380 ALOX12 polymorphisms are associated with fat mass in men W-J Xiao et al pairwise LD exceeds a threshold r2 of 0.8, tagging SNPs are selected from each bin to constitute a minimal set of highly informative markers, whereas minimizing redundant data. We selected SNPs on the basis of the following criteria: (1) degree of heterozygosity (minor allele frequencies 40.10) and (2) classification as tagging SNPs. In total, six SNPs in the ALOX12 gene were selected: rs2073438, rs2292350, rs312470, rs434473, rs1235805 and rs312462. The study subjects were genotyped for all six SNPs using the TaqMan allelic discrimination assay (Applied Biosystems, Foster City, CA, USA), whereas primer and probe sequences were optimized using the SNP assay-by-design service of Applied Biosystems. Reactions were performed on the Mx3000P Real-Time PCR System (STRATAGENE, La Jolla, CA, USA). One allelic probe was labeled with FAM dye and the other with HEX dye. Genomic DNA (20 ng) was amplified on 96-well plates in the presence of a 1 TaqMan probe assay and 1 TaqMan Universal PCR Master Mix (Applied Biosystems). LD and haplotype analyses Haplotypes were constructed from the population genotype data by the algorithm of Stephens using the Phase program, version 2.0.2. Significant levels of LD between the markers of this gene were assessed based on the observed haplotype and allele frequencies using Haploview 3.2.25 We examined Lewontin’s D and LD coefficient r2 between all pairs of biallelic loci. The frequencies of genotypes and haplotypes were calculated using the unrelated parents of nuclear families. Statistical analyses The allele frequencies were estimated by gene counting. Hardy–Weinberg equilibrium was tested by a w2 goodness- of-fit statistic. To ensure unrelated individual samples, only genotype data from the parents of each nuclear family were used in the statistical analysis. The heritability estimate was performed using a linear regression of the parents’ mean value and the offspring’s value of every phenotype (described at http://www.heritability.com). The statistical power was estimated with Piface software (version 1.65) (http:// www.math.uiowa.edu/~rlenth/Power/) on our current sample size according to the minor allele frequency of every genotype and the variation of the BMD and obesity phenotypes. The QTDT program using the orthogonal model was used to test for population stratification, linkage and within-family association between SNPs and haplotypes and BMD phenotypes, TLM, TFM and BMI. The QTDT software package is available online (http://www.sph.umich.edu/csg/ abecasis/QTDT/). This method, as implemented in the QTDT software,26 extends the trio-based TDT to quantitative trait data and uses genotype data from available siblings and parents. As in our nuclear families, all of the children were sons and the effects of parent phenotypes were excluded in the QTDT, sex was not used as a covariate to adjust the International Journal of Obesity variations in the sons’ bone phenotypes. The raw BMD values were adjusted by age, height and weight as covariates, and BMI, TFM and TLM were adjusted by age as covariates. As false-positive results might be generated in multiple tests, which were used in this study, permutations (1000 simula- tions) were performed to generate the empirical P-values26,27 to assess the reliability of the results. The QTDT program generates P-values for various tests through an asymptotic w2 distribution. Po0.05 was considered significant for all analyses. In unrelated sons, the differences in the BMD among the genotype and haplotype groups were tested using general linear model ANOVA (GLM-ANOVA) adjustments for the confounding variables of age, height and weight. The differences in the BMI, TFM and TLM among the genotype and haplotype groups were performed using GLM-ANOVA adjusted for age. To evaluate the associations of the TFM and TLM with the BMD, we used Pearson’s correlation analyses to examine the coefficient (r) and multiple linear regression to estimate the relative contribution of the TFM and TLM to the BMD at various sites in young men. Statistical analysis was performed using SPSS, version 11.0 (SPSS, Chicago, IL, USA). Results Basic characteristics of the study subjects Of the males sampled from 427 nuclear families, 15 individuals could not be genotyped because of the poor quality of the DNA sample, and 12 sons deviated from Mendelian inheritance. Ultimately, there were 400 male nuclear families composed of 1215 individuals in this study. The basic characteristics of the study subjects are shown in Table 1. As the effects of parent phenotypes were excluded in the statistical analysis (both the QTDT and ANOVA), we only obtained the TFM and TLM of sons in this study. Peak BMD is thought to be under strong genetic control. In our sample, the heritability estimates for the peak BMD in the Table 1 Basic characteristics of the subjects (mean±s.d.) Variables Father (n ¼ 400) Age (years) 61.1±7.1 Height (cm) 167.8±6.0 Weight (kg) 69.7±9.5 BMI (kg m–2) 25.2±2.7 Lumbar spine BMD (g cm–2) 1.139±0.171 Femoral neck BMD (g cm–2) 0.892±0.132 Total hip BMD (g cm–2) 0.958±0.138 TFM (kg) F TLM (kg) F PFM (%) F Mother (n ¼ 400) 58.4±6.3 155.7±5.5 58.2±8.2 24.0±3.1 0.992±0.168 0.796±0.144 0.852±0.162 F F F Son (n ¼ 415) 30.4±6.1 172.9±5.9 70.7±10.8 24.2±3.2 1.138±0.137 0.995±0.141 1.008±0.142 16.31±7.56 51.43±5.76 21.89±7.17 Abbreviations: BMD, bone mineral density; BMI, body mass index; PFM, percentage fat mass; TFM, total fat mass; TLM, total lean mass.