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一个工具包，以在作物育种群体中注入密集的基因组信息。密集的基因组信息使遗传学家可以利用全基因组关联研究来揭示性状的遗传学，育种者可以利用基因组选择和基因组学辅助育种来加快遗传改良。这些方法在基因组信息密度很高、基因分型个体数量很大时最有效，但收集基因信息来建立这样的数据集的成本令人望而却步。一个灵活和有效的推算工具包将使使用推算数据以低成本建立这种数据集成为可能。在遗传学和基因组学的背景下，归罪是从另一个人的已知基因类型中预测一个人的未知基因(举个简单的例子，如果已知个人‘X’和‘Y’分别具有AA和CC基因，那么他们的后代‘Z’被推定为AC)。归罪的价值在于，当与少数个体的高密度基因信息相结合时，可以将高密度信息归因于许多以低密度进行基因分型的个体，从而极大地降低了密集基因组信息数据集的成本。我们将开发启发式的补偿算法，利用作物系谱中的信息，纠正系谱错误，并生成基因组的近似物理图。现有的针对牲畜设计的启发式推算算法不适用于农作物，因为农作物系谱比牲畜系谱更复杂，作物数据有许多不同的类型，而牲畜数据在类型上相当同质。我们将开发与启发式算法相结合的概率算法，以产生一种结合了启发式算法的速度和概率算法的灵活性和健壮性的作物混合补偿算法。现有的概率算法速度太慢，需要太多的内存才能很好地处理作物数据。我们将打包该软件，将其应用于KWS的一些特定案例数据集和育种计划，KWS是世界四大领先作物育种公司之一。

项目成果

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

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

Plant breeders should be determining economic weights for a selection index instead of using independent culling for choosing parents in breeding programs with genomic selection

植物育种者应该确定选择指数的经济权重，而不是在基因组选择育种计划中使用独立剔除来选择亲本

• DOI: 10.1101/500652
• 发表时间: 2018
• 作者: Batista L

Phasing and imputation of single nucleotide polymorphism data of missing parents of biparental plant populations.

• DOI: 10.1002/csc2.20409
• 发表时间: 2021-07
• 期刊: Crop science
• 影响因子: 2.3
• 作者: Gonen S;Wimmer V;Gaynor RC;Byrne E;Gorjanc G;Hickey JM
• 通讯作者: Hickey JM

A hybrid method for the imputation of genomic data in livestock populations.

• DOI: 10.1186/s12711-017-0300-y
• 发表时间: 2017-03-03
• 期刊: Genetics, selection, evolution : GSE
• 作者: Antolín R;Nettelblad C;Gorjanc G;Money D;Hickey JM
• 通讯作者: Hickey JM