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得能够快速且成本有效地生成序列数据。不久，全基因组测序将成为常规检测，为生物医学研究和相关领域开辟新的机会。该提案的总体目标是通过开发用于研究群体遗传学模型的新理论框架和改进用于基因组分析的各种统计工具的关键数学组成部分，为全基因组变异研究开发准确的、可扩展的计算方法。贯穿这一提议的一条共同主线是重组。将考虑交叉和基因转换重组。 本研究的具体目标是：（1）发展和应用一个新的理论框架，在重组率中等到大的情况下补充标准的合并理论，从而为群体基因组学社区提供一套新的有用的分析工具。(2)设计原则性的方法，直接从潜在的群体遗传学模型中推导出准确的多位点条件抽样分布。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数据联合估计交叉率、基因转换率和平均转换道长度。将现实的生物学场景与重叠的基因转换模型，并扩展该模型来处理多基因家族中的异位（或非等位基因）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近严格和准确的数学结果来实现的。在这项研究中开发的新的理论框架将允许进行解析计算，这被认为是棘手的标准合并理论与重组。此外，将根据扩散过程设计数学上合理的近似值，并将开发用于人口基因组学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提供对人群基因组变异的全面了解，有助于揭示人类生物学和疾病风险的遗传基础。变异是一种主要的生物学机制，负责在人群中产生遗传变异，并在基因组分析中的许多计算问题，包括疾病关联映射和检测自然选择的签名具有重要意义。拟议的研究将有助于分析和解释全基因组变异数据，通过开发新的数学框架和统计工具来研究具有重组的群体遗传学模型。

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.