measurements of thyroid function, and circulating TSH and thyroid hormone levels may be regarded as phenotypes that, to some extent, are involved in the pathways that lead to thyroid disease (21). Thus, genetic and environmental agents that influence the normal range of interindividual variability among the healthy may also influence the development of disease, and using circulating thyroid hormone levels as phenotypes (so-called endophenotypes) may assist in understanding the thyroid disease processes (21).

The aims of our study were 1) to examine the association between serum TSH and thyroid hormone levels and 2) to test whether the circulating levels of TSH and thyroid hormones are influenced by a common set of genes (genetic pleiotropy) and, if so, to estimate the magnitude of this shared genetic background.


   METHODS
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 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
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Subjects. The present study is part of a nationwide project (GEMINAKAR) investigating the relative influences of genetic and environmental factors on a variety of different traits among Danish twins. Based on a questionnaire survey performed in 1994 concerning physical health and health-related behavior, a representative sample of complete twin pairs was recruited from the population-based Danish Twin Registry (22). A detailed description of the ascertainment procedure can be found elsewhere (5, 6). Briefly, the twins included in the GEMINAKAR study were healthy as reported by themselves. With the exception of contraceptives, no twins were taking medicine known to affect the pituitary-thyroid axis or thyroid size. To get an equal distribution of twin pairs, sampling was stratified according to age, sex, and zygosity. The twins of a pair were examined on the same day. All twins with at least one partner living in the western part of Denmark were examined in Odense, whereas all those where both partners were living in the eastern part of Denmark were examined in Copenhagen. With the exception of 39 twin pairs, both twins in a pair lived in the same geographic region of Denmark. Blood and urine samples as well as DNA were collected without regard to any phenotype feature between 8 AM and 9 AM after a 12-h fast. Thereafter, a clinical examination was performed, and the twins filled out health-related questionnaires including questions regarding thyroid disease, smoking habits, and medicine intake.

In all, 1,512 individuals (756 twin pairs) were examined. Blood samples were available from 1,473 individuals. Serum TSH, free T4, free T3, thyroid peroxidase antibodies (TPOab), and thyroglobulin antibodies (Tgab) were measured. Individuals with self-reported thyroid disease (32 persons in 28 twin pairs) and overt biochemical thyroid disease (19 persons in 18 twin pairs) were excluded, as were their co-twins. Thus, the final study population included 1,380 healthy, euthyroid individuals (690 twin pairs) distributed in 284 monozygotic (MZ), 286 dizygotic same-sex (DZ), and 120 opposite-sex (OS) twin pairs. From among the 690 twin pairs, 649 were without subclinical thyroid disease (defined as serum TSH >0.3 or TSH <4.0), and 580 twin pairs were thyroid antibody negative (values >60 KU/l were regarded as positive for TPOab as well as for Tgab).

Assays. Serum TSH was measured using a time-resolved fluoroimmunometric assay (AutoDELFIA hTSH Ultra Kit; PerkinElmer/Wallac, Turku, Finland). Reference range is 0.30–4.00 mU/l. Serum free T4 and serum free T3 were determined using the AutoDELFIA FT4 and FT3 (PerkinElmer/Wallac), respectively. For free T4 the reference range is 9.9–17.7 pmol/l, and for free T3 it is 4.3–7.4 pmol/l. TPOab and Tgab were measured by solid phase, two-step, time-resolved fluoroimmunoassays (AutoDELFIA TPOab and hTgab kits, respectively, Perkin Elmer/Wallac). Twin pairs were analyzed within the same run. All of the serum samples were analyzed at the same laboratory in Odense. Zygosity was established by analysis of nine highly polymorphic restriction fragment length polymorphisms and microsatellite markers widely scattered through the genome with an Applied Biosystems AmpFISTR Profiles Plus kit (PerkinElmer) (25).

Statistical methods. The distribution of TSH levels was skewed and was therefore transformed by the natural logarithm. Pearson's correlations for lnTSH, free T4, and free T3 were computed separately for the five groups of twins: MZM (monozygotic males), DZM (dizygotic males), MZF (monozygotic females), DZF (dizygotic females), and DOS (DZ opposite-sex).

Moreover, Pearson's correlation coefficients were used to assess the associations between the phenotypes. These correlations were adjusted for the possible confounding effects of age and sex. These statistical analyses were carried out using STATA statistical software (23).

Quantitative genetic analyses. Model fitting was done with Mx, a computer program designed for the analysis of genetically informative data (15). In these analyses, the phenotypic variances are decomposed into genetic and environmental contributions (16). The genetic variance is further subdivided into an additive (A) component and a dominance (D) component. The environmental contribution is divided into a common environmental component (C) and a unique environmental (E) component. Heritability is defined as the proportion of the total variance attributable to total additive genetic variance (2, 16).

Based on twin correlations, the ACE model was selected as a starting point of the modeling, and the significance of A, C, and E was tested by removing them sequentially in specific nested submodels (16). Model fit was assessed using the –2 log likelihood (–2LL). Submodels were compared with full models by use of hierarchical {chi}2 tests. The difference between minus twice the log-likelihood for a reduced model and that of the full model ({Delta}2lnL) is approximately {chi}2 distributed. To identify the most parsimonious model consistent with the data, parameters were removed from the model if the removal did not result in a significant degradation of model fit.

According to standard biometric practice (16), we assumed equal environment for MZ and DZ twins, no epistasis (gene-gene interaction), and no gene-environment interaction or correlation.

Sex limitation models. To test for sex differences, a full model, allowing for qualitative and quantitative sex differences was compared with alternative models by adding constraints. Thus, a model in which the genetic correlation between males and females in the OS twins was estimated freely was compared with a model in which this correlation was fixed to be the same as in DZ twins. Moreover, to test for whether the magnitude of the A and E estimates differed across sex, a model with separate parameters for males and females was compared with a constrained model with equal standardized genetic and unique environmental estimates for males and females by use of {chi}2 statistics. In the analyses, the mean levels for lnTSH, free T3, and free T4 were age and sex adjusted.

Bivariate genetic models. Well-established statistical genetic methods were applied to address whether genetic or environmental factors that affect one trait are the same as those that affect a correlated trait (16). By use of a bivariate Cholesky decomposition model (Fig. 1), it is assumed that a genetic component underlying the control of one phenotype affects another phenotype as well. Summing the genetic paths that load on a phenotype gives the heritability. With such a model, the phenotypic relationship between two traits can be subdivided into genetic and environmental correlations (11, 13, 16). The genetic correlation (rg) between two traits indicates the amount of overlap between the genes influencing those traits. The magnitude of the genetic correlation between traits corresponds to the degree of pleiotropy, and rg = 1 indicates that the two sets of genes overlap completely. Evidence of pleiotropy (a common set of genes influence more than one trait) is indicated by a genetic correlation significantly different from zero. The significance of the correlations was determined by comparing the likelihood of an unrestricted model in which the correlation was estimated freely with the likelihood of a submodel in which the correlation was fixed at zero.


Figure 1
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  		Fig. 1. The bivariate Cholesky model, with serum free thyroxine (T4) levels entered as the 1st and serum free triiodothyronine (T3) levels as the 2nd variable. The measured/observed phenotypes for twin 1 and for twin 2 are shown in boxes. Latent factors are shown as circles. The additive genetic (A) and unique environmental effects (E) on serum free T3 are decomposed into components in shared with and independent of the components underlying variance on serum free T4 concentration. Although ACE models were tested, C is not shown in the figure for reasons of clarity. a11, Additive genetic influence acting on free T4 concentration; e11, unique environmental influence acting on free T4 concentration; a21, additive genetic influence common to free T4 and free T3 concentrations; e21, unique environmental influence common to free T4 and free T3 concentrations; a22, additive genetic influence specific for free T3 concentration; e22, unique environmental influence specific for free T3 concentration. The estimated values of the standardized genetic factor loadings were as follows: a11 = 0.78, a21 = 0.19, and a22 = 0.75.

 
The genetic contribution to the observed phenotypic correlation can be derived as the product of the genetic correlation and the square roots of the heritabilities of the two phenotypes (16). Similarly, the unique environmental contribution to the observed phenotypic correlation corresponds to the environmental correlation and the square roots of environmental components (16). The proportion of the observed phenotypic correlation that is mediated by a correlation at the genetic level is defined as the bivariate heritability.

An alternative approach to describe the results is to subdivide the total heritability for a specific phenotype into a genetic component that is shared by the two phenotypes, as well as a residual component (13). Genetic variance that is shared by the two phenotypes is reflected by the a21 path in Fig. 1. The a21 equals the product of the genetic correlation and the square root of the heritability of free T3 (11). By squaring this specific genetic loading, the proportion of serum free T3 heritability shared with free T4 heritability can be calculated.

Finally, trying to find the model that in the best way explained our data, we tested the independent pathway model (16). Since univariate analyses found no consistent evidence for significant differences across sex, the DZ and OS twins were pooled.


   RESULTS
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DISCUSSION
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The means (SD) as well as twin correlations (r) for serum TSH and thyroid hormone levels across zygosity are given in Table 1. The differences between the opposite-sex correlations and the same-sex DZ correlations were not significant.


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  		Table 1. Means values (SD) and twin correlations (r) for serum TSH and thyroid hormone concentrations across zygosity groups

 
The results from univariate analyses are given in table 2. Models including additive genetic effects and unique environmental effects (AE models) were considered as the most parsimonious. Constraining the genetic correlation between males and females in the OS twins so as to be the same as in DZ twins, did not provide a worse model fit (P = 0.267), indicating that the same set of genes act in males and females. The heritability for serum TSH was 0.64 (95% CI: 0.54–0.71) in males and 0.71 (95% CI: 0.62–0.77) in females. A constrained model with equal heritability estimates across sex did not provide a significantly worse model fit (P = 0.225) suggesting that the relative contribution of genetic and environmental effects are equal across sex.


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  		Table 2. Results of univariate model fitting analyses

 
For free T4 levels no significant sex differences were found, neither in the genetic source (P = 0.406) nor in the magnitude of the genetic effects [heritability estimates: males 0.63 (95% CI: 0.52–0.71) and females 0.60 (95% CI: 0.49–0.68), P = 0.656].

For free T3 levels, the same genetic factors were operating in males and females (P = 0.742), and the magnitude of the heritability estimates were the same across sex: 0.60 (95% CI: 0.50–0.69) in males and 0.60 (95% CI: 0.49–0.69) in females, P > 0.999.

Excluding the twin pairs in which one or both twins had subclinical thyroid disease and/or positive thyroid antibody status did not change the results (data not shown).

Bivariate twin analyses. Figure 2 shows scatter plots of the relationships between unadjusted serum TSH and thyroid hormone levels. The transformed serum TSH levels were virtually uncorrelated with free T4 (r = –0.03, P > 0.05) as well as free T3 levels (r = 0.07, P < 0.05). In contrast, serum free T4 levels were significantly and positively correlated with serum free T3 levels (r = 0.37, P < 0.001). Adjusting for the confounding effects of age and sex, the correlation was r = 0.32, P < 0.001. In the following, these phenotypic correlations were further explored.


Figure 2
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  		Fig. 2. Scatterplots and phenotype correlations between serum TSH, free T4, and free T3 concentrations in euthyroid individuals.

 
As univariate analyses found no evidence for common environmental influences, only genetic and unique environmental factors were modeled. Entering free T4 as the first and free T3 levels as the second variable, a substantial heritability was detected for free T4 levels (0.6086) as well as free T3 levels (0.6016). The correlation between the genetic effects acting on serum free T4 and free T3 levels was rg = 0.25 (95% CI 0.14–0.35), whereas the correlation between the environmental effects was re = 0.41 (0.32–0.50). The observed phenotypic correlation between free T4 and free T3 (r = 0.32) could be subdivided into a genetic part Formula= 0.15 and into an environmental part Formula= 0.16. Thus, the proportion of the observed phenotypic correlation explained by correlation at the genetic level was 0.15/0.32, or 48%; this is the bivariate heritability estimate.

The alternative approach to describe our results is to estimate the extent to which genetic effects acting on serum free T4 levels account for genetic effects on serum free T3 levels. Figure 1 displays the path-model with the estimated standardized genetic factor loadings. The overall heritability estimate for free T3 levels could be subdivided into a small portion that was attributable to genetic effects acting on serum free T4 levels Formula= (0.19)2 = 0.04 as well as a residual part (0.6016 – 0.04 = 0.56) that was unique for serum free T3 levels. Stated differently, only 7% of the genetic component of serum free T3 levels is shared with serum free T4.

The independent pathway model provided a very poor fit to the data compared with the Cholesky model and was rejected (data not shown).

In the analyses between TSH and free T4 levels and between TSH and free T3 levels, the genetic correlations were rg = –0.06 (95% CI –0.16–0.05) and rg = 0.07 (95% CI –0.04–0.17), respectively. Constraining these genetic correlations to be zero could be done without a significant loss of fit, suggesting that neither of these was significantly different from zero.


   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Serum TSH, free T4, and free T3 levels illustrate the thyroid function set point from different angles, and it intuitively seems plausible that a set of common genes influences more than one of these biochemical phenotypes. With sophisticated genetic modeling, this is the first study quantifying the shared genetic contribution between these phenotypes. Surprisingly, we found evidence of a discrete genetic pleiotropy between serum free T4 and serum free T3 levels.

The present study extends our previous findings (6). The inclusion of opposite-sex twin pairs allows for the evaluation of possible sex-associated differences, which were not found. A stronger genetic influence on serum TSH and serum free T3 levels was demonstrated compared with previous studies of thyroid function (12, 14, 20). A recent study (20) demonstrated a low heritability for serum TSH: only 0.32 and dropping to 0.20 when individuals with elevated TSH values (TSH >4.5 mIU/l) were excluded. In the present study, excluding twin pairs in which one or both twins had TSH >4.0 as well as twins with thyroid antibodies, had only a minute influence on the heritability estimates.

On the basis of this well-characterized euthyroid population, we demonstrate that serum TSH levels and circulating thyroid hormone levels are virtually uncorrelated. In contrast, a significant correlation between serum free T4 and serum free T3 levels was found. These findings are in accord with previous studies (3, 4, 7, 9, 17, 27). The absence of a phenotypic correlation between serum TSH and the thyroid hormone levels in a healthy population attracts particular attention. If two phenotypes have a high positive genetic correlation but a negative environmental correlation (or vice versa), then we might expect to find near-zero phenotypic correlations. Therefore, two traits that appear phenotypically unrelated may actually share genetic or environmental causes. But intriguingly, in our analyses, the genetic correlations between TSH and free T4 and free T3 levels were not significantly different from zero. Therefore, we conclude that these biochemical measurements are highly heritable but do not share any genetic influences. The thyrotropin-releasing hormone (TRH) gene is regarded as a central control point in the regulation of thyroid homeostasis (1), and we hypothesized that a gene like this would affect circulating TSH as well as thyroid hormone levels. However, our results imply that, in the euthyroid state, the small but significant association between TSH and T3 arises solely because of the reciprocal influence of TSH on T3 and of T3 on TSH (8, 24). The strong trait-specific genetic influence may be regarded as an advantage for the overall control of thyroid homeostasis, because it may guarantee a more stable control. The homeostasis is not as sensitive to genetic mutations as would be the case given a strong common genetic influence.

In the present study, a modest genetic correlation (rg = 0.25) between serum free T4 and serum free T3 levels was found, suggesting the existence of genes with influence on both traits. The circulating levels of free T4 and free T3 are determined by a balance of activating and deactivating pathways. In an attempt to place our results into a specific physiological context, it is well known that enzymes such as the selenodeiodinases play a pivotal role in the regulation of thyroid hormone concentrations (18, 26). The gene encoding for an enzyme such as the type III deiodinase (the major T3- and T4-inactivating enzyme) would have a decreasing regulatory effect on the circulating levels of free T4 as well as on free T3. Thus, this gene represents a gene with an influence on both traits. Another example of a regulatory overlap would be the gene encoding for the type I deiodinase (being responsible for the T4-to-T3 conversion in peripheral tissues). However, in this case, the regulatory input would be a decreasing effect on the circulating levels of serum free T4 as opposed to an increasing effect on serum free T3 levels. Nevertheless, the deiodinase genes represent genetic factors shared between these traits. Yet, superimposed on these important regulatory pathways, our study suggests the effect of other regulatory inputs to be strong. The extent to which genetic effects acting on serum free T4 levels could account for genetic effects on serum free T3 levels was small. The total heritability of serum free T3 levels (0.60) could be subdivided into a very small but significant component accounted for by genetic effects acting on free T4 concentration (0.04) and a large residual genetic component (0.56), implying that the majority of the genetic variance for these measurements is actually trait specific. The logic underlying the sequence in which the variables are considered in the Cholesky decomposition model is based on the fact that most circulating free T3 arises from the conversion of free T4 (10, 26).

In conclusion, thyroid hormone levels are partly genetically correlated. However, the majority of the genetic variance is specific to serum free T4 and free T3. Serum TSH and thyroid hormone levels do not share any genetic influences.


   GRANTS
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 ABSTRACT
 METHODS
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 DISCUSSION
 GRANTS
REFERENCES
 
The present work was supported by grants from the Danish Research Agency; the Foundation of 17-12-1981; the Agnes and Knut Mørk Foundation; the Novo Nordisk Foundation; the Foundation of Medical Research in the County of Funen; Else Poulsens Mindelegat; the Foundation of Direktør Jacob Madsen and Hustru Olga Madsen; the Foundation of Johan Boserup and Lise Boserup; the A. P. Møller and Hustru Chastine McKinney Møllers Foundation; the A. P. Møller Relief Foundation; and the Clinical Research Institute, Odense University.


   ACKNOWLEDGMENTS
 
PerkinElmer/Wallac, Turku, Finland, kindly provided the kits for determination of TPOab, Tgab, serum TSH, free T4, and free T3. Ole Blaabjerg and Esther Jensen are acknowledged for supervising the biochemical thyroid analyses. We gratefully acknowledge Jacob v. B. Hjelmborg for valuable statistical advise and discussions.

Presented at the 13th International Thyroid Congress, Buenos Aires, Argentina, October 30-November 4, 2005, Poster 221.


   FOOTNOTES
 

Address for reprint requests and other correspondence: P. S. Hansen, The Danish Twin Registry, Epidemiology, Institute of Public Health, Univ. of Southern Denmark, Odense, JB Winsløwsvej 9B, DK-5000 Odense C, Denmark (e-mail: piaskovhansen{at}dadlnet.dk)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   REFERENCES
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Familial risks for hospitalized Graves' disease and goiter
Eur. J. Endocrinol., October 1, 2009; 161(4): 623 - 629.
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