CORE--BIOSTATISTICS

核心9002，生物统计学核心（PI Marazita），将主要负责统计 基因分析是该中心产生人类数据的项目所必需的，特别是项目0001-P.I. Murray，Project 0002-Marazita和Lidral Pilot Project。Diana Juriloff博士是Lidral试点项目的共同研究者，也是小鼠遗传学项目分析方法的专家;她将担任该核心的顾问，负责对项目0003-Schutte、项目0004-狄克逊、项目0005 Jiang和Lidral试点项目中产生的小鼠数据进行任何分析。 博士Marazita在统计遗传学的各个方面都有相当的专业知识和经验; 例如，分离分析方法，双研究分析方法，分离分析的方差分量和回归方法，连锁分析（参数和无模型），关联分析（病例对照，TDT），多变量分析（聚类分析，主成分分析等）。Marazita博士的局域网和Linux集群的设施可通过该核心使用，并将用于所有中心统计分析。 通过核心可获得的主要统计遗传学方法包括连锁分析 （两点、多点、方差分量、相对对、非参数）、关联分析（病例对照、TDT）、混合分析、单体型分析、多变量分析（主要用于鉴定数据中的相互作用和复杂模式，包括基因X基因相互作用）、分离分析（包括系谱判别分析）。还可使用更一般的统计分析方法：例如标准卡方、ANOVA、功效计算、主成分以及用于分析小鼠数据的此类一般方法。

Core 9002, the Biostatistics Core (PI Marazita), will have the primary responsibility for the statistical genetic analyses necessary for the Center projects generating human data, particularly Project 0001--P.I. Murray, Project 0002---Marazita, and Lidral Pilot Project. Dr. Diana Juriloff is a co-Investigator on the Lidral Pilot Project and is an expert on analysis methods for mouse genetics projects; she will serve as a consultant to this core for any analyses of the murine data generated in Project 0003--Schutte, Project 0004-- Dixon, Project 0005 Jiang, and the Lidral Pilot Project. Dr. Marazita has considerable expertise and experience in all aspects of statistical genetics; for example, segregation analysis methods, twin-study analysis methods, variance component and regressive methods for segregation analysis, linkage analysis (both parametric and model-free), association analysis (case-control, TDT), multivariate analysis (cluster analysis, principal component analyses, etc). The facilities of Dr. Marazita's local area network and LINUX cluster are available through this Core and will be used for all Center statistical analyses. The major statistical genetic methods that are available through the Core include linkage analysis (two-point, multipoint, variance component, relative pair, nonparametric), association analysis (case-control, TDT), admixture analysis, haplotype analysis, multivariate analysis (primarily for identifying interactions and complex patterns in the data including gene x gene interaction), segregation analysis (including pedigree discriminant analysis). More general statistical analysis methods are also available: e.g. standard chi-square, ANOVA, power calculations, principal components, and such general methods used for the analyses of mouse data.