Statistical Genetics

生物统计学核心将负责实验设计和数据分析的统计方面，

The Biostatistics Core will be responsible for statistical aspects of experimental design and data analysis, and the development of new statistical methods required by Projects 1 and 2. In collaboration with the Bioinformatics Core, the Biostatistics Core will design and implement analysis methods and associated software for use by investigators in the analysis of resequencing and genotype data from the Projects. The specific aims are: 1) design and implement an integrated approach for data analysis to support the day to day data analysis demands of the Projects by developing standardized protocols for the statistical analysis of both family-based association studies and case-control studies to maintain adequate statistical power while providing appropriate control of Type I error in the face of a potentially large number of tests; 2) develop novel statistical genetics methods and software to meet the specific needs of each of the Projects and the other Cores and in collaboration with the Bioinformatics Core, make these new implementations available to the research community through the public web interfaces provided by the Bioinformatics Core. The Biostatistics Core will be directed by Dr Christoph Lange and co-directed by Dr Nan Laird. Drs Lange and Laird are world-renowned statisticians with extensive experience in the development of important new methodology for statistical genetic analyses, developing and distributing suites of data analysis software, and in the analysis of genetic data sets and disease association studies. They have a well-established track record of highly productive collaboration with each other and with the other investigators assembled for this project. The work of the Biostatistics core will contribute to our understanding of genetic and environmental influences in asthma and COPD, two common and important human diseases which display complex patterns of genetic influences and important effects from environmental exposures. In addition, the work will lead to improvements in statistical genetics methods for complex traits.