Impact of including information from bulls and their daughters in the training population of multiple‐step genomic evaluations in dairy cattle: A simulation study

Oliveira, Hinayah Rojas de; Brito, Luiz F.; Sargolzaei, Mehdi; Silva, Fabyano F.; Jamrozik, J.; Lourenço, Daniela; Schenkel, F.S.

doi:10.1111/jbg.12407

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…By contrast to female genotyping, the relative accuracy difference decrease was slight, whereas the number of genotyped rams was notably reduced (even 50%), and the average number of daughters in data was increased (around 150%), which reflects the importance of genotyping animals well connected in the population. These results confirmed that the genotyping of rams in flocks have a limited effect on prediction accuracy if they are isolated in the pedigree and without offspring records (Pszczola et al, 2012;Shabalina et al, 2017;de Oliveira et al, 2019).…”

Section: Maximum Genotypingsupporting

confidence: 67%

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Granado-Tajada

Varona

Ugarte

2021

Journal of Dairy Science

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Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau.

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Section: Maximum Genotypingsupporting

confidence: 67%

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Granado-Tajada

Varona

Ugarte

2021

Journal of Dairy Science

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“…Thus, we hypothesize that haplotype-based methods could result in more accurate and less biased GEBV prediction when compared to SNP-based models in populations with high genetic diversity because of their development process (e.g., relatively lower selection pressures, crossbreeding) and more complex haplotype structure than observed in populations with low genetic diversity. Simulated data is an interesting approach to investigate this hypothesis because the true breeding values (TBVs) are known ( Morris et al, 2019 ; Oliveira et al, 2019 ). Therefore, we simulated sheep populations with different genetic diversity levels to test our hypothesis.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study

et al. 2021

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The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix (G) in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second G (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne ≅ 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between −0.14 and −0.08 and from −0.62 to −0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne ≅ 250) and less (Ne ≅ 100) genetically diverse populations, respectively, and the bias ranged between −0.36 and −0.32 and from −0.78 to −0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two G matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

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“…The use of simulated data is an effective way to evaluate the prediction process, since the TBVs are known and can be used to validate the EBVs (Oliveira et al, 2019). Our results showed high accuracy and low bias of EBVs, indicating that the two-step process was…”

Section: Bibliographymentioning

confidence: 75%

Análise genética de curvas de crescimento de suínos: um estudo de simulação

et al. 2021

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Objetivamos estimar parâmetros genéticos para os parâmetros da curva de crescimento não-linear de Gompertz com base no método em duas etapas e em dados simulados de uma população de suínos. Dados de valores genéticos reais, dos parâmetros da curva de crescimento (a, b e k) e dos pesos corporais aos 1, 90, 210, 360 e 540 dias de idade foram simulados para 1700 indivíduos. Posteriormente, na primeira etapa do método em duas etapas, as curvas de Gompertz foram ajustadas para cada animal e os parâmetros da curva de crescimento foram estimados. Na segunda etapa, considerando os parâmetros estimados como fenótipos, os valores genéticos dos animais foram preditos por análises multicaracterísticas. Os processos de simulação e de duas etapas foram replicados 10 vezes. Para cada réplica, os parâmetros genéticos, acurácias e vieses foram calculados. Herdabilidades médias moderadas a altas para os parâmetros da curva [0,62 (0,04), 0,56 (0,04) e 0,57 (0,04) para a, b e k, respectivamente] mostram a possibilidade de seleção eficaz de animais com base nesses parâmetros. Correlações genéticas médias altas e favoráveis, especialmente entre os parâmetros mais importantes da curva de crescimento [a e k, 0,93 (0,01)], mostram que a seleção para o aumento do peso à maturidade (a) também aumentará a taxa de maturação (k). Acurácias médias altas [0,84 (0,02), 0,82 (0,02) e 0,83 (0,02)] e vieses médios baixos [0,06 (0,06), 0,06 (0,06) e 0,07 (0,07) para a, b e k, respectivamente] indicam que a abordagem em duas etapas é viável para predizer valores genéticos para os parâmetros da curva de crescimento e para trabalhar com medidas longitudinais de peso corporal.

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Impact of including information from bulls and their daughters in the training population of multiple‐step genomic evaluations in dairy cattle: A simulation study

Cited by 8 publications

References 25 publications

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study

Análise genética de curvas de crescimento de suínos: um estudo de simulação

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