- Research Article
- Open Access
Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs
- Zulma G. Vitezica^{1, 2}Email author,
- Luis Varona^{3, 4},
- Jean-Michel Elsen^{2},
- Ignacy Misztal^{5},
- William Herring^{6} and
- Andrès Legarra^{2}
Received: 28 July 2015
Accepted: 12 January 2016
Published: 29 January 2016
Abstract
Background
Most developments in quantitative genetics theory focus on the study of intra-breed/line concepts. With the availability of massive genomic information, it becomes necessary to revisit the theory for crossbred populations. We propose methods to construct genomic covariances with additive and non-additive (dominance) inheritance in the case of pure lines and crossbred populations.
Results
We describe substitution effects and dominant deviations across two pure parental populations and the crossbred population. Gene effects are assumed to be independent of the origin of alleles and allelic frequencies can differ between parental populations. Based on these assumptions, the theoretical variance components (additive and dominant) are obtained as a function of marker effects and allelic frequencies. The additive genetic variance in the crossbred population includes the biological additive and dominant effects of a gene and a covariance term. Dominance variance in the crossbred population is proportional to the product of the heterozygosity coefficients of both parental populations. A genomic BLUP (best linear unbiased prediction) equivalent model is presented. We illustrate this approach by using pig data (two pure lines and their cross, including 8265 phenotyped and genotyped sows). For the total number of piglets born, the dominance variance in the crossbred population represented about 13 % of the total genetic variance. Dominance variation is only marginally important for litter size in the crossbred population.
Conclusions
We present a coherent marker-based model that includes purebred and crossbred data and additive and dominant actions. Using this model, it is possible to estimate breeding values, dominant deviations and variance components in a dataset that comprises data on purebred and crossbred individuals. These methods can be exploited to plan assortative mating in pig, maize or other species, in order to generate superior crossbred individuals in terms of performance.
Keywords
Background
Crossbreeding schemes are widely used in animal and plant breeding for the purpose of exploiting the heterosis and breed complementarity that often occur in crosses [1]. The main goal of crossbreeding is to improve the performance of crossbred populations. Pure breed/line performance is an imperfect predictor of crossbred performance; there are two reasons that explain this incomplete correlation between purebred and crossbred populations. First, phenotypic measurements on purebred/line individuals are often recorded in only one environment (e.g., management) that differs from the environment in which the crossbred individuals are raised (genotype-by-environment interaction). Second, non-additive genetic effects, such as dominance and/or epistasis, which likely determine heterosis, may result in different breeding values between purebreds and crossbreds.
In the case of dominant inheritance, the theory of pedigree-based genetic evaluation and estimation of genetic parameters for crossbred populations was proposed by Lo et al. [2, 3]. In this model, each individual has two genetic values, one on the purebred scale and one on the crossbred scale. In the absence of inbreeding, it is necessary to estimate nine genetic variance components for an \(F_{1}\) cross between breeds/lines (A and B): additive variance for breed A, dominance variance for breed A, additive variance for breed B, dominance variance for breed B, additive variance for the \(F_{1}\) population due to the effects of the alleles inherited from breed A, additive variance for the \(F_{1}\) population due to the effects of the alleles inherited from breed B, the dominance variance for the \(F_{1}\) population, additive covariance between a parent from breed A and an \(F_{1}\) offspring, and the additive covariance between a parent from breed B and an \(F_{1}\) offspring. Although the relevance of the crossbred model has been shown [4, 5], its use in applied breeding programs is limited, because pedigree relationships between purebred and crossbred individuals are often not known, and large datasets on crosses are needed [6].
Genomic approaches offer tools that allow to perform much deeper analyses, and thus, to understand the effects and the mechanisms of the genes and their interactions that underlie complex traits and to explore new directions for their improvement [7]. In addition, genomic evaluation renews the interest in crossbred individuals because they can be used as training animals [8]. In the case of additive inheritance, a joint genomic evaluation of purebred and crossbred individuals was proposed [9]. Toosi et al. [10] and Zeng et al. [11] extended this approach in order to include dominance. All these studies focused on the selection of purebred individuals for crossbred performance. However, the formal definition of substitution effects and dominant deviations and the estimation of genetic variance components in two breeds/lines and the \(F_{1}\) population have not been revisited so far within the genomic framework. This is needed for correct genetic evaluation and for planning selection schemes. The additive variances due to the gametes from the pure lines that compose the \(F_{1}\) population are an indicator of how much can be gained by selection. Estimation of dominance variance for the \(F_{1}\) individuals can be considered as a predictor of the variability of specific combining ability, i.e. how relevant is assortative mating between purebred lines to maximize the phenotype at a trait of interest in the \(F_{1}\) population. As an example, a common procedure in maize breeding is to use “testers” to evaluate the performance of a pure line as a parent in a cross. If the level of dominance variance is high, the use of testers might severely bias selection towards those lines that combine adequately with a particular tester. In practice, the estimated variance components serve as a guide for choosing breeds/lines with good combining abilities (e.g., pigs, corn, etc.) in animal and plant breeding schemes.
The objective of this work was twofold. First, we decomposed variance components for an \(F_{1}\) population using a genomic model with additive and non-additive (dominance) inheritance. Second, we applied the approach to estimate variance components using pig data. To our knowledge, there is no published description of the theoretical variance components (additive and dominant) in terms of substitution effect across two pure populations and the crossbred population. The next section describes the theory on which the estimation of genotypic values is based using GBLUP.
Theory
An \(F_{1}\) population involves gametes from the parental populations 1 and 2. If dominance is present, and because allelic frequencies differ in each breed, the within-breed (additive) substitution effects are not equal to the substitution effects across the \(F_{1}\) population. Thus, purebred individuals have different breeding values depending on whether they are mated to individuals from the same or another breed/line. This situation is well known [3, 12, 13], and holds even if the genotype effects are constant across breeds or crossbred individuals.
\(\sigma_{{A_{1} }}^{2}\) \((\sigma_{{A_{2} }}^{2} )\) is the variance of the GCA of the alleles of individuals from population 1 crossed to individuals from population 2 (alleles of individuals from population 2 crossed with individuals from population 1) or it can also be considered as the additive variance of gametes inherited from population 1 (from population 2) in the \(F_{1}\) population as in Lo et al. [3].
The variance of the GCA (\(\sigma_{A}^{2}\)) is an important parameter to understand if selection of purebred individuals can increase crossbred performance [1]. If variance of the GCA explains a large part of the total genetic variance for the \(F_{1}\) population, it means that within-population selection will result in a large increase of the crossbred performance, without resorting to specific matings to create crossbreds with large dominance deviations.
The term \(ad\) appears in \(\sigma_{G}^{2}\) but is completely embedded in \(\sigma_{{A_{1} }}^{2}\) and \(\sigma_{{A_{2} }}^{2}\). This term differs from 0 if there is covariance between \(a\) and \(d\), i.e. if \(a\) and \(d\) are of the same magnitude and direction or if there is overdominance. This covariance between additive and dominant effects of genes implies the presence of inbreeding depression or heterosis. Different models have been proposed to take the dependency between additive and dominant effects into account [15].
If \(a\) and \(d\) effects are considered as random variables with a covariance of 0 between \(a\) and \(d\), variance components for the \(F_{1}\) population can be obtained from these expressions using markers in a GBLUP context as detailed in the next section.
Equivalent genomic model based on SNPs
The division by \(\left\{ {tr\left[ {{\mathbf{ZZ}}\varvec{'}} \right]} \right\}/n\) where n is the number of individuals scales the matrix to an average of the diagonal elements equal to 1. This covariance matrix is similar to the classical \({\mathbf{G}}\) matrix of genomic BLUP [19], but with a different variance component i.e. \(\sigma_{{A^{*} }}^{2}\), the variance component that is associated to the genotypic additive values (this is not a genetic variance per se since it cannot be interpreted as the variance of the population). Based on \(\sigma_{{A^{*} }}^{2}\), the SNP variance for the additive component can be obtained as \(\sigma_{a}^{2} = \frac{{\sigma_{{A^{*} }}^{2} }}{{\left\{ {tr\left[ {\varvec{ZZ'}} \right]} \right\}/n}}\).
Therefore, the genotypic model is an equivalent model, which is useful to go from variance components (\(\sigma_{{A^{*} }}^{2}\), \(\sigma_{{D^{*} }}^{2}\)), with no particular interpretations, to marker variances (\(\sigma_{a}^{2} , \sigma_{d}^{2}\)).
Therefore, this approach allows to estimate variance components for the \(F_{1}\) population under a genomic model with additive and non-additive (dominance) inheritance. The three variance components in Eqs. (5), (6) and (7) do have an interpretation in terms of variances of breeding values (or GCA) and of dominant deviations (or SCA).
The biological additive and dominant effects of SNPs may not be the same across the different populations, due to genotype by environment or genotype by genotype (i.e. epistasis) interactions.
A simple alternative is to model marker effects as correlated across populations [20], which implies correlated \({\mathbf{u}}^{ *}\) and \({\mathbf{v}}^{ *}\) [21, 22]. This generalizes the methods above.
Methods
In this section, we illustrate the partition of variance components (additive and dominant) across two pig lines 1 and 2 and the crossbred population. Data for this study were provided by Genus plc (Hendersonville, TN, USA). Animal Care and Use Committee approval was not obtained for this study because the data were obtained from an existing database.
Lines 1 and 2 were two unrelated lines, and population 12 consisted of both reciprocal crosses of animals from lines 1 and 2. Data on litter size (total number of piglets born per litter) were analyzed. The average litter size was equal to 12.68 ± 3.07, 13.15 ± 3.20 and 13.64 ± 3.16 for lines 1 and 2 and population 12, respectively. A total of 34,753 records were available for 8265 sows. Genotypes for all sows were generated using the Illumina PorcineSNP60 BeadChip (Illumina, San Diego, CA). After quality control, i.e. after excluding genotypes with a minor allele frequency lower than 0.05 and a SNP call rate less than 0.90 in the overall population, 40,634 SNPs remained and were used to build genomic relationship matrices. Animals with a call rate less than 0.90 were removed. Thus, the number of sows with genotypes was equal to 3509, 2706 and 2050 in lines 1 and 2 and population 12, respectively.
Phenotypes were collected for the genetic nucleus (pure lines) and commercial herds (crosses). Records were analyzed using a GBLUP (mixed) model. Fixed effects included parity order, farm, year and month of farrowing, and mating type (artificial insemination or natural service).
To estimate the variance components, lines 1 and 2 and population 12 were considered as three different traits with correlations between pure and cross lines [3]. This model is equivalent to a model where marker effects are correlated across populations [20–22] and assumes that additive and dominant effects of a gene (\(a_{1} , a_{2} , a_{12}\) and \(d_{1} , d_{2} , d_{12} )\) are not necessarily the same in the three populations. Quantitative trait loci (QTL) that segregate in different breeds are not necessarily identical. In addition, linkage disequilibrium between SNPs and QTL can differ between populations. Even with causal genes, the effects may differ, which was confirmed by experimental results. One example is the bovine myostatin gene (GDF8), i.e. both the Belgian Blue and South Devon breeds carry the same GDF8 mutation, but they have different conformation and double-muscling phenotypes [23]. Functional mutations in the GDF8 gene appear to be breed-specific [24]. Effects can be population-specific and the variation can be interpreted as a dependency of the gene effect on the environmental (GxE) and genetic (i.e. epistasis) backgrounds. Parental pure lines and the \(F_{1}\) population have only half of their genetic background in common.
SNP dominance variances \((\sigma_{{d_{1} }}^{2} , \sigma_{{d_{2} }}^{2} ,\sigma_{{d_{12} }}^{2} )\) are obtained similarly, e.g., as \(\sigma_{{d_{1} }}^{2} = \hat{\sigma }_{{D_{1}^{*} }}^{2} /\left( {\left\{ {tr\left[ {{\mathbf{WW^{\prime}}}} \right]} \right\}/n} \right)\). The covariance matrices for permanent environmental and residual effects are as follows: \( Var\left[ {\begin{array}{*{20}c} {{\mathbf{p}}_{1} } \\ {{\mathbf{p}}_{2} } \\ {{\mathbf{p}}_{12} } \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} {\sigma_{{p_{1} }}^{2} } & {\bf 0} & {\bf 0} \\ {\bf 0} & {\sigma_{{p_{2} }}^{2} } & {\bf 0} \\ {\bf 0} & {\bf 0} & {\sigma_{{p_{12} }}^{2} } \\ \end{array} } \right]\varvec{ } \otimes \;{\mathbf{I}}_{3} \), and \(Var\left[ {\begin{array}{*{20}c} {{\mathbf{e}}_{1} } \\ {{\mathbf{e}}_{2} } \\ {{\mathbf{e}}_{12} } \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} {\sigma_{{e_{1} }}^{2} } & {\bf 0} & {\bf 0} \\ {\bf 0} & {\sigma_{{e_{2} }}^{2} } & {\bf 0} \\ {\bf 0} & {\bf 0} & {\sigma_{{e_{12} }}^{2} } \\ \end{array} } \right]\varvec{ } \otimes \;{\mathbf{I}}_{3}\), respectively.
Variance components for the genomic model (GBLUP model) and for a pedigree-based model (PED model, not including dominance) were estimated by EM-REML (expectation maximization restricted maximum likelihood) using the software remlf90 ([26]; available at http://nce.ads.uga.edu/wiki/doku.php), plus an additional iteration of AIREML to obtain the average information matrix. It should be noted that estimated values of \(\sigma_{{A^{*} }}^{2}\) and \(\sigma_{{D^{*} }}^{2}\) have per se no meaningful genetic interpretation.
Additive and dominance variance components at the SNP level (\(\sigma_{{a_{1} }}^{2} , \sigma_{{a_{2} }}^{2} ,\sigma_{{a_{12} }}^{2}\) and \(\sigma_{{d_{1} }}^{2} ,\sigma_{{d_{2} }}^{2} ,\sigma_{{d_{12} }}^{2}\)) were back-solved (dividing by \(\left\{ {tr\left[ {{\mathbf{ZZ}}^{'} } \right]} \right\}/n)\) and \(\left\{ {tr\left[ {{\mathbf{WW}}\varvec{'}} \right]} \right\}/n\)) from variance component estimates (\(\sigma_{{A_{1}^{*} }}^{2} ,\sigma_{{A_{2}^{*} }}^{2} ,\sigma_{{A_{12}^{*} }}^{2}\) and \(\sigma_{{D_{1}^{*} }}^{2} ,\sigma_{{D_{2}^{*} }}^{2} ,\sigma_{{D_{12}^{*} }}^{2}\), respectively) for the three populations. Genetic variance components for the \(F_{1}\) population were obtained from Eqs. (5), (6) and (7). Asymptotic standard errors of variance component estimates were obtained as in [27].
Results and discussion
Heritability
Narrow-sense heritabilities for litter size in pure pig lines under pedigree-based (PED) and genomic multiple-trait (GBLUP) models
Model | Line 1 | Line 2 |
---|---|---|
PED | 0.101 ± 0.019 | 0.102 ± 0.021 |
GBLUP | 0.094 ± 0.014 | 0.103 ± 0.015 |
Estimated heritability coefficients across models (PED vs. GBLUP) were similar. They were close to 0.10 and consistent with those reported by Nielsen et al. [28] and Guo et al. [29]. Our estimated heritability coefficients for total number of piglets born per litter were also consistent with the average heritability (0.11) reported in the review by Rothschild and Bidanel [30].
Genetic variances
Estimates of variances for litter size obtained with the genomic multiple-trait (GBLUP) model
Population | Within line | Crossbred | Permanent environmental variance | Residual variance | ||
---|---|---|---|---|---|---|
Additive variance | Dominance variance | Additive variance^{a} | Dominance variance^{b} | |||
1 | 0.81 ± 0.13 | 0.14 ± 0.09 | 1.47 ± 0.20 | 0.63 ± 0.15 | 7.02 ± 0.12 | |
2 | 0.92 ± 0.14 | 0.22 ± 0.12 | 1.44 ± 0.19 | 0.37 ± 0.18 | 7.40 ± 0.14 | |
12 | 1.45 ± 0.19 | 0.22 ± 0.14 | 0.94 ± 0.20 | 6.84 ± 0.12 |
The theory presented in this paper and illustrated with these results makes it possible to estimate breeding values and dominance deviations, and to estimate dominance variance for a crossbred population for different traits. It can also be used for more accurate predictions and to assess the relevance of assortative mating in species such as pigs or maize, in order to increase the performance of offspring.
Genomic correlations
Additive genotypic correlation (in italics) and dominance genotypic correlation between pure and \(F_{1}\) populations
Line 1 | Line 2 | \(F_{1}\) | |
---|---|---|---|
Line 1 | 1.00 | 0.78 ± 0.21 | 0.60 ± 0.14 |
Line 2 | 0.54 ± 0.38 | 1.00 | 0.83 ± 0.15 |
\(F_{1}\) | 0.47 ± 0.41 | 0.59 ± 0.36 | 1.00 |
Table 3 presents the additive and dominance genotypic correlations for markers (\(a\) and \(d\)) between pure lines and the \(F_{1}\) population. The estimated additive genotypic correlation between lines 1 and 2 was equal to 0.78 (Table 3). This indicates that the biological additive effects of SNPs are similar between these lines. Estimating correlations between nominally unrelated lines may seem strange, but genomic relationships allow this estimation. Similar correlations for milk yield were obtained by Karoui et al. [21] between dairy breeds.
For dominance genotypic correlations (Table 3), the values were low regardless of the population, which indicates that dominant effects differ in each population, and that, in practice, assortative mating between two genotypes that would be profitable within, say, line 1 may not be so profitable in the \(F_{1}\) population.
Estimates of inbreeding depression, for which the inbreeding coefficient was calculated as the average homozygosity for litter size, were equal to −12.90 ± 2.29 and −10.74 ± 3.03 for lines 1 and 2, respectively. Estimates of inbreeding depression for pure lines expressed as the change in phenotypic mean per 10 % increase in inbreeding were equal to −1.29 and −1.07 piglets born.
Conclusions
Assuming that SNP effects are independent of the origin of alleles and that allelic frequencies differ between parental populations, we show that the genetic variance for the \(F_{1}\) population includes the biological additive and dominant effects of the gene and a covariance term. Genetic variance can be partitioned into additive variance (due to substitution effects of the parental gametes) and dominance deviations. Breeding values of crossbred individuals are generated by substitution effects, where the effects for each parental line depend on the allele frequencies from the other line. In addition, dominance variance is proportional to the product of the heterozygosities of both lines. If the biological additive and dominant effects of markers are considered random with the covariance between them equal to 0, genetic variance components for the \(F_{1}\) population can be obtained using an equivalent GBLUP model based on SNPs. The method presented here allows selection for specific combining ability, i.e. selection of a specific pair of parents to produce superior \(F_{1}\) individuals, in a GBLUP evaluation framework. The identification of superior \(F_{1}\) individuals between inbred/pure lines is an important focus of research in animals and plants [33].
Declarations
Authors’ contributions
ZGV, AL and JME derived the theory. WH provided the pig data. ZGV developed the Fortran program to create the relationship matrices and AL parallelized it. ZGV analyzed the data and wrote the first draft of the manuscript. All authors discussed the results, made suggestions and corrections. All authors read and approved the final manuscript.
Acknowledgements
We are grateful to members of X-GEN, SelDir projects, and Miguel Toro for their helpful and constructive comments. This work was financed by the INRA SELGEN metaprogram - project X-GEN and EpiSel (ZV, AL), as well as AGL2010-15903 (LV). The project was partly supported by the Toulouse Midi-Pyrénées Bioinformatics platform.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
- Falconer DS, Mackay TFC. Introduction to quantitative genetics. New York: Longman; 1981.Google Scholar
- Lo LL, Fernando RL, Cantet RJ, Grossman M. Theory for modelling means and covariances in a two-breed population with dominance inheritance. Theor Appl Genet. 1995;90:49–62.PubMedView ArticleGoogle Scholar
- Lo LL, Fernando RL, Grossman M. Genetic evaluation by BLUP in two-breed terminal crossbreeding systems under dominance. J Anim Sci. 1997;75:2877–84.PubMedGoogle Scholar
- Lutaaya E, Misztal I, Mabry JW, Short T, Timm HH, Holzbauer R. Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model. J Anim Sci. 2001;79:3002–7.PubMedGoogle Scholar
- Lutaaya E, Misztal I, Mabry JW, Short T, Timm HH, Holzbauer R. Joint evaluation of purebreds and crossbreds in swine. J Anim Sci. 2002;80:2263–6.PubMedGoogle Scholar
- Misztal I. Estimation of variance components with large-scale dominance models. J Dairy Sci. 1997;80:965–74.View ArticleGoogle Scholar
- Mäki-Tanila A, Hill WG. Influence of gene interaction on complex trait variation with multilocus models. Genetics. 2014;198:355–67.PubMedPubMed CentralView ArticleGoogle Scholar
- Bijma P, Bastiaansen JW. Standard error of the genetic correlation: how much data do we need to estimate a purebred-crossbred genetic correlation. Genet Sel Evol. 2014;46:79.PubMedPubMed CentralView ArticleGoogle Scholar
- Ibánẽz-Escriche N, Fernando RL, Toosi A, Dekkers JCM. Genomic selection of purebreds for crossbred performance. Genet Sel Evol. 2009;41:12.PubMedPubMed CentralView ArticleGoogle Scholar
- Toosi A, Fernando RL, Dekkers JCM. Genomic selection in admixed and crossbred populations. J Anim Sci. 2010;88:32–46.PubMedView ArticleGoogle Scholar
- Zeng J, Toosi A, Fernando RL, Dekkers JC, Garrick DJ. Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action. Genet Sel Evol. 2013;45:11.PubMedPubMed CentralView ArticleGoogle Scholar
- Wei M, van der Werf JHJ. Maximizing genetic response in crossbreds using both purebred and crossbred information. Anim Prod. 1994;59:401–13.View ArticleGoogle Scholar
- Bernardo R. Breeding for quantitative traits in plants. Woodbury: Stemma Press; 2002.Google Scholar
- Stuber C, Cockerham CC. Gene effects and variances in hybrid populations. Genetics. 1966;54:1279–86.PubMedPubMed CentralGoogle Scholar
- Wellmann R, Bennewitz J. Bayesian models with dominance effects for genomic evaluation of quantitative traits. Genet Res (Camb). 2012;94:21–37.View ArticleGoogle Scholar
- Toro MA, Varona L. A note on mate allocation for dominance handling in genomic selection. Genet Sel Evol. 2010;42:33.PubMedPubMed CentralView ArticleGoogle Scholar
- Hill WG, Goddard ME, Visscher PM. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 2008;4:e1000008.PubMedPubMed CentralView ArticleGoogle Scholar
- Vitezica ZG, Varona L, Legarra A. On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics. 2013;195:1223–30.PubMedPubMed CentralView ArticleGoogle Scholar
- VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414–23.PubMedView ArticleGoogle Scholar
- Varona L, Moreno C, Ibanez-Escriche N, Altarriba J. Whole genome evaluation for related populations. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production: 1–6 August 2010; Leipzig; 2010. http://www.kongressband.de/wcgalp2010/assets/pdf/0460.pdf.
- Karoui S, Carabaño MJ, Díaz C, Legarra A. Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genet Sel Evol. 2012;44:39.PubMedPubMed CentralView ArticleGoogle Scholar
- Wientjes YC, Veerkamp RF, Bijma P, Bovenhuis H, Schrooten C, Calus MP. Empirical and deterministic accuracies of across population genomic prediction. Genet Sel Evol. 2015;47:5.PubMedPubMed CentralView ArticleGoogle Scholar
- Smith JA, Lewis AM, Wiener P, Williams JL. Genetic variation in the bovine myostatin gene in UK beef cattle: allele frequencies and haplotype analysis in the South Devon. Anim Genet. 2000;31:306–9.PubMedView ArticleGoogle Scholar
- Dunner S, Miranda ME, Amigues Y, Cañón J, Georges M, Hanset R, et al. Haplotype diversity of the myostatin gene among beef cattle breeds. Genet Sel Evol. 2003;35:103–18.PubMedPubMed CentralView ArticleGoogle Scholar
- Silió L, Rodríguez MC, Fernández A, Barragán C, Benítez R, Óvilo C, Fernandez AI. Measuring inbreeding and inbreeding depression on pig growth from pedigree or SNP-derived metrics. J Anim Breed Genet. 2013;130:349–60.PubMedGoogle Scholar
- Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). In Proceedings of the 7th World Congress on Genetics Applied to Livestock Production: 19–23 August 2002; Montpellier; 2002. http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=28-07.pdf.
- Meyer K, Houle D. Sampling based approximation of confidence intervals for functions of genetic covariance matrices. Proc Assoc Advmt Anim Breed Genet. 2013;20:523–6.Google Scholar
- Nielsen B, Su G, Lund MS, Madsen P. Selection for increased number of piglets at d 5 after farrowing has increased litter size and reduced piglet mortality. J Anim Sci. 2013;91:2575–82.PubMedView ArticleGoogle Scholar
- Guo X, Christensen OF, Ostersen T, Wang Y, Lund MS, Su G. Improving genetic evaluation of litter size using a single-step model. J Anim Sci. 2015;93:503–12.PubMedView ArticleGoogle Scholar
- Bidanel J. Biology and genetics of reproduction. In: Rothschild MF, Ruvinsky A, editors. The genetics of the pig. 2nd ed. London: CAB International; 1998. p. 313–43.Google Scholar
- Culbertson MS, Mabry JW, Misztal I, Gengler N, Bertrand JK, Varona L. Estimation of dominance variance in purebred Yorkshire swine. J Anim Sci. 1998;76:448–51.PubMedGoogle Scholar
- Misztal I, Varona L, Culbertson M, Bertrand JK, Mabry J, Lawlor TJ, et al. Studies on the value of incorporating the effect of dominance in genetic evaluations of dairy cattle, beef cattle and swine. Biotechnol Agron Soc Environ. 1998;2:227–33.Google Scholar
- Charcosset A, Bonnisseau B, Touchebeuf O, Burstin J, Barrière Y, Gallais A, et al. Prediction of maize hybrid silage performance using marker data: comparison of several models for specific combining ability. Crop Sci. 1998;38:38–44.View ArticleGoogle Scholar