Crop Science Department, Faculty of Agriculture, University of Zimbabwe, P. O. Box MP167, Mt Pleasant, Harare, Zimbabwe; Department of Plant Biology, Faculty of Science, University of Yaounde 1, Yaounde, Cameroon.
CIRAD, UMR AGAP, F-34398 Montpellier, France; AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
Genomics. 2021 Mar;113(2):655-668. doi: 10.1016/j.ygeno.2021.01.012. Epub 2021 Jan 27.
Genotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4.3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally. These results will contribute to the efficient implementation of genomic selection with GBS. For Hevea, GBS is promising for rubber yield improvement, with GP accuracies reaching 0.52.
基于测序的基因分型（GBS）为基因组预测（GP）提供了所需的标记密度。然而，GBS 会产生大量缺失的 SNP 数据，对于没有染色体水平基因组组装的物种，必须在不知道 SNP 物理位置的情况下进行推断。在这里，我们比较了七种无图谱依赖和两种图谱依赖的推断方法的 GP 准确性，以及使用所有 SNP 与遗传映射 SNP 子集的情况。我们使用了两个橡胶树（Hevea brasiliensis）数据集和三个性状。结果表明，最佳的推断方法是 LinkImputeR、Beagle 和 FImpute。使用遗传映射 SNP 可将 GP 准确性提高 4.3%。使用所有标记的 LinkImputeR 可以避免遗传图谱构建，GP 准确性略有下降。LinkImputeR 给出了最高水平的正确推断基因型，其性能通过能够定义一组最佳推断 SNP 的能力进一步提高。这些结果将有助于高效实施 GBS 的基因组选择。对于橡胶树，GBS 有望提高橡胶产量，GP 准确性达到 0.52。
