Jérémie Vandenplas

Comparison and improvements of different Bayesian procedures to integrate external information into genetic evaluations

The aim of this research was to compare different Bayesian procedures to integrate information from outside a given evaluation system, hereafter called external information, and in this context estimated breeding values (EBV), into this genetic evaluation, hereafter called internal evaluation, and to improve the Bayesian procedures to assess their potential to combine information from diverse sources. The 2 improvements were based on approximations of prior mean and variance. The first version of modified Bayesian evaluation considers all animals as animals associated with external information. For animals that have no external information (i.e., internal animals), external information is predicted from available external information. Thereby, propagation of this external information through the whole pedigree is allowed. Furthermore, the prediction of external information for internal animals allows large simplifications of the computational burden during setup and solving of mixed model equations. However, double counting among external animals (i.e., animals associated with available external information) is not avoided. Double counting concerns multiple considerations of contributions due to relationships by integration of external EBV for related external animals and is taken into account by the second version of modified Bayesian evaluation. This version includes the estimation of double counting before integration of external information. To test the improvements, 2 dairy cattle populations were simulated across 5 generations. Milk production for the first lactation for each female was simulated in both populations. Internal females were randomly mated with internal males and 50 external males. Results for 100 replicates showed that rank correlations among Bayesian EBV and EBV based on the joint use of external and internal data were very close to 1 for both external and internal animals if all internal and external animals were associated with external information. The respective correlations for the internal evaluation were equal to 0.54 and 0.95 if no external information was integrated. If double counting was avoided, mean squared error, expressed as a percentage of the internal mean squared error, was close to zero for both external and internal animals. However, computational demands increased when double counting was avoided. Finally, the improved Bayesian procedures have the potential to be applied for integrating external EBV, or even genomic breeding values following some additional assumptions, into routine genetic evaluations to evaluate animals more reliably.

Genetic analysis of heat stress effects on yield traits, udder health, and fatty acids of Walloon Holstein cows

Genetic parameters that considered tolerance for heat stress were estimated for production, udder health, and milk composition traits. Data included 202,733 test-day records for milk, fat, and protein yields, fat and protein percentages, somatic cell score (SCS), 10 individual milk fatty acids (FA) predicted by mid-infrared spectrometry, and 7 FA groups. Data were from 34,468 first-lactation Holstein cows in 862 herds in the Walloon region of Belgium and were collected between 2007 and 2010. Test-day records were merged with daily temperature-humidity index (THI) values based on meteorological records from public weather stations. The maximum distance between each farm and its corresponding weather station was 21km. Linear reaction norm models were used to estimate the intercept and slope responses of 23 traits to increasing THI values. Most yield and FA traits had phenotypic and genetic declines as THI increased, whereas SCS, C18:0, C18:1 cis-9, and 4 FA groups (unsaturated FA, monounsaturated FA, polyunsaturated FA, and long-chain FA) increased with THI. Moreover, the latter traits had the largest slope-to-intercept genetic variance ratios, which indicate that they are more affected by heat stress at high THI levels. Estimates of genetic correlations within trait between cold and hot environments were generally high (>0.80). However, lower estimates (<=0.67) were found for SCS, fat yield, and C18:1 cis-9, indicating that animals with the highest genetic merit for those traits in cold environments do not necessarily have the highest genetic merit for the same traits in hot environments. Among all traits, C18:1 cis-9 was the most sensitive to heat stress. As this trait is known to reflect body reserve mobilization, using its variations under hot conditions could be a very affordable milk biomarker of heat stress for dairy cattle expressing the equilibrium between intake and mobilization under warm conditions.

Unified method to integrate and blend several, potentially related, sources of information for genetic evaluation.

BackgroundA condition to predict unbiased estimated breeding values by best linear unbiased prediction is to use simultaneously all available data. However, this condition is not often fully met. For example, in dairy cattle, internal (i.e. local) populations lead to evaluations based only on internal records while widely used foreign sires have been selected using internally unavailable external records. In such cases, internal genetic evaluations may be less accurate and biased. Because external records are unavailable, methods were developed to combine external information that summarizes these records, i.e. external estimated breeding values and associated reliabilities, with internal records to improve accuracy of internal genetic evaluations. Two issues of these methods concern double-counting of contributions due to relationships and due to records. These issues could be worse if external information came from several evaluations, at least partially based on the same records, and combined into a single internal evaluation. Based on a Bayesian approach, the aim of this research was to develop a unified method to integrate and blend simultaneously several sources of information into an internal genetic evaluation by avoiding double-counting of contributions due to relationships and due to records.ResultsThis research resulted in equations that integrate and blend simultaneously several sources of information and avoid double-counting of contributions due to relationships and due to records. The performance of the developed equations was evaluated using simulated and real datasets. The results showed that the developed equations integrated and blended several sources of information well into a genetic evaluation. The developed equations also avoided double-counting of contributions due to relationships and due to records. Furthermore, because all available external sources of information were correctly propagated, relatives of external animals benefited from the integrated information and, therefore, more reliable estimated breeding values were obtained.ConclusionsThe proposed unified method integrated and blended several sources of information well into a genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. The unified method can also be extended to other types of situations such as single-step genomic or multi-trait evaluations, combining information across different traits.

Validation of simultaneous deregression of cow and bull breeding values and derivation of appropriate weights

Training of genomic prediction in dairy cattle may use deregressed proofs (DRP) as phenotypes. In this case, DRP should be estimated breeding values (EBV) corrected for information of relatives included in the data used for genomic prediction, and adjusted for regression to the mean (i.e., their reliability). Deregression is especially important when combining animals with EBV with low reliability, as commonly the case for cows, and high reliability. The objective of this paper, therefore, was to compare the performance of different deregression procedures for data that include both cow and bull EBV, and to develop and test procedures to obtain the appropriate deregressed weights for the DRP. Considered DRP were EBV: without any adjustment, adjusted for information of parents and regression to the mean, or adjusted for information of all relatives and regression to the mean. Considered deregressed weights were weights of initial EBV: without any adjustment, adjusted for information of parents, or adjusted for information of all relatives. The procedures were compared using simulated data based on an existing pedigree with 1,532 bulls and 13,720 cows that were considered to be included in the data used for genomic prediction. For each cow, 1 to 5 records were simulated. For each bull, an additional 50 to 200 daughters with 1 record each were simulated to generate a source of data that was not used for genomic prediction. The simulated trait had either a heritability of 0.05 or 0.3. The validation involved 3 steps: (1) computation of initial EBV and weights, (2) deregression of those EBV and weights, (3) using deregressed EBV and weights to compute final EBV, (4) comparison of the initial and final EBV and weights. The methods developed to compute appropriate weights for the DRP were either very precise and computationally somewhat demanding for larger data sets, or were less precise but computationally trivial due their approximate nature. Adjusting DRP for all relatives, known as matrix deregression, yields by definition final EBV that are identical to the original EBV. Matrix deregression is therefore preferred over other approaches that only correct for information of parents or not performing any deregression at all. It is important to use appropriate weights for the DRP, properly corrected for information of relatives, especially when individual reliabilities of final EBV are computed based on the prediction error variance of the model.

Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra

The aim of this study was to estimate phenotypic and genetic correlations between methane production (Mp) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation. Calibration equations predicting daily Mp (g/d) and milk fatty acid contents (g/100 dL of milk) were applied on milk mid-infrared spectra related to Walloon milk recording. A total of 241,236 predictions of Mp and milk fatty acids were used. These data were collected between 5 and 305 d in milk in 33,555 first-parity Holstein cows from 626 herds. Pedigree data included 109,975 animals. Bivariate (i.e., Mp and a fatty acid trait) random regression test-day models were developed to estimate phenotypic and genetic parameters of Mp and milk fatty acids. Individual short-chain fatty acids (SCFA) and groups of saturated fatty acids, SCFA, and medium-chain fatty acids showed positive phenotypic and genetic correlations with Mp (from 0.10 to 0.16 and from 0.23 to 0.30 for phenotypic and genetic correlations, respectively), whereas individual long-chain fatty acids (LCFA), and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed null to positive phenotypic and genetic correlations with Mp (from -0.03 to 0.13 and from -0.02 to 0.32 for phenotypic and genetic correlations, respectively). However, these correlations changed throughout lactation. First, de novo individual and group fatty acids (i.e., C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, SCFA group) showed low phenotypic or genetic correlations (or both) in early lactation and higher at the end of lactation. In contrast, phenotypic and genetic correlations between Mp and C16:0, which could be de novo synthetized or derived from blood lipids, were more stable during lactation. This fatty acid is the most abundant fatty acid of the saturated fatty acid and medium-chain fatty acid groups of which correlations with Mp showed the same pattern across lactation. Phenotypic and genetic correlations between Mp and C17:0 and C18:0 were low in early lactation and increased afterward. Phenotypic and genetic correlations between Mp and C18:1 cis-9 originating from the blood lipids were negative in early lactation and increased afterward to become null from 18 wk until the end of lactation. Correlations between Mp and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed a similar or intermediate pattern across lactation compared with fatty acids that compose them. Finally, these results indicate that correlations between Mp and milk fatty acids vary following lactation stage of the cow, a fact still often ignored when trying to predict Mp from milk fatty acid profile.

Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × test-day had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01×10(-3) and 4.17×10(-3) for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × test-day effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also contributed substantially to micro-environmental sensitivity. Addition of random regressions to the mean model did not reduce heterogeneity in residual variance and that genetic heterogeneity of residual variance was not simply an effect of an incomplete mean model.

Empirical determination of breed-of-origin of alleles in three-breed cross pigs

BackgroundAlthough breeding programs for pigs and poultry aim at improving crossbred performance, they mainly use training populations that consist of purebred animals. For some traits, e.g. residual feed intake, the genetic correlation between purebred and crossbred performance is low and thus including crossbred animals in the training population is required. With crossbred animals, the effects of single nucleotide polymorphisms (SNPs) may be breed-specific because linkage disequilibrium patterns between a SNP and a quantitative trait locus (QTL), and allele frequencies and allele substitution effects of a QTL may differ between breeds. To estimate the breed-specific effects of alleles in a crossbred population, the breed-of-origin of alleles in crossbred animals must be known. This study was aimed at investigating the performance of an approach that assigns breed-of-origin of alleles in real data of three-breed cross pigs. Genotypic data were available for 14,187 purebred, 1354 F1, and 1723 three-breed cross pigs.ResultsOn average, 93.0xa0% of the alleles of three-breed cross pigs were assigned a breed-of-origin without using pedigree information and 94.6xa0% with using pedigree information. The assignment percentage could be improved by allowing a percentage (fr) of the copies of a haplotype to be observed in a purebred population different from the assigned breed-of-origin. Changing fr from 0 to 20xa0%, increased assignment of breed-of-origin by 0.6 and 0.7xa0% when pedigree information was and was not used, respectively, which indicates the benefit of setting fr to 20xa0%.ConclusionsBreed-of-origin of alleles of three-breed cross pigs can be derived empirically without the need for pedigree information, with 93.7xa0% of the alleles assigned a breed-of-origin. Pedigree information is useful to reduce computation time and can slightly increase the percentage of assignments. Knowledge on the breed-of-origin of alleles allows the use of models that implement breed-specific effects of SNP alleles in genomic prediction, with the aim of improving selection of purebred animals for crossbred offspring performance.

An integration of external information for foreign stallions into the Belgian genetic evaluation for jumping horses

The aim of this study was to test the integration of external information, i.e. foreign estimated breeding values (EBV) and the associated reliabilities (REL), for stallions into the Belgian genetic evaluation for jumping horses. The Belgian model is a bivariate repeatability Best Linear Unbiased Prediction animal model only based on Belgian performances, while Belgian breeders import horses from neighbouring countries. Hence, use of external information is needed as prior to achieve more accurate EBV. Pedigree and performance data contained 101382 horses and 712212 performances, respectively. After conversion to the Belgian trait, external information of 98 French and 67 Dutch stallions was integrated into the Belgian evaluation. Resulting Belgian rankings of the foreign stallions were more similar to foreign rankings according to the increase of the rank correlations of at least 12%. REL of their EBV were improved of at least 2% on average. External information was partially to totally equivalent to 4 years of contemporary horses performances or to all the stallions own performances. All these results showed the interest to integrate external information into the Belgian evaluation.

Multi-population Genomic Relationships for Estimating Current Genetic Variances Within and Genetic Correlations Between Populations

Relationships between individuals are important to estimate genetic variances within a population and covariances between populations. Here, Wientjes..... Different methods are available to calculate multi-population genomic relationship matrices. Since those matrices differ in base population, it is anticipated that the method used to calculate genomic relationships affects the estimate of genetic variances, covariances, and correlations. The aim of this article is to define the multi-population genomic relationship matrix to estimate current genetic variances within and genetic correlations between populations. The genomic relationship matrix containing two populations consists of four blocks, one block for population 1, one block for population 2, and two blocks for relationships between the populations. It is known, based on literature, that by using current allele frequencies to calculate genomic relationships within a population, current genetic variances are estimated. In this article, we theoretically derived the properties of the genomic relationship matrix to estimate genetic correlations between populations and validated it using simulations. When the scaling factor of across-population genomic relationships is equal to the product of the square roots of the scaling factors for within-population genomic relationships, the genetic correlation is estimated unbiasedly even though estimated genetic variances do not necessarily refer to the current population. When this property is not met, the correlation based on estimated variances should be multiplied by a correction factor based on the scaling factors. In this study, we present a genomic relationship matrix which directly estimates current genetic variances as well as genetic correlations between populations.

Assigning breed origin to alleles in crossbred animals

BackgroundFor some species, animal production systems are based on the use of crossbreeding to take advantage of the increased performance of crossbred compared to purebred animals. Effects of single nucleotide polymorphisms (SNPs) may differ between purebred and crossbred animals for several reasons: (1) differences in linkage disequilibrium between SNP alleles and a quantitative trait locus; (2) differences in genetic backgrounds (e.g., dominance and epistatic interactions); and (3) differences in environmental conditions, which result in genotype-by-environment interactions. Thus, SNP effects may be breed-specific, which has led to the development of genomic evaluations for crossbred performance that take such effects into account. However, to estimate breed-specific effects, it is necessary to know breed origin of alleles in crossbred animals. Therefore, our aim was to develop an approach for assigning breed origin to alleles of crossbred animals (termed BOA) without information on pedigree and to study its accuracy by considering various factors, including distance between breeds.ResultsThe BOA approach consists of: (1) phasing genotypes of purebred and crossbred animals; (2) assigning breed origin to phased haplotypes; and (3) assigning breed origin to alleles of crossbred animals based on a library of assigned haplotypes, the breed composition of crossbred animals, and their SNP genotypes. The accuracy of allele assignments was determined for simulated datasets that include crosses between closely-related, distantly-related and unrelated breeds. Across these scenarios, the percentage of alleles of a crossbred animal that were correctly assigned to their breed origin was greater than 90xa0%, and increased with increasing distance between breeds, while the percentage of incorrectly assigned alleles was always less than 2xa0%. For the remaining alleles, i.e. 0xa0toxa010xa0% of all alleles of a crossbred animal, breed origin could not be assigned.ConclusionsThe BOA approach accurately assigns breed origin to alleles of crossbred animals, even if their pedigree is not recorded.