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快速和经济有效地生成序列数据成为可能。不久，全基因组测序将成为一种常规分析方法，为生物医学研究和相关领域开辟新的机会。本提案的总体目标是通过开发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成分，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。交叉重组和基因转换重组都将被考虑。本研究的具体目标是：(1)发展和应用一个新的理论框架，在重组率为中等到较大时补充标准凝聚理论，从而为群体基因组学界提供一套新的有用的分析工具。(2)设计原则性方法，直接从潜在的群体遗传学模型中获得精确的多位点条件抽样分布。提高广泛的基因组分析统计方法的准确性，利用条件抽样分布。(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化束长度。将现实的生物学情景纳入一个重叠基因转换的模型，并扩展该模型以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近严格而准确的数学结果来实现的。本研究建立的新的理论框架将允许人们进行解析计算，这在标准的复合聚结理论中被认为是难以解决的。此外，将设计基于扩散过程的数学上合理的近似值，并将开发用于种群基因组学分析的便携式软件包。

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.