Statistical Methods to Interpret Genomic Data

最终目标是开发和应用计算和统计技术，对最近出现的基因组数据进行大规模分析，以提取最佳和有意义的生物信息。(1)深入了解基因定位和微阵列实验的技术和生物学方面，并确定功能基因组研究中涉及的统计问题。这个学习阶段将使PI，一个数学统计学家，熟悉人类遗传学的生物学。(2)建立单核苷酸多态性（SNPs）与特定遗传性疾病之间有效关联分析的方法。对于许多复杂的性状，研究中受影响的家族或个体的数量小于（或不太大）基因组筛选中使用的SNP标记的数量。这就排除了在全基因组基础上进行有意义的多变量分析。为了选择适当的标记物子集用于进一步研究，并在多变量分析中结合联合收割机多个标记物的信息，将开发新的统计bootstrap（基于重采样）方法。将通过logistic或其他多元回归模型进一步评估SNP标记物的子集用于风险评估。(3)开发基于微阵列技术获得的基因表达数据的统计分析方法。 在多个实验和信号功能的再现性等问题经常混淆的微阵列数据的分析。提出了一种基于寡核苷酸表达阵列原始数据的多步骤程序，并将开发计算机程序。在培训期间开发的方法将最终允许改进对基因组和基因表达阵列数据的分析。

The ultimate goal is to develop and apply computational and statistical techniques for large-scale analysis of recently emerged genomic data in order to extract optimal and meaningful biological information Specific aims to reach this goal are threefold. (1) To thoroughly understand the technological and biological aspects of gene mapping and microarray- based experiments and to identify the statistical problems involved in functional genomic studies. This learning phase will make the PI, a mathematical statistician, familiar with the biology of human genetics. (2) To develop methods for efficient association analysis between Single Nucleotide Polymorphisms (SNPs) and specific heritable diseases. For many complex traits, the number of families or affected individuals in a study is smaller (or not much larger) than the number of SNP markers used in a genomic screen. This precludes meaningful multi-variate analyses on a genome-wide basis. To selective appropriate subsets of markers for further study and to combine information over multiple markers in multi-variate analyses, novel statistical bootstrap (resampling-based) methods will be developed The resulting subset of SNP markers will be further evaluated by logistic or other multiple regression models for risk assessment. (3) To develop statistical analysis methods for gene expression data obtained through microarray-based technologies. Issues such as reproducibility in multiple experiments and signal function frequently confound the analysis of microarray data. A multi-step procedure based on raw data from oligonucleotide expression array is proposed and computer programs will be developed. The approaches developed in the training period will ultimately allow improved analysis of both genomic and gene expression array data.