Parameter configurations used in our simulation study.

a<p>denote the phenotypic variance explained by additive effects of causal SNP 6 and SNP 10 as well as interactive effect between SNP 6 and SNP 10, respectively.</p>b<p>denote the phenotypic variance explained by additive effect of causal SNP 8.</p>c<p>the basic parameter configuration is highlighted in bold. Each possible parameter setting can be obtained by replacing one entry of the basic parameter configuration with a different entry of corresponding parameter.</p

Plot of genome-wide MLAS results of lean body mass implemented by PLS-based MLAS.

<p>Significant genes are highlighted in red.</p

Comparison of MLAS and SLAS results of the 17 genes detected by PLS-based MLAS of lean body mass.

a<p>denote the smallest P value of each gene obtained from our previous genome-wide SLAS of lean body mass.</p

Power comparing results of PLS-based MLAS (PLS_MLAS), PCA-based MLAS (PCA_MLAS), tagSNPs-based MLAS (tagSNPs_MLAS), TSM-based MLAS using F test (FTSM) and TSM-based MLAS using Wald test (WTSM) under the epistatic model.

<p>Power comparing results of PLS-based MLAS (PLS_MLAS), PCA-based MLAS (PCA_MLAS), tagSNPs-based MLAS (tagSNPs_MLAS), TSM-based MLAS using F test (FTSM) and TSM-based MLAS using Wald test (WTSM) under the epistatic model.</p

Power comparing results of PLS-based MLAS (PLS_MLAS), PCA-based MLAS (PCA_MLAS), tagSNPs-based MLAS (tagSNPs_MLAS), TSM-based MLAS using F test (FTSM) and TSM-based MLAS using Wald test (WTSM) under the additive model.

<p>Power comparing results of PLS-based MLAS (PLS_MLAS), PCA-based MLAS (PCA_MLAS), tagSNPs-based MLAS (tagSNPs_MLAS), TSM-based MLAS using F test (FTSM) and TSM-based MLAS using Wald test (WTSM) under the additive model.</p

KBD/control sample pairs used for microarray analysis.

<p>KBD/control sample pairs used for microarray analysis.</p

COL9A1 Gene Polymorphism Is Associated with Kashin-Beck Disease in a Northwest Chinese Han Population

<div><p>Objective</p><p>We sought to determine whether genomic polymorphism in collagen IX genes (COL9A) was associated with Kashin-Beck disease (KBD).</p><p>Methods</p><p>Twenty seven single nucleotide polymorphisms (SNPs) in COL9AI, COL9A2 and COL9A3 were genotyped in 274 KBD cases and 248 healthy controls using the Sequenom MassARRAY system. Associations between the COL9A polymorphism and KBD risk were detected using an unconditional logistic regression model. Linkage disequilibrium (LD) and haplotypes analysis were performed with the Haploview software.</p><p>Results</p><p>After Bonferroni correction, the frequency distribution of genotypes in rs6910140 in COL9A1 was significantly different between the KBD and the control groups (<i>X</i>² = 16.74, <i>df</i> = 2, <i>P</i> = 0.0002). Regression analysis showed that the allele “C” in SNP rs6910140 had a significant protective effect on KBD [odds ratio (OR) = 0.49, 95% confidence interval (CI) = 0.34–0.70, P = 0.0001]. The frequencies of alleles and genotypes in rs6910140 were significantly different among subjects of different KBD stages (allele: <i>X</i>² = 7.82, <i>df</i> = 2, <i>P</i> = 0.02, genotype: <i>X</i>² = 14.81, <i>df</i> = 4, <i>P</i> = 0.005). However, haplotype analysis did not detect any significant association between KBD and COL9A1, COL9A2 and COL9A3.</p><p>Conclusions</p><p>We observed a significant association between rs6910140 of COL9A1 and KBD, suggesting a role of COL9A1 in the development of KBD.</p></div

Primers used in qRT-PCR validation of microarray data.

<p>Primers used in qRT-PCR validation of microarray data.</p

Flow chart for the identification of the differentially expressed genes in PBMCs of KBD patients and healthy controls.

<p>The terms “present” and “absent” represent the expression levels of the transcripts described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028439#s2" target="_blank">Materials and Methods</a> section. Values are the mean±SD number and percentage of transcripts. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028439#s2" target="_blank">Materials and Methods</a> for details of the selection criteria.</p

PAPSS2 is prominently expressed in osteoblasts derived from MC3T3 cells stimulated by ascorbic acid and β-glycerol-phosphate (OS media).

<p>(A) Tissue distribution of PAPSS2 mRNA expression in lean control mice. Total RNA was extracted from the indicated tissues, reverse transcribed, and assessed using quantitative real-time PCR. PAPSS2 mRNA expression was calculated relative to the expression of B2m mRNA, and values were expressed relative to the kidney level. (^a<i>P</i><0.05 between calvaria/bone and the other tissues) (B) RT-PCR analysis of PAPSS2 mRNA in MC3T3, for 0, 3, 7, and 14 days, showing that PAPSS2 is expressed in OB. (C) Normalized mRNA level of <i>B</i>. (^a<i>p</i><0.05 compared with 0 days) (D) Western blot analysis of PAPSS2 protein expression in OB derived from MC3T3 cells induced with OS media for 0, 3, 7, and 14 days. Equivalent amounts (20 μg) of total protein were loaded in each lane. (E) Quantification of protein levels from immunoblots, as in <i>D</i>. The protein levels of PAPSS2 were normalized to GAPDH. The levels of PAPSS2 were gradually increased in timeline. (^a<i>P</i><0.05 compared with 0 day) (F) Real-time PCR analysis of PAPSS2 expression pattern during differentiation of osteoclast and osteoblasts. RAW264.7 (Raw) cells were induced with 10 ng/ml of RANKL and 20 ng/ml M-CSF for 0, 24, and 96 hours for osteoclastogenesis; and MC3T3-E1 clone 4 cells were induced with OS media for 0, 7, and 21 days, respectively. (^a<i>P</i><0.05 between raw and MC3T3 for 0, 7, 21 days). The results showed that PAPSS2 is specifically and highly expressed in both pre-osteoblasts and osteoblast.</p