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

该项目将开发和测试一个工具包，以估算作物育种群体中的密集基因组信息。密集的基因组信息允许遗传学家使用全基因组关联研究来解开性状的遗传学，并且育种者使用基因组选择和基因组学辅助育种来加速遗传改良。当基因组信息的密度非常高并且基因分型的个体的数量非常大时，这些方法是最强大的，但是收集基因型信息以构建这样的数据集的成本是过高的。一个灵活和有效的估算工具包将使人们有可能利用估算数据以低成本建立此类数据集。在遗传学和基因组学背景下，插补是从其他个体的已知基因型预测一个个体中的未知基因型（举一个简单的例子，如果已知个体“X”和“Y”分别具有基因型AA和CC，则其后代“Z”被插补为AC）。插补的价值在于，当与来自少数个体的高密度基因型信息相结合时，高密度信息可以被插补到许多已在低密度下进行基因分型的个体，这极大地降低了密集基因组信息数据集的成本。我们将开发启发式插补算法，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谱。现有的启发式插补算法，这是专为牲畜，不适用于作物，因为作物谱系比牲畜谱系更复杂，作物数据是许多不同的类型，而牲畜数据是相当同质的类型.我们将开发与启发式算法相结合的概率算法，以产生一个混合的农作物插补算法，结合了启发式算法的速度与概率算法的灵活性和鲁棒性。现有的概率算法速度太慢，需要太多的内存，无法很好地处理作物数据。我们将把软件打包应用于世界四大作物育种公司之一的KWS的一些特定案例数据集和育种程序。

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.