Quantitative traits often underlie risk for complex diseases. Many studies collect multiple correlated quantitative phenotypes and perform univariate analyses on each of them respectively. However, this strategy may not be powerful and has limitations to detect plei- otropic genes that may underlie correlated quantitative traits. In addition, testing multiple traits individually will exacerbate perplexing problem of multiple testing. In this study, generalized estimating equation 2 （GEE2） is applied to association mapping of two correlated quantitative traits. We suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In that region, multiple SNPs are genotyped. Genotypes of these SNPs and the two quantitative traits affected by a causal SNP were simulated under various parameter values： residual correlation coefficient between two traits, causal SNP heritability, minor allele frequency of the causal SNP, extent of linkage disequilibrium with the causal SNP, and the test sample size. By power ana- lytical analyses, it is showed that the bivariate method is generally more powerful than the univariate method. This method is robust and yields false-positive rates close to the pre-set nominal significance level. Our real data analyses attested to the usefulness of the method.
Osteoporosis is a highly heritable common bone disease leading to fractures that severely impair the life quality of patients.Wrist fractures caused by osteoporosis are largely due to the scarcity of wrist bone mass.Here we report the results of a genome-wide association study (GWAS) of wrist bone mineral density (BMD).We examined ～500000 SNP markers in 1000 unrelated homogeneous Caucasian subjects and found a novel allelic association with wrist BMD at rs11023787 in the SOX6 (SRY (sex determining region Y)-box 6) gene (P=9.00×10-5).Subjects carrying the C allele of rs11023787 in SOX6 had significantly higher mean wrist BMD values than those with the T allele (0.485:0.462 g cm-2 for C allele vs.T allele carriers).For validation,we performed SOX6 association for BMD in an independent Chinese sample and found that SNP rs11023787 was significantly associated with wrist BMD in the Chinese sample (P=6.41×10-3).Meta-analyses of the GWAS scan and the replication studies yielded P-values of 5.20×10-6 for rs11023787.Results of this study,together with the functional relevance of SOX6 in cartilage formation,support the SOX6 gene as an important gene for BMD variation.
Copy number variation （CNV） is a type of genetic variation which may have important roles in phenotypic variability and disease susceptibility. To hunt for genetic variants underlying human height variation, we performed a genome wide CNV association study for human height in 618 Chinese unrelated subjects using Affymetrix 500K array set. After adjusting for age and sex, we found that four CNVs at 6p21.3, 8p23.3-23.2, 9p23 and 16p12.1 were associated with human height （with borderline significant p value： 0.013, 0.011, 0.024, 0.049; respectively）. However, after multiple tests correction, none of them was associated with human height. We observed that the gain of copy number （more than 2 copies） at 8p23.3-23.2 was associated with lower height （normal copy number vs. gain of copy number： 161.2 cm vs. 153.7 cm, p = 0.011）, which accounted for 0.9% of height variation. Loss of copy number （less than 2 copies） at 6p21.3 was associated with 0.8% lower height （loss of copy number vs. normal copy number： 154.5 cm vs. 161.1 cm, p = 0.013）. Since no important genes influencing height located in CNVs at loci of 8p23.3-23.2 and 6p21.3, the two CNVs may cause the structural rear- rangements of neighbored important candidate genes, thus regulates the variation of height. Our results expand our knowledge of the genetic factors underlying height variation and the biological regulation of human height.
