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2. Genetic inbreeding load and its individual prediction for milk yield in French dairy sheep.

Author

Antonios, Simona, Rodríguez-Ramilo, Silvia T., Legarra, Andres, Astruc, Jean-Michel, Varona, Luis, and Vitezica, Zulma G.

Subjects
GENETIC load, LIKELIHOOD ratio tests, MILK yield, INBREEDING, LIFE sciences, GENETICS
Abstract

Background: The magnitude of inbreeding depression depends on the recessive burden of the individual, which can be traced back to the hidden (recessive) inbreeding load among ancestors. However, these ancestors carry different alleles at potentially deleterious loci and therefore there is individual variability of this inbreeding load. Estimation of the additive genetic value for inbreeding load is possible using a decomposition of inbreeding in partial inbreeding components due to ancestors. Both the magnitude of variation in partial inbreeding components and the additive genetic variance of inbreeding loads are largely unknown. Our study had three objectives. First, based on substitution effect under non-random matings, we showed analytically that inbreeding load of an ancestor can be expressed as an additive genetic effect. Second, we analysed the structure of individual inbreeding by examining the contributions of specific ancestors/founders using the concept of partial inbreeding coefficients in three French dairy sheep populations (Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse). Third, we included these coefficients in a mixed model as random regression covariates, to predict genetic variance and breeding values of the inbreeding load for milk yield in the same breeds. Results: Pedigrees included 190,276, 166,028 and 633,655 animals of Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse, respectively, born between 1985 and 2021. A fraction of 99.1% of the partial inbreeding coefficients were lower than 0.01 in all breeds, meaning that in practice inbreeding occurs in pedigree loops that span several generations backwards. Less than 5% ancestors generate inbreeding, because mating is essentially between unrelated individuals. Inbreeding load estimations involved 658,731, 541,180 and 2,168,454 records of yearly milk yield from 178,123, 151,863 and 596,586 females in Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse, respectively. Adding the inbreeding load effect to the model improved the fitting (values of the statistic Likelihood Ratio Test between 132 and 383) for milk yield in the three breeds. The inbreeding load variances were equal to 11,804 and 9435 L squared of milk yield for a fully inbred (100%) descendant in Manech Tête Noire and Manech Tête Rousse. In Basco-Béarnaise, the estimate of the inbreeding load variance (11,804) was not significantly different from zero. The correlations between (direct effect) additive genetic and inbreeding load effects were − 0.09, − 0.08 and − 0.12 in Basco-Béarnaise, Manech Tête Noire and Manech Tête Rousse. Conclusions: The decomposition of inbreeding in partial coefficients in these populations shows that inbreeding is mostly due to several small contributions of ancestors (lower than 0.001) going back several generations (5 to 7 generations), which is according to the policy of avoiding close matings. There is variation of inbreeding load among animals, although its magnitude does not seem enough to warrant selection based on this criterion. [ABSTRACT FROM AUTHOR]

Published
2025
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9. The long-term effects of genomic selection : 2. Changes in allele frequencies of causal loci and new mutations

Author

Wientjes, Yvonne C.J., Bijma, Piter, Van Den Heuvel, Joost, Zwaan, Bas J., Vitezica, Zulma G., Calus, Mario P.L., Wientjes, Yvonne C.J., Bijma, Piter, Van Den Heuvel, Joost, Zwaan, Bas J., Vitezica, Zulma G., and Calus, Mario P.L.

Abstract

Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.

Published
2023

10. Genetic determinism of sensitivity to environmental challenges using daily feed intake records in three lines of pigs.

Author

Tusingwiire, Tomasi, Garcia-Baccino, Carolina, Carillier-Jacquin, Céline, Ligonesche, Bruno, Larzul, Catherine, and Vitezica, Zulma G

Subjects
GENETIC determinism, GENETIC correlations, ANIMAL welfare, ENVIRONMENTAL indicators, SWINE
Abstract

In pig breeding, environmental challenges can affect the welfare and productivity of animals. Resilient animals have the capacity to be minimally affected by these environmental challenges. Understanding the genetic basis of sensitivity to these environmental challenges is crucial for selecting more resilient animals, thereby enhancing welfare and productivity. The aims of this study were to 1) estimate the probability of the occurrence of an unrecorded environmental challenge on a given day using daily feed intake (DFI) data and 2) evaluate the genetic determinism of environmental sensitivity in three pig lines bred in real selection conditions. Data comprised of 100,799, 186,247, and 304,826 DFI records from 1,618, 2,517, and 3,788 Landrace (LA), Large White (LW), and Piétrain (PI) male pigs, respectively. The pedigree included 3,730, 5,649, and 9,293 animals for LA, LW, and PI, respectively. The probabilities of the occurrence of an unrecorded environmental challenge on a given day were estimated via a mixture model. The probabilities (⁠ p ⁠) of being "high coefficient of variation days" were then taken as reference and used in genetic analysis as an environmental descriptor to describe the environment. DFI records were analyzed using two linear models: a linear reaction norm animal model (RNAM) and the animal model. (Co)variance components were estimated using average-information restricted maximum likelihood (AI-REML). The means of the probabilities of the occurrence of an environmental challenge for LA, LW, and PI were 0.24, 0.10, and 0.22, respectively, indicating that the probability of an environmental challenge was low for most of the days. The genetic correlations between the intercept and the slope obtained from the RNAM for LA, LW, and PI were −0.52, 0.06, and −0.36, respectively. These findings suggest that selecting hypothetically for decreased DFI in nonstressful conditions would result in pigs with increased DFI in stressful conditions in the LA and PI lines, whereas it would have a minor impact on the environmental sensitivity of LW. The proportion of resilient animals for LA, LW, and PI was 75.0, 74.2, and 72.2%, respectively, implying that most of the animals were resilient. The study demonstrated that the slope of DFI is heritable and can effectively be used as an indicator of sensitivity to environmental challenges. These results are valuable in improving the resilience of livestock species to environmental challenges through genetic selection. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Supplemental Figure S1. Boxplot of log10 of contributions of AI Males, Dam of Males and Dam of Females per generation from G0 (base generation) to G7 (the last generation) in Basco-Béarnaise (BB) and Lacaune (LAC) breeds

Author

Antonios, Simona, Legarra, Andres, Pong-Wong, Ricardo, Astruc, Jean-Michel, Rodríguez-Ramilo, Silvia T., and Vitezica, Zulma G.

Abstract

Supplementary Materials to "Partition of the genetic trend of French dairy sheep in Mendelian samplings and long-term contributions" S. Antonios, A. Legarra, R. Pong-Wong, J.M. Astruc, S.T. Rodríguez-Ramilo and Z.G. Vitezica

Published
2023
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13. Supplemental Figure S2. Long-term contribution (log10 of the contributions) plotted against EBV of the first-generation (G0) individuals in Basco-Béarnaise (BB) and Lacaune (LAC) breeds

Author

Antonios, Simona, Legarra, Andres, Pong-wong, Ricardo, Astruc, Jean-Michel, Rodríguez-Ramilo, Silvia T., and Vitezica, Zulma G.

Abstract

Supplementary Materials to "Partition of the genetic trend of French dairy sheep in Mendelian samplings and long-term contributions" S. Antonios, A. Legarra, R. Pong-Wong, J.M. Astruc, S.T. Rodríguez-Ramilo and Z.G. Vitezica

Published
2023
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15. The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

Author

Wientjes, Yvonne C.J., Bijma, Piter, Calus, Mario P.L., Zwaan, Bas J., Vitezica, Zulma G., van den Heuvel, Joost, Wientjes, Yvonne C.J., Bijma, Piter, Calus, Mario P.L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost

Abstract

Background Genomic selection has revolutionized genetic improvement in animals and plants, but little is known about its long-term effects. Here, we investigated the long-term effects of genomic selection on response to selection, genetic variance, and the genetic architecture of traits using stochastic simulations. We defined the genetic architecture as the set of causal loci underlying each trait, their allele frequencies, and their statistical additive effects. We simulated a livestock population under 50 generations of phenotypic, pedigree, or genomic selection for a single trait, controlled by either only additive, additive and dominance, or additive, dominance, and epistatic effects. The simulated epistasis was based on yeast data. Results Short-term response was always greatest with genomic selection, while response after 50 generations was greater with phenotypic selection than with genomic selection when epistasis was present, and was always greater than with pedigree selection. This was mainly because loss of genetic variance and of segregating loci was much greater with genomic and pedigree selection than with phenotypic selection. Compared to pedigree selection, selection response was always greater with genomic selection. Pedigree and genomic selection lost a similar amount of genetic variance after 50 generations of selection, but genomic selection maintained more segregating loci, which on average had lower minor allele frequencies than with pedigree selection. Based on this result, genomic selection is expected to better maintain genetic gain after 50 generations than pedigree selection. The amount of change in the genetic architecture of traits was considerable across generations and was similar for genomic and pedigree selection, but slightly less for phenotypic selection. Presence of epistasis resulted in smaller changes in allele frequencies and less fixation of causal loci, but resulted in substantial changes in statistical additive effects acro, Background Genomic selection has revolutionized genetic improvement in animals and plants, but little is known about its long-term effects. Here, we investigated the long-term effects of genomic selection on response to selection, genetic variance, and the genetic architecture of traits using stochastic simulations. We defined the genetic architecture as the set of causal loci underlying each trait, their allele frequencies, and their statistical additive effects. We simulated a livestock population under 50 generations of phenotypic, pedigree, or genomic selection for a single trait, controlled by either only additive, additive and dominance, or additive, dominance, and epistatic effects. The simulated epistasis was based on yeast data. Results Short-term response was always greatest with genomic selection, while response after 50 generations was greater with phenotypic selection than with genomic selection when epistasis was present, and was always greater than with pedigree selection. This was mainly because loss of genetic variance and of segregating loci was much greater with genomic and pedigree selection than with phenotypic selection. Compared to pedigree selection, selection response was always greater with genomic selection. Pedigree and genomic selection lost a similar amount of genetic variance after 50 generations of selection, but genomic selection maintained more segregating loci, which on average had lower minor allele frequencies than with pedigree selection. Based on this result, genomic selection is expected to better maintain genetic gain after 50 generations than pedigree selection. The amount of change in the genetic architecture of traits was considerable across generations and was similar for genomic and pedigree selection, but slightly less for phenotypic selection. Presence of epistasis resulted in smaller changes in allele frequencies and less fixation of causal loci, but resulted in substantial changes in statistical additive eff

Published
2022

16. Additional file 5 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

Author

Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost

Abstract

Additional file 5: Figure S1. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under RANDOM selection. Figure S2. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under MASS selection. Figure S3. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under PBLUP selection with own performance (PBLUP_OP). Figure S4. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection without own performance (GBLUP_NoOP). Figure S5. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection with own performance (GBLUP_OP). Figure S6. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under RANDOM selection. Figure S7. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under MASS selection. Figure S8. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under PBLUP selection with own performance (PBLUP_OP). Figure S9. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection without own performance (GBLUP_NoOP). Figure S10. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection with own performance (GBLUP_OP).

Published
2022
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17. Additional file 4 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

Author

Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost

Abstract

Additional file 4: Figure S1. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under RANDOM selection. Figure S2. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under MASS selection. Figure S3. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under PBLUP selection with own performance (PBLUP_OP). Figure S4. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under GBLUP selection without own performance (GBLUP_NoOP). Figure S5. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under GBLUP selection with own performance (GBLUP_OP). Figure S6. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under RANDOM selection. Figure S7. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under MASS selection. Figure S8. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under PBLUP selection with own performance (PBLUP_OP). Figure S9. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection without own performance (GBLUP_NoOP). Figure S10. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection with own performance (GBLUP_OP). Figure S11. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under RANDOM selection. Figure S12. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under MASS selection. Figure S13. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under PBLUP selection with own performance (PBLUP_OP). Figure S14. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection without own performance (GBLUP_NoOP). Figure S15. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection with own performance (GBLUP_OP).

Published
2022
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18. Additional file 2 of Impacts of additive, dominance, and inbreeding depression effects on genomic evaluation by combining two SNP chips in Canadian Yorkshire pigs bred in China

Author

Mei, Quanshun, Vitezica, Zulma G., Li, Jielin, Zhao, Shuhong, Legarra, Andres, and Xiang, Tao

Abstract

Additional file 2: Figure S1. Distribution of the minor allele frequencies of the Illumina array-specific SNPs in Scenario 1 and the Affymetrix array-specific SNPs in Scenario 2. Figure S2. Effect of allele frequency on $${2{p}_{j}{q}_{j}\left({q}_{j}-{p}_{j}\right)}^{2}$$ 2 p j q j q j - p j 2 .

Published
2022
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19. Additional file 2 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

Author

Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost

Abstract

Additional file 2: Figure S1. Allele frequency distribution of segregating causal loci over 50 generations without selection. Figure S2. Extent of LD (r2) in the simulated population before selection as a function of distance in one random replicate. Figure S3. Trend in the variation in minor allele frequency (MAF) of segregating causal loci for the five selection methods and three genetic models. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S4. The difference between additive genic and additive genetic variance which represents a transient loss in genetic variance for the five selection methods and three genetic models. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S5. Phenotypic trend for the GBLUP model with own performance records and with and without a dominance effect for the genetic models with non-additive effects. The phenotypic trend is scaled by the additive genetic standard deviation in the generation before selection in order to make the results comparable across the genetic models. The two genetic models were a model with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S6. Trend in additive genetic (A, B) and additive genic (C, D) variance for the GBLUP model with own performance and with and without a dominance effect for the genetic models with non-additive effects. The trend is scaled by the additive genetic or additive genic variance in the generation before selection in order to make the results comparable across the genetic models. The two genetic models were a model with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error.

Published
2022
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20. Additional file 1 of Impacts of additive, dominance, and inbreeding depression effects on genomic evaluation by combining two SNP chips in Canadian Yorkshire pigs bred in China

Author

Mei, Quanshun, Vitezica, Zulma G., Li, Jielin, Zhao, Shuhong, Legarra, Andres, and Xiang, Tao

Abstract

Additional file 1: Table S1. Estimates of variance components, standard error (SE) of parameters, − 2 log likelihood (− 2LogL), AIC (Akaike’s Information Criterion) from models MA, MAD, MAD*, MAI, MAID and MAID*. Table S2. Contributed additive genetic variance from the inbreeding depression effect. Table S3. P-value of likelihood ratio test based on model MA. Table S4. P-value of likelihood ratio test based on models MAI, MAD and MAD*. Table S5. P-value of inbreeding depression effect based on the Wald test. Table S6. Average genomic relationships between animals in the imputed and reference sets. Table S7. Converted classical variance components based on the genotypic variance component in models MAD and MAID.

Published
2022
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21. Additional file 3 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

Author

Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost

Subjects
ComputingMethodologies_SIMULATIONANDMODELING, Computer Science::Neural and Evolutionary Computation, Data_FILES, ComputingMethodologies_GENERAL, Quantitative Biology::Genomics, Computer Science::Operating Systems
Abstract

Additional file 3. Decomposition of additive genetic variance. This file provides a theoretical decomposition of the additive genetic variance.

Published
2022
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22. Additional file 6 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

Author

Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost

Abstract

Additional file 6: Table S1. Maximum genetic gain1 that is still possible after 50 generations of selection for the five selection methods and three genetic models2. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1The maximum genetic gain in generation 50 is estimated as the genetic gain when all loci would be fixed for the favourable allele, using the statistical additive effects of generation 50 and neglecting mutations. 2Results are shown as averages across the 20 replicates with their corresponding standard errors of the mean between brackets. Table S2. Percentual change in the components of the genetic variance after 10 and 50 generations of selection for the five selection methods and three genetic models1. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets. Increases in the value of a component are represented in bold. Table S3. Average pedigree inbreeding coefficient after 50 generations of selection for the five selection methods and three genetic models1. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets. Table S4. Average and variance of change in allele frequency of causal loci1 across 50 generations of selection for the five selection methods and three genetic models2. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Causal loci included only the causal loci segregating in generation 0. 2Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets.

Published
2022
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23. Quantitative trait loci linked to PRNP gene controlling health and production traits in INRA 401 sheep

Author

Brunel Jean-Claude, Barillet Francis, Allain Daniel, Bouix Jacques, François Dominique, Roig Anne, Schibler Laurent, Lantier Isabelle, Lantier Frederic, Moreno Carole R, Vitezica Zulma G, and Elsen Jean-Michel

Subjects
association analysis, production trait, health trait, gene PRNP, sheep, Animal culture, SF1-1100, Genetics, QH426-470
Abstract

Abstract In this study, the potential association of PrP genotypes with health and productive traits was investigated. Data were recorded on animals of the INRA 401 breed from the Bourges-La Sapinière INRA experimental farm. The population consisted of 30 rams and 852 ewes, which produced 1310 lambs. The animals were categorized into three PrP genotype classes: ARR homozygous, ARR heterozygous, and animals without any ARR allele. Two analyses differing in the approach considered were carried out. Firstly, the potential association of the PrP genotype with disease (Salmonella resistance) and production (wool and carcass) traits was studied. The data used included 1042, 1043 and 1013 genotyped animals for the Salmonella resistance, wool and carcass traits, respectively. The different traits were analyzed using an animal model, where the PrP genotype effect was included as a fixed effect. Association analyses do not indicate any evidence of an effect of PrP genotypes on traits studied in this breed. Secondly, a quantitative trait loci (QTL) detection approach using the PRNP gene as a marker was applied on ovine chromosome 13. Interval mapping was used. Evidence for one QTL affecting mean fiber diameter was found at 25 cM from the PRNP gene. However, a linkage between PRNP and this QTL does not imply unfavorable linkage disequilibrium for PRNP selection purposes.

Published
2007
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25. Using genotype probabilities in survival analysis: a scrapie case

Author

Díaz Clara, Rupp Rachel, Elsen Jean-Michel, and Vitezica Zulma G

Subjects
genotype probabilities, survival analysis, PrP genotypes, scrapie, Animal culture, SF1-1100, Genetics, QH426-470
Abstract

Abstract The objective was to evaluate the potential use of genotype probabilities to handle records of non-genotyped animals in the context of survival analysis. To do so, the risks associated with the PrP genotype and other transmission factors in relation to clinical scrapie were estimated. Data from 4049 Romanov sheep affected by natural scrapie were analyzed using survival analysis techniques. The original data set included 1310 animals with missing genotypes; five of those had uncensored records. Different missing genotype-information patterns were simulated for uncensored and censored records. Three strategies differing in the way genotype information was handled were tested. Firstly, records with unknown genotypes were discarded (P1); secondly, those records were grouped in an unknown class (P2). Finally the probabilities of genotypes were assigned (P3). Whatever the strategy, the ranking of relative risks for the most susceptible genotypes (VRQ-VRQ, ARQ-VRQ and ARQ-ARQ) was similar even when the non-genotyped animals were not a negligible part of uncensored records. However, P3 had a more efficient way of handling missing genotype information. As compared to P1, either P2 or P3 avoided discarding the records of non-genotyped animals; however, P3 eliminated the unknown class and the risk associated with this group. Genotype probabilities were shown to be a useful technique to handle records of individuals with unknown genotype.

Published
2005
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29. The correlation of substitution effects across populations and generations in the presence of nonadditive functional gene action

Author

Legarra, Andres, Garcia-Baccino, Carolina A., Wientjes, Yvonne C.J., Vitezica, Zulma G., Legarra, Andres, Garcia-Baccino, Carolina A., Wientjes, Yvonne C.J., and Vitezica, Zulma G.

Abstract

Allele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In the presence of nonadditive functional gene action, substitution effects are not constant across populations. We develop an original approach to model the difference in substitution effects across populations as a first order Taylor series expansion from a “focal” population. This expansion involves the difference in allele frequencies and second-order statistical effects (additive by additive and dominance). The change in allele frequencies is a function of relationships (or genetic distances) across populations. As a result, it is possible to estimate the correlation of substitution effects across two populations using three elements: magnitudes of additive, dominance, and additive by additive variances; relationships (Nei’s minimum distances or Fst indexes); and assumed heterozygosities. Similarly, the theory applies as well to distinct generations in a population, in which case the distance across generations is a function of increase of inbreeding. Simulation results confirmed our derivations. Slight biases were observed, depending on the nonadditive mechanism and the reference allele. Our derivations are useful to understand and forecast the possibility of prediction across populations and the similarity of GWAS effects.

Published
2021

32. Genotype-by-environment interaction with highdimensional environmental data: an example in pigs.

Author

Bussiman, Fernando, Lourenco, Daniela, Ching-Yi Chen, Holl, Justin W., Misztal, Ignacy, and Vitezica, Zulma G.

Subjects
GENOTYPE-environment interaction, ENVIRONMENTAL auditing, GENETIC correlations, HUMIDITY, RAINFALL, DEW
Abstract

This study investigated using environmental covariates to model correlated herd effects and genotype-by-environment interaction (GE), as well as their impact on the prediction accuracy of genomic evaluations in pigs. We used air, dew/frost, wet-bulb, and earth-skin temperatures, relative humidity, rainfall, wind speed and direction. Average daily gain (ADG) and backfat thickness (BFT) from a terminal pig line were analyzed by 6 different models: MG, which included herd as an uncorrelated random effect; ME30, which considered 30 d of daily weather information to correlate herd effects; and ME100, which used 100 d of daily climate covariates; MGE30, same as ME30 but also considering the GE as the Hadamard product of the genomic relationship matrix and the environmental covariates matrix; MGE100, same as ME100 but including the GE; and MTM, a traditional multiple-trait model with each herd as a different trait. All animals were genotyped and phenotyped; therefore, the statistical model of choice was GBLUP. Validation was based on LR method, and focal animals were those born in the last year. Accuracy, bias, and dispersion did not change among models, except for MTM. Comparing all models against MTM, the accuracy increased by 38% for BFT, whereas only slightly increased for ADG. Among all 11 herds, only 4 showed a genetic correlation stronger than 0.80 for BFT, while this number was much greater for ADG. When the model has a variable accounting for environmental effects like herd in the case of MG, using weather information to correlate environments or adding the GE term has little benefit for genomic predictions. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Association analysis of loci implied in “buffering” epistasis

Author

Legarra, Andrés, primary, Vitezica, Zulma G., additional, Naval-Sánchez, Marina, additional, Henshall, John, additional, Raidan, Fernanda, additional, Li, Yutao, additional, Meyer, Karin, additional, Hudson, Nicholas J., additional, Porto-Neto, Laercio R., additional, and Reverter, Antonio, additional

Published
2019
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39. Evaluation of nonadditive effects in yearling weight of tropical beef cattle

Author

Raidan, Fernanda S S, Porto-Neto, Laercio R, Li, Yutao, Lehnert, Sigrid A, Vitezica, Zulma G, Reverter, Antonio, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and INRA SELGEN metaprogram (project EpiSel) / INRA-CSIRO linkage action

Subjects
Analysis of Variance, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], Genotype, dominance effect, Body Weight, Animal Genetics and Genomics, Australia, breeding value, Epistasis, Genetic, Breeding, Polymorphism, Single Nucleotide, genetic evaluation, Phenotype, Animals, epistatic effect, Cattle, Genes, Dominant
Abstract

International audience; Nonadditive effects may contribute to genetic variation of complex traits. Their inclusion in genetic evaluation models may therefore improve breeding value estimates and lead to more accurate selection decisions. In this study, we evaluated a systematic series of models accounting for additive, dominance and first-order epistatic interaction (additive by additive, GxG; additive by dominance, GxD; and dominance by dominance, DxD) on body yearling weight (YWT) of 2,550 Tropical Composite (TC) and 2,111 Brahman (BB) cattle in Australia. For both breeds, similar estimates of additive and phenotypic variances and narrow and broad-sense heritability values were obtained across the evaluated models except when GxG effect was considered. In this case, additive variance was slightly lower than that obtained in the models which do not consider this effect. The estimated dominance and epistatic variances from additive and dominance effects (AD) and additive, dominance and epistatic effects models (ADE) were greater than that ADH and ADEH models (as described above plus heterozygosity as a covariate). However, all genetic parameter estimates were associated with a large standard deviation. Averaged across ADH and ADHE models, the magnitude of dominance variance compared to the phenotypic variance of YWT was 14% (BB) and 10% (TC). The magnitude of epistasis compared to the phenotypic variance for BB and TC was 17% and 29%, respectively for GxG; 0.7% and 0% for GxD; and 0% and 0% for DxD. The inclusion of nonadditive effects slightly improves the predictive accuracy of breeding values from 0.28 for A to 0.33 for ADHE GxG and from 0.18 for A to 0.23 ADE GxD in BB and TC cattle. Models that included heterozygosity (ADH and ADHE) must be used to estimate nonadditive genetic variance components. A 1 Mb sliding window analysis identified a region on BTA 14 explaining 10.08% and 1.21% of total genetic variance (additive + dominance) of YWT in BB and TC, respectively. Our results suggest that dominance, epistasis, and heterozygosity should be included in models for genetic parameters estimation. To identify the animals with the highest additive genetic value in selection decisions, only the additive effect should be used in evaluation models.

Published
2018
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46. Estimating dominance genetic variances for growth traits in American Angus males using genomic models.

Author

Garcia-Baccino, Carolina A., Lourenco, Daniela A. L., Miller, Stephen, Cantet, Rodolfo J. C., and Vitezica, Zulma G.

Abstract

Estimates of dominance variance for growth traits in beef cattle based on pedigree data vary considerably across studies, and the proportion of genetic variance explained by dominance deviations remains largely unknown. The potential benefits of including nonadditive genetic effects in the genomic model combined with the increasing availability of large genomic data sets have recently renewed the interest in including nonadditive genetic effects in genomic evaluation models. The availability of genomic information enables the computation of covariance matrices of dominant genomic relationships among animals, similar to matrices of additive genomic relationships, and in a more straightforward manner than the pedigree-based dominance relationship matrix. Data from 19,357 genotyped American Angus males were used to estimate additive and dominant variance components for 3 growth traits: birth weight, weaning weight, and postweaning gain, and to evaluate the benefit of including dominance effects in beef cattle genomic evaluations. Variance components were estimated using 2 models: the first one included only additive effects (MG) and the second one included both additive and dominance effects (MGD). The dominance deviation variance ranged from 3% to 8% of the additive variance for all 3 traits. Gibbs sampling and REML estimates showed good concordance. Goodness of fit of the models was assessed by a likelihood ratio test. For all traits, MG fitted the data as well as MGD as assessed either by the likelihood ratio test or by the Akaike information criterion. Predictive ability of both models was assessed by cross-validation and did not improve when including dominance effects in the model. There was little evidence of nonadditive genetic variation for growth traits in the American Angus male population as only a small proportion of genetic variation was explained by nonadditive effects. A genomic model including the dominance effect did not improve the model fit. Consequently, including nonadditive effects in the genomic evaluation model is not beneficial for growth traits in the American Angus male population. [ABSTRACT FROM AUTHOR]

Published
2020
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47. Influence of epistasis on response to genomic selection using complete sequence data

Author

Ministerio de Economía y Competitividad (España), Région Midi-Pyrénées, Generalitat de Catalunya, Forneris, Natalia, Vitezica, Zulma G., Legarra, A., Pérez-Enciso, Miguel, Ministerio de Economía y Competitividad (España), Région Midi-Pyrénées, Generalitat de Catalunya, Forneris, Natalia, Vitezica, Zulma G., Legarra, A., and Pérez-Enciso, Miguel

Abstract

[Background]: The effect of epistasis on response to selection is a highly debated topic. Here, we investigated the impact of epistasis on response to sequence-based selection via genomic best linear prediction (GBLUP) in a regime of strong non-symmetrical epistasis under divergent selection, using real Drosophila sequence data. We also explored the possible advantage of including epistasis in the evaluation model and/or of knowing the causal mutations., [Results]: Response to selection was almost exclusively due to changes in allele frequency at a few loci with a large effect. Response was highly asymmetric (about four phenotypic standard deviations higher for upward than downward selection) due to the highly skewed site frequency spectrum. Epistasis accentuated this asymmetry and affected response to selection by modulating the additive genetic variance, which was sustained for longer under upward selection whereas it eroded rapidly under downward selection. Response to selection was quite insensitive to the evaluation model, especially under an additive scenario. Nevertheless, including epistasis in the model when there was none eventually led to lower accuracies as selection proceeded. Accounting for epistasis in the model, if it existed, was beneficial but only in the medium term. There was not much gain in response if causal mutations were known, compared to using sequence data, which is likely due to strong linkage disequilibrium, high heritability and availability of phenotypes on candidates., [Conclusions]: Epistatic interactions affect the response to genomic selection by modulating the additive genetic variance used for selection. Epistasis releases additive variance that may increase response to selection compared to a pure additive genetic action. Furthermore, genomic evaluation models and, in particular, GBLUP are robust, i.e. adding complexity to the model did not modify substantially the response (for a given architecture).

Published
2017

49. Use of NIRS for the genetic study of the quality of duck fatty liver

Author

Bastianelli, Denis, Fernandez, Xavier, Davrieux, Fabrice, Vitezica, Zulma G., Robert-Granié, Christelle, and Marie-Etancelin, Christelle

Subjects
Spectroscopie infrarouge, Canard, U30 - Méthodes de recherche, Q04 - Composition des produits alimentaires, L10 - Génétique et amélioration des animaux, Foie, Corps gras
Published
2012

50. Genetic parameters of product quality and hepatic metabolism in fattened mule ducks

Author

Marie-Etancelin, Christelle, Basso, B., Davail, S., Gontier, K., Fernandez, Xavier, Vitezica, Zulma G., Bastianelli, Denis, Baéza, Elisabeth, Bernadet, Marie-Dominique, Guy, G., Brun, J.M., and Legarra, Andrés

Subjects
Hybride, L52 - Physiologie animale - Croissance et développement, Canard, Q04 - Composition des produits alimentaires, Génétique, Métabolisme des lipides, Paramètre génétique, Viande de canard, U10 - Informatique, mathématiques et statistiques, Abats, Canard de Barbarie, Composition chimique, L10 - Génétique et amélioration des animaux, Anas, Amélioration des animaux, Foie, Modèle mathématique
Abstract

Genetic parameters of traits related to hepatic lipid metabolism, carcass composition, and product quality of overfed mule ducks were estimated on both parental lines of this hybrid: the common duck line for the maternal side and the Muscovy line for the paternal side. The originality of the statistical model was to include simultaneously the additive genetic effect of the common ducks and that of the Muscovy ducks, revealing a greater genetic determinism in common than in Muscovy. Plasma metabolic indicators (glucose, triglyceride, and cholesterol contents) were heritable, in particular at the end of the overfeeding period, and heritabilities increased with the overfeeding stage. Carcass composition traits were highly heritable in the common line, with values ranging from 0.15 for liver weight, 0.21 for carcass weight, and 0.25 for abdominal fat weight to 0.32 for breast muscle weight. Heritabilities of technological outputs were greater for the fatty liver (0.19 and 0.08, respectively, on common and Muscovy sides for liver melting rate) than for the pectoralis major muscle (between 0.02 and 0.05 on both parental sides for cooking losses). Fortunately, the processing industry is mainly facing problems in liver quality, such as too high of a melting rate, than in meat quality. The meat quality appraisal criteria (such as texture and cooking losses), usually dependent on pH and the rate of decline of pH, were also very lowly heritable. This study demonstrated that genetic determinism of meat quality and ability of overfeeding is not similar in the common population and in the Muscovy population; traits related to fattening, muscle development, and BW have heritability values from 2 to 4 times greater on the common line than on the Muscovy line, which is relevant for considering different selection strategies.

Published
2011

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