Effectiveness of SNPs for Sibship Assignment in Farmed Banana Shrimp (Penaeus merguiensis)

Author:

Phuthaworn Chontida12,Nguyen Nguyen Hong3ORCID,Knibb Wayne1

Affiliation:

1. Centre for Bioinnovation, University of the Sunshine Coast, Locked Bag 4, Maroochydore DC, QLD 4558, Australia

2. Department of Aquaculture, Faculty of Fisheries, Kasetsart University, 50 Ngamwongwan Rd, Chatuchak, Bangkok 10900, Thailand

3. School of Science, Engineering and Technology, University of the Sunshine Coast, Locked Bag 4, Maroochydore DC, QLD 4558, Australia

Abstract

Pedigrees are essential components in selective breeding programs to manage genetic diversity and obtain accurate genetic parameter estimates to ensure long-term response to selection in captive populations. High throughput and cost-effective sequencing technologies has offered opportunities of using single nucleotide polymorphisms (SNPs) to resolve penaeid shrimp pedigrees from mass spawning cohorts and communal rearing. Effects of SNPs for sibship assignment were investigated on 546 shrimp using two software programs, Colony and Sequoia. Assignment rates and accuracies using SNP subsets with six different minor allele frequencies (MAFs), four sets of SNPs, and five genotyping error rates were compared to the microsatellite-based pedigree established in a previous study. High MAFs and numbers of SNPs contributed to significant increases in assignment rates and accuracies, whereas genotyping error rates showed negligible impacts on assignment results. Sibship assignments achieved rates and accuracies of 98% and 83%, respectively, with a minimum number of 91 SNPs (average MAF ≥ 0.14), and the two different programs exhibited similar resulting patterns for different SNP subsets. High consistencies between SNP-based and microsatellite-based pedigrees showed that accurate pedigrees could be achieved by using SNPs and thus contribute to the long-term response to selection in farmed banana shrimp.

Funder

Australian Seafood Cooperative Research Center

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

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