Lack of Association Between Vitamin D Receptor Genotypes and ...

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Journal of Gerontology: BIOLOGICAL SCIENCES 2007, Vol. 62A, No. 9, 966–972

Copyright 2007 by The Gerontological Society of America

Lack of Association Between Vitamin D Receptor Genotypes and Haplotypes With Fat-Free Mass in Postmenopausal Brazilian Women Ricardo Moreno Lima,1 Breno Silva de Abreu,2 Paulo Gentil,1 Tulio Cesar de Lima Lins,2 Da´rio Grattapaglia,2 Rinaldo Wellerson Pereira,1,2 and Ricardo Jaco´ de Oliveira1 1

Programa de Po´s-Graduac¸a˜o em Educac¸a˜o Fı´sica da Universidade Cato´lica de Brası´lia, Brazil. Programa de Po´s-Graduac¸a˜o em Cieˆncias Genoˆmicas e Biotecnologia da Universidade Cato´lica de Brası´lia, Brazil.

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The relationship between vitamin D receptor (VDR) ApaI, CDX2, BsmI, FokI, and TaqI polymorphisms and fat-free mass (FFM) were examined in 191 postmenopausal Brazilian women (mean age 67.87 6 5.22 years). Participants underwent FFM measurements by dualenergy x-ray absorptiometry (DEXA). Whole-blood-extracted genomic DNA was genotyped to the aforementioned polymorphisms and to ancestry-informative markers through minisequencing, using the SNaPshot Multiplex System. Association between VDR polymorphisms and FFM variables was assessed by analysis of covariance. Haplotypes were estimated, and regressionbased, haplotype-specific association tests were carried out with the studied phenotypes. No departure from Hardy–Weinberg equilibrium was detected for any polymorphism. None of the investigated VDR allelic variations, individually or analyzed as haplotypes, was associated with FFM phenotypes. The inclusion of individual African genomic ancestry was used as an attempt to correct for population stratification. Further studies in larger sample population are required to confirm these findings.

S

ARCOPENIA, defined as the age-associated progressive loss of skeletal muscle mass and strength (1), is a well documented phenomenon strongly related to physical disability among elderly persons (2–6). The health care costs attributable to sarcopenia in the United States during 2000 were estimated to be around $18.5 billion ($10.8 billion in men, $7.7 billion in women) (7). Therefore, development of strategies to minimize skeletal muscle decline will improve quality of life in elderly persons and decrease public health care costs. In order to reach this aim, detailed comprehension of sarcopenia mechanisms is mandatory. Loss of fat-free mass (FFM) associated with sarcopenia is a typical complex phenotype in which multiple environmental and genetic factors are thought to interact in its path (8–11). Nutritional habits and physical activity status are major environmental determinants of FFM loss (12), and in a broad perspective, both play their roles through our genome (13). The human genome normal variation influence on FFM is poorly understood, and a small number of candidate genes have been investigated so far (14). Nevertheless, new technologies emerging from the human postgenomic era will bring new candidate genes to the field of sarcopenia genetics (15). The description that vitamin D deficiency contributes to age-related muscle function decline (16) and the identification of vitamin D receptor (VDR) in the nucleus of myocytes (17,18) introduced the VDR gene as a potential candidate in association studies involving muscle phenotypes. It has been suggested that vitamin D modulates

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calcium (Ca2þ) levels into muscle cells and that nuclear VDR is the receptor that mediates the effects of this hormone on contractility (19). In addition, VDR knockout mice exhibit abnormal muscle development and unregulated expression of myogenic transcription factors (20). Despite the above evidence, few studies had investigated VDR gene variation and its association with muscle strength and/or FFM. A significant association was found between BsmI genotypes and muscle strength in postmenopausal (21) and premenopausal (22) women. However, the G/G genotype was found to be associated with higher muscle strength in the postmenopausal women, whereas the opposite association was observed in the premenopausal women. Regarding other frequently investigated VDR polymorphisms, the results are also controversial. There are results showing an absence of association between ApaI, TaqI, and FokI VDR polymorphisms and muscle strength in young and older men (23). Conversely, there are results showing that ApaI was associated with muscle strength in Chinese women (24) and that the FokI polymorphism was associated with FFM in older Caucasian men (25). The CDX2 (G to A substitution) is another functional VDR polymorphism frequently investigated in bone phenotype variation (26), but it has not been examined in relation to muscle phenotypes. The purpose of the present study was to examine the association between FFM and TaqI, ApaI, BsmI, FokI, and CDX2 VDR gene polymorphisms in a sample of postmenopausal Brazilian women. Aware of the high admixture

FFM AND VDR POLYMORPHISMS IN POSTMENOPAUSAL WOMEN

in Brazilians (27–29) and also aware of the potential complications when performing association studies in such population (30,31), we genotyped 13 ancestry-informative single nucleotide polymorphisms (SNPs) and took the estimated genetic ancestry values as covariates during the analysis. In an effort to get rid of potential factors that may have influenced FFM variation other than the VDR polymorphisms, we also included body mass index, calcium supplementation, smoking status, percent body fat, and hormone replacement therapy as covariates. The results presented here failed to detect an association between VDR polymorphisms and FFM in postmenopausal Brazilian women. METHODS

Participants Participants in the present study were recruited from a full assistance for the elderly program developed at the Catholic University of Brasilia, which offers physical activity, psychological and medical assistance, nutritional assessment, and English classes to the local elderly population. The present cross-sectional study involved 191 postmenopausal women aged between 56 and 84 years. The 191 volunteers did not have metallic implants, artificial pacemakers, or hip replacement surgery. They also did not have a metabolic or endocrine disorder known to affect the musculoskeletal system. Prior to venous blood collection, all individuals answered a questionnaire addressing medical history, comorbidities, hormone replacement therapy, lifestyle habits, and self-reported skin color. Another questionnaire was applied to assess physical activity level. All volunteers provided written informed consent approved by the Institutional Review Board. Anthropometric Measurements and Body Composition Standard procedures were used to gauge weight with 0.1kg precision on a physician’s balance beam scale, and height was measured at the nearest 0.1 cm with a stadiometer. Body mass index (BMI) was derived as body weight divided by height squared (kg/m2). Body composition measurements were conducted using dual-energy x-ray absorptiometry (DEXA; DPX-L; Lunar Radiation Corporation, Madison, WI). All measurements were carried out by the same expert technician to avoid interpretation errors. Regional measurements (arms, legs, and trunk) were determined on the basis of bone landmarks, with vertical boundaries separating the arms from the body at the shoulder, and angled boundaries separating the legs from the trunk at the hips. Appendicular FFM (AFFM) was calculated as the sum of both arms’ and legs’ FFM (32). Because absolute FFM correlates directly with height, whole-body FFM and AFFM were also considered relative to body height squared (kg/m2), analogous to the use of BMI (33). In addition, a second measure of relative FFM, fat-adjusted AFFM, was defined with the use of a linear regression that predicted volunteers’ AFFM [AFFM (kg) ¼ 14.15 þ 18.14 3 height þ 0.09 3 fat mass] from height (in meters) and whole-body fat mass (in kg), as proposed by Newman and colleagues (34). The residuals of the re-

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gression were used in subsequent analyses. This measure has been shown to be related to functional limitations (34) and to markers of inflammation (35) in older individuals.

Physical Activity Level The official Portuguese short version of the International Physical Activity Questionnaire (IPAQ) was applied to assess the physical activity level of each postmenopausal woman volunteer in this study. The questionnaire was administered at face-to-face interviews, as is recommended for use in developing countries. The IPAQ was developed by investigators from all over the world supported by the World Health Organization as a tool to follow up physical activity level. It has been reported as an instrument that has acceptable measurement properties in various countries (36), and it has been previously applied in a postmenopausal Brazilian population study (37). Based on the questionnaire results, all individuals were categorized as sedentary, insufficiently active, active, or very active. Genotyping The polymorphisms analyzed in this study are commonly reported as TaqI, ApaI, BsmI, FokI, and CDX2. These polymorphisms are found in the National Center for Biotechnology Information (NCBI) dbSNP data bank (http://www.ncbi.nlm.nih.gov/projects/SNP/) under the respective denominations: rs731236, rs7975232, rs1544410, rs10735810, and rs11568820. In order to make comprehension easier, the present study uses the restriction fragment length polymorphism (RFLP; TaqI, ApaI, BsmI, and FokI) and the transcription factor binding site (CDX2) nomenclature to refer to each one of the polymorphisms. The alleles, genotypes, and haplotypes are described using the dbSNP reference alleles. Genotyping was performed on DNA extracted from peripheral venous blood using a modified salting-out protocol (38). The polymerase chain reaction (PCR) protocol was carried out in 12.5 lL as follows: 1X Taq polymerase buffer, 2.5 mM MgCl2, 250 mM dNTPs, bovine serum albumin (BSA) at 1.6 mg/mL, 0.50 lM of each primer, 10–40 ng of DNA and 1 U of Taq polymerase. The PCR amplification was performed in an ABI9700 thermocycler using an initial denaturation step at 958C for 5 minutes, followed by 15 cycles of 40 seconds at 958C, 40 seconds at 628C decreasing 0.58C per cycle, 40 seconds at 728C, and 15 cycles of 40 seconds at 958C, 40 seconds at 568C, 40 seconds at 728C, and a final extension step at 728C for 5 minutes. The purification was carried out on 3 lL of PCR volume adding 1 U of ExoI, 0.95 U of SAP, and 0.5 X SAP Reaction Buffer, which were incubated for 90 minutes at 378C following 20 minutes at 808C. The minisequencing was performed using 1.25 lL of SNaPshot Multiplex minisequencing kit reaction mix (Applied Biosystems, Foster City, CA), 1.25 lL of Big Dye Sequencing Buffer, 1 lL of Purified PCR product, 0.4 lM single base extension primers each, and sterile autoclaved Milli-Q water up to 5 lL. Reaction conditions were performed as follows: 25 cycles of 968C for 10 seconds, 508C for 5 seconds, and 608C for 30 seconds. Purification was carried out in order to degrade ddNTPs not incorporated on reaction by adding 0.5 U

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Table 1. Thirteen Ancestry-Informative Markers with Chromosomal Positions, Allele Frequencies, and Their Respective Differences (d) Between European (EUR), African (AFR), and Amerindians (AMR) Allele Frequency

Allele Frequency Differences (d)

Locus

Position

Allele

EUR

AFR

AMR

EUR/AFR

EUR/AMR

AFR/AMR

rs3138521 rs3176921 rs2740574 rs2814778 rs285 rs1800404 rs1129048 rs1426654 rs1480642 rs1871534 rs3768641 rs4766807 rs735665

1q25 8q13.1 7q22.1 1q23.2 8p21.3 15q13.1 1p35 15q21 6q23 5p15.2 2p13 12q24.2 11q24

Insertion G G C G G C C C C C T C

0.280 0.073 0.042 0.998 0.508 0.210 0.224 0.001 0.994 0.999 0.083 0.378 0.244

0.860 0.680 0.802 0.001 0.029 0.910 0.995 0.980 0.106 0.017 0.999 0.970 0.939

0.061 0.017 0.041 0.998 0.558 0.570 0.983 0.950 0.621 0.999 0.068 0.052 0.018

0.580 0.607 0.760 0.997 0.479 0.700 0.771 0.979 0.888 0.982 0.916 0.592 0.695

0.219 0.056 0.001 0.000 0.050 0.360 0.759 0.949 0.373 0.000 0.015 0.326 0.226

0.799 0.663 0.761 0.997 0.529 0.340 0.012 0.030 0.515 0.982 0.931 0.918 0.921

of SAP and 0.2X SAP reaction buffer for each sample, followed by an incubation at 378C for 60 minutes and a step at 758C for 20 minutes. Samples were subject to electrophoresis on an ABI prism 3100 Genetic Analyzer (Applied Biosystems) and analyzed with GeneScan Analysis 3.7 and Genotyper 3.7 software (Applied Biosystems). Genotyping of 13 ancestry-informative markers (AIMs) was performed under the same conditions described above.

Genomic Ancestry Inference Thirteen AIMs were genotyped for all the samples. Detailed information about these AIMs are provided in Table 1. The up-to-date frequencies of the AIMs on all parental populations used in the computational inference of genomic ancestry were retrieved from the NCBI dbSNP, and such analysis was performed by the LOD-Score–based software IAE3CI, which was provided by Dr. Mark D. Shriver (Pennsylvania State University). This analysis software estimates the amount of genomic contribution from each of the parental population on one’s genome. Statistical Analysis The Kolmogorov–Smirnov test was used to verify data distribution normality. Compliance of VDR genotype frequencies to Hardy–Weinberg equilibrium expectancy were analyzed by exact test. Each polymorphism was individually analyzed using one-way analysis of variance (ANOVA) models to test for differences in physical activity levels, age, height, BMI, genomic ancestry levels, and percent body fat among VDR genotype groups. A stepwise multiple regression model including all potential covariates was analyzed for each of the investigated FFM variables when searching for differences between VDR genotype groups in AFFM, relative AFFM, whole-body FFM, relative whole-body FFM, and fat-adjusted AFFM. Variables with p , .10 were included as covariates in the subsequent analysis of covariance (ANCOVA) performed for each polymorphism separately. The Bonferroni procedure was adopted to correct for multiple comparisons. Data were considered significant at p , .05. All the above statistical

analyses were performed using the Statistical Package for the Social Sciences, version 10.0 (SPSS, Chicago, IL). All haplotype estimates and regression-based, haplotypespecific association tests were carried out using Whap (http://www.genome.wi.mit.edu/;shaun/whap/) software, which is based on an expectation-maximization (EM) algorithm and considers the estimates of posterior probabilities to account for the ambiguity of haplotype phase estimates on regression-based association tests on unrelated individuals. The software package is able to handle quantitative traits and covariates for regression analysis. The haplotype association analyses were performed considering whole-body FFM, AFFM, relative whole-body FFM, relative AFFM, and fat-adjusted AFFM as dependent variables. The model used included height, weight, percent body fat, and individual African ancestry levels. RESULTS

Baseline Characteristics The Kolmogorov–Smirnov test showed that all variables were normally distributed. Therefore, all genotyped participants were included in subsequent analyses. The population’s physical characteristics are outlined in Table 2. It was observed that 55 (21.2%) participants were classified as Table 2. Volunteers’ Characteristics Variable N Age, y Weight, kg Height, m BMI, kg/m2 AFFM, kg Relative AFFM, kg/m2 Whole-body FFM, kg Relative whole-body FFM, kg/m2 Percent body fat, %

67.87 64.55 1.52 27.91 15.85 6.85 38.20 16.52 38.29

191 6 5.22 6 11.73 6 0.06 6 4.49 6 2.65 6 0.95 6 5.23 6 1.82 6 6.35

Notes: Values are expressed as the mean 6 standard deviation. AFFM ¼ appendicular fat-free mass; FFM ¼ fat-free mass; BMI ¼ body mass index.

FFM AND VDR POLYMORPHISMS IN POSTMENOPAUSAL WOMEN

Table 3. Allelic Frequencies and dbSNP Numbers of the Studied Polymorphisms in the VDR Gene SNP

dbSNP

Allele

Frequency

ApaI (A/C)

rs7975232

BsmI (A/G)

rs1544410

Cdx-2 (G/A)

rs11568820

FokI (C/T)

rs10735810

TaqI (T/C)

rs731236

A C A G G A C T T C

0.586 0.414 0.317 0.683 0.646 0.354 0.648 0.352 0.668 0.332

Note: dbSNP ¼ single nucleotide polymorphism data bank (National Center for Biotechnology Information); VDR ¼ vitamin D receptor.

obese (BMI . 30 kg/m2), and 33 (17.28%) were taking hormone replacement therapy. The most common chronic diseases verified among the volunteers were hypertension and type II diabetes, respectively, affecting 127 (66.5%) and 32 (16.8%) volunteers. Physical activity levels according to IPAQ classification were as follows: 8 (4.2%) were sedentary, 67 (35.1%) were insufficiently active, 113 (59.1%) were active, and 3 (1.6%) were very active. Stepwise multivariate regression analysis revealed that height, weight, and body fat percentage were the most important predictors of AFFM and whole-body FFM, whereas the model that better predicted relative AFFM and relative whole-body FFM included BMI and percent body fat. For fat-adjusted AFFM, the significant predictor was BMI. Consequently, these variables were used as covariates in subsequent analyses of covariance, along with the individual African ancestry level.

Genotyping VDR genotype results were available for the majority of participants. However, in some samples it could not be precisely identified, so those were not included in association analyses. Reasons for incomplete genotype data included unsuccessful PCR assays or single base extension reaction. Therefore, when presenting the results of each locus, the numbers of participants are slightly different. More precisely, a total of 187, 183, 189, 189, and 184 postmenopausal women were genotyped for the VDR ApaI, BsmI, CDX2, FokI, and TaqI polymorphisms, respectively. Allelic frequencies and genotypic distribution are presented in Tables 3 and 4, respectively. No departures from Hardy– Weinberg equilibrium were detected for any of the studied polymorphisms. Baseline Characteristics and FFM Phenotypes in Relation to VDR Genotype The studied population characteristics according to VDR genotypes are presented in Table 4. No significant differences were observed among VDR polymorphism genotype distribution regarding age, height, weight, BMI, ancestry levels, or percent body fat. Additionally, there were no significant differences between VDR genotypes for physical activity level or hormone replacement therapy.

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As shown in Table 5, ANCOVA revealed no statistically significant difference between ApaI, BsmI, CDX2, FokI, or TaqI VDR genotypes for AFFM, relative AFFM, whole-body FFM, relative whole-body FFM, or fat-adjusted AFFM.

Haplotype Association Tests Haplotype estimates from TaqI, ApaI, BsmI, FokI, and CDX2 polymorphism genotypes revealed 15 haplotypes with frequencies . 2% spanning 96.4% of haplotypic diversity (Table 6). Omnibus permutation tests for haplotypic association were carried out considering both the five marker haplotypes and the 39 end TaqI-ApaI-BsmI haplotypes. The regression-based association tests for the TaqI, ApaI, and BsmI haplotype did not give significant results for any of the phenotypic traits described here ( p ¼ 1). No significant results from the association analyses of AFFM, FFM, relative AFFM, relative FFM, and fat-adjusted AFFM to the five marker haplotypes were detected when no covariates were included in the model ( p ¼ 1). The inclusion of each of the covariates alone to correct for height, weight, BMI, and percent body fat and all possible combinations among them were not able to modify the previous observation (p ¼ 1). The inclusion of Africanicity level as a measure to correct for population stratification was also unable to evidence haplotype association as well ( p ¼ 1). DISCUSSION The role played by the vitamin D endocrine system in muscle mass is clearly shown in VDR knockout mice (20). Although it is known that vitamin D shows its genomic and rapid response effects through nuclear and membrane-bound VDR, little is known about the role played by VDR gene normal variation over muscle phenotypes, more specifically, the loss of muscle mass and strength associated with aging. So far, this question has been addressed through genetic association studies investigating the VDR polymorphisms ApaI, TaqI, BsmI, FokI, 39 poly A repeat, and their association with muscle mass and strength (21–25). By the time this article was submitted, we were not aware of any publication that evaluated the association between the CDX2 polymorphism and muscle phenotypes. In genotyping ApaI, TaqI, BsmI, FokI, and CDX2 polymorphisms in a sample of postmenopausal Brazilian women, we did not find an association with FFM phenotypes. The association was not found with any allele, genotype, or haplotype. In fact, available data do not support evidence of associations among TaqI genotypes and muscular phenotypes (24,39). We are not aware of association studies investigating ApaI polymorphism and FFM. Its relationship with muscle strength has been previously investigated; however, the results are not consistent. For example, Iki and colleagues (39) observed no differences in any of various analyzed muscle strength indices among ApaI genotype groups in a sample of 180 postmenopausal Japanese women. Because muscle strength is positively related with FFM (40), these results may support our observation. In contrast, a recent investigation (24) reported differences among ApaI genotype groups for isokinetic muscle strength. However, the authors

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Table 4. Characteristics of the Participants According to ApaI, CDX2, BsmI, FokI, and TaqI Vitamin D Receptor Genotypes ApaI genotype N, % Age, y Weight, kg Height, cm BMI, kg/m2 Percent body fat, % BsmI genotype N, % Age, y Weight, kg Height, cm BMI, kg/m2 Percent body fat, % CDX2 genotype N, % Age, y Weight, kg Height, cm BMI, kg/m2 Percent body fat, % FokI genotype N, % Age, y Weight, kg Height, cm BMI, kg/m2 Percent body fat, % TaqI genotype N, % Age, y Weight, kg Height, cm BMI, kg/m2 Percent body fat, %

C/C

A/C

A/A

p Value

29 (15.51) 67.86 6 5.37 62.69 6 11.49 151.93 6 5.99 27.09 6 4.17 38.64 6 6.25

97 (51.87) 67.69 6 5.12 65.54 6 11.68 152.15 6 6.26 28.32 6 4.86 38.16 6 6.31

61 (32.62) 68.21 6 5.34 63.85 6 12.21 151.65 6 6.75 27.63 6 3.99 38.47 6 6.41

.829 .450 .878 .374 .882

G/G

G/A

A/A

90 (49.18) 68.02 6 5.27 64.59 6 11.42 151.43 6 6.09 28.18 6 4.76 38.71 6 6.12

70 (38.25) 67.50 6 5.33 65.74 6 13.00 153.03 6 6.89 27.94 6 4.47 38.36 6 6.24

23 (12.57) 67.13 6 4.30 62.13 6 10.08 151.00 6 5.33 27.17 6 3.55 37.31 6 6.22

Cdx-G

Cdx G/A

Cdx-A

79 (41.80) 67.65 6 4.89 63.56 6 11.21 151.66 6 7.00 27.58 6 4.23 38.56 6 5.88

86 (45.50) 67.91 6 5.86 66.06 6 11.72 152.48 6 5.53 28.39 6 4.57 38.32 6 6.11

24 (12.70) 68.38 6 3.94 62.88 6 13.53 150.63 6 6.85 27.60 6 5.07 37.96 6 8.26

C/C

C/T

T/T

79 (41.80) 67.94 6 5.33 63.77 6 10.94 152.38 6 5.54 27.43 6 4.27 37.94 6 6.62

87 (46.03) 67.68 6 5.02 65.25 6 12.01 151.37 6 6.72 28.41 6 4.51 39.06 6 5.99

23 (12.17) 68.48 6 5.95 64.65 6 13.81 152.00 6 7.29 27.94 6 5.17 37.20 6 6.23

C/C

C/T

T/T

28 (15.22) 67.54 6 5.66 62.89 6 10.04 151.25 6 6.26 27.38 6 3.17 38.75 6 5.49

66 (35.87) 67.44 6 5.14 65.03 6 11.26 153.29 6 6.21 27.58 6 3.85 37.36 6 6.02

90 (48.91) 68.32 6 5.17 64.52 6 12.42 151.28 6 6.44 28.19 6 5.12 38.67 6 6.67

.695 .447 .210 .635 .623

.832 .294 .410 .474 .915

.805 .721 .556 .380 .332

.542 .718 .120 .582 .388

Note: BMI ¼ body mass index.

compared the A/A homozygous group to the C/C and A/C groups combined, in a different statistical approach. In agreement with our results, Roth and colleagues (25) reported that the BsmI polymorphism was not significantly associated with absolute or relative FFM in a cohort of older Caucasian men. Similar findings were observed by Grundberg and colleagues (22), who reported no statistical differences among VDR BsmI genotype groups for whole-body FFM in a study sample of 175 Swedish women aged 20–39 years. Contrary to our findings, Roth and colleagues (25) reported significant FokI genotype differences for each of the same FFM variables evaluated in the present study. In elderly Caucasian men, the authors observed that the C/C group exhibited significantly lower values than both the C/T and T/T groups for all the examined FFM variables. To our knowledge, no other study examined the relationship between FFM and the CDX2 polymorphism. Nonreplication is the rule, not the exception, when dealing with association studies’ results. This is currently explained by aspects of the study design itself, by the genetic architecture of the trait under investigation, and most

frequently by the combination of both (41). Population heterogeneity may play an important role in association studies contributing to spurious results, not only falsepositives but also false-negatives. Aware of the admixed nature of the Brazilian population, we used African genomic ancestry as a covariate in an attempt to address this potential source of bias. Such a procedure intended to minimize the well characterized problem generated from the use of selfreports of ancestry or skin pigmentation on sample clustering and analysis in association studies (42). The results presented here, those already published, and others that may appear in the literature should, in the future, be summed up and submitted to meta-analysis as already happened in the case of VDR variation and bone phenotypes (43–46). Nevertheless, meta-analyses are prone to biases resulting from selective publication of positive results; therefore, publications of negative associations are needed to be considered by the scientific community. By now, the number of published articles investigating VDR gene variation and muscle phenotypes is much lower than those in which bone phenotypes were investigated. Therefore, it is

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Table 5. Relationship Between Fat-Free Mass Variables (Mean 6 Standard Deviation [SD]) and ApaI, Cdx-2, BsmI, FokI, or TaqI VDR Genotype Groups in the Study Population, by Analysis of Covariance VDR Gene ApaI Genotype Groups AFFM, kg Relative AFFM, kg/m2 Whole Body FFM, kg Relative Whole Body FFM, kg/m2 Fat-adjusted AFFM, kg VDR Gene BsmI Genotype Groups AFFM, kg Relative AFFM, kg/m2 Whole Body FFM, kg Relative Whole Body FFM, kg/m2 Fat-adjusted AFFM, kg VDR Gene Cdx-2 Genotype Groups AFFM, kg Relative AFFM, kg/m2 Whole Body FFM, kg Relative Whole Body FFM, kg/m2 Fat-adjusted AFFM, kg VDR Gene FokI Genotype Groups AFFM, kg Relative AFFM, kg/m2 Whole Body FFM, kg Relative Whole Body FFM, kg/m2 Fat-adjusted AFFM, kg VDR Gene TaqI Genotype Groups AFFM, kg Relative AFFM, kg/m2 Whole Body FFM, kg Relative Whole Body FFM, kg/m2 Fat-adjusted AFFM, kg

C/C 15.37 6.64 36.91 15.96 .33

6 6 6 6 6

A/C 2.48 0.85 5.33 1.86 1.82

16.03 6.92 38.65 16.69 .02

2.04 0.76 5.07 2.00 1.54

16.20 6.88 38.93 16.56 .06

G/G 15.67 6.81 37.78 16.48 .05

6 6 6 6 6

6 6 6 6 6

6 6 6 6 6

16.17 6.94 39.12 16.81 .05

2.97 1.13 4.36 1.56 1.44

15.76 6.86 38.11 16.60 .04

6 6 6 6 6

6 6 6 6 6

3.53 1.22 5.95 1.79 1.69

15.71 6.89 37.71 16.52 .27

6 6 6 6 6

6 6 6 6 6

16.08 6.82 39.07 16.58 .13

6 6 6 6 6

2.35 0.71 5.37 1.59 1.58

.829 .778 .412 .285 .586

6 6 6 6 6

1.89 0.76 3.79 1.22 1.60

.707 .607 .418 .322 .484

Cdx-A

3.06 1.14 5.17 1.87 1.62

15.38 6.76 37.05 16.31 .10

2.44 0.80 5.77 1.98 1.70

15.67 6.77 38.92 16.83 .04

6 6 6 6 6

2.01 0.65 4.61 1.53 1.38

.914 .911 .523 .660 .977

2.40 0.83 6.05 2.14 1.78

.303 .287 .957 .943 .982

3.06 1.18 5.32 2.07 1.78

.649 .746 .108 .640 .618

T/T

C/T 1.89 0.59 4.07 1.34 1.39

6 6 6 6 6

A/A

C/T

C/C 15.41 6.72 37.62 16.43 .18

15.78 6.84 37.95 16.46 .09

Cdx-G/A

2.33 0.78 5.41 1.85 1.64

C/C 15.99 6.87 38.02 16.36 .03

2.92 1.10 5.15 1.93 1.56

G/A

Cdx-G 15.63 6.78 37.50 16.28 .05

6 6 6 6 6

p Value

A/A

6 6 6 6 6

T/T 2.37 0.70 5.37 1.59 1.60

15.85 6.92 37.71 16.48 .02

6 6 6 6 6

Note: AFFM ¼ appendicular fat-free mass; FFM ¼ fat-free mass; VDR ¼ vitamin D receptor.

necessary that more research groups investigate and publish their results, so that a clearer picture of the relationship between VDR gene natural variation and muscle mass loss phenotypes can be seen. Table 6. List of Phased Haplotypes Reconstructed Through Expectation–Maximization Algorithm and Its Respective Frequencies ID

Haplotype

Frequency

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

CAACG TCGCG TCGCA TCGTG TAGCG CAATG TAGTG CAATA TAGCA TCGTA TAGTA CAGCA TAACA TAACG CAGTG

0.166 0.153 0.121 0.096 0.066 0.065 0.056 0.048 0.046 0.036 0.034 0.034 0.033 0.022 0.021

Note: Haplotype follows the gene sequence of markers as TaqI, ApaI, BsmI, FokI, and CDX2.

Summary The present results indicate no association between the ApaI, CDX2, BsmI, FokI, and TaqI polymorphisms in the VDR gene, individually or when analyzed as haplotypes, with indices of FFM in postmenopausal Brazilian women. These results remained unchanged with the inclusion of Africanicity levels as a measure to correct for population stratification. Further studies in larger sample populations are required to confirm these findings. ACKNOWLEDGMENTS This work was supported by Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nı´vel Superior (CAPES), Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq), and Universidade Cato´lica de Brası´lia (PRPGP–UCB). TCLL and BSA were supported by a CAPES scholarship. We thank Dr. Mark D. Shriver for providing IA3CI software and Dr. Shaun Purcell for helping with the Whap software.

CORRESPONDENCE Address correspondence to Ricardo Jaco´ de Oliveira, PhD, Universidade Cato´lica de Brası´lia–UCB, Mestrado em Educac¸a˜o Fı´sica, QS 07, Lote 01, Pre´dio Sa˜o Joa˜o Bosco, Sala 119. CEP: 71.996-700, Taguatinga–DF– Brazil. E-mail: [email protected] or [email protected]

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Received November 9, 2006 Accepted June 12, 2007 Decision Editor: Huber R. Warner, PhD