Patient bone mineral density (BMD) predicts the likelihood of osteoporotic fracture. While substantial progress has been made toward elucidating the genetic determinants of BMD, our understanding of the factors involved remains incomplete. Here, using a systems genetics approach in the mouse, we predicted that bicaudal C homolog 1 (Bicc1), which encodes an RNA-binding protein, is responsible for a BMD quantitative trait locus (QTL) located on murine chromosome 10. Consistent with this prediction, mice heterozygous for a null allele of Bicc1 had low BMD. We used a coexpression network-based approach to determine how Bicc1 influences BMD. Based on this analysis, we inferred that Bicc1 was involved in osteoblast differentiation and that polycystic kidney disease 2 (Pkd2) was a downstream target of Bicc1. Knock down of Bicc1 and Pkd2 impaired osteoblastogenesis, and Bicc1 deficiency-dependent osteoblast defects were rescued by Pkd2 overexpression. Last, in 2 human BMD genome-wide association (GWAS) meta-analyses, we identified SNPs in BICC1 and PKD2 that were associated with BMD. These results, in both mice and humans, identify Bicc1 as a genetic determinant of osteoblastogenesis and BMD and suggest that it does so by regulating Pkd2 transcript levels. IntroductionOsteoporosis is a disease characterized by low bone mass, skeletal fragility, and increased risk of fracture (1). Of the traits intrinsic to bone that influence its strength, bone mineral density (BMD) is one of the strongest predictors of fractures (2). BMD is also highly heritable, with approximately 70% of its variation being attributable to genetic factors. As a result, developing a comprehensive understanding of the genes and pathways that regulate BMD promises to lead to novel therapies aimed at preventing and treating bone fragility.Genome-wide association studies (GWAS) have significantly expanded the list of known variants and genes that influence BMD (3). A recent meta-analysis of 17 BMD GWAS (Genetic Effects For Osteoporosis [GEFOS] Consortium) involving approximately 83,000 individuals identified 56 robustly significant associations (4). Interestingly, together, the associations explained less than 5% of the total phenotypic variance in BMD, suggesting that bone mass is highly polygenic and that most of the genes influencing BMD remain to be identified.One strategy that can help fill this knowledge gap is the use of gene discovery in the mouse to inform human BMD GWAS. Several recent studies (5-10) have demonstrated the effectiveness of this strategy. To date, dozens of quantitative trait loci (QTLs) (regions of the genome harboring genetic variation influencing a quantitative trait) affecting BMD have been identified in the mouse (11), and recently the pace of identifying QTL genes has rapidly progressed. This is due in part to the development of sys-

In this paper we present a hardware-assisted rendering technique coupled with a compression scheme for the interactive visual exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 45 millions voxels each time step) at interactive rates, permitting the visual exploration of large scientific data sets in both the temporal and spatial domain.

We present a scalable volume rendering technique that exploits lossy compression and low-cost commodity hardware to permit highly interactive exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 42 millions voxels each time step) at interactive rates. By clustering multiple PCs together we demonstrate the data-size scalability of our method. The frame rates achieved make possible the interactive exploration of data in the temporal, spatial, and transfer function domains. A comprehensive evaluation of our method based on experimental studies using data sets (up to 134 millions voxels per time step) from turbulence flow simulations is also presented.

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Non-photorealistic rendering can be used to illustrate subtle spatial relationships that might not be visible with more realistic rendering techniques. We present a parallel hardware-accelerated rendering technique, making extensive use of multi-texturing and paletted textures, for the interactive non-photorealistic visualization of scalar volume data. With this technique, we can render a 512¢512¢512 volume using non-photorealistic techniques that include tone-shading, silhouettes, gradient-based enhancement, and color depth cueing, as shown in the images on the color plate, at multiple frames second. The interactivity we achieve with our method allows for the exploration of a large visualization parameter space for the creation of effective illustrations.