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Keywords:

  • Gender;
  • Heritability;
  • Genetic Polymorphisms;
  • Gene-Environment Interaction;
  • Osteoporosis

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Discussion
  5. Conclusions and Recommendations
  6. List of Abbreviations
  7. Acknowledgments
  8. References

Common diseases result from the complex relationship between genetic and environmental factors. The aim of this review is to provide perspective for a conceptual framework aimed at studying the interplay of gender-specific genetic and environmental factors in the etiology of complex disease, using osteoporosis as an example.

In recent years, gender differences in the heritability of the osteoporosis-related phenotypes have been reported and sex-specific quantitative-trait loci were discovered by linkage studies in humans and mice. Results of numerous allelic association studies also differed by gender. In most cases, it was not clear whether or not this phenomenon should be attributed to the effect of sex-chromosomes, sex hormones, or other intrinsic or extrinsic differences between the genders, such as the level of bioavailable estrogen and of physical activity. We conclude that there is need to consider gender-specific genetic and environmental factors in the planning of future association studies on the etiology of osteoporosis and other complex diseases prevalent in the general population.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Discussion
  5. Conclusions and Recommendations
  6. List of Abbreviations
  7. Acknowledgments
  8. References

Common diseases, such as diabetes, cardiovascular diseases, osteoarthritis, osteoporosis, bipolar disorder, or cancer, appear with different frequencies in men and women. Some of these gender-related differences may be explained by the influence of gonadal steroids and/or social and behavioral factors (exposure to smoking for instance). Owing to recent advances of the Human Genome project, knowledge on genomic structure variation is constantly improving. Thus, single-nucleotide polymorphisms (SNPs) have been found to occur at a rate of ∼1 in every 1,000 bp and other structural variants, such as copy number variants (CNVs), inversions, and translocations, are also quite prevalent (Conrad and Hurles, 2007, http://projects.tcag.ca/variation/). Thus, common diseases are increasingly recognized as the result of a complex interplay between environmental and genetic factors. Although many studies report gender-specific genetic factors for susceptibility to disease, the “quality” of the studies that support this concept has been challenged (Patsopoulos et al., 2007). To explore this issue in more detail, we review here the sexual dimorphism for osteoporosis and fracture risk and its underlying genotypic determinants.

The lack of consistent replication of chromosomal linkage and allelic associations in genetic epidemiological studies on osteoporosis may be a function of multiple causes. It may stem from the initial overestimate of the genetic effect due to the phenomenon of the “winner's curse”, failure to type the genuine causal locus and lack of power of the original or subsequent studies, allelic heterogeneity and the confounding introduced by the widely divergent allele frequencies in diverse ethnic populations. In this review, we will specifically focus on the non-inclusion of a relevant effect of gene-environment (gene-gender) interaction in the assessment of the relationship between phenotype and genotype, which will introduce substantial noise into this relationship and may explain, in part, the conflicting results thus far reported in the literature. In addition to the different environment, sexes may have dissimilar genetic backgrounds due to multiple interacting genes.

The aim of this perspective paper is therefore to use osteoporosis, as an example of other complex diseases, to illustrate the interplay of gender-specific environment and genetic factors that contribute to the susceptibility to this complex disease. We will provide data on the phenotype differences by sex, and gender-specific contribution of covariates, and will offer some insights into possible nature of the genetic factors, from heritability to quantitative trait loci (QTLs) to specific genetic variants. This review focuses on genotype-gender relationships rather than on epigenetic or environmental influences on the bone phenotype. We will conclude with some practical recommendations on exploring these issues in a genetic epidemiological study.

Sexual Dimorphism in Osteoporosis

Osteoporosis is a common condition that affects at least 30% of women and 12% of men at some point in life (Ralston & de Crombrugghe, 2006). Furthermore, one out of two women and one out of four men will eventually suffer from a fragility fracture as she/he ages (Burge et al., 2007). Females show a greater incidence of both stress fractures early in life (Beck et al., 2000) and fragility fractures later in life (Cummings and Melton, 2002). Bone fragility results from lower bone mineral mass or/and microstructural alterations of the skeleton (Anonymous, 2001). To date, the most widely used and reliable clinical predictor of an osteoporotic fracture remains areal bone mineral density (BMD)(Kanis et al., 2007), as evaluated by dual-energy x-ray absorptiometry (DXA), and most osteoporosis genetics studies indeed have used BMD as the phenotype of interest. However, sexual dimorphism is marked in both skeletal development and aging and is not limited to BMD.

The differences between the sexes in the structural components of bone strength (e.g. skeletal dimensions, cortical thickness), biomechanical responses, mineral mass and turnover, and even trabecular microstructure, are obvious. Sexual dimorphism in the skeletal dimensions and shape is well known and it constitutes the basis of sex evaluation for archeological and forensic applications (Karasik et al., 1998; Lovejoy et al., 1985; Lazenby, 2002; Feik et al., 2000; Duan et al., 2003). For example, in the Framingham cohorts, adult men have longer femora, with more obtuse neck-shaft angles, as well as longer and wider femoral necks (Table 1), in addition to higher BMD. Thus, the greater prevalence of fragility fractures with advancing age in women, compared to men, may largely be explained by the smaller skeletal size and bone mass of women (Seeman, 2002), even after adjustment for body size (Nieves et al., 2005; Looker et al., 2001). Note, however, that peak volumetric BMD is no different by sex (Seeman, 2003). Although women and men both lose BMD and bone microstructure with aging because of endocrine, paracrine, and cellular factors, these effects are more pronounced in women, particularly a decrease in cortical thickness, decrease in number of trabeculae, and an increase in spacing between trabeculae compared to men (Khosla et al., 2006), notably accelerating after menopause with the rapid decline in estrogen levels (Ahlborg et al., 2003; Riggs et al., 1998; Melton et al., 2000). Adaptation by periosteal apposition may also be lesser in women than men, further contributing to a structural instability of bones that occurs at an earlier point in the life of a woman than a man (Duan et al., 2003; Seeman, 2002).

Table 1.  Sexual dimorphism in bone mineral density, quantitative ultrasound, and the skeletal dimensions, in the Framingham Osteoporosis Study cohorts. (Data from (Karasik et al., 2003) and (Demissie et al., 2007))
Sex (age ±S.D) Variable Mean ± S.DOriginal CohortOffspring Cohort
Males (79 ± 5 yrs)Females (80 ± 5 yrs)Males (59 ± 9 yrs)Females (58± 10 yrs)
  1. NSA, neck-shaft angle; FNL, femoral neck length; CSA, cross-sectional area of bone surface; BUA - broadband ultrasound attenuation, SOS - speed of sound.

BMD (g/cm2± S.D.)
 Femoral neck0.875 ± 0.1340.727 ± 0.1110.996 ± 0.1390.886 ± 0.145
 Trochanter0.863 ± 0.1410.634 ± 0.1180.904 ± 0.1390.732 ± 0.138
 Lumbar spine1.353 ± 0.2181.077 ± 0.1921.318 ± 0.1941.162 ± 0.193
Femoral geometry
 NSA (degrees)131.4 ± 6.5128.1 ± 6.1130.6 ±5.7127.9 ±5.9
 FNL (cm)5.4 ± 0.84.6 ± 0.75.6 ±0.74.7 ±0.6
Narrow Neck
 Outer diameter (cm)3.4 ± 0.32.9 ± 0.33.3 ±0.32.8 ±0.2
 CSA (cm2)2.4 ± 0.51.8 ± 0.42.8 ±0.52.2 ±0.4
 Section modulus (cm3)1.4 ± 0.30.9 ± 0.21.6 ±0.31.1 ±0.3
Shaft
 Outer diameter (cm)3.3 ± 0.33.0 ± 0.23.1±0.22.9 ±0.2
 CSA (cm2)4.2 ± 0.72.7 ± 0.64.6 ±0.73.4 ±0.6
 Section modulus (cm3)2.5 ± 0.51.5 ± 0.42.7 ±0.51.8 ±0.4
Calcaneal quantitative
ultrasound
 BUA (dB/MHz)74.02 ± 21.5350.40 ± 17.6180.55± 17.1670.04 ± 16.99
 SOS (m/s)1539.40 ± 35.851510.11 ± 31.601555.13 ± 31.271547.34 ± 33.10

Hence, gender-specific predisposition to osteoporosis and fracture risk may be searched among genes determining both the structural and mineral constituents of bone strength (and fragility). It should be noted, however, that young boys have a higher incidence of childhood fractures than girls (Khosla et al., 2003), which emphasizes the role of environmental (behavioral) differences between genders on the clinical expression of bone fragility (for example, propensity to take risks in boys (Ma et al., 2004)).

Sexual Dimorphism of Bone Mass and Size: Role of Hormones

Despite pronounced sexual dimorphism in mass, structure and shape, there is no strictly gender-limited phenotype to be defined, i.e. distinctive features of the male and female skeleton result from quantitative and qualitative variations of bone modeling and remodeling, not from completely different mechanisms of regulation. Even estrogen may be as important for attaining peak bone mass in males as in females, as demonstrated by the lower BMD in young females with late menarche (Chevalley et al., 2005) as well as in men with loss-of-function mutations in the estrogen receptor α gene (Smith et al., 1994) and aromatase gene (Bilezikian et al., 1998). Age-related declines in BMD in men are also directly related to declining levels of estradiol, which may be even more prominent than the BMD relationship to testosterone in aging men (Amin et al., 2000; Khosla et al., 2005; Mellstrom et al., 2006). Conversely, the androgen receptor also plays a role in modulating bone mass and structure in females (Windahl et al., 2006), and periosteal expansion in women may also be enhanced by androgens, as seen in polycystic ovary syndrome (PCOS). These observations justify the analysis of genes in both the androgen and estrogen pathways, including androgen receptor (AR), estrogen receptors α (ESR1) and β(ESR2), and aromatase (CYP19), in association with osteoporosis in men as well as women.

Notably, differences in response to estrogen and testosterone have been shown for male and female chondrocytes, osteoblasts, myoblasts, and other cells.(Tosi et al., 2005; Corsi et al., 2007) Corsi et al. (Corsi et al., 2007) recently found that, when stimulated with bone morphogenetic protein 4 (BMP4), skeletal muscle-derived stem cells (MDSCs) from male mice had a larger increase in osteogenic gene expression and a higher alkaline phosphatase (ALP) activity than cells derived from female mice. These results suggest that male MDSCs have a significantly greater osteoprogenitor potential, which may serve a basis for sex dimorphism in bone regeneration (Corsi et al., 2007). In an earlier study, sex differences in response to progesterone have also been reported in cells derived from rat lumbar vertebrae (Ishida & Heersche, 1997).

The above paragraphs provide evidence that there is undeniable dimorphism in multiple measurable phenotypic determinants of bone strength (risk factors of bone fragility). The question is whether this means that different genes are at work on the male and female skeleton, or whether expression and penetrance of the same genes are modulated by gender-specific environments, such as exposure to gonadal steroids, differences in physical activity, and muscle strength.

Gender-Specific Heritability of BMD: Human Studies

Studies over the last four decades have documented the major contribution of genes to osteoporosis. The majority of the studies focused on areal BMD, which has been shown to be highly heritable (Slemenda et al., 1991; Deng et al., 2002a; Peacock et al., 2002). However the susceptibility to fractures depends on many factors, including non-skeletal factors, such as propensity to fall, diminished soft tissue cushion etc. The age-adjusted heritability of osteoporotic fractures is modest but consistently reported (Deng et al., 2000; Deng et al., 2002b; Michaelsson et al., 2005). The most reliable predictor of fracture is bone mineral density. However, heritability of fracture is notably independent of BMD (Deng et al., 2002b; Kanis et al., 2004), and it is confirmed by the findings of genes that contribute to variation in BMD but do not always contribute to osteoporotic fractures per se (Andrew et al., 2005; van Meurs et al., 2003). The assumption for studying the genetics of BMD as a phenotype of choice, is that genes regulating the proxy for the osteoporotic fractures are also playing a role in the pathogenesis of the fracture, a frequently challenged assumption (Deng et al., 2002b; Andrew et al., 2005; van Meurs et al., 2003). Therefore, the ‘end-point disease’, e.g. fracture, may still be a valuable phenotype for genetic studies of osteoporosis.

Several studies found differences in the heritability (h2) between the sexes, including the genotype-by-sex and environment-by-sex interactions on BMD. Thus, correlations between BMD of the opposite-sex twin pairs were lower than those of same-sex twin pairs (Naganathan et al., 2002), leading to the proposal that such differences could arise from many different mechanisms, including genetic imprinting and different environments in males and females. As is presented in Table 2, our study (Karasik et al., 2003) as well as most of the studies that followed to date, found gender-related differences in the percentage of variance explained by additive genetic effects. Some of these differences are striking, such as h2 for femoral neck (FN) BMD in men ∼67% and in women ∼47% (Karasik et al., 2003) or for lumbar spine (LS) BMD, 72% in men but only 55% in women (Ralston et al., 2005). In other studies, residual heritability in women was either identical to that in men (Kammerer et al., 2003) or not statistically significantly different (Brown et al., 2004). However, h2 in the combined sample of men and women was lower than in either men or women — only 51% (Kammerer et al., 2003), which may indicate that some factors (such as sex-specific gene-environment interactions, which are indiscriminately included within the h2 estimate unless specified) indeed differed between the genders and when combined, could have been lost or masked.

Table 2.  Heritability estimates (h2± S.E.) of bone mineral density phenotypes after adjustment for covariates, sex-specific subsamples.
Site SexAdjusted forFemur BMDSpine BMDReference
  1. *Standard error not available.

  2. **total hip BMD in Brown et al. (Brown et al., 2004), Yang et al. (Yang et al., 2006) and Xiao et al. (Xiao et al., 2006).

Malesage, age2, BMI, height, alcohol, caffeine, smoking, physical activity, calcium, vitamin D0.67 ± 0.110.62 ± 0.11(Karasik et al., 2003)
FemalesSame as above + estrogen therapy0.47 ± 0.090.67 ± 0.10 
Malesage, age2, height, and BMI0.72*0.63*(Brown et al., 2004)
FemalesSame as above0.80*0.87* 
Malesage2, BMI, and diabetes status0.67 ± 0.13(Kammerer et al., 2003)
Femalesage, age2, BMI, and diabetes status0.68 ± 0.20 
Malesage, age2, weight, height0.74 ±0.120.75 ± 0.13(Ng et al., 2006)
FemalesSame as above0.63 ±0.040.71 ± 0.04 
Malesage0.63±0.070.72±0.07(Ralston et al., 2005)
FemalesSame as above0.66 + 0.050.55±0.05 
Malesage, age2, height, and weight0.74 ± 0.06**0.76 ± 0.06(Yang et al., 2006)
FemalesSame as above + estrogen therapy and menopause0.71 ± 0.04**0.63 ± 0.04 
Malesage, height, and weight0.74 ± 0.06**0.76 ± 0.06(Xiao et al., 2006)
FemalesSame as above0.74 ± 0.04**0.67 ± 0.04 

Some of the apparent differences in the heritability of bone mass between genders, however, might be explained by our (in)ability to account for the contribution of some “environmental” covariates, particularly in men. In the study of the Framingham families, we (Karasik et al., 2002) adjusted BMD not only for age, body mass index, height, and estrogen use in females, as was done in many other quantitative genetic studies of bone mass, but also for such covariates as alcohol, caffeine, calcium, and vitamin D consumption, smoking, and physical activity, which are known to be correlated to BMD. It should be noted that some of these ‘environmental’ factors are heritable (an obvious example being anthropometric traits). We found that in females, percentage of variation explained by all the covariates was much higher than in males. Similarly, in a study of 3,320 Chinese subjects from 1,019 families (Ng et al., 2006), age, weight, height, as well as intake of isoflavones and calcium, physical activity, smoking, drinking, contributed from only 13% to 31% among men; for women, addition of age at menarche and menopause resulted in 32% to 46% of explained total variance in BMD. Similar results were reported in the Framingham families (R2 from 8% to 20% in men but 19% to 41% in women (Karasik et al., 2002)) and in the Amish families (Brown et al., 2004).

These observations may reflect the sexual dimorphic nature of the bone phenotypes and covariates, but more obviously, they indicate that the sex-specific covariate models are fitted better to explain variance in BMD than sex-adjusted models with the standard set of covariates, obviously because of inclusion of estrogen exposure in women. Since hypogonadism affects nearly 30% of aging men and up to 60% of older men with osteoporosis (Orwoll et al., 2000), including testosterone and/or estradiol levels as a covariate might therefore narrow the gap between male and female statistical models in magnitude of explained variance.

The necessity and importance of adjusting for covariates is indisputable. By eliminating the contribution of as many covariates as possible, the analyst decreases variability of the trait and thus increases the genetic signal-to-noise ratio. The main point of looking for the gender-specific analytical constructs is that failing to model for sex-specific architecture may substantially hamper detection of susceptibility loci (Towne et al., 1997). Along with this, however, some shared genetic predispositions can be eliminated, such as the genetic co-variance between body size and BMD (see discussions in Karasik et al., 2002; Demissie et al., 2007). Similarly, age at menarche and menopause have heritabilities in the order of ∼50%, hence adjusting in women for these variables may in fact trim down some genetic effects from the overall heritability of BMD.

In summary, sex-specific models adjusting for an extended set of covariates both common and individual for each sex, such as hormonal background, are needed to definitely conclude that apparent differences in the heritability for bone traits between genders actually reflect the greater contribution of skeletal-specific genes in one gender.

Gender-Specific Linkage Analysis for BMD in Humans

Although many studies recoiled from performing sex-specific linkage analyses due to sample size restrictions (Devoto et al., 2001; Wilson et al., 2003), some genome-wide linkage analyses in humans produced gender-specific results. Thus, in a whole-genome linkage analysis stratified by gender, gender-specific QTLs were found in the Framingham sample (Karasik et al., 2003), as well as by several groups (Table 3). Noteworthy the FAMOS study from the UK (Ralston et al., 2005) had a large sample size (3691 individuals from 715 families) and thus was sufficiently powered to allow stratification by age in each sex. The median age of this study sample was 50 yrs, which is close to the average age of menopause and may also grossly segregate subjects of both genders who are still close to peak bone mass vs those already experiencing bone loss. This study identified a unique QTL for BMD in men on chromosome 10q21 (LOD = 4.42). Also, suggestive linkage to FN BMD was found in both men and women with a locus on chromosome 4q25, but the linkage signal was in men below 50 yrs and in women above 50 yrs (Table 3). This subgroup specificity presumably explains why a clear signal at chromosome 4q25 could not be detected in the sex-pooled analysis (Ralston et al., 2005). Moreover, absence of stratification by age may explain why results of published linkage analyses from different groups may vary even in homogeneous ethnic samples (Shen et al., 2005a). In another study, a very promising sex-specific QTL for FN BMD was identified at 15q in young women (Peacock et al., 2004), with LOD score 4.3, but not in younger men from the same geographic region (Peacock et al., 2005a). This finding was also supported by a QTLs meta-analysis in women (Ioannidis et al., 2007). Similarly, QTLs for femoral bone geometry were different between genders (Peacock et al., 2005b).

Table 3.  Results of genome-wide linkage analyses of BMD stratified by sex.
 Lumbar spineMenWomen
 N age (SD)(Ioannidis et al., 2007) 3,411(Karasik et al., 2003) 661 64 (8)(Kammerer et al., 2003) 259 42 (16)(Peacock et al., 2005) 482 35 (11)(Ralston et al., 2005) 1398 48 (17)(Streeten et al., 2006) 371 50 (16)(Xiao et al., 2006) 2682 48 (16)(Ioannidis et al., 2007) 7,741(Karasik et al., 2003) 887 64 (8)(Kammerer et al., 2003) 261 43 (15)(Peacock et al., 2004) 636 34 (7)Ralston et al., 2005) 2260 48 (17)(Streeten et al., 2006) 593 50 (16)(Xiao et al., 2006) 1816 48 (16)
  1. ‡weighted mean rank scores corresponding to maximum multipoint LOD scores in each genomic bin studied (higher score corresponds to higher magnitude of LODs)

  2. *age group ≤50 yrs; ** age group >50 yrs.

  3. †total hip in Xiao et al. (Xiao et al., 2006).

  4. In Kammerer et al. (Kammerer et al., 2003) study, lumbar spine BMD was not measured.

binchr. regionScore‡LODLODLODLODLODLODScoreLODLODLODLODLODLOD
1.61p13.3-q23.3  87.71  
1.71q23.3-q31.1   2.11 (176 cM) 
1.81q31.1-q32 3.13 (226 cM) 2.44 (205 cM) 
2.42p16.2-p12 3.16 (81 cM)  
3.23p25.3-p22.1  85.93  2.61 (27 cM)
3.73q25.31-q27.3  2.43 (177 cM)  
4.74q32.1-q35.1 1.52 (176 cM)   
6.56q23.2-q25.395.6   
7.27p21.1-p14.196.6   
12.612q24.31-qter92.2   
14.114pter-q13.1 4.6 (35 cM)  
15.215q14-q21.394.4   4.49(19 cM)
16.116pter-p13   2.28 (31 cM)* 
18.218p11-q12.3   2.83 (48 cM)** 
20.420q13.13-qter   3.2 (90 cM)* 
21.221q21.3-qter  3.36 (27 cM)  
 Femoral neckMenWomen
 N2,902 4,179 
1.21p36.23-1p35.3 2.02 (34 cM) 
2.12pter-p25.1 3.98 (0 cM) 
2.42p16.2-p1296.0 
2.92q34-q35 2.99 (216 cM) 
4.54q24-q28.3 2.22 (117 cM)* 2.55 (117 cM)** 
4.64q28.3-q32.1 82.1 
4.74q34.1 (176 cM) 2.06 (176 cM) 
7.27p21.1-p14.1 2.28 (57 cM)** 3.01† (74 cM) 1.83 (57 cM) 
7.57q31.1-q34 3.09 (14 cM) 
8.28p22-p21.1 2.15 (48 cM) 
10.310q11.21-q22.1 4.42 (80 cM)* 
12.312p11.21-q1591.4 
12.512q23-12q24 1.91 (137 cM) 
13.313q22.2-q33.1 2.51 (60 cM) 
14.214q13.1-q24.1 1.83 (73 cM) 
15.115pter-q14 90.8 
15.215q14-q21.3 101.8 
15.315q21.3-q26.1 74.8 4.3 (62 cM) 
16.116pter-p13 2.52 (31 cM)* 
17.117pter-p12 101.4 
18.218p11-q12.3 2.07 (41 cM) 
18.318q12.3-q22.1 2.37 (89 cM) 
18.418q22.1-qter 86.1 

As a caveat to these findings however, is the poor overlap in QTLs between the results of the referenced studies. Hence, many of the QTLs identified in these individual studies were not confirmed by the recent meta-analysis of Ioannidis et al. (Ioannidis et al., 2007) that included data from the 9 (mostly Caucasian) whole-genome scans for BMD, suggesting that many of the original findings could be false-positives. In other words, an apparent sex-specific QTL is as likely to be missing in the same-sex result from another study population as it was in the opposite-sex from the original study population. Nevertheless, the meta-analysis also produced several new sex-specific QTLs that did not appear in any of the original studies of BMD linkage, which may be attributed to the increased power due to a large sample size (adjusted for the sample heterogeneity).

It is apparent from the review of the above studies that limited sample size, as well as low density of genomic markers employed in most linkage studies so far may produce many false negative as well as false positive results, thereby precluding unambiguous identification of common and distinct QTLs for bone density and structure in men and women (Blangero et al., 2003; Shen et al., 2005a). Moreover, the majority of the quantitative genetic and linkage studies of BMD and related phenotypes usually did not correct for estrogen use in postmenopausal females, nor made adjustment for the menopause per se (see Table 2 and Ioannidis et al., 2007). Ongoing genome-wide association (GWA) studies using high-density polymorphic markers (300K, 500K and up to >1 million SNPs) throughout the human genome will increase the likelihood to detect common and unique genetic determinants of osteoporosis risk in males and females, although this approach also increases the risk of false positive results (due to a logarithmic increase in the number of comparisons). It can therefore be advocated to take into consideration a balance between sample size (therefore, power) and biological plausibility of stratification by sex vs. simple adjustment for sex when planning an association analysis (see Patsopoulos et al., 2007).

Sex-Specific Heritability and QTLs for BMD in Animal Models

Experimental animal models provide a means to largely circumvent the complex effects of environmental (at least extrinsic) variations which are present in human studies (Orwoll et al., 2001; Rosen et al., 2001). Longer exposure to diverse environmental factors in humans may mask effects of genes more easily observed in experimental models. Specifically for the field of osteoporosis it is important to note that in mice, however, growth plates do not close until well after maturation; also, there is virtually no prolonged period in life during which bone mass and structure is maintained constant, contrary to humans (Glatt et al., 2007).

In keeping with some observations in humans, studies of animals found differences in the heritability between the sexes. In the study of Orwoll et al. (Orwoll et al., 2001), the heritability of peak BMD was studied in male and female recombinant inbred (RI) strains of mice, whose whole body BMD values were corrected for the influence of body weight. The study found an estimated narrow sense heritability of 45% and 22% in male and female mice, respectively. Quantitative trait locus analysis of the BXD RI strain series provisionally identified nine chromosomal sites linked to peak bone mass development in males and seven regions in females. In two cases, the chromosomal loci were shared between genders, but the rest were distinctly sex-specific (Orwoll et al., 2001). This study raises another interesting question: was the rather low heritability estimate for BMD in this mouse strain a result of elimination of the covariance with weight/mechanical loading? In this regard, the simultaneous study of genetic effects on bone and body size (body weight, body length, and adipose mass) may be critical (Lang et al., 2005). QTLs for BMD have also been identified in mice by crossing inbred strains that differ in BMD and further testing for association with genetic markers in the progeny. Several of these studies have focused on bone strength phenotypes in female mice (Beamer et al., 2001; Bouxsein et al., 2004; Kesavan et al., 2006), and thus it is difficult to generalize their findings; their results may be perceived as sex-specific until proven otherwise.

Similarly, evidence of sex-specific genetic influences on femoral geometry (cross-sectional area, CSA) was identified at several chromosomal sites, using RI mouse strains (Klein et al., 2002). QTLs on four chromosomes were linked to CSA in both genders, while three other chromosomal loci were sex-specific. Interestingly, none of these QTLs governing bone geometry were identified to regulate areal BMD, findings consistent with studies in humans (Shen et al., 2005b; Demissie et al., 2007).

Congenic strains are especially useful for the study of QTLs. They are created by moving a chromosomal region containing a QTL from one donor strain to a recipient strain via a series of backcrosses. For example, Beamer and colleagues have generated congenic mouse strains for several of the bone size and strength QTLs, which were moved from C3H/HeJ (C3H) onto a pure C57BL/6J (B6) background (Bouxsein et al., 2002; Robling et al., 2007). Using congenic mice, Turner et al. (Turner et al., 2003) also revealed sex-specific genetic regulation of femoral structure and strength. They found the loci on mouse Chr. 1, 6 and 18, to show sex-specific effects on femoral volumetric BMD and bone structure. For example, the QTL on Chr. 1 suppresses femoral polar moment of inertia (and index of resistance to fracture) in male mice but enhances it in female mice. This reversion of trend between the genders (also seen for other measures of bone strength) is very interesting, since it means that the QTL gene(s) may be truly sex-specific, influencing male bones in an opposite direction from female bones. Robling et al. (Robling et al., 2007) further tested the mechano-responsiveness in the Chr. 1 congenic strain and confirmed that the male congenics were less responsive than B6 male controls, but the 1T females were more responsive than B6 female controls. Similarly, Beamer et al. (Beamer et al., 2007) most recently found that the Chr. 1 QTL also affects cortical and trabecular bone phenotypes in a sex-specific manner (similar results also obtained by Edderkaoui et al., 2007).

In summary, data in mice suggest the presence of sex-specific genes governing bone macro- and microstructure, which may in part occur via differences in skeletal mechanosensitivity among sexes. Whether this phenomenon would be amplified, or blunted, in the context of extended environmental variation and interactions (as present in the human world), is currently unknown, although some studies start to point towards a sex-specific interaction between genetic variants in osteoporosis candidate genes and physical activity (see below).

Despite their intrinsic limitations, the above studies provide some evidence that there may be sex-specific heritabilities and QTLs for bone strength and fragility in humans and mice. The tantalizing question is therefore whether the sexual dimorphism for osteoporosis risk is determined by the variable penetrance of common genetic susceptibility factors on a background of specific environments in males and females, or whether there might be genes that determine bone strength in a sex-specific manner (Fig. 1). We may identify here some of the genes for both scenarios.

image

Figure 1. Schematic diagram of genetic determinants of sexual dimorphism for osteoporosis risk. It is suggested that most genes determining peak bone mass, microstructure and turnover are common to males and females, with only a few gender-specific genes. The thin vs. large arrows represent genetic factors of variable expression or penetrance, which may particularly depend on interactions with the sex-specific environment.

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Contribution of Candidate Genes to Bone Phenotypes in Humans: Differences by Sex

The disease susceptibility genes may be roughly categorized into several broad categories according to the function(s) of the coded molecules, mostly involved in metabolism of bone cells (osteoblasts and osteoclasts), structure and turnover of collagen and mineral (calcium and phosphorus), and regulatory/hormonal (obviously, sex-hormone) pathways. Multiple genes in each category were proposed in the last decade as candidates for osteoporosis (see (Liu et al., 2006; Ferrari, 2005) for review). Among the most extensively studied so far are the vitamin D receptor, VDR, estrogen receptor alpha and beta, ESR1 and ESR2, collagen 1 alpha 1 chain, COL1A1, transforming growth factor beta, TGFβ, interleukin-6, IL-6, methylenetetrahydrofolate reductase, MTHFR, and more recently the LDL-receptor related protein 5, LRP5, gene. Both retrospective and prospective meta-analyses have been published for VDR (Uitterlinden et al., 2006), ESR1 (Ioannidis et al., 2004), and COL1A1 (Ralston et al., 2006), and most recently, for TGFβ (Langdahl et al., 2008) and LRP5 (van Meurs et al., 2008), resulting in an evidence for an association with BMD and/or fracture. Most association studies have been performed in women, less commonly in men, and rarely in both men and women from the same cohort.

Very recently, Patsopoulos et al.(Patsopoulos et al., 2007) critically reviewed the claims of sex-specific associations in complex diseases and proposed criteria for validating such claims. In particular, (a) sex differences should be specified a priori, (b) interaction tests and adjustment for multiple comparisons should be performed, (c) the subgroups should be adequately powered, and finally, (d) the results should be replicated by several other studies. These requirements have rarely been met in the osteoporosis genetic literature; this should be kept in mind as we will now specifically discuss the main association studies that reported sex-specific associations with osteoporosis and their limitations.

Estrogen Receptors α (ESR1) and β (ESR2) Gene Polymorphisms

As indicated above, lack of estrogen plays a critical role in postmenopausal osteoporosis (Sievänen, 2005). In addition, estradiol is important for the acquisition and maintenance of peak bone mass in both females (Cummings et al., 1998; Rizzoli and Bonjour, 1997) and males (Amin et al., 2000; Riggs et al., 1998). ESR1 gene polymorphisms have been analyzed extensively for association with BMD, bone loss, turnover markers and/or fractures in women, at first with contrasting results (Gennari et al., 2005). Nevertheless, a meta-analysis of the association between ESR1 genotypes and BMD including more than five thousand women from 22 eligible studies (n = 11 in Caucasians and n = 11 in Asians), concluded that homozygotes for the XbaI (rs9340799) XX genotype have a modestly but significantly higher BMD (+1-2%) at lumbar spine or hip compared to xx (Ioannidis et al., 2002). No differences were found between PvuII (rs2234693) genotypes, despite the fact that XbaI and PvuII sites are only 46 base pairs apart and are in strong linkage disequilibrium with one another in Caucasians. In this meta-analysis, BMD differences between genotypes tended to be up to five times larger in pre-menopausal compared to post-menopausal women, despite the fact that only three studies included premenopausal women. On the other hand, differences in fracture risk were large compared to the small differences of bone mass observed between genotypes (odds ratio, 0.66 [95% CI, 0.47-0.93] in XX vs xx) (Ioannidis et al., 2002). Similarly, a recent study of these polymorphisms in 18,917 individuals from 8 European centers found evidence of association with fracture risk but not BMD (Ioannidis et al., 2004). These meta-analyses therefore suggest that ESR1 genetic variation may influence the age-related changes in bone structure that underlie bone strength/fragility, whereas ESR1 association with bone mass may be better discernible above a certain estradiol threshold, as met mostly in females between menarche and menopause.

Against this background, few studies of modest size investigated association of ESR1 with BMD in both male and female children and/or adolescents. Hence, in 139 pre-pubertal girls and 232 pre-pubertal boys (mean age ±SD, 7.6±0.4 yrs) we found that ESR1 genotypes were associated with BMD at most skeletal sites (p = 0.01-0.06 adjusted for age, weight and height), but with a borderline interaction involving sex (Pinteraction≤ 0.09) and calcium intake (Pinteraction= 0.03)(Pennisi et al., 2005). Notably, however, Boot et al. (Boot et al., 2004) reported that PvuII/XbaI polymorphisms are associated with BMD in both boys and girls. Similarly, Willing et al. (Willing et al., 2003) have identified significant gene-by-gene interaction between the ESR1 and VDR genes on BMD and BMC in very young children from both genders.

Large-scale evidence of the contribution of ESR1 genetic variation to osteoporosis risk in men is lacking; a few studies reported association with BMD in men (Long et al., 2004) but most found no association (Langdahl et al., 2000; Khosla et al., 2004; Koh et al., 2002; van Meurs et al., 2003). In this context, it is notable that Khosla et al. (Khosla et al., 2004) found significant interactions between bioavailable estradiol levels and the XbaI and PvuII genotypes on rates of bone loss in men aged 22-90 years. Interpretation of the above findings is limited by the lack of biological evidence that ESR1 intron 1 alleles may directly affect estrogen receptor α levels and/or activity, especially in men. Unfortunately, there are few studies that have tested for an association between ESR1 polymorphisms and BMD in both adult men and women from the same population (Gennari et al., 2005).

Similarly, few studies have examined the polymorphisms in the estrogen receptor β gene (ESR2) for association with BMD and fracture risk. The majority of the studies focused on the number of CA repeats and was done in postmenopausal women: Japanese (Ogawa et al., 2000), southern Chinese (Lau et al., 2002), and US Caucasian (Scariano et al., 2004). A haplotype of ESR2 alone and in interaction with ESR1 and IGF1 genetic variation influenced the risk of vertebral and fragility fractures in postmenopausal Dutch women (Rivadeneira et al., 2006). Similarly, epistatic interactions between ESR1, ESR2 and nuclear receptor interacting protein 1 (NRIP1) genes were strongly associated with osteoporosis in postmenopausal Spanish women (Moron et al., 2006).

We assessed whether the ESR2 CA repeat and four other intronic polymorphisms were associated with BMD in 723 men and 795 women (mean age 60 yrs) from the Offspring Cohort of the Framingham Study (Shearman et al., 2004). In both women and men, there was significant association of the CA repeat with measures of femoral BMD; similar to other studies (Lau et al., 2002; Scariano et al., 2004), the higher BMD values were observed in subjects who were homozygous for a lower number of repeats (CA < 23). Furthermore, two common single nucleotide polymorphisms (rs1256031 and rs1256059) in strong linkage disequilibrium with one another but not with the CA repeat, showed an association with femoral BMD in men but not in women. We thus concluded that genetic variation in the ESR2 might play a prominent role in men. Since other ESR2 studies were mostly performed in small samples of either pre- or postmenopausal women (Arko et al., 2002; Gennari et al., 2005), there is a need for additional studies of ESR2 alleles in males along with further meta-analysis of the results of these studies.

In summary, it is not yet entirely clear whether or not ESR1 and ESR2 are associated with osteoporosis risk in a sex-specific manner. More likely, the strength of the association depends on the age-related level of endogenous hormones in both genders.

Interleukin-6 (IL-6) Gene Promoter Polymorphisms

The decline in endogenous estrogen after menopause leads to increased production of pro-inflammatory cytokines, including interleukin 6 (IL-6) (Manolagas & Jilka, 1995). Increased levels of cytokines such as IL-6, in turn, promote the differentiation of osteoclast precursor cells into mature osteoclasts, which induce bone resorption (Moffett et al., 2004).

Several studies have identified the IL-6 gene locus (7p21) to be linked to BMD in postmenopausal women (Murray et al., 1997; Tsukamoto et al., 1999) and in families of osteoporotic probands (Duncan et al., 1999; Hamanaka et al., 1999), whereas no linkage was found in young, healthy sister pairs (Lakatos, 2000). Ferrari and colleagues reported contribution of functional polymorphisms in the IL-6 promoter, -572 GC (rs1800796) and -174 GC (rs1800795), to the C-reactive protein and markers of bone resorption in postmenopausal women (Ferrari et al., 2003). Subsequently, Yamada et al. (Yamada et al., 2003) examined the −634 CG polymorphism of the IL-6 gene in ∼2200 Japanese subjects, men and women. This polymorphism was significantly associated with BMD of total body and lumbar spine in postmenopausal women but not in men (Table 4).

Table 4.  Genetic polymorphisms in two candidate genes for osteoporosis that manifest sex specific associations
Gene/PolymorphismSample: ethnicity, age, NWomenMenReference
  1. ΔBMD – change in BMD.

  2. CRP – C-reactive protein.

  3. CTx – carboxy terminal telopeptide of collagen 1.

  4. Ns – non-significant (p ≥ 0.05).

IL-6: -174 CG (rs1800795)Caucasian, 819 women and 755 men, age 60 ± 9 yrspostmenopausal, femoral neck/trochanter BMD (p= 0.024-0.027)nsFerrari et al. (Ferrari et al., 2004b)
IL-6: −634 CG (no rs number)Japanese, Women: 1108 to 1113; Men: 1116 to 1130 ages 40–79postmenopausal, total body BMD (p= 0.011); lumbar spine BMD (p= 0.007)nsYamada et al.(Yamada et al., 2003)
IL-6: -174 CG (rs1800795) and -572 GC (rs1800796)Caucasian, 495 women Age: 71.9 ± 5.7 yrsPostmenopausal, CRP (p=0.007), CTx (p≤0.016), LS BMD (p=0.037),n.aFerrari et al. (Ferrari et al., 2003)
IL-6: -174 CG (rs1800795)Caucasian, 3376 women only, age 73 ± 5Femoral neck ΔBMD (p= 0.029) Total hip ΔBMD (p= 0.004)n.a.Moffett et al. (Moffett et al., 2004)
IL-6: -174 CG (rs1800795)Caucasian, 964 women only, age 75 ± 0Quantitative ultrasound of calcaneus (p= 0.02), but not to BMDn.a.Nordstrom et al. (Nordstrom et al., 2004).
LRP5: exon 9 V667M (rs4988321)exon18 A1330V (rs3736228)Caucasian, 78 men with idiopathic osteoporosis, mean age ∼50 yrsn.a.risk of idiopathic osteoporosis, HR ∼3 (p = 0.02-0.002)Ferrari et al. (Ferrari et al., 2005)
LRP5: SameCaucasian, 460 men, 408 women, Age: 7-69 yrsBMD, BMC, projected area (ns)lumbar spine BMD (p = 0.015), BMC (p = 0.002), projected area (p=0.002)Ferrari et al. (Ferrari et al., 2004a)
LRP5: exon 18 A1330V (rs3736228)Caucasian, 980 men and 1213 women, age 69 ±9associations weaker and less consistent than in menlumbar spine BMD (p=0.001), femoral neck BMD (p= 0.01), and risk of fragility fractures (p=0.004)van Meurs et al. (van Meurs et al., 2006)
LRP5: rs2306862 (exon 10) exon 18 A1330V (rs3736228)Caucasian, 868 men and 929 women, age 62 ± 9No interactionInteraction with physical activity on spine BMD (rs2306862, p for interaction=0.02; rs3736228, p = 0.05)Kiel et al. (Kiel et al., 2007)
LRP5: rs491347 (intron)Chinese, 364 women and 369 men, age 27.2±4.5 Caucasian, 1873 members of 405 nuclear familiesAssociation with spine BMD in Caucasian females (P = 0.005)Association with spine BMD (P = 0.002) and hip BMD (P = 0.030) in Chinese malesXiong et al. (Xiong et al., 2007)

In the Offspring Cohort of the Framingham Heart Study (Ferrari et al., 2004b), BMD was found to be significantly lower in women with IL-6 genotype –174 GG compared to CC, and intermediate with GC, in women who were either more than 15 years past menopause, or estrogen-deficient, or who had insufficient calcium intake (<940 mg/day). No associations were observed in premenopausal women nor in men (n = 755). We may speculate that the mechanism of this interaction is via NF-IL-6, a transcription factor which interacts with the estrogen receptor to repress IL-6 transcription (Fishman et al., 1998; Terry et al., 2000). The IL-6–174 GC polymorphism is indeed known to affect a binding site for NF-IL-6, thus making it more or less susceptible to estrogen activity. Although estradiol levels also correlate with BMD in aging men (see above), this relationship may actually depend on aromatization at the tissue level. Therefore the estradiol-IL-6 genetic pathway may not be as directly involved in bone remodelling in men as in women.

The IL-6–174 G allele was confirmed to also be associated with lower bone ultrasound properties and an increased risk of wrist fracture (OR, 1.5, 95% CI 1.1-2.0) in a large cohort of 964 post-menopausal women aged 75 years (Nordstrom et al., 2004). Another group specifically studied the rate of decline in hip BMD with the IL-6 174GC polymorphism (Moffett et al., 2004). Compared to women with the GG phenotype, women having the CC genotype had slower rates of bone loss in the total hip and femoral neck in ∼3.5 years of follow-up and 33% lower risk of wrist fractures over an average of 10.8 years (Moffett et al., 2004). Results of these studies may be interpreted not only as a gender-specific, but also as a hormone-status-specific effect, i.e. the influence of IL-6 alleles will develop only in the context of prolonged estrogen deprivation and/or increased bone turnover. It would be interesting therefore to re-examine the (non-) association of IL-6 genetic variation with osteoporosis in men by considering their circulating estradiol and testosterone levels as potential covariates.

LDL-Receptor Related Protein 5 (LRP5) Gene Polymorphisms

LRP5 is a member of the low-density lipoprotein (LDL) receptor-related family coding for a transmembrane co-receptor for Wnt signaling. Mutations of this gene are associated either with osteoporosis-pseudoglioma syndrome (loss-of-function mutations)(Gong et al., 2001) or with high bone mass and osteosclerotic phenotypes (gain-of-function mutations) (Little et al., 2002; Boyden et al., 2002; Van Wesenbeeck et al., 2003). In an animal study, Iwaniec et al. (Iwaniec et al., 2007) recently provided some experimental evidence that Wnt-LRP5 signaling may be implicated in the sexual dimorphism of the skeleton. Indeed, the decrease in femoral bone mineral content (BMC) and cancellous bone volume observed in Lrp5-null mice compared to wild type was more prominent in males than females. In humans, LRP5 exon 9 V667M (rs4988321) and exon 18 A1330V (rs3736228) missense polymorphisms have been associated with an up to 3 fold increase risk of idiopathic osteoporosis in a case-control study in middle-aged men (mean age ∼50 yrs).(Ferrari et al., 2005) Moreover, vertebral bone mass and size in adult males as well as changes over one year in lumbar spine BMD and size in pre-pubertal boys were also significantly associated with these LRP5 variants (Ferrari et al., 2004a), whereas no association was found in females, suggesting that LRP5 polymorphisms could mainly contribute to the risk of spine osteoporosis in men by influencing vertebral bone growth during childhood. Consistent with these findings, the Rotterdam study confirmed association of LRP5 1330-valine with significantly decreased lumbar spine area and a higher risk of fragility fractures (hip, proximal humerus, and pelvis fractures) in elderly men (OR 1.6, 95% CI 1.0-2.4), but not in women (van Meurs et al., 2006).

We further performed a large-scale population-based genetic association study in the Framingham Study. In 1,797 unrelated individuals, ten single-nucleotide polymorphisms (SNPs) spanning the LRP5 gene were genotyped. Three SNPs were significantly associated with BMD in men ≤60 years of age, after adjustment for covariates. In women, we also found 3 SNPs to be significantly associated with BMD; however, these were distinct from the SNPs in men. Moreover, there was a significant interaction between physical activity and a SNP in exon 10 (rs2306862; Pinteraction= 0.02) and another in exon 18 (A1330V or rs3736228, Pinteraction= 0.05) on spine BMD in men (Table 4). In both SNPs, the TT genotype was associated with lower spine BMD in men with higher physical activity scores, conversely with preserved BMD in men with lower physical activity scores (Kiel et al., 2007). Therefore, analyses of LRP5 polymorphisms in the Framingham Osteoporosis Study suggest an interaction with physical activity in men, but not women. Findings of sex-specific associations of LRP5 SNPs with BMD were further confirmed by Xiong et al. (Xiong et al., 2007) in Chinese and Caucasians. However, a prospective meta-analysis of participant-level data on 37,760 individuals in GENOMOS (van Meurs et al., 2008), which was focused on the A1330V (rs3736228) and V667M (rs4988321) polymorphisms, could not find the gender difference suggested in the above individual studies (Ferrari et al., 2004a; van Meurs et al., 2006; Kiel et al., 2007). These two polymorphisms were strongly associated with BMD and were predictive of osteoporotic fractures in Caucasians of both sexes. This latter study is important since it again confirms that only in an adequately-powered study, it is possible to discern whether or not the subsample-specific results are false-positives or true findings. It would be also interesting to test for interaction of LRP5 with physical activity in men and women, to corroborate or disprove our results (Kiel et al., 2007).

This list of candidate genes for osteoporosis that manifest sex-specific associations is not comprehensive; others may exist. Results of association with some candidates, such as COLIA1 Sp1 polymorphism, were remarkably similar for both sexes (Ralston et al., 2006). Among candidate genes, apparent sex-specific associations are characteristic to the genes in gonadal steroids pathway (such as IL-6, ESR1, ESR2). Even if some phenotype association findings may be sex-specific, in most of the reviewed publications, genotype distribution did not differ by gender. Hence it becomes evident that these might be gender-specific environmental (such as a hormonal milieu) and/or epigenetic effects, which are playing an important role on the expression of genetic variation in osteoporosis. The “truly” sex-specific genes may potentially reside on sex-chromosomes.

X-Chromosome: Possible Culprit?

The much higher prevalence of osteoporosis in females than males may be hypothesized to be due to an X-linked, frequent and dominant allele. It appears as if a second X chromosome affords the extra probability of liability to an XX female compared to an XY male (Alternatively, we may hypothesize that Y chromosome genes play a protective role in osteoporosis!). The X-chromosome genes are expressed differently in females in part because there is a double copy of the gene. In addition, there are differences in meiotic effects, X-chromosome inactivation, and genetic imprinting (whether the active X chromosome comes from the mother or the father) (Tosi et al., 2005). It is known, however, that most X-linked genes are subject to dosage compensation and thus are not expected to be highly expressed in females; on the other hand, some X-linked genes may ‘escape’ X inactivation (reviewed in Craig et al., 2004).

Syndromes of an extra or deficient X-chromosome, such as Klinefelter and Turner syndromes respectively, are characterized by sex steroid deficiency due to gonadal dysgenesis, low peak bone mass and an increased risk of osteoporosis. It may be speculated that the Klinefelter (XXY) phenotype might also reflect X chromosome imprinting effects, as has been proposed for 45,X Turner syndrome (Zinn et al., 2005). Unfortunately, there is an obvious lack of studies on genomic imprinting in the osteoporosis field, which potentially could shed light on the sex-specificity of genetic variation.

These chromosomal disorders may implicate, among others, involvement of the androgen receptor (AR) gene, which is mapped on Xq11.2-q12 (Zinn et al., 2005). The few studies of the AR in men are not well-powered (Van Pottelbergh et al., 2001; Zitzmann et al., 2001), thus their results are inconclusive for male-specific associations. There are not many other X-linked genes that have been directly suggested for bone phenotypes in animals or humans (besides perhaps the PHEX gene mutations responsible for X-linked hypophosphatemic rickets). There are some other potential candidate genes of interest in the chromosomal regions linked to bone traits, such as NF-kappa B-repressing factor (NRF) and fibroblast growth factor 13 (FGF13). Another gene found on chromosome X in humans and mice is biglycan; its deficiency most strongly affected the male bones cross-sectional geometric and mechanical properties (Wallace et al., 2006). In this regard, two recent collaborative studies of a QTL on the X-chromosome are of interest. First, a QTL was found on the mouse X-chromosome for post-maturity change in spine BMD (Szumska et al., 2006). Then, the syntenic region on human chromosome Xp22 was tested in post-menopausal women by association mapping of the human QTL, which identified a gene that encodes a nuclear protein Pirin as possibly responsible for the observed signal (Parsons et al., 2005). This study provides a valuable example that interspecies synteny can be used to identify and refine QTLs for complex traits.

Several human studies of femoral geometry found linkages on chromosome X, namely femoral neck cortical thickness (Shen et al., 2005b; Xiong et al., 2006) and femoral shaft CSA and width (Demissie et al., 2007). This X-linkage may account for the well-recognized differences between men and women in the majority of bone geometric traits (even when adjusted for body size (Kaptoge et al., 2003)). Also of interest are linkages of murine femoral CSA (Klein et al., 2002) and periosteal circumference (Masinde et al., 2003) on mouse chromosome X (syntenic with Xq26-Xq27 in humans). These chromosomal regions thus deserve more attention for follow-up fine-mapping studies.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Discussion
  5. Conclusions and Recommendations
  6. List of Abbreviations
  7. Acknowledgments
  8. References

The contribution of genetic factors to the marked differences in bone fragility between females and males is not well understood. Because the male skeletal phenotypes are associated with considerable fracture risk reduction, an elucidation of the nature of the effects of sex and gender could provide the basis for novel diagnostic, preventative, or therapeutic approaches (Orwoll et al., 2001).

Recent case-control GWA studies show that we are now able to identify common alleles that increase the risk of many complex disorders by at least 20%, provided the sample size is sufficient, i.e. in the thousands (as in the Welcome Trust Case Control Consortium (Anonymous, 2007)). Genetic variations however do not cause complex disease per se but rather influence a person's susceptibility to the detrimental effects of environmental factors (including those imposed by the hormonal milieu, such as estrogen exposure or deprivation). Some people are thus more sensitive to their environments while others are less susceptible, while persons with different genetic make-ups react in opposite ways to the same environmental factor. Along with this, even when a specific genetic variation is identified that contributes significantly to the disease in some people, many others will be found who have the same polymorphism but do not develop the disease. This is particularly true when risk differs among genders, indicating that additional factors — genetic, epigenetic, environmental and behavioral — can mask or compensate for the disease susceptibility gene. Therefore there is a need to perform sex-specific analyses more systematically. As recently advocated (Patsopoulos et al., 2007) a formal interaction test needs to be performed, and if the subgroups are adequately powered, analysis of sub-samples is justified. However we acknowledge that there is a trade-off between the need to investigate gene-by-gender interactions based on a biological reason, on one hand, and inadequate statistical power and other limitations, on the other.

Choice of a phenotype of interest is also very important in genetic epidemiology in general, and in the osteoporosis field in particular. By definition, endophenotypes (such as bone turnover markers) are more proximate to the pathophysiologic pathways and thus may be more susceptible to gene-by-sex interaction, whereas the environment may become prominent when dealing with more distal phenotypes, i.e. fractures. Notably, difference in the trait distribution by sex (sexual dimorphism) is not a pre-requisite for sex differences in heritability, linkage, and ultimately genetic variation underlying these traits (Weiss et al., 2006); however, it is an important starting point for exploring the gender effects.

Conclusions and Recommendations

  1. Top of page
  2. Summary
  3. Introduction
  4. Discussion
  5. Conclusions and Recommendations
  6. List of Abbreviations
  7. Acknowledgments
  8. References

Personalized medicine and major advances in public health are expected to result from understanding the variation in DNA that makes humans different from one another in their susceptibility to disease and their response to medicines (pharmacogenetics). Most genetics research is still focused on identifying single causative factors and has not overcome a deterministic interpretation of complex disorders. Although there has been some progress lately in the field of gene-environment interactions, the results themselves have exposed the immense complexity that is involved. There is a need to develop new approaches to integrate this type of knowledge into existing methods, including enhancements from a statistical point of view.

All things considered, planning of future association studies will deal with gene-environment, specifically gene-gender interactions. In the case of osteoporosis, we are certain that analysis of gender differences is warranted a priori, based on the sex dimorphisms of osteoporosis-related phenotypes. In any case, the results of genetic analysis should be replicated by other studies (Patsopoulos et al., 2007). By identifying and characterizing gene-by-modifiable environment interactions, we have more opportunities to effectively target intervention strategies. Knowledge of genetic machinery underlying sex dimorphism of osteoporotic fracture risk may ultimately identify targets for specific interventions aimed at increasing bone strength in women and men. Considering the implications of both genetics and environment in complex diseases, we hope that our findings regarding osteoporosis will prompt further investigations aimed at the gene-by-gender interactions in the etiology of other complex diseases prevalent in the general population.

List of Abbreviations

  1. Top of page
  2. Summary
  3. Introduction
  4. Discussion
  5. Conclusions and Recommendations
  6. List of Abbreviations
  7. Acknowledgments
  8. References
ALP

alkaline phosphatase

AR

androgen receptor

BMI

Body mass index

BMC

bone mineral content

BMD

bone mineral density (FN, femoral neck; LS, lumbar spine)

BMP

bone morphogenetic protein

CNV

copy numbers variant

COMT

catechol-O-methyltransferase

CSA

cross-sectional area

CYP17A1

cytochrome P450 family 17 subfamily A

CYP19

aromatase

DXA

dual x-ray absorptiometry

ERT

estrogen replacement therapy

ESR

estrogen receptor

FNL

femoral neck length

GWA

Genome wide association

h2

heritability

HSA

hip structural analysis

IGF1

insulin-like growth factor I

LD

linkage disequilibrium

LOD

logarithm of the odds

LRP5

Low-density lipoprotein receptor-related protein 5

MDSC

muscle-derived stem cell

mRNA

messenger RNA

MGF

“mechanogrowth factor”

NRIP1

nuclear receptor interacting protein 1

NSA

neck-shaft angle

QTL

quantitative trait locus

PCOS

polycystic ovary syndrome

RI

recombinant-inbred

RIZ1

retinoblastoma-interacting zinc finger protein

SD

standard deviation

SNP

single nucleotide polymorphism

SRY

sex determining region Y

VDR

Vitamin D receptor

Acknowledgments

  1. Top of page
  2. Summary
  3. Introduction
  4. Discussion
  5. Conclusions and Recommendations
  6. List of Abbreviations
  7. Acknowledgments
  8. References

We gratefully acknowledge Drs D.P. Kiel, Y.H. Hsu, W.G. Beamer, and Nicole Shimabuku, for their critical reading and useful discussions in preparation of this manuscript, and Drs. T. Chevalley and R. Rizzoli for their essential contribution to studies on LRP5, ESR1 and IL-6 alleles in children, as reported in this review. We are also thankful to the anonymous reviewers who helped to shape and focus this manuscript.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Discussion
  5. Conclusions and Recommendations
  6. List of Abbreviations
  7. Acknowledgments
  8. References
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