A general method for the imputation of genomic data in crop species

作物物种基因组数据估算的通用方法

• 批准号: BB/R002061/1
• 负责人: John Hickey
• 金额: $ 40.3万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FR002061%2F1
• 关键词: general; method; imputation; genomic; data

The project will develop and test a toolkit to impute dense genomic information in crop breeding populations. Dense genomic information allows geneticists to unravel the genetics of traits using genome wide association studies, and breeders to speed up genetic improvement using genomic selection and genomics assisted breeding. These methods are most powerful when the density of genomic information is very high and the numbers of individuals genotyped are very large but the cost of collecting genotype information to build such datasets is prohibitive. A flexible and effective imputation toolkit will make it possible to build such datasets cheaply using imputed data. In a genetics and genomics context, imputation is the prediction of an unknown genotype in one individual from the known genotypes of other individuals (to give a trivial example, if individuals 'X' and 'Y' are known to have genotypes AA and CC respectively, then their offspring 'Z' is imputed to be AC). The value of imputation is that when combined with high-density genotype information from a few individuals, high-density information can be imputed for many individuals that have been genotyped at low-density, which vastly reduces the costs of datasets of dense genomic information.The project has three parts:-1. We will develop heuristic imputation algorithms that exploit the information in crop pedigrees, that correct pedigree errors and that generate approximate physical maps of the genome. Existing heuristic imputation algorithms, which were designed for livestock, do not work on crops because crop pedigrees are more complex than livestock pedigrees and crop data are of many different types, whereas livestock data is fairly homogeneous in type.2. We will develop probabilistic algorithms that integrate with the heuristic algorithms to produce a hybrid imputation algorithm for crops that combines the speed of heuristic algorithms with the flexibility and robustness of probabilistic algorithms. Existing probabilistic algorithms are too slow and require too much memory to work well with crop data.3. We will package the software apply it to a number of specific case datasets and breeding programs in KWS, which is one of the worlds four leading crop-breeding companies.

该项目将开发和测试工具包，以在农作物育种种群中估算密集的基因组信息。密集的基因组信息允许遗传学家使用基因组广泛的关联研究来阐明性状的遗传学，并使用基因组选择和基因组学辅助育种来加快遗传改善。当基因组信息的密度非常高并且基因分型非常大时，这些方法最强大，但是收集基因型信息以构建此类数据集的成本非常高。灵活而有效的归纳工具包将使使用估算的数据便宜地构建此类数据集成为可能。在遗传学和基因组学环境中，推测是一个人中从其他个体的已知基因型中预测一个未知的基因型（为了给出一个琐碎的例子，如果已知个体“ X”和“ Y”分别具有基因型AA和CC，那么将其后代“ Z”推出为AC）。插补的价值是，当与少数个体的高密度基因型信息结合使用时，可以为许多以低密度基因分型进行基因分型的个体估算高密度信息，从而大大降低了密集基因组信息的数据集的成本。该项目具有三个部分：-1。我们将开发启发式插入算法，以利用作物血统中的信息，这些算法纠正谱系误差并产生基因组的近似物理图。为牲畜设计的现有启发式插补算法不从农作物上起作用，因为农作物的血统比牲畜的谱系更为复杂，而作物数据具有许多不同的类型，而牲畜数据在类型上相当均匀。2。我们将开发与启发式算法相结合的概率算法，以生成将启发式算法速度与概率算法的灵活性和鲁棒性的作物相结合的农作物的混合归合算法。现有的概率算法太慢，需要过多的内存以至于无法与作物数据合作。3。我们将包装软件将其应用于KWS中的许多特定案例数据集和繁殖计划，这是世界四家领先的作物繁殖公司之一。

项目成果

Plant breeding simulations with AlphaSimR

• DOI: 10.1101/2023.12.30.573724
• 发表时间: 2023-12-30
• 期刊: bioRxiv
• 作者: Bancic，Jon;Greenspoon，Philip;Gorjanc，Gregor
• 通讯作者: Gorjanc，Gregor

A heuristic method for fast and accurate phasing and imputation of single nucleotide polymorphism data in bi-parental plant populations

双亲植物群体中单核苷酸多态性数据快速准确定相和插补的启发式方法

• DOI: 10.1101/330027
• 发表时间: 2018
• 作者: Gonen S

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

杂交育种项目早期一般配合力基因组预测模型的比较

• DOI: 10.1002/csc2.21105
• 发表时间: 2023
• 期刊: Crop Science
• 影响因子: 2.3
• 作者: De Jong G

A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

• DOI: 10.1007/s00122-018-3156-9
• 发表时间: 2018-11
• 期刊: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
• 作者: Gonen S;Wimmer V;Gaynor RC;Byrne E;Gorjanc G;Hickey JM
• 通讯作者: Hickey JM

Potential of Low-Coverage Genotyping-by-Sequencing and Imputation for Cost-Effective Genomic Selection in Biparental Segregating Populations

• DOI: 10.2135/cropsci2016.08.0675
• 发表时间: 2017-05-01
• 期刊: CROP SCIENCE
• 影响因子: 2.3
• 作者: Gorjanc, Gregor;Dumasy, Jean-Francois;Hickey, John M.
• 通讯作者: Hickey, John M.