Statistical methods for genetic data

描述(申请人提供)：遗传学在许多人类疾病中发挥作用，无论疾病本身是遗传的，还是与基因活动的实质性变化有关。通过利用最近发展的技术，人们可以在全基因组水平上进行生物学研究，从而更好地了解和诊断人类疾病，这是一个巨大的机会。很有必要开发新的量化工具，专门用来分析这些研究产生的大量数据。拟议研究的总体目标是开发有助于理解基因组数据的统计方法和软件。 特别关注的是功能基因组学，来自基因表达阵列和大规模基因分型的数据可以用来研究大量基因如何发挥作用来完成各种功能角色。将开发用于DNA微阵列实验的统计推断技术，特别是识别在两种或两种以上生物条件下差异表达的基因。这些技术既适用于静态实验，也适用于时间进程实验。还将开发转录调控的遗传解剖的统计方法。这包括在基因组范围和基因特定水平上估计基因表达的遗传控制的方法，以及绘制与基因表达相关联的基因座的方法。特别是，将研究从模型选择方法进行的多基因连锁分析，其中将开发新的方法来计算高效的模型生成、选择和显著性分析。 所有这些方法都将被落实到用户友好的软件中，免费分发给学术界。这些方法还将与实验者合作，在公开可用的数据上进行测试，以努力验证这些方法提供了具有生物学意义的结果。总体而言，这项工作的目的是通过为基因组研究提供严格的分析工具，帮助理解人类疾病的分子生物学和遗传学基础。

DESCRIPTION (provided by applicant): Genetics plays a role in many human diseases, whether the disease itself is inherited or it is associated with a substantial change in the activity of genes. A great opportunity exists to better understand and diagnose human disease by utilizing recently developed technologies that allow one to carry out biological studies at the genome-wide level. There is a substantial need to develop new quantitative tools specifically designed to analyze the enormous amounts of data generated by these studies. The overall goal of the proposed research is to develop statistical methods and software useful in understanding genomic data. The particular focus is in functional genomic, where data from gene expression arrays and large-scale genotyping can be used to study how large numbers of genes work to accomplish various functional roles. Statistical inference techniques for DNA micro array experiments will be developed, specifically identifying genes that are differentially expressed among two or more biological conditions. These techniques will be applicable to both static experiments and time course experiments. Statistical methods for the genetic dissection of transcriptional regulation will also be developed. This includes methods to estimate the genetic control of gene expression at both genome-wide and gene-specific levels, and methods to map loci showing linkage to gene expression. In particular, multiple locus linkage analysis from a model selection approach will be investigated, where new methods will be developed for computationally efficient model generation, selection, and significance analysis. All of these methods will be implemented into user-friendly software that will be freely distributed to the academic community. The methods will also be tested on publicly available data in collaboration with experimentalists, in an effort to verify that the methods provide biologically meaningful results. Overall, this work is aimed at contributing to the understanding of the molecular biology and genetic basis of human disease by providing rigorous analytical tools for genomic studies.