GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits.

Identification of multifactor gene-gene (G×G) and gene-environment (G×E) interactions underlying complex traits poses one of the great challenges to today's genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates.

The effects of RNA interference mediated VEGF gene silencing on biological behavior of renal cell carcinoma and transplanted renal tumor in nude mice.

Objective: This study was to explore the effects of RNA interference mediated vascular endothelial growth factor (VEGF) gene silencing on biological behavior of renal cell carcinoma (RCC), transplanted renal tumor and angiogenesis in nude mice.

Methods: The specific siRNA sequence targeting VEGF were designed and synthesized to construct hVEGF-siRNA plasmid which was transfected into RCC 786-O cells. Reverse transcriptase-polymerase chain reaction (RT-PCR) was used for the detection of VEGF gene expression and western blot was adopted for the examination of VEGF protein expression.

Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits.

The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points).