Impact of different breeding strategies on the accuracy of genomic selection in a population of pacific white shrimp (Litopenaeus vannamei): A simulation study

The prediction accuracy of estimated breeding values using traditional best linear unbiased prediction (PBLUP) and single-step genomic BLUP (ssGBLUP) methods was investigated in two breeding populations of Litopenaeus vannamei , using simulated data with varying levels of marker density. Additionally, the impact of the number of generations with phenotypic and genotypic data on the prediction accuracy was also investigated. The simulated breeding populations consisted of 25 families with 40 animals each, or 50 families with 20 animals each. Each population consisted of 20 non-overlapping generations, with 1000 animals per generation. Only the last ten generations were genotyped, and phenotypes and pedigrees were available for all the animals. Phenotypes from the 20th generation were masked in data analysis to mimic the selection of young animals without phenotypes. A trait with a heritability of 0.1 was simulated, and 100 % of the genetic variance was explained by 1000 QTLs evenly distributed across chromosomes and randomly located across the genome. Genomic information consisted of 44 chromosomes that mimicked the genome structure of L. vannamei , with 6, 11, 22, 45, and 90 K markers evenly distributed across the 44 chromosomes. Results indicated that expanding family sizes with fewer family numbers improved the prediction accuracy in both PBLUP and ssGBLUP. In all scenarios, ssGBLUP outperformed PBLUP in terms of the prediction accuracy. The greater the number of generations of data in which genotypes and phenotypes were included in the ssGBLUP approach, the better the prediction accuracy. However, genotyping for more than six generations did not substantially improve prediction accuracy. Additionally, maximum accuracy was achieved with 45 K SNPs in both scenarios, with no substantial improvement in accuracy by increasing the marker density to 90 K. Overall, more accurate predictions were obtained with ssGBLUP with more genotyped animals per family and could be a suitable method for genomic selection for traits with low heritability in L. vannamei . • Genomic selection enhances accuracy in shrimp breeding compared to traditional family-based selection. • Genetic gains are higher after five generations of genotyping and phenotyping. • Genotyping fewer families with more individuals results in higher accuracies. • Medium to high density SNP chips may be beneficial on longer programs.

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Publication Details

Journal
Aquaculture Reports
Published
2024-11-11
DOI
https://doi.org/10.1016/j.aqrep.2024.102463
Citations
1
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
Field-Weighted Citation Impact
0.55

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article

Impact of different breeding strategies on the accuracy of genomic selection in a population of pacific white shrimp (Litopenaeus vannamei): A simulation study

Breno de Oliveira Fragomeni, Arun K. Dhar, Kent E. Holsinger, Issabelle Ampofo et al.
1 citations
Aquaculture Reports
Genetic and phenotypic traits in livestock
0.55
article

Impact of different breeding strategies on the accuracy of genomic selection in a population of pacific white shrimp (Litopenaeus vannamei): A simulation study

Breno de Oliveira Fragomeni, Arun K. Dhar, Kent E. Holsinger, Issabelle Ampofo, Shauneen O’Neill
article en
1 citations

Abstract

The prediction accuracy of estimated breeding values using traditional best linear unbiased prediction (PBLUP) and single-step genomic BLUP (ssGBLUP) methods was investigated in two breeding populations of Litopenaeus vannamei , using simulated data with varying levels of marker density. Additionally, the impact of the number of generations with phenotypic and genotypic data on the prediction accuracy was also investigated. The simulated breeding populations consisted of 25 families with 40 animals each, or 50 families with 20 animals each. Each population consisted of 20 non-overlapping generations, with 1000 animals per generation. Only the last ten generations were genotyped, and phenotypes and pedigrees were available for all the animals. Phenotypes from the 20th generation were masked in data analysis to mimic the selection of young animals without phenotypes. A trait with a heritability of 0.1 was simulated, and 100 % of the genetic variance was explained by 1000 QTLs evenly distributed across chromosomes and randomly located across the genome. Genomic information consisted of 44 chromosomes that mimicked the genome structure of L. vannamei , with 6, 11, 22, 45, and 90 K markers evenly distributed across the 44 chromosomes. Results indicated that expanding family sizes with fewer family numbers improved the prediction accuracy in both PBLUP and ssGBLUP. In all scenarios, ssGBLUP outperformed PBLUP in terms of the prediction accuracy. The greater the number of generations of data in which genotypes and phenotypes were included in the ssGBLUP approach, the better the prediction accuracy. However, genotyping for more than six generations did not substantially improve prediction accuracy. Additionally, maximum accuracy was achieved with 45 K SNPs in both scenarios, with no substantial improvement in accuracy by increasing the marker density to 90 K. Overall, more accurate predictions were obtained with ssGBLUP with more genotyped animals per family and could be a suitable method for genomic selection for traits with low heritability in L. vannamei . • Genomic selection enhances accuracy in shrimp breeding compared to traditional family-based selection. • Genetic gains are higher after five generations of genotyping and phenotyping. • Genotyping fewer families with more individuals results in higher accuracies. • Medium to high density SNP chips may be beneficial on longer programs.

Aquaculture ReportsVol. 39
University of Arizona (US), University of Connecticut (US)
U.S. Department of Agriculture, National Institute of Food and Agriculture
Life below water
Openalex Percentile: Top 24%
Genetic and phenotypic traits in livestock
0.55
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