Impact of accounting for allele dosage on genomic prediction in a simulated autopolyploid sturgeon population

文献类型： 外文期刊

第一作者： Song, Hailiang

作者： Song, Hailiang;Hu, Hongxia

作者机构：

关键词： Genomic prediction; Autopolyploid sturgeons; Allele dosage; Reference population

期刊名称：AQUACULTURE （ 影响因子：5.135； 五年影响因子：5.125 ）

ISSN： 0044-8486

年卷期： 2022 年 548 卷

页码：

收录情况： SCI

摘要： Genomic selection is an efficient approach to get shorter breeding cycles in selection programs and greater genetic gains with the selection of superior individuals. Despite advances in genotyping techniques, genetic studies for polyploid species are limited to a rough approximation due to lacking of proper method and having to use the method of diploid species. The major challenge of genetic evaluation is to distinguish the different types of heterozygotes presented in polyploid populations. The main objective of this study was to investigate the impact of accounting for allele dosage on the accuracy and bias of genomic prediction in autopolyploid sturgeons with different ploidy levels. We simulated an autopolyploid sturgeon population of tetraploid and octoploid. Three methods, including BLUP, genomic BLUP (GBLUP) and reproducing kernel Hilbert space (RKHS), were implemented to investigate their prediction abilities. Moreover, the impact of double reduction on prediction accuracy and the impact of the reference population composition on genomic prediction were also surveyed. The results showed that there was no difference in prediction accuracy between pseudo diploid and autopolyploid with different double reductions by BLUP method. However, considering the allele dosage in genomic prediction models promoted approximately 5.1% and 13.0% genomic prediction accuracy in tetraploid and octoploid respectively, compared with using pseudo diploid allele dosage. GBLUP and RKHS models showed superiority over BLUP model, and RKHS performed equal to GBLUP in all scenarios. In addition, reference population only comprising top individuals led to low accuracy and serious bias of genomic prediction. Our results can be applied to optimize genomic prediction for other species, especially polyploids.

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