Rita Cantor

Statistical Genetics With the advent of new molecular genetic technologies, the mapping and identification of complex disease genes and their interactions has become feasible. Statistical analyses are often a necessary component of these gene mapping efforts. The statistical approaches needed to map disease genes vary with the particular research question, the population and disease under analysis. These are the design issues which are a focus of this research effort. We are applying statistical methodologies to identify genes contributing to lipid disorders which result in coronary artery disease, and in particular Familial Combined Hyperlipidemia, a common complex genetic disorder which often leads to premature death. We have conducted a genome scan to identify chromosomal regions likely to harbor the disease causing genes. Additionally we are looking at candidate genes in the linked and other chromosomal regions for association with the disease. We have also been analyzing Single Nucleotide Polymorphism(SNP) data from a chromosomal region of a gene family linked to the disorder. Patterns of linkage disequilibrium illustrate the research paradigms which may be necessary to identify the disease causing variants. We are also applying statistical methods to map genes contributing to Systemic Lupus Erythematosus(SLE). We have capitalized upon mouse-human synteny and conducted linkage analysis to identify a human chromosomal region likely to harbor an SLE gene. The positional candidate PARP has been shown to have a distorted allele transmission pattern in this sample, implicating it in the development of SLE. Future endeavors are to apply similar methodologies to infectious disease syndromes such as Kawasaki disease and birth defects such as spina bifida.

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