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Computational and mathematical methods for population genetics analysis of multi-locus data under selection and strong recombination

选择和强重组下多位点数据群体遗传学分析的计算和数学方法

基本信息

批准号：
191584514
负责人：
Dr. Matthias Steinrücken
金额：
--
依托单位：
Department of Statistics
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2010
资助国家：
德国
起止时间：
2009-12-31 至 2012-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/191584514?language=en
关键词：
Computational mathematical methods population genetics

项目摘要

Markov diffusion processes are used to model the dynamics of genetic information in a population. The neutral one-locus case has been extended to include selection and recombination. This is complemented by coalescent processes describing the ancestry of individuals. The probability of sampling a certain genetic configuration from a population can be estimated via importance sampling on coalescent histories. A central object in this approach is the conditional sampling distribution of an additionally sampled individual, having already observed a given genetic configuration.I will apply the conditional sampling distribution introduced by Paul and Song (2010) to impute missing sequence data and to infer local ancestry in admixed populations using established algorithms that update individual genetic information based on known data for related individuals using the conditional sampling distribution. I will also develop an approach to impute missing data via importance sampling on incomplete histories.Since it has been argued that detection of selection has to account for epistatic interaction between loci to explain the inheritance of certain diseases, I will investigate the influence of epistatic and epistasis-free selection on statistical measures of correlation among loci to develop a test for epistasis. To foster this, I will also introduce epistatic selection into the asymptotic expansion of the sampling probability given by Jenkins and Song (2010).Genome-wide association studies detecting genetic markers that influence diseases rely on the linkage structure of genetic sequences. Linkage disequilibrium plays an important role and understanding its dynamics is essential. The hosts postdoc has shown that for large recombination probabilities the linkage disequilibrium can be approximated by an amenable diffusion process. I want to derive a process that approximates the ancestry for large recombination probabilities and analyse its duality to this diffusion.

马尔可夫扩散过程用于模拟种群中遗传信息的动态。中性单基因座情况已扩展到包括选择和重组。这是补充的结合过程描述的祖先的个人。从一个种群中抽取某种遗传构型的概率可以通过对结合历史的重要性抽样来估计。该方法的中心目标是附加采样个体的条件采样分布，我已经观察到了一个给定的遗传配置。我将应用Paul和Song（2010）介绍的条件抽样分布。使用基于相关个体的已知数据更新个体遗传信息的已建立算法，条件抽样分布我还将开发一种方法来填补缺失的数据，通过重要性抽样不完整的histors.Since它一直认为，检测选择要考虑基因座之间的上位性相互作用，以解释某些疾病的遗传，我将调查上位性和无上位性选择对基因座之间的相关性的统计测量的影响，发展上位性的测试。为了促进这一点，我还将把上位选择引入到Jenkins和Song（2010）给出的抽样概率的渐近展开中。全基因组关联研究检测影响疾病的遗传标记依赖于遗传序列的连锁结构。连锁不平衡起着重要的作用，了解其动态是必不可少的。宿主博士后已经表明，对于大的重组概率，连锁不平衡可以近似为一个顺从的扩散过程。我想推导出一个过程，它近似于大重组概率的祖先，并分析它对这种扩散的对偶性。