Dissection of additive genetic variability for quantitative traits in chickens using SNP markers

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10–0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R² = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R² = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.     

Introgressing multiple QTL in breeding programmes of limited size.

The ability to enrich a breed with favourable alleles from multiple unlinked quantitative trait loci (QTL) of a donor breed through marker-assisted introgression (MAI) in a population of limited size was evaluated by considering the effects of the proportion selected, the size of the marker intervals, the number of introgressed QTL and the uncertainty of QTL position. Informative flanking markers were used to select progeny with the largest expected number of donor QTL alleles over five generations of backcrossing and five generations of intercrossing. In the backcrossing phase, with 5% selected and 20 cM marker intervals for three QTL, there were sufficient backcross progeny that were heterozygous for all markers, and QTL frequencies dropped below 0.5 only because of double recombinants. For higher fractions selected, longer marker intervals, and more QTL, frequency reductions from 0.5 were greater and increased with additional generations of backcrossing. However, even with 20% selected, three QTL, and marker intervals of 5 or 20 cM, mean QTL frequencies in generation 5 were 0.35 and 0.30, sufficient to allow subsequent selection of QTL in the intercrossing phase. After five generations of intercrossing, over 90% of individuals were homozygous for all QTL, and 85% when five QTL were introgressed. The higher the proportions selected, the longer the marker intervals, and larger numbers of introgressed QTL increased the number of intercrossing generations required to achieve fixation of QTL. Location of the QTL in the marked intervals did not affect QTL frequencies or the proportion of QTL lost at the end of the introgression programme. In conclusion, introgressing multiple QTL can be accomplished in a MAI programme of limited size without requiring that all individuals selected during the backcrossing phase to be carriers of favourable alleles at all QTL.     

Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

Background: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci(QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Methods: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect.A commonly used method for genome wide association studies is to fit a mixture model such as Bayes B that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a42 K SNP panel.Results: Using data from the first 7 generations, both Bayes B and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with Bayes B and less than 1 % with GBLUP. When predicting phenotypes in generation 8 Bayes B accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with Bayes B in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with Bayes B but only6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using Bayes B but had no predictive power with GBLUP.Conclusions: In conclusion, In the presence of large effect QTL, Bayes B did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.     

Linkage disequilibrium and selection response in two-stage marker-assisted selection of dairy cattle over several generations

A stochastic simulation was carried out to investigate the advantage of marker‐assisted selection (MAS) in comparison with traditional selection over several generations. The selection goal was a sex‐limited trait or a linear combination of traits with a polygenic component, two unlinked additive QTL and a non‐genetic component. The simulated QTL were moderate or large and the allele frequencies were varied. Two stages of selection among the male offspring were carried out. In the first stage marker information was used to select among full sibs (MAS) or one full sib was chosen at random. In the second stage young bulls were selected based on a progeny test. The response in total genetic gain was faster with MAS than with traditional selection and persisted over several generations. With a QTL of moderate size and initial allele frequencies of the favourable allele of 0.05 the response with MAS was 6% higher than with traditional selection in the sires selected after progeny test. MAS in a within‐family two‐stage selection scheme improved the genetic merit of selected bulls even when linkage disequilibrium between QTL and polygenes was initially increased.     

标记辅助导入中不同背景选择方法的比较

模拟比较了随机选择、标记值选择及BLUP选择3种背景选择方法在标记辅助导入(利用标记辅助将供体群中的一个有利QTL等位基因导入到受体群中)的选择效果。前景选择是借助与目标基因连锁的两侧标记对目标基因进行间接选择。研究结果表明,在背景选择中,利用标记值选择能使受体基因组很快得到恢复,2个世代的回交就能恢复90%以上,4个世代的回交就能完全恢复。利用BLUP选择虽然不能使受体基因组迅速全部恢复,但能使特定的背景性状得到最大的遗传进展。     

标记辅助选择在猪育种中应用效果的研究

本研究针对猪育种中重点考虑的窝产活仔猪数（NBA）、达100 kg体重日增重（ADG）、饲料利用率（FCR）、达100 kg体重的背膘厚（BF）、肌内脂肪含量（IMF） 5个性状,利用连锁平衡（linkage equilibrium,LE）、连锁不平衡（linkage disequilibrium,LD）标记和直接标记（direct marker,DR）3种类型的分子遗传标记,设计了3个规模不同的基础群,母猪数分别为100、200、300头,公猪数都为10头,基础群个体间无亲缘关系,育种群实施闭锁繁育。用Monte Carlo方法模拟了MAS的5个世代选择试验。育种值估计采用标准BLUP（Standard BLUP,SBLUP）模型（此育种值作为对照）、QBLUP模型（使用DR标记）、MBLUP模型（使用LD和LE标记）。结果表明,利用DR标记在各种情况下都比利用LD和LE标记获得的选择效率高;5个性状中,MAS对低遗传力、限性性状NBA的选择效率最高;当性状的QTL方差占遗传方差基本相同时,中等遗传力性状FCR的选择效率比高遗传力性状BF的更高;当性状的遗传力差异不大时,QTL方差占遗传方差比例大的性状FCR的选择效率比QTL方差占遗传方差比例小的性状ADG的更高。当利用QBLUP模型时,MAS对NBA的选择效率最高,ADG的选择效率最低。     

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.     

Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP

Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A . Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A- inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A- inverse and in the computation of reliability from PEVs.     

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.     

BC1群体单标记分析检测QTL的效率

[目的]为QTL定位中进一步估计QTL的位置和影响效应提供必要的依据。[方法]以BC1设计为资源群体,通过计算机模拟研究了不同群体规模、标记-QTL间图距、性状遗传力和QTL效应（QTL方差占加性遗传方差的比例）对单标记分析检测QTL效率的影响。[结果]表明：资源群体规模较大,标记与QTL的间距较小（或标记与QTL连锁紧密）,目标性状的遗传力较高,且QTL效应较大时,采用单标记分析方法检测QTL的检出率较高。当所检测的标记距离QTL较近时,获得相同的QTL检出率所需的资源群体规模更小。[结论]QTL效应对QTL检出率的影响会受性状遗传力的制约,如果性状的遗传力过低,即使QTL方差占遗传方差的比例很高,也很难获得理想的QTL检出率。     

Single and composite influence of growth-related candidate gene polymorphisms on additive genetic variation of birth weight in charolais beef cattle

The objective of the present experiment work was to evaluate the effect of the inclusion of genomic information on the additive genetic variance of birth weight (BW) of Charolais cattle in Mexico. Variance components and heritability were estimated using four linear models. The first model was the base model (BM) from which single and composite effects of selected single-nucleotide polymorphism (SNP) markers were evaluated (BM1, BM2, and a composite BM3). Genetic markers were included in a regression model and analyzed by stepwise regression against adjusted BW from a panel of growth-related traits candidate gene markers. After two regression rounds, two SNPs (R ²?>?0.02) were chosen to include into the animal models as fixed effects. Growth hormone receptor gene GHR 4.2 and GHR 6.1 SNPs were selected from a panel of 39 SNPs. GHR 4.2 had a negligible effect on BW, whilst GHR6.1, interestingly, explained ～9 % of genetic variance (p?=?0.0877) with an α_G>A?=?0.509. The inclusion of markers in M2 and M3 reduced 19 and 15 % of the additive genetic variance, respectively. Both adjusted significantly better the linear model (LRT?=?p?<?0.01). Results obtained suggest that the previous selection of markers in a candidate gene approach and subsequent inclusion of selected SNPs into animal model might provide a better fit, avoiding the overestimation of genetic variance components and breeding values for BW.     

Estimation of additive and non-additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information

Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and reproduction traits. In this study, components of genetic variance for additive and non-additive genetic effects were estimated for a variety of fertility and reproduction traits in Holstein cattle using pedigree and genomic relationship matrices. Four linear models were used: (a) an additive genetic model; (b) a model including both additive and epistatic (additive by additive) genetic effects; (c) a model including both additive and dominance effects; and (d) a full model including additive, epistatic and dominance genetic effects. Nine fertility and reproduction traits were analysed, and models were run separately for heifers (N = 5,825) and cows (N = 6,090). For some traits, a larger proportion of phenotypic variance was explained by non-additive genetic effects compared with additive effects, indicating that epistasis, dominance or a combination thereof is of great importance. Epistatic genetic effects contributed more to the total phenotypic variance than dominance genetic effects. Although these models varied considerably in the partitioning of the components of genetic variance, the models including a non-additive genetic effect did not show a clear advantage over the additive model based on the Akaike information criterion. The partitioning of variance components resulted in a re-ranking of cows based solely on the cows’ additive genetic effects between models, indicating that adjusting for non-additive genetic effects could affect selection decisions made in dairy cattle breeding programs. These results suggest that non-additive genetic effects play an important role in some fertility and reproduction traits in Holstein cattle.     

Linkage disequilibrium and effective population size in Gir cattle selected for yearling weight

Linkage disequilibrium (LD) plays an important role in genomic selection and mapping of quantitative trait loci (QTL). This study investigated the pattern of LD and effective population size (N_e) in Gir cattle selected for yearling weight. For this purpose, 173 animals with imputed genotypes (from 18 animals genotyped with the Illumina BovineHD BeadChip and 155 animals genotyped with the Bovine LDv4 panel) were analysed. The LD was evaluated at distances of 25–50 kb, 50–100 kb, 100–500 kb and 0.5–1 Mb. The N_e was estimated based on 5 past generations. The r² values (a measure of LD) were, respectively, .35, .29, .18 and .032 for the distances evaluated. The LD estimates decreased with increasing distance of SNP pairs and LD persisted up to a distance of 100 kb (r² = .29). The N_e was greater in generations 4 and 5 (24 and 30 animals, respectively) and declined drastically after the last generation (12 animals). The results showed high levels of LD and low N_e, which were probably due to the loss of genetic variability as a consequence of the structure of the Gir population studied.     

Genome‐wide association study for egg production and quality in layer chickens

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome‐wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13 000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. ( 2012 ). The Bayesian whole genome prediction model – BayesB (Meuwissen et al. 2001 ) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non‐zero effect with posterior probability > 0.9 for one or more traits.     

Genotype by environment interaction and model comparison for growth traits of Santa Ines sheep

The objectives of the present study were to compare alternative models for the genetic evaluation and assess the importance of genotype by environment interaction (G×E) in the estimation of genetic parameters and genetic evaluation of birth weight (BW), weight at 60 days of age (W60) and weight at 180 days of age (W180) of Santa Ines sheep. Data comprise 7622 BW, 4673 W60 and 2830 W180 records from animals born in 44 Brazilian herds. Four models were used for the analyses: animal model (AM) with homogeneous residual variance (1), or heterogeneous residual variance (2), hierarchical reaction norms model (HRNM) with homogeneous (1) or heterogeneous residual variance (2). The models that best fit the BW, W60 and W180 data were AM2, HRNM1 and HRNM2 respectively. Thus, models for genetic evaluation that consider heterogeneity of variances are recommended to evaluate growth traits of sheep. The correlation between intercept and slope of the HRNM was higher than 0.70 for all traits studied, indicating that animals with higher average breeding values responded better to improvement in environmental conditions, a fact characterizing the scale effect of G×E. Therefore, G×E is an important factor to be considered in the estimation of genetic parameters and genetic evaluation of growth traits of sheep.     

Genomic selection

Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used so that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. This approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and new methods to efficiently genotype large number of SNP. Simulation results and limited experimental results suggest that breeding values can be predicted with high accuracy using genetic markers alone but more validation is required especially in samples of the population different from that in which the effect of the markers was estimated. The ideal method to estimate the breeding value from genomic data is to calculate the conditional mean of the breeding value given the genotype of the animal at each QTL. This conditional mean can only be calculated by using a prior distribution of QTL effects so this should be part of the research carried out to implement genomic selection. In practice, this method of estimating breeding values is approximated by using the marker genotypes instead of the QTL genotypes but the ideal method is likely to be approached more closely as more sequence and SNP data is obtained. Implementation of genomic selection is likely to have major implications for genetic evaluation systems and for genetic improvement programmes generally and these are discussed.     

Evaluation of genetic architecture and mutation drift equilibrium of Marathwada buffalo population in Central India

In the present study, microsatellite data on 24 loci were generated and utilized to evaluate the genetic architecture and mutation drift equilibrium of Marathwada buffaloes, a Central Indian population maintained under low input system. Sufficient allelic diversity was observed with a total of 109 alleles across different loci. The genetic diversity analysis of Marathwada buffaloes displayed moderate level of within breed variability in terms of mean number of alleles per locus (4.48) and heterozygosity values (Ho = 0.532, He = 0.624). The studied Indian buffalo population showed considerable heterozygote deficiency (F_IS = 0.138) and deviation from HWE at many investigated loci. Three quantitative tests viz. sign test, standardized difference test and Wilcoxon sign rank test and a qualitative test for mode shift distortion of allelic frequencies were employed to evaluate mutation drift equilibrium under three different models of microsatellite evolution. The population was found to deviate significantly under IAM and TPM, while it was reverse under SMM. The qualitative test for mode shift supported the results under SMM indicating the absence of genetic bottleneck in the recent past in Marathwada buffaloes.     

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0–0.86) and the heritability of residual variance is low (median = 0.01; range 0–0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.     

Benefits from marker-assisted selection under an additive polygenic genetic model

This study investigated, through stochastic computer simulation, the extra gains expected from marker-assisted selection (MAS) in an infinitesimal model with linkage. The trait under selection was assumed to be controlled by 2,000 loci of additive small effect and evenly distributed in c chromosomes of one Morgan each (and c = 5, 10, 20, or 30). This approach differs from previous studies on the benefits of MAS that have considered mixed inheritance models. Marker information was used together with pedigree information to compute the relationship matrix used in BLUP genetic evaluations. The MAS schemes were compared with schemes where genetic evaluations were performed using standard BLUP (i.e., the relationship matrix is obtained using pedigree information only). When the number of markers was large enough (approximately one marker every 10 cM), there were increases in the accuracy of selection with MAS, and this led to extra gains compared with standard BLUP for all genome sizes considered. The benefit from MAS increased over generations. At the last generation of selection (Generation 10), the response from MAS was 11, 9, 7, and 5% greater than with standard BLUP for genomes with 5, 10, 20, and 30 chromosomes, respectively. Thus, although small, gains from MAS were nonetheless detectable for genome sizes typical of livestock populations.     

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.