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See detailSequence-based association analysis identifies coding and non-coding variants in HFM1, MLH3, MSH4, MSH5, RNF212 and RNF212B with large effects on male and female recombination rate in cattle
Kadri, Naveen Kumar ULg; Harland, Chad ULg; Faux, Pierre ULg et al

Poster (2016, June 15)

We herein study genetic recombination in three dairy cattle populations from France, New-Zealand and the Netherlands. We identify 2,395,177 crossover (CO) events in sperm cells transmitted by 2,940 sires ... [more ▼]

We herein study genetic recombination in three dairy cattle populations from France, New-Zealand and the Netherlands. We identify 2,395,177 crossover (CO) events in sperm cells transmitted by 2,940 sires to 94,516 offspring, and 579,996 CO events in oocytes transmitted by 11,461 cows to 25,332 offspring. When measured in identical family structures, the average number of CO in males (23.3) was found to be larger than in females (21.4). The heritability of global recombination rate (GRR) was estimated at 0.13 in males and 0.08 in females. The genetic correlation was equal to 0.66, indicating that shared variants are influencing GRR in both genders. Haplotype-based genome-wide association studies revealed seven genome-wide significant QTL. Variants identified by next-generating sequencing in 5 Mb windows encompassing the QTL peaks were imputed in order to perform a sequence-based association analysis. For four QTLs, we identified missense mutations in genes known to be involved in meiotic recombination among the most significantly associated variants. Most of the identified mutations had significant effects in both genders with three of them accounting each for approximately 10% of the genetic variance in males (the allelic substitution effect being approximately equal to one additional CO per genome). Thus, a large fraction of the genetic variance is associated with missense mutations in genes known to be involved in meiotic recombination. Our results are very different from reports of recombination in other species. For instance, in human, recombination rate is higher in females, distinct variants affect recombination rate in males and females, and the genetic correlation is close to 0, whereas in cattle, we observed a higher recombination rate in males controlled by shared variants effective in both sexes. [less ▲]

See detailHIGHER MALE THAN FEMALE RECOMBINATION RATE LARGELY CONTROLLED BY MISSENSE VARIANTS IN RNF212, MLH3, HFM1, MSH5 AND MSH4 IN CATTLE
Kadri, Naveen Kumar ULg; Harland, Chad ULg; Faux, Pierre ULg et al

Poster (2016, May 11)

We herein study genetic recombination in three dairy cattle populations from France, New-Zealand and the Netherlands. We identify 2,395,177 crossover (CO) events in sperm cells transmitted by 2,940 sires ... [more ▼]

We herein study genetic recombination in three dairy cattle populations from France, New-Zealand and the Netherlands. We identify 2,395,177 crossover (CO) events in sperm cells transmitted by 2,940 sires to 94,516 offspring, and 579,996 CO events in oocytes transmitted by 11,461 cows to 25,332 offspring. When measured in identical family structures, the average number of CO in males (23.3) was found to be larger than in females (21.4). The heritability of global recombination rate (GRR) was estimated at 0.13 in males and 0.08 in females. The genetic correlation was equal to 0.66, indicating that shared variants are influencing GRR in both genders. Haplotype-based genome-wide association studies revealed seven genome-wide significant QTL. Variants identified by next-generating sequencing in 5 Mb windows encompassing the QTL peaks were imputed in order to perform a sequence-based association analysis. For four QTLs, we identified missense mutations in genes known to be involved in meiotic recombination among the most significantly associated variants. The P259S variant identified in RNF212 had already been reported, whereas missense mutations in MLH3 (N408S), HFM1 (S1189L), MSH5 (R631Q), MSH4 (C342Y) and a second in RNF212 (A77T) are new. Surprisingly, variants previously identified in REC8 were not associated with a QTL detected on BTA10 whereas variants in RNF212B, a paralog of RNF212, showed much stronger association with the phenotype in this region. This suggests that RNF212B might be involved in the recombination process. Most of the identified mutations had significant effects in both genders with three of them accounting each for approximately 10% of the genetic variance in males (the allelic substitution effect being approximately equal to one additional CO per genome). Thus, a large fraction of the genetic variance is associated with missense mutations in genes known to be involved in meiotic recombination. Our results are very different from reports of recombination in other species. For instance, in human, recombination rate is higher in females, distinct variants affect recombination rate in males and females, and the genetic correlation is close to 0, whereas in cattle, we observed a higher recombination rate in males controlled by shared variants effective in both sexes. [less ▲]

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See detailCoding and noncoding variants in HFM1, MLH3, MSH4, MSH5, RNF212, and RNF212B affect recombination rate in cattle.
Kadri, Naveen Kumar ULg; Harland, Chad ULg; Faux, Pierre ULg et al

in Genome Research (2016)

We herein study genetic recombination in three cattle populations from France, New Zealand, and the Netherlands. We identify 2,395,177 crossover (CO) events in 94,516 male gametes, and 579,996 CO events ... [more ▼]

We herein study genetic recombination in three cattle populations from France, New Zealand, and the Netherlands. We identify 2,395,177 crossover (CO) events in 94,516 male gametes, and 579,996 CO events in 25,332 female gametes. The average number of COs was found to be larger in males (23.3) than in females (21.4). The heritability of global recombination rate (GRR) was estimated at 0.13 in males and 0.08 in females, with a genetic correlation of 0.66 indicating that shared variants are influencing GRR in both sexes. A genome-wide association study identified seven quantitative trait loci (QTL) for GRR. Fine-mapping following sequence-based imputation in 14,401 animals pinpointed likely causative coding (5) and noncoding (1) variants in genes known to be involved in meiotic recombination (HFM1, MSH4, RNF212, MLH3, MSH5) for 5/7 QTL, and noncoding variants (3) in RNF212B for 1/7 QTL. This suggests that this RNF212 paralog might also be involved in recombination. Most of the identified mutations had significant effects in both sexes, with three of them each accounting for approximately 10% of the genetic variance in males. [less ▲]

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See detailComputation and approximation of the inverse of relationship matrices between genotyped animals: Algorithms and Applications
Faux, Pierre ULg

Doctoral thesis (2014)

The recent developments in molecular biology have made available thousands of genetic markers, allowing livestock genotyping at a reasonable cost and the subsequent development of genomic prediction. The ... [more ▼]

The recent developments in molecular biology have made available thousands of genetic markers, allowing livestock genotyping at a reasonable cost and the subsequent development of genomic prediction. The single-step procedure, a unified approach of genomic prediction, requires inversion of two matrices gathering additive relationships between genotyped animals: the genomic relationship matrix (G) and a part of the additive relationship matrix (A22). The inverse of A22 may also be interesting for other applications. Matrix inverse can be constructed successively by, first, computing, for each animal, the vector containing contributions of other animals to its relationship and, secondly, adding the product of each vector of contributions by itself to a zeroed matrix. The objectives of this thesis were (1) to propose algorithms to compute or to approximate the vector of contributions and (2) to test the numerical efficiency of these algorithms (computing speed, memory use and, if needed, approximation accuracy). Computing contributions covered two points: (1) finding or approximating which contributions are different from zero, and (2) computing the value of contributions considered as non-zero. In the first approach, we considered that animals closely related have non-zero contributions and approximated their values by linear regression. This approach was extended in a recursive way. In the second approach, we empirically determined the set of non-zero contributions by a heuristic algorithm of pedigree exploration (only for the case of A22). Values were then computed either by linear regression, or using the already computed inverse. We also tested an approximation strategy: limiting the number of extracted generations of non-genotyped ancestors to reduce pedigree complexity. In a third approach, we followed the same heuristic algorithm as before but restricted the pedigree exploration to find out which animals have a non-zero contribution. Their values were approximated by linear regression. The presentation of the different approaches is followed by a general discussion in which the approaches are compared. It was found that the best compromise between speed, memory and approximation accuracy was achieved by the last approach for the case of A22. Use of this last approach simplified computations and therefore made predictions more feasible. However, for the case of G, no sufficient approximations could be reach in a reasonable time. Perspectives of other uses of algorithms developed and of future researches were drawn, as well as practical perspectives for animal breeding. [less ▲]

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See detailEfficient computation of genomically-enhanced inbreeding coefficients
Faux, Pierre ULg; Gengler, Nicolas ULg

Poster (2014, February 07)

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See detailA review of inversion techniques related to the use of relationship matrices in animal breeding
Faux, Pierre ULg; Gengler, Nicolas ULg

in Biotechnologie, Agronomie, Société et Environnement = Biotechnology, Agronomy, Society and Environment [=BASE] (2014), 18(3), 319-468

In animal breeding, prediction of genetic effects is usually obtained through the use of mixed models. For any of these genetic effects, mixed models require the inversion of the covariance matrix ... [more ▼]

In animal breeding, prediction of genetic effects is usually obtained through the use of mixed models. For any of these genetic effects, mixed models require the inversion of the covariance matrix associated to that effect, which is equal to the associated relationship matrix times the associated component of the genetic variance. Given the size of many genetic evaluation systems, computing the inverses of these relationship matrices is not trivial. In this review, we aim to cover computational techniques that ease inversion of relationship matrices used in animal breeding for prediction of the following different types of genetic effects: additive effect, gametic effect, effect due to presence of marked quantitative trait loci, dominance effect and different epistasis effects. Construction rules and inversion algorithms are detailed for each relationship matrix. In the final discussion, we draw up a common theoretical frame to most of the reviewed techniques. Two computational constraints come out of this theoretical frame: setting up the matrix of dependencies between levels of the effect and setting up some parts (diagonal or block-diagonal elements) of the relationship matrix to be inverted. [less ▲]

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See detailA method to approximate the inverse of a part of the additive relationship matrix
Faux, Pierre ULg; Gengler, Nicolas ULg

in Journal of Animal Breeding & Genetics (2014)

Single-step genomic predictions need the inverse of the part of the additive relationship matrix between genotyped animals (A22). Gains in computing time are feasible with an algorithm that sets up the ... [more ▼]

Single-step genomic predictions need the inverse of the part of the additive relationship matrix between genotyped animals (A22). Gains in computing time are feasible with an algorithm that sets up the sparsity pattern of A22inv (SP algorithm) using pedigree searches, when A22inv is close to sparse. The objective of this study is to present a modification of the SP algorithm (RSP algorithm) and to assess its use in approximating A22inv when the actual A22inv is dense. The RSP algorithm sets up a restricted sparsity pattern of A22inv by limiting the pedigree search to a maximum number of searched branches. We have tested its use on four different simulated genotyped populations, from 10 000 to 75 000 genotyped animals. Accuracy of approximation is tested by replacing the actual A22inv by its approximation in an equivalent mixed model including only genotyped animals. Results show that limiting the pedigree search to four branches is enough to provide accurate approximations of A22inv, which contain approximately 80% of zeros. Computing approximations is not expensive in time but may require a great amount of memory (at maximum, approximately 81 min and approximately 55 Gb of RAM for 75 000 genotyped animals using parallel processing on four threads). [less ▲]

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See detailEstimation of dominance variance for live body weight in a crossbred population of pigs
Dufrasne, Marie ULg; Faux, Pierre ULg; Piedboeuf, Maureen et al

in Journal of Animal Science (2014), 92

The objective of this study was to estimate the dominance variance for repeated live BW records in a crossbred population of pigs. Data were provided by the Walloon Pig Breeding Association and included ... [more ▼]

The objective of this study was to estimate the dominance variance for repeated live BW records in a crossbred population of pigs. Data were provided by the Walloon Pig Breeding Association and included 22,197 BW records of 2,999 crossbred Piétrain × Landrace K+ pigs from 50 to 210 d of age. The BW records were standardized and adjusted to 210 d of age for analysis. Three single-trait random regression animal models were used: Model 1 without parental subclass effect, Model 2 with parental subclasses considered unrelated, and Model 3 with the complete parental dominance relationship matrix. Each model included sex, contemporary group, and heterosis as fixed effects as well as additive genetic, permanent environment, and residual as random effects. Variance components and their SE were estimated using a Gibbs sampling algorithm. Heritability tended to increase with age: from 0.50 to 0.64 for Model 1, from 0.19 to 0.42 for Model 2, and from 0.31 to 0.53 for Model 3. Permanent environmental variance tended to decrease with age and accounted for 29 to 44% of total variance with Model 1, 29 to 37% of total variance with Model 2, and 34 to 51% of total variance with Model 3. Residual variance explained <10% of total variance for the 3 models. Dominance variance was computed as 4 times the estimated parental subclass variance. Dominance variance accounted for 22 to 40% of total variance for Model 2 and 5 to 11% of total variance for Model 3, with a decrease with age for both models. Results showed that dominance effects exist for growth traits in pigs and may be reasonably large. The use of the complete dominance relationship matrix may improve the estimation of additive genetic variances and breeding values. Moreover, a dominance effect could be especially useful in selection programs for individual matings through the use of specific combining ability to maximize growth potential of crossbred progeny. [less ▲]

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See detailEstimation of dominance variance for growth traits with sire-dam subclass effects in a crossbred population of pigs
Dufrasne, Marie ULg; Faux, Pierre ULg; Piedboeuf, Maureen et al

Poster (2014)

Nonadditive genetic effects may be not negligible but are often ignored in genetic evaluations. The most important nonadditive effect is probably dominance. Prediction of dominance effects should allow a ... [more ▼]

Nonadditive genetic effects may be not negligible but are often ignored in genetic evaluations. The most important nonadditive effect is probably dominance. Prediction of dominance effects should allow a more precise estimation of the total genetic merit, particularly in populations that use specialized sire and dam lines, and with large number of full-sibs, like pigs. Computation of the inverted dominance relationship matrix, D-1, is difficult with large datasets. But, D-1 can be replaced by the inverted sire-dam subclass relationship matrix F-1, which represents the average dominance effect of full-sibs. The aim of this study was to estimate dominance variance for longitudinal measurements of body weight (BW) in a crossbred population of pigs The dataset consisted of 20,120 BW measurements recorded between 50 and 210 d of age on 2,341 crossbred pigs (Piétrain X Landrace). A random regression model was used to estimate variance components. Fixed effects were sex and date of recording. Random effects were additive genetic, permanent environment, parental dominance and residual. Dominance variance represented 7 to 9% of the total variance and 11 to 30% of additive variance. Those results showed that dominance variance exists for growth traits in pigs and may be relatively large. The estimation of dominance effects may be useful for mate selection program to maximize genetic merit of progeny. [less ▲]

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See detailConstruction et approximation de l'inverse de sous-matrices de parenté
Faux, Pierre ULg

Speech/Talk (2013)

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See detailInversion of a part of the numerator relationship matrix using pedigree information
Faux, Pierre ULg; Gengler, Nicolas ULg

in Genetics, Selection, Evolution (2013), 45

Background. In recent theoretical developments, the information available (e.g. genotypes) divides the original population into two groups: animals with this information (selected animals) and animals ... [more ▼]

Background. In recent theoretical developments, the information available (e.g. genotypes) divides the original population into two groups: animals with this information (selected animals) and animals without this information (excluded animals). These developments require inversion of the part of the pedigree-based numerator relationship matrix that describes the genetic covariance between selected animals (A22). Our main objective was to propose and evaluate methodology that takes advantage of any potential sparsity in the inverse of A22 in order to reduce the computing time required for its inversion. This potential sparsity is brought out by searching the pedigree for dependencies between the selected animals. Jointly, we expected distant ancestors to provide relationship ties that increase the density of matrix A22 but that their effect on A22i might be minor. This hypothesis was also tested. Methods. The inverse of A22 can be computed from the inverse of the triangular factor (T-1 ) obtained by Cholesky root-free decomposition of A22 . We propose an algorithm that sets up the sparsity pattern of T-1 using pedigree information. This algorithm provides positions of the elements of T-1 worth to be computed (i.e. different from zero). A recursive computation of A22i is then achieved with or without information on the sparsity pattern and time required for each computation was recorded. For three numbers of selected animals (4000; 8000 and 12 000), A22 was computed using different pedigree extractions and the closeness of the resulting A22i to the inverse computed using the fully extracted pedigree was measured by an appropriate norm. Results. The use of prior information on the sparsity of T-1 decreased the computing time for inversion by a factor of 1.73 on average. Computational issues and practical uses of the different algorithms were discussed. Cases involving more than 12 000 selected animals were considered. Inclusion of 10 generations was determined to be sufficient when computing A22. Conclusions. Depending on the size and structure of the selected sub-population, gains in time to compute A22 are possible and these gains may increase as the number of selected animals increases. Given the sequential nature of most computational steps, the proposed algorithm can benefit from optimization and may be convenient for genomic evaluations. [less ▲]

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See detailEstimation of dominance variance with sire-dam subclass effects in a crossbred population of pigs
Dufrasne, Marie ULg; Faux, Pierre ULg; Piedboeuf, Maureen et al

Poster (2013, August 26)

Nonadditive genetic effects may be not negligible but are often ignored in genetic evaluations. The most important nonadditive effect is probably dominance. Prediction of dominance effects should allow a ... [more ▼]

Nonadditive genetic effects may be not negligible but are often ignored in genetic evaluations. The most important nonadditive effect is probably dominance. Prediction of dominance effects should allow a more precise estimation of the total genetic merit, particularly in populations that use specialized sire and dam lines, and with large number of full-sibs, like pigs. Computation of the inverted dominance relationship matrix, D-1, is difficult with large datasets. But, D-1 can be replaced by the inverted sire-dam subclass relationship matrix F-1, which represents the average dominance effect of full-sibs. The aim of this study was to estimate dominance variance for longitudinal measurements of body weight (BW) in a crossbred population of pigs, assuming unrelated sire-dam subclass effects. The edited dataset consisted of 20,120 BW measurements recorded between 50 and 210 d of age on 2,341 crossbred pigs from 89 Piétrain sires and 169 Landrace dams. A random regression model was used to estimate variance components. Fixed effects were sex and date of recording. Random effects were additive genetic, permanent environment, sire-dam subclass and residual. Random effects, except residual, were modeled with linear splines. Only full-sib contributions were considered by using uncorrelated sire-dam classes. Estimated heritability of BW increased with age from 0.40 to 0.60. Inversely, estimated dominance decreased with age, from 0.28 to 0.01. Ratio of dominance relative to additive variance was high at early age (58.3% at 50 d) and decreased with age (2.6% at 200 d). Those results showed that dominance effects might be important for early growth traits in pigs. However, this need to be confirmed and dominance relationships will be included in the next steps. [less ▲]

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See detailGenetic evaluation of calving ease for Walloon Holstein dairy cattle
Vanderick, Sylvie ULg; Troch, Thibault ULg; Gillon, Alain et al

Conference (2013, August 25)

Calving complications have an incidence on the economic profitability of dairy herds. In the Walloon Region of Belgium, calving ease data recording is being done on voluntary basis since 2000. This allows ... [more ▼]

Calving complications have an incidence on the economic profitability of dairy herds. In the Walloon Region of Belgium, calving ease data recording is being done on voluntary basis since 2000. This allows now the implementation of a genetic evaluation of Holstein dairy cattle addressing the need of dairy breeders to select bulls in order to reduce frequency of calving problems. Calving ease scores were analyzed using univariate animal linear models, which were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Variance components and related genetic parameters were estimated from a dataset including 33,155 calving records. Included in the models were fixed season effects, fixed herd effects and fixed sex of calf*age of dam classes*group of calvings interaction effects, random herd*year of calving effects, random maternal permanent environment effects, and random animal direct and maternal additive genetic effects. For both models, direct and maternal heritabilities for calving ease were about 8% and about 2%, respectively. Genetic correlation between direct and maternal additive effects was found to be non-significantly different from zero. So, an animal linear model with genetic correlation between direct and maternal effects constrained to zero was adopted for the routine genetic evaluation of calving ease for Walloon Holstein dairy cattle. This model was validated by Interbull in January 2013 and, since April 2013, the Walloon Region of Belgium has officially participated to the international MACE evaluation for calving traits. [less ▲]

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See detailGenetic evaluation of calving ease for Walloon Holstein dairy cattle.
Vanderick, Sylvie ULg; Troch, Thibault ULg; Gillon, Alain et al

in Interbull Bulletin (2013), 47

Calving complications have an incidence on the economic profitability of dairy herds. In the Walloon Region of Belgium, calving ease data recording is being done on voluntary basis since 2000. This allows ... [more ▼]

Calving complications have an incidence on the economic profitability of dairy herds. In the Walloon Region of Belgium, calving ease data recording is being done on voluntary basis since 2000. This allows now the implementation of a genetic evaluation of Holstein dairy cattle addressing the need of dairy breeders to select bulls in order to reduce frequency of calving problems. Calving ease scores were analyzed using univariate animal linear models, which were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Variance components and related genetic parameters were estimated from a dataset including 33,155 calving records. Included in the models were fixed season effects, fixed herd effects and fixed sex of calf*age of dam classes*group of calvings interaction effects, random herd*year of calving effects, random maternal permanent environment effects, and random animal direct and maternal additive genetic effects. For both models, direct and maternal heritabilities for calving ease were about 8% and about 2%, respectively. Genetic correlation between direct and maternal additive effects was found to be non-significantly different from zero. So, an animal linear model with genetic correlation between direct and maternal effects constrained to zero was adopted for the routine genetic evaluation of calving ease for Walloon Holstein dairy cattle. This model was validated by Interbull in January 2013 and, since April 2013, the Walloon Region of Belgium has officially participated to the international MACE evaluation for calving traits. [less ▲]

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See detailDevelopment of a genomic evaluation for milk production for a local bovine breed
Colinet, Frédéric ULg; Vandenplas, Jérémie ULg; Faux, Pierre ULg et al

in Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science (2013, August)

Detailed reference viewed: 27 (11 ULg)