Effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci detection and genomic evaluation in a simulated cattle population

Takeda, Masayuki; Uemoto, Yoshinobu; Satoh, Masahiro

doi:10.1111/asj.13432

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…To address this issue, we considered different phenotypic scenarios in our simulation. In all studied scenarios, with decreasing phenotypic records, prediction accuracy also decreases, which indicates a direct relationship between phenotypic records and prediction accuracy (Goddard, 2009;Takeda et al, 2020).…”

Section: Discussionmentioning

confidence: 80%

Selective genotyping to implement genomic selection in beef cattle breeding

et al. 2023

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Genomic selection (GS) plays an essential role in livestock genetic improvement programs. In dairy cattle, the method is already a recognized tool to estimate the breeding values of young animals and reduce generation intervals. Due to the different breeding structures of beef cattle, the implementation of GS is still a challenge and has been adopted to a much lesser extent than dairy cattle. This study aimed to evaluate genotyping strategies in terms of prediction accuracy as the first step in the implementation of GS in beef while some restrictions were assumed for the availability of phenotypic and genomic information. For this purpose, a multi-breed population of beef cattle was simulated by imitating the practical system of beef cattle genetic evaluation. Four genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed an improvement in prediction accuracy, albeit a limited number of animals being genotyped (i.e., 3% of total animals in genetic evaluation). The comparison of genotyping scenarios revealed that selective genotyping should be on animals from both ancestral and younger generations. In addition, as genetic evaluation in practice covers traits that are expressed in either sex, it is recommended that genotyping covers animals from both sexes.

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Section: Discussionmentioning

confidence: 80%

Selective genotyping to implement genomic selection in beef cattle breeding

et al. 2023

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“…Our findings support this assumption, suggesting that high-reliability GEBV can improve the accuracy of variant discovery and downstream analyses. In a study by Takeda et al [ 33 ], no significant differences were observed in the power to detect QTL when simulated heritability values of 0.2 and 0.5 were compared. However, it was noted that QTL detection improved with an increasing number of phenotyped progenies (N = 1500, 4500, and 9000), which led to a higher reliability of GEBV.…”

Section: Discussionmentioning

confidence: 99%

Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study

et al. 2023

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Background Identifying true positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of genomic information may give insights into the optimal number of individuals to be used in GWA. This study investigated different discovery set sizes based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). In addition, we investigated the impact on the prediction accuracy by adding variants, which were selected based on different set sizes, to the regular single nucleotide polymorphism (SNP) chips used for genomic prediction. Methods We simulated sequence data that included 500k SNPs with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one in every ten simulated SNPs. Effective population size (Ne) was set to 20 or 200. GWA were performed using a number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and large-effect size SNPs were added to the 50k panel and used for single-step genomic best linear unbiased prediction (ssGBLUP). Results Using a number of genotyped animals corresponding to at least EIG98 allowed the identification of QTN with the largest effect sizes when Ne was large. Populations with smaller Ne required more than EIG98. Furthermore, including genotyped animals with a higher reliability (i.e., a higher trait heritability) improved the identification of the most informative QTN. Prediction accuracy was highest when the significant or the large-effect SNPs representing twice the number of simulated QTN were added to the 50k panel. Conclusions Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.

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“…Our findings highlighted that regardless of the number of QTN, N e , M e , and sample size, high heritability scenarios could capture more significant QTN explaining a larger portion of the variance. However, Takeda et al [32] observed no differences in power to detect QTL when heritability 0.2 and 0.5 were simulated but outlined that QTL detection was better with the increasing number of phenotyped progenies (N = 1,500, 4,500, 9,000). As the use of more phenotyped progeny data indicated higher reliability of EBV of the parents, which is the case of genotyped animals in the higher heritability scenarios in our study, those findings agreed with the current results.…”

Section: Discussionmentioning

confidence: 99%

Dimensionality of genomic information and its impact on GWA and variant selection: a simulation study

Jang

Tsuruta

Leite

et al. 2022

Preprint

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Background: Identifying true-positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of the genomic information may give insights into the optimal number of individuals to use in GWA. This study investigated different discovery set sizes in GWA based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). An additional investigation included the change in accuracy by adding variants, selected based on different set sizes, to the regular SNP chips used for genomic prediction. Methods: Sequence data were simulated containing 500k SNP with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one every ten simulated SNP. Effective population size (Ne) was 20 and 200. The GWA was performed with the number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and high effect size SNP were added to the 50k panel and used for single-step GBLUP with and without weights. Results: Using the number of genotyped animals corresponding to at least EIG98 enabled the identification of QTN with the largest effect sizes when Ne was large. Smaller populations required more than EIG98. Furthermore, using genotyped animals with higher reliability (i.e., higher trait heritability) helped better identify the most informative QTN. The greatest prediction accuracy was obtained when the significant or the high effect SNP representing twice the number of simulated QTN were added to the 50k panel. Weighting SNP differently did not increase prediction accuracy, mainly because of the size of the genotyped population. Conclusions: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, the dimensionality of genomic information. This dimensionality can help identify the suitable sample size for GWA and could be considered for variant selection. Even when variants are accurately identified, their inclusion in prediction models has limited implications.

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Effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci detection and genomic evaluation in a simulated cattle population

Cited by 3 publications

References 33 publications

Selective genotyping to implement genomic selection in beef cattle breeding

Selective genotyping to implement genomic selection in beef cattle breeding

Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study

Dimensionality of genomic information and its impact on GWA and variant selection: a simulation study

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