References of "Misztal, Ignacy"
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See detailGenetic analysis of pig survival up to commercial weight in a crossbred population
Dufrasne, Marie ULg; Misztal, Ignacy; Tsuruta, Shogo et al

in Livestock Science (2014), 167

Records from 99,384 crossbred pigs from Duroc sires and Large White x Landrace dams were used to estimate genetic parameters for survival traits at different stages of the fattening period, and their ... [more ▼]

Records from 99,384 crossbred pigs from Duroc sires and Large White x Landrace dams were used to estimate genetic parameters for survival traits at different stages of the fattening period, and their relations with final weight. Traits analyzed were preweaning mortality (PWM), culling between weaning and harvesting (Call), culling during the farrowing period (Cfar), in the nursery site (Cnur), during the finishing phase (Cfin), and hot carcass weight (HCW). Because of the binary nature of PWM and culling traits, threshold-linear models were used: Model 1, including PWM, Call, and HCW; Model 2, including PWM, Cfar, Cnur, Cfin, and HCW. Both models included sex and parity number as fixed effects for all traits. Contemporary groups were considered as fixed effect for HCW and as random effects for the binary traits. Random effects were sire additive genetic, common litter, and residual effects for all traits and models. Heritability estimates were 0.03 for PWM, and 0.15 for HCW with both models, 0.06 for Call with Model 1, and 0.06 for Cfar, 0.14 for Cnur, and 0.10 for Cfin with Model 2. Litter variance explained a large part of the total variance and its influence declined slightly with age. For Model 1, genetic correlations were -0.36 between PWM and Call, -0.02 between PWM and HCW, and -0.25 between Call and HCW; correlations for litter effect were -0.15 between PWM and Call, -0.19 between PWM and HCW, and -0.21 between Call and HCW. For Model 2, genetic correlations were all positive between PWM and culling traits, except between PWM and Cnur (-0.61). Genetic correlations between HCW and the other traits were moderate and negative to null. Correlations for common litter effect were all negative between traits, except between Cfar and Cfin, and between Cnur and Cfin. Heritability of PWM and culling traits increased with age period. Therefore, selection for survival after weaning may be more efficient. The low genetic correlations between PWM and culling traits suggest that different genes influence pre- and postweaning mortality. The HCW was not correlated with the other traits. However, relationships are not strongly unfavorable, therefore selection for survival and high final weight is possible. [less ▲]

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See detailGenetic analysis of pig survival in a crossbred population
Dufrasne, Marie ULg; Misztal, Ignacy; Tsuruta, Shogo et al

Conference (2013, July 09)

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See detailEstimation of genetic parameters for birth weight, preweaning mortality, and hot carcass weight of crossbred pigs
Dufrasne, Marie ULg; Misztal, Ignacy; Tsuruta, Shogo et al

in Journal of Animal Science (2013), 91

Genetic parameters for birth weight (BWT), preweaning mortality (PWM), and hot carcass weight (HCW) were estimated for a crossbred pig population to determine if BWT could be used as an early predictor ... [more ▼]

Genetic parameters for birth weight (BWT), preweaning mortality (PWM), and hot carcass weight (HCW) were estimated for a crossbred pig population to determine if BWT could be used as an early predictor for later performances. Sire genetic effects for those traits were estimated to determine if early selection of purebred sires used in crossbreeding could be improved. Data were recorded from one commercial farm between 2008 and 2010. Data were from 24,376 crossbred pigs from Duroc sires and crossbred Large White × Landrace dams and included 24,376 BWT and PWM records, and 13,029 HCW records. For the analysis, PWM was considered as a binary trait (0 for live or 1 for dead piglet at weaning). A multi-trait threshold-linear animal model was used, with animal effect divided into sire genetic and dam effects; the dam effects included both genetic and environmental variation due to the absence of pedigree information for crossbred dams. Fixed effects were sex and parity for all traits, contemporary groups for BWT and HCW, and age at slaughter as a linear covariable for HCW. Random effects were sire additive genetic, dam, litter, and residual effects for all traits, and contemporary group for PWM. Heritability estimates were 0.04 for BWT, 0.02 for PWM, and 0.12 for HCW. Ratio between sire genetic and total estimated variances was 0.01 for BWT and PWM, and 0.03 for HCW. Dam and litter variances explained respectively 14% and 15% of total variance for BWT, 2% and 10% for PWM, and 3% and 8% for HCW. Genetic correlations were −0.52 between BWT and PWM, 0.55 between BWT and HCW, and -0.13 between PWM and HCW. Selection of purebred sires for higher BWT of crossbreds may slightly improve survival until weaning and final market weight at the commercial level. [less ▲]

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See detailGenetic analysis of pig survival in a crossbred population
Dufrasne, Marie ULg; Misztal, Ignacy; Tsuruta, Shogo et al

in Journal of Animal Science (2013), 91(E-Suppl.2), 193

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See detailEstimation of genetic parameters for birth weight, pre-weaning mortality and hot carcass weight in a crossbred population of pigs
Dufrasne, Marie ULg; Misztal, Ignacy; Tsuruta, Shogo et al

Conference (2012, July 18)

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See detailBayesian integration of external information into the single step approach for genomically enhanced prediction of breeding values
Vandenplas, Jérémie ULg; Misztal, Ignacy; Faux, Pierre ULg et al

Conference (2012, July 17)

An assumption to compute unbiased estimated breeding values (EBV) is that all information, i.e. genomic, pedigree and phenotypic information, has to be considered simultaneously. However, current ... [more ▼]

An assumption to compute unbiased estimated breeding values (EBV) is that all information, i.e. genomic, pedigree and phenotypic information, has to be considered simultaneously. However, current developments of genomic selection will bias evaluations because only records related to selected animals will be available. The single step genomic evaluation (ssGBLUP) could reduce pre-selection bias by the combination of genomic, pedigree and phenotypic information which are internal for the ssGBLUP. But, in opposition to multi-step methods, external information, i.e. information from outside ssGBLUP, like EBV and associated reliabilities from Multiple Across Country Evaluation which represent a priori known phenotypic information, are not yet integrated into the ssGBLUP. To avoid multi-step methods, the aim of the study was to assess the potential of a Bayesian procedure to integrate a priori known external information into a ssGBLUP by considering simplifications of computational burden, a correct propagation of external information and no multiple considerations of contributions due to relationships. To test the procedure, 2 dairy cattle populations (referenced by “internal” and “external”) were simulated as well as milk production for the first lactation of each female in both populations. Internal females were randomly mated with internal and 50 external males. Genotypes of 3000 single-nucleotide polymorphisms for the 50 males were simulated. A ssGBLUP was applied as the internal evaluation. The external evaluation was based on phenotypic and pedigree external information. External information integrated into the ssGBLUP consisted to external EBV and associated reliabilities of the 50 males. Results showed that rank correlations among Bayesian EBV and EBV based on the joint use of external and internal data and genomic information were higher than 0.99 for the 50 males and internal animals. The respective correlations for the internal evaluation were equal to 0.50 and 0.90. Thereby, the Bayesian procedure can integrate external information into ssGBLUP. [less ▲]

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See detailBayesian integration of external information into the single step approach for genomically enhanced prediction of breeding values
Vandenplas, Jérémie ULg; Misztal, Ignacy; Faux, Pierre ULg et al

in Journal of Dairy Science (2012), 95(Supplement 2),

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See detailA recursive algorithm for decomposition and creation of the inverse of the genomic relationship matrix
Faux, Pierre ULg; Gengler, Nicolas ULg; Misztal, Ignacy

in Journal of Dairy Science (2012), 95(10), 6093-6102

As the number of genotyped animals increases, some genomic prediction models have issues related to inversion of the genomic relationship and related matrices. We developed a recursive algorithm to ... [more ▼]

As the number of genotyped animals increases, some genomic prediction models have issues related to inversion of the genomic relationship and related matrices. We developed a recursive algorithm to approximate the inverses of those matrices. The algorithm converges after a few rounds of recursion, but additional work is needed to reduce computing costs further. [less ▲]

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See detailEstimation of genetic parameters for birth weight, pre-weaning mortality and hot carcass weight in a crossbred population of pigs
Dufrasne, Marie ULg; Misztal, Ignacy; Tsuruta, Shogo et al

in Journal of Animal Science (2012), 90(E-Suppl.3), 721

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See detailA recursive method of approximation of the inverse of genomic relationships matrix
Faux, Pierre ULg; Gengler, Nicolas ULg; Misztal, Ignacy

Conference (2011, July 11)

Genomic evaluations by some procedures such as genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP) use the inverse of the genomic relationship matrix (G). The cost to create such an inverse is cubic and ... [more ▼]

Genomic evaluations by some procedures such as genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP) use the inverse of the genomic relationship matrix (G). The cost to create such an inverse is cubic and becomes prohibitively expensive after 30–100k genotypes. The purpose of this study was to develop methodologies, which eventually could compute a good approximation of G−1 at reduced cost. A recursive approximation of the inverse is based on a decomposition similar to that for the pedigree-based relationship: G−1 = (T−1)’D−1T−1, where T is a triangular and D a diagonal matrix. In the first step, animals are processed from the oldest to the youngest. For each animal, a subset of ancestors is selected with coefficients of genomic relationship to that animal greater than a threshold. A system of equations is created where the coefficients of G for the selected ancestors are in the left hand side and the coefficients of G for the given animal corresponding to the ancestors are the right hand side. The solution to that system of equation is stored in one line of T. Then, D is computed as diagonal elements of T−1G(T−1)’. If off-diagonals of D are too large, the approximation to G can be improved by repeated applications of G−1 = (T−1)’D−1T−1 and D = T−1G(T−1)’. After n rounds, the approximation of G inverse is a product of 2n triangular matrices and one diagonal matrix. This recursive method has been assessed on a sample of 1,718 genotyped dairy bulls. The correlation between GEBV using the complete or approximated G were 0.54 in the first round, 0.96 in the second, and 0.99 in the third. The cost of the proposed method depends on the population structure. It is likely to be high for closely related animals but lower for populations where few animals are strongly related. Additional research is needed to identify near-sparsity in T and D to eliminate unimportant operations. [less ▲]

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See detailA recursive method of approximation of the inverse of genomic relationship matrix
Faux, Pierre ULg; Misztal, Ignacy; Gengler, Nicolas ULg

Conference (2011, July 11)

Approximation of the inverse of the genomic relationship matrix is obtained by a similar algorithm to that of the pedigree-based relationship matrix. A recursive use of this algorithm returns a better ... [more ▼]

Approximation of the inverse of the genomic relationship matrix is obtained by a similar algorithm to that of the pedigree-based relationship matrix. A recursive use of this algorithm returns a better approximation of the inverse after each round of recursion, until it reaches the real inverse. [less ▲]

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See detailWorking on a Method to Compute Inverse of Genomic Relationship Matrix from Sparse Matrices
Faux, Pierre ULg; Misztal, Ignacy; Gengler, Nicolas ULg

Conference (2010, July 12)

Genomic relationships matrix (G) are dense matrices used in genomic prediction of dairy cattle. As the number of genotyped animals will increase, an approximation of the inverse of genomic relationships ... [more ▼]

Genomic relationships matrix (G) are dense matrices used in genomic prediction of dairy cattle. As the number of genotyped animals will increase, an approximation of the inverse of genomic relationships matrix is needed. We have developed a method based on a decomposition of G similar to decomposition of additive relationships matrix A. The method of inversion is tested on a set of simulated data. [less ▲]

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See detailPrinicpal components approach for estimating heritability of mid-infrared (MIR) spectrum in bovine milk
Soyeurt, Hélène ULg; Tsuruta, Shogo; Misztal, Ignacy et al

in Book of Abstracts of the 58th Annual Meeting of the European Association for Animal Production (2007)

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See detailVariation of lactoferrin content predicted by mid-infrared spectrometry (MIR)
Soyeurt, Hélène ULg; Colinet, Frédéric ULg; Arnould, Valérie ULg et al

in Book of Abstracts of the 58th Annual Meeting of the European Association for Animal Production (2007)

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See detailGenetic parameters of the major fatty acid (FA) contents in cow milk
Soyeurt, Hélène ULg; Gillon, Alain ULg; Vanderick, Sylvie ULg et al

in Book of Abstracts of the 58th Annual Meeting of the European Association for Animal Production (2007)

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See detailPrincipal components approach for estimating heritability of mid-infrared spectrum in bovine milk
Soyeurt, Hélène ULg; Tsuruta, Shogo; Misztal, Ignacy et al

in Journal of Dairy Science (2007)

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