The JUNO experiment locates in Jinji town, Kaiping city, Jiangmen city, Guangdong province. The geographic location is east longitude 112 • 31'05' and North latitude 22 • 07'05'. The experimental site is 43 km to the southwest of the Kaiping city, a county-level city in the prefecture-level city Jiangmen in Guangdong province. There are five big cities, Guangzhou, Hong Kong, Macau, Shenzhen, and Zhuhai, all in ∼200 km drive distance, as shown in figure 3.

We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using a linear mixed model. Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed model formulation. The smoothing parameter and the kernel parameter can be estimated as variance components using restricted maximum likelihood. A score test is developed to test for the genetic pathway effect. Model/variable selection within the LSKM framework is discussed. The methods are illustrated using a prostate cancer data set and evaluated using simulations.

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The cell surface receptor, low-density lipoprotein receptorrelated protein 5 (LRP5) is a key regulator of bone mass. Lossof-function mutations in LRP5 cause the human skeletal disease osteoporosis-pseudoglioma syndrome, an autosomal recessive disorder characterized by severely reduced bone mass and strength. We investigated the role of LRP5 on bone strength using mice engineered with a loss-of-function mutation in the gene. We then tested whether the osteogenic response to mechanical loading was affected by the loss of Lrp5 signaling. In addition to studies in humans, mice have been created with loss-of-function mutations in the mouse ortholog of LRP5, called Lrp5 (9 -11). These mice recapitulate the clinical features observed in OPPG patients, suggesting that the mouse is a useful animal model for delineating the role of Lrp5 in the mammalian skeleton (9 -11). Additionally, transgenic mice that overexpress wild-type Lrp5 or a high bone mass causing missense allele of LRP5 (G171V) under control of the type I collagen promoter, have increased bone mass and skeletal strength (12). Taken together, these data indicate that LRP5 has an important role in determining skeletal mass, strength, and function. Lrp5-null (Lrp5Although loss-of-function mutations in LRP5 impart clear deficiencies on the skeleton, it is unclear how LRP5 participates in the modulation of bone mass. The striking similarity between * This work was supported by National Institutes of Health Grants AR046530 (to C. H. T.) and AR053237 (to A. G. R.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.