Accuracy of genotype imputation of a low-density SNP array for the Amazon fish Colossoma macropomum

亚马逊鱼类巨骨舌鱼低密度SNP芯片基因型推断的准确性

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Abstract

In South America, Tambaqui (Colossoma macropomum) stands as the primary target for aquaculture, yet breeding programs for this Amazon native species are in their early stages. While high-density single nucleotide polymorphism (SNP) arrays are pivotal for aquaculture breeding, their costs can be prohibitive for non- or semi-industrial species. To overcome this, a cost-effective approach involves developing low-density SNP arrays followed by genotype imputation to higher densities. In this study, a 1K SNP array for tambaqui was created and validated, offering a balance between SNP quantity and genome representativity. The imputation accuracy from various SNP densities to a medium-density array was evaluated, with the 1K density demonstrating the best trade-off (accuracy of 0.93). This subset was further utilized to construct a commercial array through Agriseq™ targeted genotyping-by-sequencing, validated in 192 DNA samples, affirming its high quality for genotyping tambaqui. The low-density SNP array, with genome-wide coverage and high polymorphism, emerges as an effective tool for exploring genetic variation within diverse populations. Population analyses using the 1K panel proved to be an efficient tool for genetic characterization of sampled broodstocks, making it a valuable resource for genetic improvement programs targeting this Amazon native species.

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