School of Health Science, Griffith University, Gold Coast, Australia. E-mail: n.morrison{at}griffith.edu.au
Vitamin D receptor (VDR) is a nuclear hormone receptor that acts as a transcriptional regulator in response to circulating 1,25 dihydroxyvitamin D3, the active hormonal form of vitamin D. VDR gene polymorphism (VDRGP) have been extensively studied in different diseases, with over 700 primary research articles, although this has focused mainly on the same markers. The VDRGP experience, with its huge literature and appearance of apparently contradictory reports each month, may provide an example of what to expect with other genes in the growing field of analysis of common gene polymorphisms with complex common disorders. Morita et al. provide a typical example of a moderately sized population study of the relationship of VDRGP to bone density and rate of bone loss in Japanese.1 Reviewing the VDRGP literature is beyond the scope of this commentary which will only refer to a limited number of publications. For those interested, Zmuda et al. provide two comprehensive reviews of the literature of VDR related to disease.2,3 Suffice to say that VDRGP have shown positive association to a wide range of divergent diseases, and due to the pleiotropic mode of action of a nuclear-hormone receptor such as the VDR, plausible molecular scenarios of involvement can be constructed for many different diseases. In fact, if functional genetic polymorphism occurs in a transcriptional regulator, one should expect pleiotropism, due to the fact that VDR controls the expression of a large and unknown number of subordinate genes, in both positive and negative senses and in cell-specific manners. The VDR protein is at the centre of the vitamin D endocrine system, a complex physiological system with substantial feedback regulatory mechanisms involved in maintaining serum calcium and 1,25 dihydroxyvitamin D3 within narrow bounds and now known to affect a large number of organs.4 It is possible that the self-regulatory nature of the VDR endocrine system moderates the effect of VDRGP. VDR gene polymorphisms are looking for phenotypes, and judging from the literature, are related to numerous different traits, reflecting pleiotropism. Therefore, although literature has accumulated concerning VDR and bone mineral density (BMD) in particular, this may not necessarily be the most potent effect of genetic variation in VDR.
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VDR polymorphisms |
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Multifactorial traits and BMD |
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BMD is strongly related to anthropometric variables such as age, height, and weight. This fact complicates simple analysis of genetic markers in relation to BMD, especially if the effect of a genetic locus varies with age. It becomes difficult to model all the possible regression equations including anthropometric variables with genotype as additive, dominant, or recessive, without a degree of arbitrariness in the analysis. Authors tend to use linear or higher than linear quadratic adjustment for the age-related effect on BMD. Of particular relevance to BMD changes in Asians, Liao et al. analysed 2702 Chinese aged from 5 to 96 years and found that cubic relationships between age and BMD fit best for all skeletal sites tested.11 The cubic relationship implies that BMD is continually changing with age; a reasonable proposition. Genotype effects may be modelled in ANCOVA with a cubic age relationship, but the possibility that adolescent rates of bone gain1214 and rates of bone loss after the menopause15 may be affected themselves by VDR genotype severely complicates the analysis of the age effect with genotype. As cross-sectional population-based studies become larger, a more-attractive analytical alternative might be to use a regression spline approach with age knot selection based on important life events, such as skeletal maturity and the onset of menopause. Similar issues of non-linearity exist with the height and weight variables. To complicate matters further, BMD itself is not a true density, but an areal projection with units of grams per square centimetre and is influenced by bone size. More advanced measures of bone mineral content and micro-architecture may provide better target phenotypes for genetic studies.
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The standard quantitative model |
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What is the effect size of VDRGP? |
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The power of a study can be estimated by:
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Table 3 shows that large numbers are required to have reasonable power to detect an effect of the magnitude suggested by meta analysis (0.13SD difference between the two groups). Japanese allele frequencies were estimated as b = 0.885 and B = 0.115. For 80% power to detect the effect at P = 0.01, 3046 Caucasians or 4700 Japanese are required. Basing the power calculation expressly on the estimates of and
shown in Table 2, the difference is slightly smaller (0.11 SD) resulting in larger prospective numbers; for 80% power 2680 and 4000 Caucasians (or 3600 and 5400 Japanese) are needed at P of 0.05 and 0.01 respectively. These are sobering numbers, considering that the meta-analysis demonstrated an effect predominately in postmenopausal females.
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Twin studies have been proposed in order to reduce the complications of the age variable. Does the dominance deviation shown above fit with twin data? A recent study used 889 individuals in Caucasian twin ships to analyse the effect of the Taq1 VDR marker.32 Using twin path analysis, the authors concluded that VDRGP had significant effect sizes for BMD of 0.17, 0.18, and 0.13SD between extreme homozygotes for lumbar spine, forearm, and hip, respectively. Low BMD was associated with the same genotypes determined in the above meta-analysis, with a dominant model. For lumbar spine BMD, the age-adjusted means for the Taq1 marker were: tt, 0.934; Tt, 0.939; TT, 0.961. These data fit well with = 0.093 and
= 0.060, again suggesting dominance, at least in Caucasians.
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Haplotypes in different ethnic groups |
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In the present study, Morita et al.1 report effect sizes between 0.09 and 0.12SD, with genotype aaTT lower than that of AaTT, with the effect at the mid radius but no appreciable effect at the other sites of the spine and the femoral neck. This compares homozygous haplotype aT with individuals heterozygous for haplotypes aT and AT. Since Bsm1 was not genotyped, it is not possible to discriminate between haplotypes baT and BaT in the aaTT homozygotes, meaning that the aaTT genotypes are admixed.
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Conclusions |
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Genetic risk factors in cross-sectional association studies are not different in character to any other type of predictor that may be detected sporadically in an epidemiology study due to effect size. False positive signals do not tend to be replicated, especially with the regularity of the VDRGP. It is currently easier to collect quality clinical data, as in Morita et al.1 than to obtain dense genetic information. Alternatively, different study designs, based on large multigenerational pedigrees may provide more power to detect VDR linkage and association simultaneously.35
In part as a response to the concerns regarding power in genetic analysis of complex traits, very large genetic epidemiology studies are contemplated with numbers in the hundreds of thousands. The success of such whole genome association studies will depend on the quality of clinical data, genetic data, and selection of participants. With companies like Perlegen developing million SNP analyses per patient, the dream of whole genome sequence for each participant of a mass epidemiology study is not distant fantasy. One suspects that even these large whole population studies will depend on meta-analysis.
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References |
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