Computational Statistical Methods for Population Genomics

群体基因组学的计算统计方法

• 批准号: EP/C533542/1
• 负责人: David Balding
• 金额: $ 23.11万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FC533542%2F1
• 关键词: Computational; Statistical; Methods; Population; Genomics

It is now possible, and relatively cheap, to scan the entire genomes of multiple individuals within a population. The resulting data can be used to infer aspects of the history of a population, including the values of parameters such as population growth and migration rates, recombination rates, and selection coefficients, as well as levels of admixture. The Bayesian statistical paradigm offers a good framework for such inferences, because it allows maximal extraction of information from data under the specified model, and because background information can be incorporated via the prior distribution. Although straightforward in principle, exact application of the Bayesian paradigm is virtually impossible in practice in this setting because the large datasets and complex models mean that computation times are prohibitively large.In the past few years a number of exciting developments have arisen that push back the boundaries of the model complexity and dataset size that can be analysed, at the cost of an extra approximation (see for example Hey J, Machado CA, NATURE REVIEWS GENETICS, 4 (7): 535-543 JUL 2003). In the presence of ample data, this approximation is often worthwhile to achieve inferences in more realistic models than would otherwise be possible. Two such advances are:(a) Computation of the likelihood may be replaced by a simulation step in which data are simulated under the model given the current parameter settings, and these are accepted if the simulated data are close to the observed data.(b) Instead of the full likelihood, an analogous function is calculated or approximated but with the full data replaced by a vector of summary statistics. Computational Bayesian methods based on this approach have come to be known as ABC, Approximate Bayesian Computation.The applicants have contributed substantially to both these advances, and now propose to investigate systematically ways to make them work more efficiently, and to develop user-friendly computer software to make them more widely available to research workers in population genomics, conservation genetics, and related fields. These tasks will be pursued by a post-doctoral research associate at Imperial College. At the same time, a PhD student at Reading will work on applications of the new methods developed at Imperial to specific problems in population genomics. The result will be that at least approximate inferences will be possible for many more complex situations than was previously feasible, for example detailed aspects of the history of entire animal species. Other researchers will also have explicit examples of the usefulness of this new methodology.The methods we will be developing are very general, and can be applied in any area of science that uses complex models and large amounts of data. Although our project focusses on population genomics, which seems the most fruitful area for application, disease transmission models in epidemiology is an example of another field that is likely to benefit from the methods that we will develop.

现在可以扫描一个群体中多个个体的整个基因组，而且相对便宜。所得数据可用于推断种群历史的各个方面，包括种群增长率和迁移率、重组率、选择系数以及混合水平等参数值。贝叶斯统计范式为此类推论提供了一个良好的框架，因为它允许从指定模型下的数据中最大限度地提取信息，并且可以通过先验分布合并背景信息。虽然原则上很简单，但在这种情况下，贝叶斯范式的精确应用实际上是不可能的，因为大型数据集和复杂的模型意味着计算时间过大。在过去的几年中，出现了许多令人兴奋的发展，这些发展推回了可分析的模型复杂性和数据集大小的界限，但代价是额外的近似（例如参见 Hey J, Machado CA, NATURE REVIEWS GENETICS, 4 (7)：535-543 2003 年 7 月）。在存在充足数据的情况下，这种近似通常值得在比其他方式更现实的模型中实现推断。两个这样的进步是：(a) 似然性的计算可以被模拟步骤取代，其中在给定当前参数设置的模型下对数据进行模拟，如果模拟数据接近观察到的数据，则可以接受这些数据。(b) 计算或近似类似函数而不是完全似然性，但用汇总统计向量代替完整数据。基于这种方法的计算贝叶斯方法被称为 ABC，近似贝叶斯计算。申请人对这两项进步做出了重大贡献，现在提议系统地研究使它们更有效地工作的方法，并开发用户友好的计算机软件，使它们更广泛地供群体基因组学、保护遗传学和相关领域的研究人员使用。这些任务将由帝国理工学院的一名博士后研究员完成。与此同时，雷丁大学的一名博士生将致力于将帝国理工学院开发的新方法应用于群体基因组学的具体问题。结果将是，对于比以前可行的许多更复杂的情况，至少可以进行近似推断，例如整个动物物种历史的详细方面。其他研究人员也将提供这种新方法的实用性的明确示例。我们将开发的方法非常通用，可以应用于使用复杂模型和大量数据的任何科学领域。尽管我们的项目侧重于群体基因组学，这似乎是最富有成效的应用领域，但流行病学中的疾病传播模型是另一个可能受益于我们将开发的方法的领域的例子。

项目成果

A simulation approach for change-points on phylogenetic trees.

系统发育树上变化点的模拟方法。

• DOI: 10.1089/cmb.2014.0218
• 发表时间: 2015
• 期刊: a journal of computational molecular cell biology
• 作者: Persing A

On optimal selection of summary statistics for inference from high-dimensional datasets

用于从高维数据集进行推理的汇总统计量的最佳选择

• 发表时间: 2010
• 期刊: Statistical Applications in Genetics and Molecular Biology
• 影响因子: 0.9
• 作者: M Nunes

Using Genetic Distance to Infer the Accuracy of Genomic Prediction.

• DOI: 10.1371/journal.pgen.1006288
• 发表时间: 2016-09
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Scutari M;Mackay I;Balding D
• 通讯作者: Balding D

A genome-wide association scan in admixed Latin Americans identifies loci influencing facial and scalp hair features.

• DOI: 10.1038/ncomms10815
• 发表时间: 2016-03-01
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Adhikari K;Fontanil T;Cal S;Mendoza-Revilla J;Fuentes-Guajardo M;Chacón-Duque JC;Al-Saadi F;Johansson JA;Quinto-Sanchez M;Acuña-Alonzo V;Jaramillo C;Arias W;Barquera Lozano R;Macín Pérez G;Gómez-Valdés J;Villamil-Ramírez H;Hunemeier T;Ramallo V;Silva de Cerqueira CC;Hurtado M;Villegas V;Granja V;Gallo C;Poletti G;Schuler-Faccini L;Salzano FM;Bortolini MC;Canizales-Quinteros S;Rothhammer F;Bedoya G;Gonzalez-José R;Headon D;López-Otín C;Tobin DJ;Balding D;Ruiz-Linares A
• 通讯作者: Ruiz-Linares A

In defence of model-based inference in phylogeography.

• DOI: 10.1111/j.1365-294x.2009.04515.x
• 发表时间: 2010-03
• 期刊: Molecular ecology
• 影响因子: 4.9
• 作者: Beaumont MA;Nielsen R;Robert C;Hey J;Gaggiotti O;Knowles L;Estoup A;Panchal M;Corander J;Hickerson M;Sisson SA;Fagundes N;Chikhi L;Beerli P;Vitalis R;Cornuet JM;Huelsenbeck J;Foll M;Yang Z;Rousset F;Balding D;Excoffier L
• 通讯作者: Excoffier L