Browsing by Author "Chen, Hsiang-Cheng"

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    Genetics and Biomarkers of Osteoarthritis and Joint Hypermobility
    (2009) Chen, Hsiang-Cheng

    Osteoarthritis (OA) is the most common joint disorder causing chronic disability in the world population. By the year 2030, an estimated one fifth of this population will be affected by OA. Although OA is regarded as a multi-factorial disorder with both environmental and genetic components, the exact pathogenesis remains unknown.

    In this study, we hypothesize that biomarkers associated with OA can be used as quantitative traits of OA, and provide enough power to identify new genes or replicate known gene associations for OA. We established an extensive family called the CARRIAGE (CARolinas Region Interaction of Aging, Genes and Environment) family. Then, we measured and analyzed seven OA-related biomarkers (HA, COMP, PIIANP, CPII, C2C, hs-CRP and GSP) in this extensive family to evaluate their association with OA clinical phenotypes. These findings suggest that OA biomarkers can reflect hand OA in this large multigenerational family. Therefore, we performed nonparametric variance components analysis to evaluate heritability for quantitative traits for those biomarkers. Finally, based upon OA biomarkers with high heritability, we performed a genome-wide linkage scan. Our results provide the first evidence of genetic susceptibility loci identified by OA-related biomarkers, indicating several genetic loci potentially contributing to the genetic diversity of OA.

    Meanwhile, we identified joint hypermobility as a factor which reduces OA risk and has an inverse association with serum COMP levels in this family. The relationship between lower serum COMP and OA have been further validated in another Caucasian GOGO (Genetics of Generalized Osteoarthritis) population. Therefore, we further hypothesize that joint hypermobility, having the characteristic of a decreased OA risk, can serve as a quantitative trait for identifying protective loci for OA. Then, we performed nonparametric variance components analysis to evaluate the heritability of joint hypermobility. The result also shows joint hypermobility has substantial heritable components in this family. Lastly, based on the same genome-wide linkage scan, we identify genetic susceptibility loci for joint hypermobility.

    In conclusion, our work provides the first linkage study to identify genetic loci associated with OA using biological markers. Furthermore, we have also shown genetic susceptibility loci for joint hypermobility, possibly implying protective loci for OA.

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    Genome-wide linkage analysis of cardiovascular disease biomarkers in a large, multigenerational family.
    (PLoS One, 2013) Nolan, Daniel; Kraus, William E; Hauser, Elizabeth; Li, Yi-Ju; Thompson, Dana K; Johnson, Jessica; Chen, Hsiang-Cheng; Nelson, Sarah; Haynes, Carol; Gregory, Simon G; Kraus, Virginia B; Shah, Svati H
    Given the importance of cardiovascular disease (CVD) to public health and the demonstrated heritability of both disease status and its related risk factors, identifying the genetic variation underlying these susceptibilities is a critical step in understanding the pathogenesis of CVD and informing prevention and treatment strategies. Although one can look for genetic variation underlying susceptibility to CVD per se, it can be difficult to define the disease phenotype for such a qualitative analysis and CVD itself represents a convergence of diverse etiologic pathways. Alternatively, one can study the genetics of intermediate traits that are known risk factors for CVD, which can be measured quantitatively. Using the latter strategy, we have measured 21 cardiovascular-related biomarkers in an extended multigenerational pedigree, the CARRIAGE family (Carolinas Region Interaction of Aging, Genes, and Environment). These biomarkers belong to inflammatory and immune, connective tissue, lipid, and hemostasis pathways. Of these, 18 met our quality control standards. Using the pedigree and biomarker data, we have estimated the broad sense heritability (H2) of each biomarker (ranging from 0.09-0.56). A genome-wide panel of 6,015 SNPs was used subsequently to map these biomarkers as quantitative traits. Four showed noteworthy evidence for linkage in multipoint analysis (LOD score ≥ 2.6): paraoxonase (chromosome 8p11, 21), the chemokine RANTES (22q13.33), matrix metalloproteinase 3 (MMP3, 17p13.3), and granulocyte colony stimulating factor (GCSF, 8q22.1). Identifying the causal variation underlying each linkage score will help to unravel the genetic architecture of these quantitative traits and, by extension, the genetic architecture of cardiovascular risk.