- Research article
- Open Access
Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle
© Rolf et al; licensee BioMed Central Ltd. 2010
Received: 28 October 2009
Accepted: 19 April 2010
Published: 19 April 2010
Molecular estimates of breeding value are expected to increase selection response due to improvements in the accuracy of selection and a reduction in generation interval, particularly for traits that are difficult or expensive to record or are measured late in life. Several statistical methods for incorporating molecular data into breeding value estimation have been proposed, however, most studies have utilized simulated data in which the generated linkage disequilibrium may not represent the targeted livestock population. A genomic relationship matrix was developed for 698 Angus steers and 1,707 Angus sires using 41,028 single nucleotide polymorphisms and breeding values were estimated using feed efficiency phenotypes (average daily feed intake, residual feed intake, and average daily gain) recorded on the steers. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population.
Results were compared to estimates produced from pedigree-based mixed model analysis of 862 Angus steers with 34,864 identified paternal relatives but no female ancestors. Estimates of additive genetic variance and breeding value accuracies were similar for AFI and RFI using the numerator and genomic relationship matrices despite fewer animals in the genomic analysis. Bootstrap analyses indicated that 2,500-10,000 markers are required for robust estimation of genomic relationship matrices in cattle.
This research shows that breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations when at least 2,500 SNPs are available for the generation of a genomic relationship matrix.
The advent of national genetic evaluation in beef cattle was made possible by the formulation of best linear unbiased prediction (BLUP) via the mixed model equations  and most livestock species now use BLUP for the evaluation of additive genetic merit and selection of parents to produce the next generation of progeny. However, most traits for which estimated breeding values or expected progeny differences (EPDs) are computed measure animal outputs rather than inputs. Because of the increased cost of production system inputs, interest has recently been stimulated for the development of efficient methods for producing phenotypes and EPDs for the efficiency of feed utilization. Feed costs in calf feeding and yearling finishing systems account for approximately 66% and 77% of total costs, respectively  and while increasing growth rate by 10% has been estimated to increase profitability by 18%, increasing the efficiency of growth of feedlot cattle by 10% is expected to increase profitability by 43% . Other studies have suggested that increasing the feed efficiency of feedlot cattle has seven to eight times the economic impact of similar increases in growth . Selection to improve feed efficiency in cattle has been difficult to accomplish  and little progress has been made. Furthermore, guidelines have not yet been created to define the optimal trait upon which to practice selection. While early research focused on growth rate , unfavorable correlated responses in other traits, such as mature size, result in economic penalties in other sectors of the production system [4, 5]. The popularity of residual feed intake (RFI), or net feed intake, first proposed by KOCH et al.  as a measure of feed efficiency, is increasing. RFI is phenotypically independent of growth rate and metabolic body weight and can, if desired, be forced to be independent of other factors such as body composition. However, phenotypic independence does not guarantee genetic independence between RFI and the traits upon which it has been conditioned  and undesirable correlated responses can occur if producers fail to select on appropriate indexes. RFI also requires the routine and accurate collection of average daily feed intake (AFI) data on large numbers of individuals. Because AFI can relatively easily be assigned an economic value, unlike RFI , it is the most logical input trait to include in a selection index  which also includes economically relevant output traits, to produce the optimal selection tool .
The cost and logistical difficulty of collecting feed intake data on large numbers of animals necessitates the consideration of alternative approaches to the estimation of EPDs for this trait and the application of genomic information is very appealing. While marker assisted selection could be employed, the approach explains only a small portion of the genetic variation within a trait and neglects the variation due to quantitative trait loci (QTL) with small effects for which markers have not been identified [11, 12]. Conversely, Genomic Selection (GS) is an option which allows simultaneous selection on all of the QTL that underlie a trait. GS constructs prediction models for EPDs using a training population that possesses phenotypes or EPDs and is genotyped at high density using tens, or hundreds, of thousands of markers. Key to the approach is to calibrate the number of markers that are scored to the extent of linkage disequilibrium (LD) that is present in the genome of the species. By genotyping an appropriately large number of evenly spaced markers which span the entire genome, most QTL are expected to be in LD with at least some of the markers . Provided the training population is appropriately large, GS prediction models can greatly increase the accuracy of EPDs for traits on which phenotypes are especially difficult or expensive to collect. The improvement in selection response due to the application of GS has been estimated to be twice that of traditional selection schemes due to dramatic reductions in generation interval  and increases in selection intensity [15, 16].
The U.S. dairy industry has aggressively developed systems to utilize genomic information in animal selection, and has provided a model for implementation of GS in the beef industry. Several methods have been proposed for the design of GS programs within dairy breeding programs, primarily using simulated data [15, 17–21], although it has not been clear that the applied marker densities have been calibrated to the LD present within the simulated populations. Three methods proposed for the estimation of molecular breeding values include the estimation and summation of individual allele or haplotype effects across all marker loci [15, 17, 21, 22], the replacement of the pedigree-derived numerator relationship matrix with a genomic relationship matrix (GRM) in traditional mixed models [14, 16, 19–21, 23–25] or a hybrid approach involving the use of a GRM to estimate EPDs which are then combined with traditional BLUP of EPDs in a selection index [14, 16, 21]. The multiple trait derivative free restricted maximum likelihood (MTDFREML)  software has been modified to facilitate the prediction of breeding values using GRMs . While GS is now being tested within commercial dairy cattle populations [14, 28, 29], traditional progeny testing schemes used to achieve high accuracies on the bulls released for widespread use are being modified to reflect the gains in selection response that are possible when bulls, at birth, have estimates of genetic merit with accuracies that are similar to those achieved, on average, with 11 daughter equivalents .
The objective of this study was to evaluate the use of GRMs within mixed linear model analyses for variance component estimation, the correction of observations for fixed effects, and for the estimation of molecular breeding values for commercially important sires for traits recorded in experimental populations with incomplete pedigree data using feed efficiency traits as an example. The number of SNP markers needed for accurate generation of a GRM was also explored.
Descriptive Statistics: Descriptive statistics for three feed efficiency traits and estimates of variance components and heritability from linear model analyses incorporating either numerator or genomic relationship matrices.
Cryopreserved units of semen were also obtained for 1,721 registered Angus sires born between 1956 and 2003 that were used in artificial insemination (AI) within the U.S. Angus population. These animals formed paternal lineages which included the sires of the steer calves and their male ancestors. Genomic DNA was isolated from both the white blood cell and semen samples by proteinase-K digestion followed by phenol:chloroform:isoamyl alcohol extraction, and ethanol precipitation . Additionally, complete pedigrees spanning up to 62 ancestral generations were obtained for the AI sires from the American Angus Association.
Dams of steers were from a population of unregistered commercial purebred Angus cows with pedigree information that was determined to be unreliable based on our attempts to phase chromosomes and infer missing genotypes using linkage information. Because the Circle A Ranch utilized an animal identification system based upon year of birth (two digits) and birth order within each year and the pedigree file did not contain birth date, several alternate pedigrees were possible for many of the steers. Since not all of the putative maternal grandsires had been genotyped with the BovineSNP50 assay, we were unable to correctly identify the maternal pedigree on many of the steers and the parents of each of the dams were treated as unknown for all analyses. However, identifiers for dams were retained to preserve the identification of progeny that were maternal half-sibs. The 862 (698 with DNA) steers had 118 (100) sires and half-sib family sizes ranged from 1 to 81 progeny.
Intake and gain data were obtained on feeding groups of 96 steers gathered over a five year period from 1999 to 2003. Cattle were an average of 326 days of age when entering the trial and were fed for an average of 110 days. This feeding period has been found to be sufficient to accurately measure both gain and intake in British breeds . Weights were measured at the start of ration acclimation, on the first day of the test, mid-test and at the end of the test. As these cattle were commercially owned, the specific ration composition is not known, however all animals within a feeding group were fed the same ration. RFI was calculated individually for each feeding group and the mean R2 value for the regression models was 0.49.
SNP genotypes were acquired using the Illumina BovineSNP50 assay [32–34] and genotypes were called using the BeadStudio genotyping module 3.2.32 (ILLUMINA Inc., San Diego, CA). After screening for Mendelian inheritance to verify the accuracy of the sire pedigrees, genotypes for nine of the sires were found to be inconsistent with their paternal pedigree and two were identical twins produced by embryo transfer and these animals were removed from the data set. The genotypes were also filtered to require minor allele frequency (MAF) to be ≥0.05, and call rate to be > 95% which resulted in 41,028 SNP being retained for analysis on 698 steers and 1,707 AI sires. Average MAF for the 41,028 SNP was 0.28 and the average spacing between the 39,971 SNPs assigned to chromosomes (including 487 on BTAX) in the Btau4.0 assembly was 65.73 ± 68.45 kb. Finally, a total of 0.58% of the genotypes in this dataset were missing and these were imputed using fastPHASE  with the Btau4.0 positions and the -T10 and -K20 options.
Additive effects analysis
where: y is a vector of phenotypes on the 862 steers, X is an incidence matrix relating observations to feeding pens, b is a vector of pen effects, Z is an incidence matrix relating observations to animals, u is a vector of normally distributed breeding values, and e is a vector of independent normally distributed random residuals. The variance of u is A where A is the NRM and is the additive genetic variance; the variance of e is I where I is the identity matrix and is the residual variance; u and e were assumed to be uncorrelated.
Preliminary analyses indicated that birth year (Y), birth season (S) and feeding pen (PEN) were significant sources of variation for almost all traits (AFI, Y p < 0.0001, S p < 0.0006, PEN p < 0.0001; and RFI, Y p < 0.0001, S p < 0.7318, PEN p < 0.0001). However, levels of Y and S were nested within levels of PEN, and PEN was the only fixed effect incorporated into the analysis models.
Genomic relationship matrix
Construction of Marker Panel Subsets
MATLAB (The Mathworks, Natick, MA) was used to test the number of markers necessary to precisely estimate the GRM by the regression approach in cattle. Subsets (n = 100, 500, 1000, 2500, 5000, 10000, 15000, 20000, 25000, 30000, 35000 and 40000) of markers were randomly sampled with replacement from the full set of 41,028 markers to ensure a random representation of the entire genome within the marker subset. For each of i = 1,...,50 replicates at each value of n, a GRM (G ni ) was estimated using the regression approach described above and correlations were estimated between the upper triangular elements of G ni and G (the GRM estimated from all 41,028 SNP) for all 2,405 animals and between G ni and A for all 1,707 AI sires and averages were produced across replicates.
To simulate the reduced marker panels that are most likely to be commercialized in the beef industry, a panel of 384 SNPs most significant for AFI was selected for the estimation of a GRM. SNPs were first individually screened for their association with AFI using one-way analyses of variance. Subsequently, a chromosome-by-chromosome analysis was performed using a forward selection algorithm in which the SNP with the highest F-statistic for the chromosome was sequentially added to the model until no further SNPs could be added that exceeded a predetermined significance threshold. For this process, the significance threshold was initialized at a genome-wide p-value of 0.05 (F>23.7163) and was relaxed to F>6.33 until a total of 384 SNP were retained for this analysis.
Results and discussion
Summary statistics presented in Table 1 indicate that there were no appreciable differences between phenotypes for the total sample and for the genotyped subset of animals. Estimates of variance components and of narrow sense heritability using the numerator and genomic relationship matrices are also presented in Table 1. The additive genetic variance components for AFI and RFI were larger when estimated from the full sample using the NRM than when estimated from the subsample using the GRM. However the opposite was true for ADG and rather than reflecting an effect due to sample size, this likely reflects the lack of pedigree information on the dams of these steers which causes them to be treated as unrelated members of the base generation in the analyses that incorporated the NRM. However, the use of the GRM corrects for the identity by descent between these dams, which are all derived from a single herd, and should produce higher allele sharing in their sons than would unrelated females.
Nevertheless, the heritability estimates for all three traits were lower than literature estimates (ADG 0.28 ; RFI 0.08-0.44 [5, 10]; AFI 0.39 ). The reasons underlying the disparity in heritability estimates are unclear, but for ADG and RFI may be due to imprecise estimates of growth, since taking weights at least every 2 weeks during the feeding trial has been recommended . Regardless of the cause, the low heritabilities further reduced the power of this study for the estimation of genomic breeding values.
Accuracies of EBVs estimated using either a NRM or GRM: Average accuracies of estimated breeding value for three feed efficiency traits estimated using mixed linear animal models incorporating either additive numerator (NRM) or genomic relationship matrices (GRM).
Sires of Steers
AI Sires Pedigree
Bootstrap analysis: Correlations between the upper triangular elements of GRMs estimated from subsamples of SNPs with the GRM estimated from 41,028 SNPs and with the NRM computed for 1,707 AI sires with extensive pedigree records.
No. SNPs (n)
Correlation between elements of Gni and A
Correlation between elements of Gni and G
While estimates of genomic relationship coefficients based upon at least 10,000 SNPs appear to be extremely robust, estimates appear to be very sensitive to SNP sample size when fewer than 2,500 SNP are used (Figure 3). This has very significant consequences for both conservation genetic and GS applications because there are currently no cost effective technologies available for genotyping 2,500-10,000 SNP markers. Reagent costs for available high density (≥50 K SNPs) assays are in the range $175-$250 per sample and from there, current genotyping technologies allow the genotyping of 1,536 SNPs for ~$70 or 384 SNPs for ~$16 per sample. Consequently, we bootstrap sampled 200 replicates of 384 and 1,536 randomly sampled SNPs from the 41,028 available SNPs and found minimum, mean and maximum correlations between the reduced sample and full GRMs of 0.6046, 0.6536 and 0.6868 and 0.8465, 0.8690 and 0.8821, respectively. However, the 384 or 1,536 SNP panels likely to be commercialized within the livestock industries will not utilize randomly sampled SNP, but will be based on those SNP subsets that are predicted to explain the greatest amount of genetic variation within a trait or set of traits. The effects of computing the GRM from such selected SNP panels will depend on the distribution of LD and MAF among the loci.
When we estimated the GRM using the 1,536 SNPs with the highest MAF (average MAF = 0.4953), the correlation between genomic relationship coefficients was 0.8394, less than the minimum correlation obtained from 50 replicates of random sampling, suggesting that the sampled loci were more strongly linked than the majority of randomly sampled sets of 1,536 SNPs. To test this hypothesis, the average spacing between markers on the same chromosome was tested for the SNP panel with the highest MAF and also for the 200 bootstrap samples. The average spacing between markers in the high MAF panel was 1.8 Mb, and 298 of these markers were less than 250-kb apart on the same chromosome. Conversely, the average spacing of markers on the same chromosome across the 200 bootstrap samples was 38.69 Mb and their average MAF was 0.29. Thus, the SNP with the highest MAF within the Angus genome are tightly linked and provide less information about the relatedness of animals than a randomly sampled panel of SNPs. Just why this is the case is not clear.
When we estimated the GRM using the 1,536 SNP with the lowest MAF (average MAF = 0.0614) the correlation between genomic relationship coefficients was 0.4802. However, when we sampled the 384 SNPs with the highest and lowest MAF (average 0.4980 and 0.0527, respectively), the correlations between genomic relationship coefficients estimated using 384 and 41,028 SNPs were 0.6947 and 0.2226, respectively. The former exceeds the largest correlation obtained in 50 replicate random samples of 384 SNP suggesting that by reducing the number of SNP from 1,536 to 384, the pattern of LD among the 384 loci with the highest MAF is not significantly different to that among randomly sampled loci. Finally, using a forward selection process we identified a panel of 384 SNPs that were most strongly associated with AFI and that had an average MAF of 0.2884. The correlation between genomic relationship coefficients estimated using this sample and the complete set of 41,028 SNPs was 0.6198, slightly lower than the average for randomly sampled SNPs.
These results suggest that the small panels of SNPs that are soon likely to be commercialized within the beef and dairy cattle industries will have some utility for the estimation of genomic relationship coefficients and that this will allow the estimation of molecular breeding values for traits other than those targeted by the SNPs within the panels. However, our results also indicate that the greatest benefits of the technology will not be realized until inexpensive assays can be produced which query ≥2,500 SNPs. When smaller panels of 60- 90 SNPs are used for parentage identification a NRM could be constructed based on the inferred pedigree . We found a correlation between elements of the NRM and GRM based on 41,028 SNPs to be 0.8663, equivalent to the estimation of the GRM with 1,536 randomly sampled SNPs. Thus, the greatest utility from the use of small SNP panels may be the estimation of pedigree to correctly establish the parents of calves since the rates of misidentified parents in the U.S. beef and dairy industries are in the range 3-30% .
Using a genomic relationship matrix, breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations. This matrix should ideally consist of at least 2,500 SNP in cattle populations, preferably those that are unlinked and not in extremely high linkage disequilibrium with one another. While sufficient numbers of SNPs are not yet available for all species to allow the precise estimation of genomic relationship coefficients, there are no technical limits to rapid and inexpensive SNP development using the deep sequencing of reduced representation libraries with next generation sequencing platforms [32, 33]. The most significant limitation to be overcome before the approach will have widespread impact within conservation genetics and livestock improvement communities is the development of inexpensive assays which can simultaneously query from 2,500 to 10,000 SNPs. The number of SNPs available in this population was more than sufficient to generate an accurate GRM, thus the methods applied in this study appear to be viable for the generation of genomic breeding values for feed efficiency traits despite low estimated trait heritabilities. Genomic breeding values for AFI, ADG and RFI were generated for 1,707 Angus AI sires using information on 698 steer progeny from commercial dams with missing pedigree data. These EBVs and accuracies were similar to those obtained from analyses using a NRM despite a 19% difference in the number of animals with phenotypic data. Pooling available data sets on Angus animals should increase the heritability and accuracy of genomic breeding values for feed efficiency traits.
We would like to thank all of the people who donated samples to this project and provided data on the animals. Special thanks to Circle A Ranch and the MFA Inc., for providing samples and data and the American Angus Association for providing pedigree data for all animals in this manuscript. This project was supported by National Research Initiative Grants no. 2008-35205-04687 and 2008-35205-18864 from the USDA Cooperative State Research, Education, and Extension Service and grant 13321 from the Missouri Life Science Research Board.
- Henderson CR: Best Linear Unbiased Estimation and Prediction under a selection model. Biometrics. 1975, 31: 423-447. 10.2307/2529430.View ArticlePubMedGoogle Scholar
- Anderson RV, Rasby RJ, Klopfenstein TJ, Clark RT: An evaluation of production and economic efficiency of two beef systems from calving to slaughter. J Anim Sci. 2005, 83: 694-704.PubMedGoogle Scholar
- Fox DG, Tedeschi LO, Guiroy PJ: Determining feed intake and feed efficiency of individual cattle fed in groups. Proc Beef Impr Fed 33rd Ann Res Symp Annu Meet. 2001, 33: 80-98.Google Scholar
- Okine EK, Basarab J, Goonewardene LA, Mir P: Residual feed intake and feed efficiency: Difference and implications. Florida Ruminant Nutrition Symposium. 2004, 27-38.Google Scholar
- Archer JA, Richardson EC, Herd RM, Arthur PF: Potential for selection to improve efficiency of feed use in beef cattle: a review. Aust J Exp Agric. 1999, 50: 147-161.View ArticleGoogle Scholar
- Koch RM, Swiger LA, Chambers D, Gregory KE: Efficiency of feed use in beef cattle. J Anim Sci. 1963, 22: 486-494.Google Scholar
- Kennedy BW, Werf Van Der JH, Meuwissen TH: Genetic and statistical properties of residual feed intake. J Anim Sci. 1993, 71: 3239-3250.PubMedGoogle Scholar
- Garrick DJ: Development of genetic evaluations and decision support to improve feed efficiency. Proc Beef Impr Fed 38th Ann Res Symp Annu Meet. 2006, 38: 32-40.Google Scholar
- Hazel LN: The genetic basis for constructing selection indexes. Genetics. 1943, 28: 476-490.PubMed CentralPubMedGoogle Scholar
- Herd RM, Archer JA, Arthur PF: Reducing the cost of beef production through genetic improvement in residual feed intake: Opportunity and challenges to application. J Anim Sci. 2003, 81: E9-17.Google Scholar
- Meuwissen TH: Genomic Selection: The future of animal breeding. 2007, 88-91. [http://www.umb.no/statisk/husdyrforsoksmoter/2007/23.pdf]Google Scholar
- Spangler ML, Bertrand JK, Rekaya R: Combining genetic test information and correlated phenotypic records for breeding value estimation. J Anim Sci. 2007, 85: 641-649. 10.2527/jas.2006-617.View ArticlePubMedGoogle Scholar
- Meuwissen TH: Genomic selection: marker assisted selection on a genome wide scale. J Anim Breed Genet. 2007, 124: 321-322.View ArticlePubMedGoogle Scholar
- VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS: Invited Review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci. 2009, 92: 16-24. 10.3168/jds.2008-1514.View ArticlePubMedGoogle Scholar
- Schaeffer LR: Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet. 2006, 123: 218-223. 10.1111/j.1439-0388.2006.00595.x.View ArticlePubMedGoogle Scholar
- Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME: Invited Review: Genomic selection in dairy cattle: Progress and challenges. J Dairy Sci. 2009, 92: 433-443. 10.3168/jds.2008-1646.View ArticlePubMedGoogle Scholar
- Meuwissen TH, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157: 1819-1829.PubMed CentralPubMedGoogle Scholar
- Dekkers JCM: Prediction of response to marker-assisted and genomic selection using selection index theory. J Anim Breed Genet. 2007, 124: 331-341.View ArticlePubMedGoogle Scholar
- Garrick DJ: Equivalent mixed model equations for genomic selection [abstract]. J Dairy Sci. 2007, 90: 376-10.3168/jds.S0022-0302(07)72639-5.View ArticleGoogle Scholar
- Hayes BJ, Goddard ME: Technical note: Prediction of breeding values using marker derived relationship matrices. J. Anim Sci. 2008, 86: 2089-2092. 10.2527/jas.2007-0733.View ArticlePubMedGoogle Scholar
- VanRaden PM: Efficient Methods to Compute Genomic Predictions. J Dairy Sci. 2008, 91: 4414-4423. 10.3168/jds.2007-0980.View ArticlePubMedGoogle Scholar
- Calus MPL, Meuwissen THE, De Roos APW, Veerkamp RF: Accuracy of genomic selection using different methods to define haplotypes. Genetics. 2008, 178: 553-561. 10.1534/genetics.107.080838.PubMed CentralView ArticlePubMedGoogle Scholar
- Van-Arendonk JA, Tier MB, Kinghorn BP: Use of multiple genetic markers in prediction of breeding values. Genetics. 1994, 137: 319-329.PubMed CentralPubMedGoogle Scholar
- Matsuda H, Iwaisaki H: A recursive procedure to compute the gametic relationship matrix and its inverse for marked QTL clusters. Genes Genet Syst. 2002, 77: 123-130. 10.1266/ggs.77.123.View ArticlePubMedGoogle Scholar
- Hayes BJ, Visscher PM, Goddard ME: Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res, Camb. 2009, 91: 47-60. 10.1017/S0016672308009981.View ArticleGoogle Scholar
- Boldman KG, Kriese LA, Van Vleck LD, Van Tassell CP, Kachman SD: A manual for use of MTDFREML. A set of programs to obtain estimates of variance and covariance. 1995, USDA, Agriculture Research Service, Clay Center, NEGoogle Scholar
- Zhang Z, Todhunter RJ, Buckler ES, Van Vleck LD: Technical note: Use of marker-based relationships with multiple-trait derivative-free restricted maximal likelihood. J Anim Sci. 2007, 85: 881-885. 10.2527/jas.2006-656.View ArticlePubMedGoogle Scholar
- De Roos APW, Schrooten C, Mullaart E, Calus MPL, Veerkamp RF: Breeding value estimation for fat percentage using dense markers on Bos taurus autosome 14. J Dairy Sci. 2007, 90: 4821-4829. 10.3168/jds.2007-0158.View ArticlePubMedGoogle Scholar
- Guillaume F, Fritz S, Boichard D, Druet T: Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle. Genet Sel Evol. 2008, 40: 91-102.PubMed CentralPubMedGoogle Scholar
- Sambrook J, Fritsch EF, Maniatis T: Molecular Cloning: A Laboratory Manual. 1989, Plainview: Cold Spring Harbor Laboratory PressGoogle Scholar
- Archer JA, Arthur PF, Herd RM, Parnell PF, Pitchford WS: Optimum postweaning test for measurement of growth rate, feed intake, and feed efficiency in British breed cattle. Journal of Animal Science. 2009, 75: 2024-32.Google Scholar
- Van Tassell CP, Smith TPL, Matukumalli LK, Taylor JF, Schnabel RD, Lawley CT, Haudenschild CD, Moore SS, Warren WC, Sonstegard TS: SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods. 2008, 5: 247-252. 10.1038/nmeth.1185.View ArticlePubMedGoogle Scholar
- Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O'Connell J, Moore SS, Smith TP, Sonstegard TS, Van Tassell CP: Development and characterization of a high density SNP genotyping assay for cattle. PLOS One. 2009, 4: e5350-10.1371/journal.pone.0005350.PubMed CentralView ArticlePubMedGoogle Scholar
- Steemers FJ, Chang W, Lee G, Barker DL, Shen R, Gunderson KL: Whole-genome genotyping with the single-base extension assay. Nat Methods. 2006, 3: 31-33. 10.1038/nmeth842.View ArticlePubMedGoogle Scholar
- Scheet P, Stephens M: A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet. 2006, 78: 629-644. 10.1086/502802.PubMed CentralView ArticlePubMedGoogle Scholar
- Quaas RL, Pollak EJ: Mixed Model Methodology for Farm and Ranch Beef Cattle Testing Programs. J Anim Sci. 1980, 51: 1277-1287.Google Scholar
- Hudson GFS, Quaas RL, Van Vleck LD: Computer algorithm for the recursive method of calculating large numerator relationship matrices. J Dairy Sci. 1982, 65: 2018-2022. 10.3168/jds.S0022-0302(82)82454-5.View ArticleGoogle Scholar
- Arthur PF, Archer JA, Johnston DJ, Herd RM, Richardson EC, Parnell PF: Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. J Anim Sci. 2001, 79: 2805-2811.PubMedGoogle Scholar
- Sherman EL, Nkrumah JD, Moore SS: Whole genome SNP associations with feed intake and feed efficiency in beef cattle. J Anim Sci . 2010, 88 (1): 16-22. 10.2527/jas.2008-1759.View ArticlePubMedGoogle Scholar
- Weaber RL: A simulation study of replacement sire selection and genetic evaluation strategies for large commercial ranches. PhD Dissertation. 2006, Cornell University, Animal Sciences Department, 193-Google Scholar
- Heaton MP, Harhay GP, Bennett GL, Stone RT, Grosse WM, Casas E, Keele JW, Smith TPL, Chitko-McKown CG, Laegreid WW: Selection and use of SNP markers for animal identification and paternity analysis in U.S. beef cattle. Mammalian Genome. 2002, 13: 272-281. 10.1007/s00335-001-2146-3.View ArticlePubMedGoogle Scholar
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