Applying an association weight matrix in weighted genomic prediction of boar taint compounds

Botelho, Margareth Evangelista; Lopes, M.S.; Mathur, P. K.; Knol, E.F.; Guimarães, Simone Eliza Facioni; Marques, Daniele Botelho Diniz; Lopes, Paulo Sávio; Silva, Fabyano F.; Veroneze, Renata

doi:10.1111/jbg.12528

Cited by 6 publications

(8 citation statements)

References 50 publications

(89 reference statements)

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“…Therefore, for multi-breed joint genetic evaluation between Xinjiang Brown and Chinese Holstein cattle, considering only the added number of animals is insufficient. Further in-depth analysis of important influencing factors, including assumptions about SNP effects (van den berg et al, 2019) and the weights of the A-and G-matrices in the H-matrix (Karaman et al, 2018;Botelho et al, 2021), is required to improve the accuracy and unbiasedness of predictions (Botelho et al, 2021).…”

Section: Reliability Of Genetic Evaluation In Joint Reference Populat...mentioning

confidence: 99%

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Zhang,

Xu,

et al. 2024

Front. Genet.

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Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement.Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150 K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined.Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142–0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.

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Section: Reliability Of Genetic Evaluation In Joint Reference Populat...mentioning

confidence: 99%

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Zhang,

Xu,

et al. 2024

Front. Genet.

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“…De Campos et al [19] reported accuracies, measured as the correlation between the observed and the predicted phenotype divided by the square root of trait heritability, equal to 0.63 and 0.57 for AND and SKA, respectively, using a Ridge Regression BLUP method. Lukić et al [59] and Botelho et al [60] estimated the genomic breeding values for AND and SKA through a GBLUP approach with correlations between the predicted breeding values and the phenotypes of 0.27-0.35 and 0.21-0.49 for AND and SKA, respectively. In that study, the accuracies adjusted for the square root of heritability (0.56 and 0.76 for AND and SKA, respectively) were much higher than those obtained in this study.…”

Section: Genomic Predictionsmentioning

confidence: 99%

“…In the literature, only Botelho et al [60] have reported results of genomic prediction for IND: for a dataset consisting of records from a single-line pig population, prediction accuracy computed as the Pearson's correlation between GEBV and phenotypes adjusted for fixed effects ranged between 0.24 and 0.26, whereas for a multi-line pig population, correlations were slightly lower (0.21-0.26). Those correlations are similar to that obtained in the present study.…”

Section: Genomic Predictionsmentioning

confidence: 99%

Genomic Prediction and Genome-Wide Association Study for Boar Taint Compounds

Faggion,

Boschi,

Veroneze

et al. 2023

Animals

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With a perspective future ban on surgical castration in Europe, selecting pigs with reduced ability to accumulate boar taint (BT) compounds (androstenone, indole, skatole) in their tissues seems a promising strategy. BT compound concentrations were quantified in the adipose tissue of 1075 boars genotyped at 29,844 SNPs. Traditional and SNP-based breeding values were estimated using pedigree-based BLUP (PBLUP) and genomic BLUP (GBLUP), respectively. Heritabilities for BT compounds were moderate (0.30–0.52). The accuracies of GBLUP and PBLUP were significantly different for androstenone (0.58 and 0.36, respectively), but comparable for indole and skatole (~0.43 and ~0.47, respectively). Several SNP windows, each explaining a small percentage of the variance of BT compound concentrations, were identified in a genome-wide association study (GWAS). A total of 18 candidate genes previously associated with BT (MX1), reproduction traits (TCF21, NME5, PTGFR, KCNQ1, UMODL1), and fat metabolism (CTSD, SYT8, TNNI2, CD81, EGR1, GIPC2, MIGA1, NEGR1, CCSER1, MTMR2, LPL, ERFE) were identified in the post-GWAS analysis. The large number of genes related to fat metabolism might be explained by the relationship between sexual steroid levels and fat deposition and be partially ascribed to the pig line investigated, which is selected for ham quality and not for lean growth.

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“…In certain instances, this strategy has resulted in improved accuracy of genomic prediction for growth and carcass traits in pigs, with improvements ranging from 0.9 to 46% for multi-breed populations [21][22][23]. However, it should be noted that this strategy did not result in improved prediction accuracy in all cases [21,24,25]. Fine mapping of causal variants was still challenging, and the advantages for genomic predictions were limited.…”

Section: Introductionmentioning

confidence: 99%

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs

Wei,

Chang,

Zhang

et al. 2023

Animals

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Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.

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Applying an association weight matrix in weighted genomic prediction of boar taint compounds

Cited by 6 publications

References 50 publications

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Genomic Prediction and Genome-Wide Association Study for Boar Taint Compounds

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs

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