GSS
Genomics Statistics Services
The Genomics Statistics (GS) Team for Complex Disease makes continuous efforts to maneuver statistical supports for the genomic research based on not only the gene expression profiles but also genotyping and clinical data. Novel statistical methodology can enhance understanding of the interactions between multiple genes and environmental factors on a complex disease.
Recent SNPs analysis tools are transforming the traditional genotyping and resequencing in the same way as expression arrays. After developing the user friendly statistical analysis platform “Gene Expression Design and Analysis Suite (GESDAS)” for microarray gene expression studies, the GS team has expanded statistical services from microarray gene expression studies to SNPs data analysis.
The massive amount of high-throughput microarray, SNPs and other biological data bring a great challenge of developing advanced statistical and computational data mining tools. The GS team also aims to advocate the statistical methods of information mining and visualization in genotype-to-phenotype mapping. Continuous and categorical versions of the Generalized Association Plot, statistical learning methods and nonlinear dimension reduction techniques, and latent class models are integrated into the platform by combining clever computational algorithms and flexible statistical models.
Linkage analysis and association studies have provided powerful tools for mapping disease-susceptibility genes with multiple SNPs arrays. Various robust techniques have been developed. To investigate the gene-environment interactions on phenotypes of complex diseases in case-control or familial studies, the GS team provides multipoint linkage and association mapping analysis, and incorporates the information of age at disease onset for association studies.
The GS team has been collaborating with other teams to establish clinical information systems for important disease categories by developing standard operation procedures and metadata structure with web-browser interfaces. Animal models will be considered in high throughput genomic research to validate genetic findings of human diseases and investigate systems biology in vivo.
By active and efficient cooperation with teams of information technology, functional and comparative bioinformatics in the Advanced Bioinformatics Core, the GS team will collaborate with research projects in genomic medicine to promote diagnoses and prognoses of major disease areas