Abstract

Genetic improvement programs of the Pacific white shrimp (Litopenaeus vannamei) have been implemented worldwide aiming to enhance growth, survival and disease resistance traits. The use of low-density SNP panels for performing high-throughput, low-cost paternity assignments, kinship determination and evaluating genetic variability parameters and structure in L. vannamei broodstock lines will help increase observed genetic gains. Samples from four selection lines from a breeding nucleus (N = 167), from seven retail stores in Brazil (N = 191), and from families (offspring, known dams and putative sires) from a commercial breeding nucleus (N = 257) were used in the study. All samples were genotyped with a set of 96 SNP markers mined from a dataset developed locally. Observed minor allele frequencies for 88 quality-controlled SNPs ranged between 0.11 and 0.5 with an average of 0.38. Mean observed population heterozygosity varied from 0.319 to 0.471. Mean estimated population Fis was 0.022 and ranged from −0.075 to 0.109. STRUCTURE and PCA analysis revealed retail store samples to have a mean composition of 85% from a common population, suggesting a common post larvae producer supplied the different grow out farms and/or wholesale merchants from different sampled areas. Overall average estimated probabilities of non-exclusion considering one known parent and one putative parent was 1.1 × 10–6, 7.0 × 10–4 considering no known parents, and 2.6 × 10–10 considering a putative pair of sire and dam. Parentage analysis using CERVUS resulted in correct dam assignments of all tested offspring. Sire assignments with >95% confidence were obtained in all cases. Obtained results indicate the developed SNP panel provides a viable tool for performing large-scale high-throughput kinship and paternity assignments, and population structure and assignment of commercial Pacific white shrimp broodstock. The developed tool can provide pedigree information and improve genetic gains in breeding programs that seek to increase grow-out survival rates and overall productivity in biologically and non-biologically challenging production environments.

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