Mathematical Models and Statistical Methods for Genome Analysis

描述(由申请人提供)：测序技术的最新进展使序列数据的快速和成本效益的产生成为可能。不久，全基因组测序将成为常规分析，为生物医学研究和相关领域打开新的机遇。这项提议的总体目标是开发准确、可扩展的全基因组变异研究的计算方法，既通过开发研究群体遗传学模型的新理论框架，也通过改进支持基因组分析的各种统计工具的关键数学组成部分。贯穿这一提议的一个共同主线是重组。交叉和基因转换重组都将被考虑。这项研究的具体目的是：(1)发展和应用一个新的理论框架，在重组率从中到大的情况下补充标准的合并理论，从而为种群基因组学提供一套新的有用的分析工具。(2)设计原则性方法，直接从基本的群体遗传学模型推导出准确的多点条件抽样分布。提高利用条件抽样分布进行基因组分析的各种统计方法的准确性。(3)开发可扩展的计算方法，用于从群体SNP数据中联合估计交叉率、基因转化率和平均转换道长度。将现实的生物学场景融入到具有重叠基因转换的模型中，并将该模型扩展到处理多基因家族中的异位(或非等位)基因转换。上述目标不是通过基于直觉或模拟的工程修改来实现的，而是通过应用和推广最新的严格和准确的数学结果来实现的。这项研究开发的新理论框架将允许人们进行分析计算，这在标准的复合结合理论中被认为是困难的。此外，将设计基于扩散过程的数学上合理的近似，并将开发用于种群基因组分析的便携式软件包。 公共卫生相关性：正在进行的大规模测序项目将全面了解人群中的基因组变异，帮助揭开人类生物学和疾病风险的遗传基础。重组是导致种群遗传变异的主要生物学机制，对基因组分析中的许多计算问题具有重要意义，包括疾病关联图谱和检测自然选择的特征。这项拟议的研究将通过开发新的数学框架和统计工具来研究重组的群体遗传学模型，从而有助于分析和解释全基因组变异数据。

DESCRIPTION (provided by applicant): Recent advances in sequencing technology is enabling fast and cost-effective generation of sequence data. Soon, whole-genome sequencing will become a routine assay, opening up new opportunities for biomedical research and related fields. The overall objective of this proposal is to develop accurate, scalable computational methods for whole-genome variation study, both by developing a new theoretical framework for studying population genetics models and by improving the key mathematical component underlying various statistical tools for genome analysis. A common thread that runs through this proposal is recombination. Both crossover and gene conversion recombinations will be considered. The specific aims of the proposed research are: (1) Develop and apply a new theoretical framework that complements the standard coalescent theory when the rate of recombination is moderate to large, thus providing a new set of useful analytic tools to the population genomics community. (2) Devise principled methods to derive accurate multi-locus conditional sampling distributions directly from the underlying population genetics model. Improve the accuracy of a wide range of statistical methods for genome analysis that utilize conditional sampling distributions. (3) Develop scalable computational methods for joint estimation of crossover rates, gene conversion rates, and mean conversion tract lengths from population SNP data. Incorporate realistic biological scenarios into a model with overlapping gene conversions and extend the model to handle ectopic (or non-allelic) gene conversions in multigene families. The above goals will be achieved not by engineering modifications based on intuition or simulations, but by applying and generalizing recent mathematical results that are rigorous and accurate. The new theoretical framework developed in this research will allow one to carry out analytic computation, which was considered to be intractable in the standard coalescent theory with recombination. Furthermore, mathematically justified approximations based on diffusion processes will be devised and portable software packages will be developed for population genomics analysis. PUBLIC HEALTH RELEVANCE: The ongoing large-scale sequencing projects will provide a comprehensive view of genomic variation in populations, helping to unravel the genetic basis of human biology and disease risk. Recombination is a major biological mechanism responsible for generating genetic variation in a population, and has important implications for many computational problems in genome analysis, including disease-association mapping and detecting signatures of natural selection. The proposed research will help with analyzing and interpreting whole-genome variation data, by developing novel mathematical frameworks and statistical tools for studying population genetics models with recombination.