Erratum to “Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins” (J. Dairy Sci. 93:5942–5949)

Department of Dairy Science, University of Wisconsin, Madison 53706, USA.
Journal of Dairy Science (Impact Factor: 2.57). 12/2010; 93(12):5942-9. DOI: 10.3168/jds.2010-3335
Source: PubMed


Genome-enabled prediction of breeding values using high-density panels (HDP) can be highly accurate, even for young sires. However, the cost of the assay may limit its use to elite animals only. Low-density panels (LDP) containing a subset of single nucleotide polymorphisms (SNP) may give reasonably accurate predictions and could be used cost-effectively with young males and females. This study evaluates strategies for selecting subsets of SNP for several traits, compares predictive ability of LDP with that of HDP, and assesses the benefits of including parent average (PA) as a predictor in models using LDP. Data consisting of progeny-test predicted transmitting ability (PTA) for net merit and 6 other traits of economic interest from 4,783 Holstein sires were evaluated using testing and training sets with regressions on their high-density genotypes and parent averages for net merit index. Additionally, SNP subsets of different sizes were selected using different strategies, including the "best" SNP based on the absolute values of their estimated effects from HDP models for either the trait itself or lifetime net merit, and evenly spaced (ES) SNP across the genome. Overall, HDP models had the best predictive ability, setting an upper bound for the predictive ability of LDP sets. Low-density panels targeting the SNP with strongest effects (for either a single trait or lifetime net merit) provided reasonably accurate predictions and generally outperformed predictions based on evenly spaced SNP. For example, evenly spaced sets would require at least 5,000 to 7,500 SNP to reach 95% of the predictive ability provided by HDP. On the other hand, this level of predictive ability can be achieved with sets of 2,000 SNP when SNP are selected based on magnitude of estimated effects for the trait. Accuracy of predictions based on LDP can be improved markedly by including parent average as a fixed effect in the model; for example, a set with the 1,000 best SNP using the parent average achieved the 95% of the accuracy of a HDP model.

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Available from: Guilherme J. M. Rosa
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    • "To boost the efficacy of implementing genomic prediction (GP; Meuwissen et al. 2001), reducing of genotyping cost is the subject to be tackled, since more individuals both measured and genotyped are needed to achieve more accurate GP. Studies have reported the performance of low-density single nucleotide polymorphism (SNP) genotype data for GP using simulated and real data (Weigel et al. 2009; Moser et al. 2010; Rolf et al. 2010; Vazquez et al. 2010; Zhang et al. 2011). Ogawa et al. (2014) also investigated the possibility of using low-density SNP genotyping information for GP of carcass weight and marbling score in Japanese Black cattle, giving an estimated minimum number of equally spaced SNPs necessary for the valid ranking of animals genetically for the carcass traits in this breed. "
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    ABSTRACT: The influence of genotype imputation using low-density single nucleotide polymorphism (SNP) marker subsets on the genomic relationship matrix (G matrix), genetic variance explained, and genomic prediction (GP) was investigated for carcass weight and marbling score in Japanese Black fattened steers, using genotype data of approximately 40,000 SNPs. Genotypes were imputed using equally spaced SNP subsets of different densities. Two different linear models were used. The first (model 1) incorporated one G matrix, while the second (model 2) used two different G matrices constructed using the selected and remaining SNPs. When using model 1, the estimated additive genetic variance was always larger when using all SNPs obtained via genotype imputation than when using only equally spaced SNP subsets. The correlations between the genomic estimated breeding values obtained using genotype imputation with at least 3,000 SNPs and those using all available SNPs without imputation were higher than 0.99 for both traits. While additive genetic variance was likely to be partitioned with model 2, it did not enhance the accuracy of GP compared with model 1. These results indicate that genotype imputation using an equally spaced low-density panel of an appropriate size can be used to produce a cost-effective, valid GP.
    Full-text · Article · Dec 2015 · Animal Science Journal
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    • "Additionally, the use of lower-density SNP panels (few hundreds to about 7,000) has been studied (Weigel et al., 2010a; Vazquez et al., 2010; Dassonneville et al., 2012). Compared with traditional breeding methods, higher response to selection has been reported for genomic evaluations, especially in dairy cattle (Hutchison et al., 2014). "
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    ABSTRACT: Genotype imputation is widely used as a cost-effective strategy in genomic evaluation of cattle. Key determinants of imputation accuracies, such as linkage disequilibrium patterns, marker densities, and ascertainment bias, differ between Bos indicus and Bos taurus breeds. Consequently, there is a need to investigate effectiveness of genotype imputation in indicine breeds. Thus, the objective of the study was to investigate strategies and factors affecting the accuracy of genotype imputation in Gyr (Bos indicus) dairy cattle. Four imputation scenarios were studied using 471 sires and 1,644 dams genotyped on Illumina BovineHD (HD-777K; San Diego, CA) and BovineSNP50 (50K) chips, respectively. Scenarios were based on which reference high-density single nucleotide polymorphism (SNP) panel (HDP) should be adopted [HD-777K, 50K, and GeneSeek GGP-75Ki (Lincoln, NE)]. Depending on the scenario, validation animals had their genotypes masked for one of the lower-density panels: Illumina (3K, 7K, and 50K) and GeneSeek (SGGP-20Ki and GGP-75Ki). We randomly selected 171 sires as reference and 300 as validation for all the scenarios. Additionally, all sires were used as reference and the 1,644 dams were imputed for validation. Genotypes of 98 individuals with 4 and more offspring were completely masked and imputed. Imputation algorithms FImpute and Beagle v3.3 and v4 were used. Imputation accuracies were measured using the correlation and allelic correct rate. FImpute resulted in highest accuracies, whereas Beagle 3.3 gave the least-accurate imputations. Accuracies evaluated as correlation (allelic correct rate) ranged from 0.910 (0.942) to 0.961 (0.974) using 50K as HDP and with 3K (7K) as low-density panels. With GGP-75Ki as HDP, accuracies were moderate for 3K, 7K, and 50K, but high for SGGP-20Ki. The use of HD-777K as HDP resulted in accuracies of 0.888 (3K), 0.941 (7K), 0.980 (SGGP-20Ki), 0.982 (50K), and 0.993 (GGP-75Ki). Ungenotyped individuals were imputed with an average accuracy of 0.970. The average top 5 kinship coefficients between reference and imputed individuals was a strong predictor of imputation accuracy. FImpute was faster and used less memory than Beagle v4. Beagle v4 outperformed Beagle v3.3 in accuracy and speed of computation. A genotyping strategy that uses the HD-777K SNP chip as a reference panel and SGGP-20Ki as the lower-density SNP panel should be adopted as accuracy was high and similar to that of the 50K. However, the effect of using imputed HD-777K genotypes from the SGGP-20Ki on genomic evaluation is yet to be studied.
    Full-text · Article · May 2015 · Journal of Dairy Science
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    • "Of these ways, choosing SNPs ranked highly in the magnitude of the absolute value of estimated effect is typical. In most cases, prediction of GEBVs with high-ranking SNPs is somewhat more accurate and reliable than with equally-spaced SNPs, when the same number of SNPs is used in the prediction (e.g., [35,36]). For Japanese Black cattle, only one previous study, conducted from the viewpoint of GE, performed the estimation of variance for carcass traits [37]. "
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    ABSTRACT: Japanese Black cattle are a beef breed whose meat is well known to excel in meat quality, especially in marbling, and whose effective population size is relatively low in Japan. Unlike dairy cattle, the accuracy of genomic evaluation (GE) for carcass traits in beef cattle, including this breed, has been poorly studied. For carcass weight and marbling score in the breed, as well as the extent of whole genome linkage disequilibrium (LD), the effects of equally-spaced single nucleotide polymorphisms (SNPs) density on genomic relationship matrix (G matrix), genetic variance explained and GE were investigated using the genotype data of about 40,000 SNPs and two statistical models. Using all pairs of two adjacent SNPs in the whole SNP set, the means of LD (r2) at ranges 0-0.1, 0.1-0.2, 0.2-0.5 and 0.5-1 Mb were 0.22, 0.13, 0.10 and 0.08, respectively, and 25.7, 13.9, 10.4 and 6.4% of the r2 values exceeded 0.3, respectively. While about 90% of the genetic variance for carcass weight estimated using all available SNPs was explained using 4,000-6,000 SNPs, the corresponding percentage for marbling score was consistently lower. With the conventional linear model incorporating the G matrix, correlation between the genomic estimated breeding values (GEBVs) obtained using 4,000 SNPs and all available SNPs was 0.99 for carcass weight and 0.98 for marbling score, with an underestimation of the former GEBVs, especially for marbling score. The Japanese Black is likely to be in a breed group with a relatively high extent of whole genome LD. The results indicated that the degree of marbling is controlled by only QTLs with relatively small effects, compared with carcass weight, and that using at least 4,000 equally-spaced SNPs, there is a possibility of ranking animals genetically for these carcass traits in this breed.
    Full-text · Article · Feb 2014 · BMC Genetics
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