Theoretical efficiency of multiple-trait quantitative trait loci-assisted selection

The effectiveness of five selection methods for genetic improvement of net merit comprising trait 1 of low heritability (h² = 0.1) and trait 2 of high heritability (h² = 0.4) was examined: (i) two‐trait quantitative trait loci (QTL)‐assisted selection; (ii) partial QTL‐assisted selection based on trait 1; (iii) partial QTL‐assisted selection based on trait 2; (iv) QTL‐only selection; and (v) conventional selection index without QTL information. These selection methods were compared under 72 scenarios with different combinations of the relative economic weights, the genetic correlations between traits, the ratio of QTL variance to total genetic variance of the trait, and the ratio of genetic variances between traits. The results suggest that the detection of QTL for multiple‐trait QTL‐assisted selection is more important when the index traits are negatively correlated than when they are positively correlated. In contrast to literature reports that single‐trait marker‐assisted selection (MAS) is the most efficient for low heritability traits, this study found that the identified QTL of the low heritability trait contributed negligibly to total response in net merit. This is because multiple‐trait QTL‐assisted selection is designed to maximize total net merit rather than the genetic response of the individual index trait as in the case of single‐trait MAS. Therefore, it is not economical to identify the QTL of the low heritability traits for the improvement of total net merit. The efficient, cost‐effective selection strategy is to identify the QTL of the moderate or high heritability traits of the QTL‐assisted selection index to facilitate total economic returns. Detection of the QTL of the low h² traits for the QTL‐assisted index selection is justified when the low h² traits have high negative genetic correlation with the other index traits and/or when both economic weights and genetic variances of the low h² traits are larger as compared to the other index traits of higher h². This study deals with theoretical efficiency of QTL‐assisted selection, but the same principle applies to SNP‐based genomic selection when the proportion of the genetic variance ‘explained by the identified QTLs’ in this study is replaced by ‘explained by SNPs’.     

Marker‐assisted selection reduces expected inbreeding but can result in large effects of hitchhiking

We used computer simulations to investigate to what extent true inbreeding, i.e. identity‐by‐descent, is affected by the use of marker‐assisted selection (MAS) relative to traditional best linear unbiased predictions (BLUP) selection. The effect was studied by varying the heritability (h² = 0.04 vs. 0.25), the marker distance (MAS vs. selection on the gene, GAS), the favourable QTL allele effect (α = 0.118 vs. 0.236) and the initial frequency of the favourable QTL allele (p = 0.01 vs. 0.1) in a population resembling the breeding nucleus of a dairy cattle population. The simulated genome consisted of two chromosomes of 100 cM each in addition to a polygenic component. On chromosome 1, a biallelic QTL as well as 4 markers were simulated in linkage disequilibrium. Chromosome 2 was selectively neutral. The results showed that, while reducing pedigree estimated inbreeding, MAS and GAS did not always reduce true inbreeding at the QTL relative to BLUP. MAS and GAS differs from BLUP by increasing the weight on Mendelian sampling terms and thereby lowering inbreeding, while increasing the fixation rate of the favourable QTL allele and thereby increasing inbreeding. The total outcome in terms of inbreeding at the QTL depends on the balance between these two effects. In addition, as a result of hitchhiking, MAS results in extra inbreeding in the region surrounding QTL, which could affect the overall genomic inbreeding.     

Impacts of genotyping strategies on long‐term genetic response in genomic selection

The present study investigated the effects of the choices of animals of reference populations on long‐term responses to genomic selection. Simulated populations comprised 300 individuals and 10 generations of selection practiced for a trait with heritability of 0.1, 0.3 or 0.5. Thirty individuals were randomly selected in the first five generations and selected by estimated breeding values from best linear unbiased prediction (BLUP) and genomic BLUP in the subsequent five generations. The reference populations comprise all animals for all generations (scenario 1), all animals for 6‐10 generations (scenario 2) and 2‐6 generations (scenario 3), and half of the animals for all generations (scenario 4). For all heritability levels, the genetic gains in generation 10 were similar in scenarios 1 and 2. Among scenarios 2 to 4, the highest genetic gains were obtained in scenario 2, with heritabilities of 0.1 and 0.3 as well as scenario 4 with heritability of 0.5. The inbreeding coefficients in scenarios 1, 2 and 4 were lower than those in BLUP, especially within cases with low heritability. These results indicate an appropriate choice of reference population can improve genetic gain and restrict inbreeding even when the reference population size is limited.     

Improving production efficiency in the presence of genotype by environment interactions in pig genomic selection breeding programmes

We simulated a genomic selection pig breeding schemes containing nucleus and production herds to improve feed efficiency of production pigs that were cross‐breed. Elite nucleus herds had access to high‐quality feed, and production herds were fed low‐quality feed. Feed efficiency in the nucleus herds had a heritability of 0.3 and 0.25 in the production herds. It was assumed the genetic relationships between feed efficiency in the nucleus and production were low (r_g = 0.2), medium (r_g = 0.5) and high (r_g = 0.8). In our alternative breeding schemes, different proportion of production animals were recorded for feed efficiency and genotyped with high‐density panel of genetic markers. Genomic breeding value of the selection candidates for feed efficiency was estimated based on three different approaches. In one approach, genomic breeding value was estimated including nucleus animals in the reference population. In the second approach, the reference population was containing a mixture of nucleus and production animals. In the third approach, the reference population was only consisting of production herds. Using a mixture reference population, we generated 40–115% more genetic gain in the production environment as compared to only using nucleus reference population that were fed high‐quality feed sources when the production animals were offspring of the nucleus animals. When the production animals were grand offspring of the nucleus animals, 43–104% more genetic gain was generated. Similarly, a higher genetic gain generated in the production environment when mixed reference population was used as compared to only using production animals. This was up to 19 and 14% when the production animals were offspring and grand offspring of nucleus animals, respectively. Therefore, in genomic selection pig breeding programmes, feed efficiency traits could be improved by properly designing the reference population.     

Evaluation criterion for response to selection with constraint

The aims of the present study are to represent the concept of restricted breeding values algebraically and to propose a criterion for evaluating the genetic responses achieved by using a restricted selection procedure. An additive genetic mixed model characterized by multiple traits with constraints was assumed. If the random errors approach zero and the fixed effects can be completely estimated correctly in the model, the restricted best linear unbiased predictor of breeding values ( u _R) is equal to , where G ₀, C ₀, and u are the additive genetic variance‐covariance matrix for the q traits, the matrix for restriction, and the vector of breeding values, respectively. Therefore, if we want to evaluate the response to restricted selection, such as by a stochastic computer simulation study with known breeding values, we can use u _R as only one criterion.     

Interplay between heritability,genetic correlation and economic weighting in a selection index with and without genomic information

The availability of genomic information demands proper evaluation on how the kind (phenotypic versus genomic) and the amount of information influences the interplay of heritability (h²), genetic correlation () and economic weighting of traits with regard to the standard deviation of the index (σ_I). As σ_I is directly proportional to response to selection, it was the chosen parameter for comparing the indices. Three selection indices incorporating conventional and genomic information for a two trait (i and j) breeding goal were compared. Information sources were chosen corresponding to pig breeding applications. Index I incorporating an own performance in trait j served as reference scenario. In index II, additional information in both traits was contributed by a varying number of full‐sibs (2, 7, 50). In index III, the conventional own performance in trait j was combined with genomic information for both traits. The number of animals in the reference population (N_P = 1000, 5000, 10 000) and thus the accuracy of GBVs were varied. With more information included in the index, σ_I became more independent of , and relative economic weighting. This applied for index II (more full‐sibs) and for index III (more accurate GBVs). Standard deviations of index II with seven full‐sibs and index III with N_P = 1000 were similar when both traits had the same heritability. If the heritability of trait j was reduced ( = 0.1), σ_I of index III with N_P = 1000 was clearly higher than for index II with seven full‐sibs. When enhancing the relative economic weight of trait j, the decrease in σ_I of the conventional full‐sib index was much stronger than for index III. Our results imply that N_P = 1000 can be considered a minimum size for a reference population in pig breeding. These conclusions also hold for comparing the accuracies of the indices.     

Estimates of phenotypic and genetic parameters for birth weight of Brown Swiss calves in Turkey using an animal model

A study was conducted to assess the influence of genetic and environmental factors on Brown Swiss calf birth weight, and to estimate variance components, genetic parameters, and breeding values. Data were collected on 1,761 Brown Swiss calves born from 1990 to 2005 in the Konuklar State Farm in Turkey. Mean birth weight for all calves was 39.3 ± 0.09 kg. Least squares mean birth weights for male and female Brown Swiss calves were 40.3 ± 0.02 and 39.0 ± 0.02 kg, respectively. Variance components, genetic parameters, and breeding values for birth weight in Brown Swiss calves were estimated by restricted error maximum likelihood (REML)–best linear unbiased prediction(BLUP) procedures using an MTDFREML (multiple trait derivative free restricted maximum likelihood) program employing an animal model. Direct heritability (h _d²), maternal heritability (h _m²), total heritability (h _T²), r _am and c _am estimates were 0.12, 0.09, 0.23, −0.58, and −0.06, respectively. The estimated maternal permanent environmental variance expressed as a proportion of the phenotypic variance (c ²) was 0.05. Breeding values were estimated for the trait and used to evaluate genetic trends across the time period investigated. The genetic trend linear regression was not different from zero. No genetic trend for birth weight was expected, since there had been no direct selection pressure on the trait. Absence of a trend confirms that there was no change due to selection pressure on correlated traits. Genetic and environmental parameter estimates were similar to literature values indicating that effective selection methods used in more developed improvement programs would be effective in Turkey as well.     

Transmission disequilibrium test for quantitative trait loci detection in livestock populations

The performance of several transmission disequilibrium tests (TDT) for detection of quantitative trait loci (QTL) in data structures typical of outbred livestock populations were investigated. Factorial mating designs were simulated with 10 sires mated to either 50 or 200 dams, each family having five or eight full sibs. A single marker and QTL, both bi‐allelic, were simulated using a disequilibrium coefficient based on complete initial disequilibrium and 50 generations of recombination [i.e. D = D₀(1 ? θ)⁵⁰], where θ is the recombination fraction between marker and QTL. The QTL explained either 10% (small QTL) or 30% (large QTL) of the genetic variance for a trait with heritability of 0.3. Methods were: TDT for QTL (Q‐TDT; both parents known), 1‐TDT (only one parent known) and sibling‐based TDT (S‐TDT; neither parent known, but sibs available). All were found to be effective tests for association and linkage between the QTL and a tightly linked marker (θ < 0.02) in these designs. For a large QTL, θ = 0.01, and five full sibs per family, the empirical power for Q‐TDT, 1‐TDT and S‐TDT was 0.966, 0.602 and 0.974, respectively, in a large population, versus 0.700, 0.414 and 0.654, respectively, in a small population. For a small QTL effect, θ = 0.01, large population the empirical power of these tests were 0.709, 0.287 and 0.634. The power of Q‐TDT, 1‐TDT and S‐TDT was satisfactory for large populations, for QTL with large effects and for five full sibs per family. The 1‐TDT based on a linear model was more powerful than the normal 1‐TDT. The empirical power for Q‐TDT and 1‐TDT with a linear model was 0.978 and 0.995 respectively. TDT based on analogous linear models, incorporating the polygenic covariance structure, provided only small increases in power compared with the usual TDT for QTL.     

Estimation of dominance genetic variances for reproductive traits and growth traits of calves in Japanese Black cattle

The dominance genetic effects for reproductive and calf growth abilities in the practical Japanese Black populations were examined using average information (AI) algorithm restricted maximum likelihood (REML) under animal models. The reproductive traits were observed in Japanese Black cattle maintained at Tottori and Okinawa prefectures, and growth traits of calves were observed in cattle at Okinawa. The average of dominance relationships in Tottori ranged from 0.2 to 0.4%, while the level in Okinawa was lower and sparse compared with Tottori. The proportions of the dominance variances to sum of additive and dominance variances () were all 0.02 for reproductive traits in Tottori. In contrast, the proportion was 0.02–0.64 in Okinawa regardless of the level of dominance relationships. These proportions suggested that the dominance might affect the expression of calving interval, days open and gestation length in Okinawa, where breeding units were spread over many islands. Although the dominance variances could not estimate birthweight, w as 0.34 for calf market weight and 0.27 for average daily gain from birth to calf market in Okinawa. These values also suggested that the dominance might affect the early growth of calves. In the near future, genetic relationships will become more complicated with continuation of the current selection and mating systems. Therefore, genetic evaluation accounting for dominance effects would be necessary for particular traits and populations.     

Genetic variation of in vivo muscle glycerol,glycogen, and pigment in Danish purebred pigs

Heritability values of glycerol, glycogen and pigment concentrations measured on muscle biopsy samples from longissimus dorsi obtained from 85?kg boars of Danish purebred pigs (Landrace, Yorkshire, and Duroc) and their genetic correlations to performance and meat quality traits were calculated. The heritability values of glycogen, pigment, and glycerol were 0.38±0.02, 0.17±0.02, and 0.065±0.016, respectively. Glycogen was negatively related to the feed conversion ratio (FCR) (r _g =?0.25±0.06) and ultimate meat pH (r _g =?0.41±0.04), and positively to the carcass meat percentage (r _g =0.35±0.04), L* (lightness of meat, r _g =0.32±0.04) and a* (redness of meat, r _g =0.12±0.03). Pigment was positively related to the FCR (r _g =0.12±0.05), the meat percentage (r _g =0.28±0.05), and a* (r _g =0.59±0.04), and negatively to the L* (r _g =?0.46±0.06). The concentrations of pigment and glycogen (r _g =0.27±0.05) were interrelated. Based on the heritability values and the signs of the genetic correlations the present data suggest that it is possible to select for higher ultimate pH and improved colour by selection for either lower muscle glycogen or higher pigment concentration, respectively. However, the positive genetic correlation between these two traits may restrict the efficiency of simultaneous selection for lower glycogen and higher pigment. All genetic parameter estimates presented are calculated across three purebreds. Values for each breed are warranted for use in pig production. However, this requires more animals per breed.     

Genomic best linear unbiased prediction method reflecting the degree of linkage disequilibrium

The degree of linkage disequilibrium (LD) between markers differs depending on the location of the genome; this difference biases genetic evaluation by genomic best linear unbiased prediction (GBLUP). To correct this bias, we used three GBLUP methods reflecting the degree of LD (GBLUP‐LD). In the three GBLUP‐LD methods, genomic relationship matrices were conducted from single nucleotide polymorphism markers weighted according to local LD levels. The predictive abilities of GBLUP‐LD were investigated by estimating variance components and assessing the accuracies of estimated breeding values using simulation data. When quantitative trait loci (QTL) were located at weak LD regions, the predictive abilities of the three GBLUP‐LD methods were superior to those of GBLUP and Bayesian lasso except when the number of QTL was small. In particular, the superiority of GBLUP‐LD increased with decreasing trait heritability. The rates of QTL at weak LD regions would increase when selection by GBLUP continues; this consequently decreases the predictive ability of GBLUP. Thus, the GBLUP‐LD could be applicable for populations selected by GBLUP for a long time. However, if QTL were located at strong LD regions, the accuracies of three GBLUP‐LD methods were lower than GBLUP and Bayesian lasso.     

Choice of statistical model for estimating genetic parameters using restricted maximum likelihood in swine

Summary Restricted maximum likelihood (REML) was used to determine the choice of statistical model, additive genetic maternal and common litter effects and consequences of ignoring these effects on estimates of variance–covariance components under random and phenotypic selection in swine using computer simulation. Two closed herds of different size and two traits, (i) pre‐weaning average daily gain and (ii) litter size at birth, were considered. Three levels of additive direct and maternal genetic correlations (r_dm) were assumed to each trait. Four mixed models (denoted as GRM1 through GRM4) were used to generate data sets. Model GRM1 included only additive direct genetic effects, GRM2 included only additive direct genetic and common litter effects, GRM3 included only additive direct and maternal genetic effects and GRM4 included all the random effects. Four mixed animal models (defined as EPM1 through EPM4) were defined for estimating genetic parameters similar to GRM. Data from each GRM were fitted with EPM1 through EPM4. The largest biased estimates of additive genetic variance were obtained when EPM1 was fitted to data generated assuming the presence of either additive maternal genetic, common litter effects or a combination thereof. The bias of estimated additive direct genetic variance (VA_d) increased and those of recidual variance (VE) decreased with an increase in level of r_dm when GRM3 was used. EPM1, EPM2 and EPM3 resulted in biased estimation of the direct genetic variances. EPM4 was the most accurate in each GRM. Phenotypic selection substantially increased bias of estimated additive direct genetic effect and its mean square error in trait 1, but decreased those in trait 2 when ignored in the statistical model. For trait 2, estimates under phenotypic selection were more biased than those under random selection. It was concluded that statistical models for estimating variance components should include all random effects considered to avoid bias.     

Implications of genetic selection on yolk proportion on the dry matter content of eggs in a White Leghorn population

1. The responses to genetic selection on yolk proportion as a technique for increasing egg dry matter content, an important criterion for the egg-product industry, was investigated in a pedigree flock of White Leghorn hens.

2. Parents were preselected on high and low yolk proportion from a base population. The absolute estimated breeding value for yolk proportion of both groups differed by 3%. The realised selection difference in dry matter content of eggs between groups was more than 1% in the analysed offspring population.

3. Heritability estimates were moderate and dry matter had a lower heritability (h² = 0.39) than yolk proportion (h² = 0.44).

4. The genetic correlation between yolk proportion and dry matter content was highly positive (r_g = 0.91). Genetic correlations with egg weight were negative and would have to be compensated for in a breeding programme (r_g = ?0.76 with yolk proportion and r_g = ?0.64 with dry matter content). The genetic correlation between the laying performance and yolk proportion was r_g = 0.28 and close to zero (r_g = ?0.05) for dry matter content.

5. Easy recording and lower undesirable correlations make yolk proportion more suitable for commercial selection compared with egg dry matter content in layer breeding.     

Selection for food conversion in broilers: Direct and correlated responses to selection for body‐weight gain,food consumption and food conversion ratio

1. Direct and correlated responses were determined after five generations in four lines of chickens selected either for increased body‐weight gain (line W), for increased food consumption (line F), for decreased food conversion ratio (line E), or at random (line C).

2. Realised heritability estimates calculated after five generations of selection were : 0–37 + 0.04 for weight gain (WG); 0.44 + 0.05 for food consumption (FC); 0.21 + 0.04 for food conversion ratio (FCR).

3. Realised genetic correlation estimates were: WG and FC, +0.71 ±006; WG and FCR, ‐0.40±0.09; FC and FCR, +0.27 + 0.09.

4. Zero‐generation heritability and genetic correlation estimates were greater than the realised estimates, and sex linkage appeared to be important in the expression of all three traits.

5. The genetic correlation between FC and FCR was asymmetrical with considerable positive response in FCR in line F (r_g = +0.79) but negligible response in FC in line E (r_g = —0.01).

6. There was an apparent plateau in response in FCR in line E from the third to the fifth selected generations.     

Is the use of formulae a reliable way to predict the accuracy of genomic selection?

We studied four formulae used to predict the accuracy of genomic selection prior to genotyping. The objectives of our study were to investigate the impact of the parameters of each formula on the values of accuracy calculated using these formulae, and to check whether the accuracies reported in the literature are in agreement with the formulae. First, we computed the marginal distribution of accuracy (by integration) for each parameter of all four formulae: heritability h², reference population size T, number of markers M and number of effective segments in the genome M_e. Then, we collected 145 accuracies and corresponding parameters reported in 13 publications on genomic selection (mainly in dairy cattle), and performed analysis of variance to test the differences between observed and predicted accuracy with effects of formulae and parameters. The variation of accuracy for different values of each parameter indicated that two parameters, T and M_e, had a significant impact and that considerable differences existed between the formulae (mean accuracies differed by up to 0.20 point). The results of our meta‐analysis showed a big formula effect on the accuracies predicted using each formula, and also a significant effect of the value obtained for M_e calculated from N_e (effective population size). Each formula can therefore be demonstrated to be optimal depending on the assumption used for M_e. In conclusion, no rules can be applied to predict the reliability of genomic selection using these formulae.     

Evaluating different PrP genotype selection strategies for expected severity of scrapie outbreaks and genetic progress in performance in commercial sheep

Stochastic computer simulations were used for quantifying the effect of selecting on prion protein (PrP) genotype on the risk of major outbreaks of classical scrapie and the rate of genetic progress in performance in commercial sheep populations already undergoing selection on performance. The risk of a major outbreak on a flock was measured by the basic reproduction ratio (R₀). The effectiveness of different PrP selection strategies for reducing the population risk was assessed by the percentage of flocks with R₀ < 1. When compared with the scenario where there was no selection on PrP genotype, selection against the VRQ allele had a minimal impact on genetic progress for performance traits. However, this strategy was not sufficient to eliminate the population risk after 15 years of selection when the initial frequency of the ARR allele was relatively low. More extreme PrP selection strategies aimed at increasing the frequency of the ARR allele and decreasing the frequency of the VRQ allele led to decreases in the rate of genetic progress for performance but reduced the population risk to very low values. The reduction in genetic progress was only large when the initial ARR frequency was low and, in general, the risk of major epidemics was very small when the frequency of this allele reached 0.7.     

Mixture model equations for marker-assisted genetic evaluation

Marker‐assisted genetic evaluation needs to infer genotypes at quantitative trait loci (QTL) based on the information of linked markers. As the inference usually provides the probability distribution of QTL genotypes rather than a specific genotype, marker‐assisted genetic evaluation is characterized by the mixture model because of the uncertainty of QTL genotypes. It is, therefore, necessary to develop a statistical procedure useful for mixture model analyses. In this study, a set of mixture model equations was derived based on the normal mixture model and the EM algorithm for evaluating linear models with uncertain independent variables. The derived equations can be seen as an extension of Henderson's mixed model equations to mixture models and provide a general framework to deal with the issues of uncertain incidence matrices in linear models. The mixture model equations were applied to marker‐assisted genetic evaluation with different parameterizations of QTL effects. A sire‐QTL‐effect model and a founder‐QTL‐effect model were used to illustrate the application of the mixture model equations. The potential advantages of the mixture model equations for marker‐assisted genetic evaluation were discussed. The mixed‐effect mixture model equations are flexible in modelling QTL effects and show desirable properties in estimating QTL effects, compared with Henderson's mixed model equations.     

Estimation of phenotypic and genetic parameters for weight gain and weight at fixed ages in the double-muscled Belgian Blue Beef breed using field records

In the double‐muscled Belgian Blue beef (DM‐BBB) breed, selection focuses on muscular conformation and not on weight gain and higher weight. There are very few studies on growth in the DM‐BBB using field records. Therefore, farms have no available useful figures on weight at fixed ages and weight gain for the DM‐BBB. This study describes and evaluates live weights of DM‐BBB animals. All the data were gathered on farms in Belgium. It was found that a male DM‐BBB weighs an average of 51 kg at birth, 98 kg at 3 months, 242 kg at 7 months, 430 kg at 13 months and 627 kg at 20 months. Between the age of 7 and 20 months, weight gain is more than 1200 g a day. Females weigh 47 kg at birth, 96 kg at 3 months, 189 kg at 7 months and 332 kg at 13 months. For males, estimates of heritability for weights at 7, 13 and 20 months were between 0.21 and 0.36. The heritability for weight gain between 13 and 20 months was 0.13. This demonstrates that it is possible to select for higher weights and for increased growth between 13 and 20 months. Animals having high weights at a young age (7 and 13 months) tend to have also high weight at slaughtering age (20 months; r_g between 0.81 and 0.98), but no additional growth between 13 and 20 months (r_g between −0.09 and 0.00). High weight at 20 months is partially due to growth between 13 and 20 months (r_g = 0.49).     

Allelic frequencies and association with carcass traits of six genes in local subpopulations of Japanese Black cattle

Marker‐assisted selection (MAS) is expected to accelerate the genetic improvement of Japanese Black cattle. However, verification of the effects of the genes for MAS in different subpopulations is required prior to the application of MAS. In this study, we investigated the allelic frequencies and genotypic effects for carcass traits of six genes, which can be used in MAS, in eight local subpopulations. These genes are SCD, FASN and SREBP1, which are associated with the fatty acid composition of meat, and NCAPG, MC1R and F11, which are associated with carcass weight, coat color and blood coagulation abnormality, respectively. The frequencies of desirable alleles of SCD and FASN were relatively high and that of NCAPG was relatively low, and NCAPG was significantly associated with several carcass traits, including carcass weight. The proportions of genotypic variance explained by NCAPG to phenotypic variance were 4.83 for carcass weight. We thus confirmed that NCAPG is a useful marker for selection of carcass traits in these subpopulations. In addition, we found that the desirable alleles of six genes showed no negative effects on carcass traits. Therefore, selection using these genes to improve target traits should not have negative impacts on carcass traits.     

Potential benefit from using the αs1-casein genotype information in a selection scheme for dairy goats

A stochastic approach is proposed to predict responses to selection when using α_s1‐casein genotype information in a selection scheme of a Spanish breed of dairy goats. Two independent selection objectives were considered: protein yield (PY), where the major additive gene CSN1S1, which codes for α_s1‐casein, has a small effect, and protein content (P%), where this gene has a large effect on performances. Significant differences in response between using and ignoring information on the major gene were observed only when the major gene has a large effect. The main result was in the case of P%, the total genetic gain obtained in the early generations of selection was maintained in the long‐term. Taking account of genotype information either in the evaluation model or in the selection criteria leads to a faster fixation of the favourable allele and a reduction of the total genetic variance over generations. The inbreeding rates varied across generations, the highest rates observed in later generations of selection and when the major gene has a large effect and its genotype was included in the genetic evaluation procedure. It is concluded that inclusion of the casein genotype as an additional selection criteria will improve gains for protein traits, in particular P%. Recommendations are also given in order to optimize the use of this molecular information in dairy goat selection programs.