Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

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Comparing genomic prediction and GWAS with sequence information vs HD or 50k SNP chips
Veerkamp, R.F. ; Binsbergen, R. van; Calus, M.P.L. ; Schrooten, C. ; Bouwman, A.C. - \ 2015
In: Book of Abstracts 66th Annual Meeting of the EAAP. - Wageningen Academic Publishers - p. 506 - 506.
Earlier work showed that using whole genome sequence information did not improve the accuracy for genomic prediction, because there are too many SNPs in close LD to pinpoint the functional SNP accurately. In this study we therefore compared the single SNP GWAS results using imputed whole genome information (from run4 of the 1000 bull genomes project) to the GWAS results obtained using either the 50k or777k HD SNP chips. For the analysis, (imputed) HD genotypes were available on 5,549 Dutch bulls, of which 3,416 were used for GWAS and subsequent training. Single SNP GWAS was performed using the genomic relationship matrix (based on HD) to account for population structure. In the sequence information there were 28,076,109 SNP imputed, but 10,258,688 where monomorphic in our training population. For protein yield 2,241 SNPs were significant (-log10(p)>5), and 28 (160) of those were present on the 50k (HD) SNP chips. For somatic cell score the equivalent number of SNPs were 1,545, 90 and 7 using sequence, HD or 50k, and for ‘interval first to last insemination’ the equivalent number was 952, 27 and 4 SNPs, respectively. Fitting all SNPs together (using Bayesian Variable Selection) the HD and 50k SNPs gave clear evidence for QTL, but using sequence information the signal was spread across many SNP in high LD. In conclusion, although more significant SNP were found using sequence information, relatively few new regions were identified, and every significant SNP was accompanied by several others in high LD. Therefore, to benefit from sequence data in genomic selection, more sophisticated methodology is required than currently used for genomic prediction.
Split-and-merge Bayesian variable selection enables efficient genomic prediction using sequence data
Calus, M.P.L. ; Schrooten, C. ; Veerkamp, R.F. - \ 2015
In: Book of Abstracts 66th Annual Meeting of the EAAP. - Wageningen Academic Publishers - p. 505 - 505.
Simultaneous use of more than 10,000,000 SNP imputed from sequence data hardly improved the accuracy of genomic prediction, even with commonly used Bayesian variable selection models. One reason may be that the overparametrization problem is more severe than with e.g. 50k SNPs. We hypothesize that splitand-merge Bayesian variable selection may provide a solution to overcome this issue. Our application of split-and-merge, also known as divide and conquer, combined with Bayesian variable selection involves two steps. The first step divides the SNPs in ~300 subsets of 40k SNPs. Subsets are formed by going through the list of SNP ordered by their position on the genome and assigning each next SNP to the next subset in line. In each subset, BayesC is used for genomic prediction, and SNPs are ranked based on their posterior probabilities indicating their likelihood to be strongly associated with the investigated trait. In the second step, from each subset a few hundred SNPs with the largest posterior probability will be selected into a final set of SNPs that are used to build the final genomic prediction model using BayesC. Next to attempting to alleviate the overparametrization problem, an additional practical benefit of this modelling procedure is that the first step comprises ~300 analyses with ~40,000 SNPs each, rather than one analysis with >10,000,000 SNPs. Since all these analyses can be run in parallel, computation time will be a matter of hours instead of more than a month. Results will be presented at the conference.
Genomic testing of cows and heifers: An industry perspective
Eaglen, S.A.E. ; Schopen, G.C.B. ; Stoffelen, L.I. ; Schrooten, C. ; Calus, M.P.L. ; Roos, A.P.W. de; Linde, C. van der - \ 2015
In: Book of Abstracts 66th Annual Meeting of the EAAP. - Wageningen Academic Publishers - p. 251 - 251.
Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle
Binsbergen, R. van; Calus, M.P.L. ; Bink, M.C.A.M. ; Eeuwijk, F.A. van; Schrooten, C. ; Veerkamp, R.F. - \ 2015
Genetics, Selection, Evolution 47 (2015). - ISSN 0999-193X
Background In contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data. Methods Highly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training. Results Prediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed. Conclusions Compared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.
Empirical and deterministic accuracies of across-population genomic prediction
Wientjes, Y.C.J. ; Veerkamp, R.F. ; Bijma, P. ; Bovenhuis, H. ; Schrooten, C. ; Calus, M.P.L. - \ 2015
Genetics, Selection, Evolution 47 (2015). - ISSN 0999-193X
dairy-cattle breeds - linkage disequilibrium - relationship matrix - complex traits - multi-breed - selection - values - markers - heritability - models
Background: Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which reference individuals and selection candidates are from different populations, and to investigate the impact of differences in allele substitution effects across populations and of the number of QTL underlying a trait on the accuracy. Methods: A deterministic formula to estimate the accuracy of across-population genomic prediction was derived based on selection index theory. Moreover, accuracies were deterministically predicted using a formula based on population parameters and empirically calculated using simulated phenotypes and a GBLUP (genomic best linear unbiased prediction) model. Phenotypes of 1033 Holstein-Friesian, 105 Groninger White Headed and 147 Meuse-Rhine-Yssel cows were simulated by sampling 3000, 300, 30 or 3 QTL from the available high-density SNP (single nucleotide polymorphism) information of three chromosomes, assuming a correlation of 1.0, 0.8, 0.6, 0.4, or 0.2 between allele substitution effects across breeds. The simulated heritability was set to 0.95 to resemble the heritability of deregressed proofs of bulls. Results: Accuracies estimated with the deterministic formula based on selection index theory were similar to empirical accuracies for all scenarios, while accuracies predicted with the formula based on population parameters overestimated empirical accuracies by ~25 to 30%. When the between-breed genetic correlation differed from 1, i.e. allele substitution effects differed across breeds, empirical and deterministic accuracies decreased in proportion to the genetic correlation. Using a multi-trait model, it was possible to accurately estimate the genetic correlation between the breeds based on phenotypes and high-density genotypes. The number of QTL underlying the simulated trait did not affect the accuracy. Conclusions: The deterministic formula based on selection index theory estimated the accuracy of across-population genomic predictions well. The deterministic formula using population parameters overestimated the across-population genomic accuracy, but may still be useful because of its simplicity. Both formulas could accommodate for genetic correlations between populations lower than 1. The number of QTL underlying a trait did not affect the accuracy of across-population genomic prediction using a GBLUP method
Added value of whole-genome sequence data to genomic predictions in dairy cattle
Binsbergen, R. van; Calus, M.P.L. ; Bink, M.C.A.M. ; Schrooten, C. ; Eeuwijk, F.A. van; Veerkamp, R.F. - \ 2014
Integrate cow and bull data in genomic evaluation for conformation traits and claw health
Schopen, G.C.B. ; Vessies, J. ; Calus, M.P.L. ; Schrooten, C. ; Roos, A.P.W. de - \ 2014
In: Proceedings, 10th World Congress of Genetics Applied to Livestock Production. - - p. 299 - 299.
The two objectives of this study were to investigate and find methods to successfully integrate cow data in the bull reference population for genomic evaluation and to investigate the effect of adding reference cows on the DGV reliability for conformation traits and claw health. Information from about 25,000 bulls and about 15,000 cows was available. Bulls were genotyped with the Illumina 50K SNP chip and the cows with the Illumina 10K SNP chip. All animals were imputed to an equal 50K SNP set. After SNP edits 37,995 SNP remain for all animals. As phenotypes, yield deviations, deregressed proofs (DRPs) with adjustments for cows and DRPs calculated based on matrix deregression will be used. The three kinds of phenotypes will be validated to investigate the effect on the reliability of direct genomic value for conformation traits and claw health.
Genomic prediction of breeding values using previously estimated SNP variances
Calus, M.P.L. ; Schrooten, C. ; Veerkamp, R.F. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 13 p.
preconditioned conjugate-gradient - dairy-cattle - genotyping strategies - genetic evaluations - complex traits - selection - accuracy - information - preselection - population
Background Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely. Methods SNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors. Results BLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10-8 instead of 10-10 for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS. Conclusions Combining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model.
Genomic Prediction with 12.5 Million SNPs for 5503 Holstein Friesian Bulls
Binsbergen, R. van; Calus, M.P.L. ; Bink, M.C.A.M. ; Schrooten, C. ; Eeuwijk, F.A. van; Veerkamp, R.F. - \ 2014
This study reports the first preliminary results of genomic prediction with whole-genome sequence data (12,590,056 SNPs) for 5503 bulls with accurate phenotypes. Two methods were compared: genome-enabled best linear unbiased prediction (GBLUP) and a Bayesian approach (BSSVS). Results were compared with results using BovineHD genotypes (631,428 SNPs). Results were reported for somatic cell score, interval between first and last insemination, and protein yield. For all traits, and both methods genomic prediction with sequence data showed similar results compared to BovineHD and GBLUP showed similar results compared to BSSVS. However, it remains to be seen if reliability of BSSVS with sequence data will improve after more sampling cycles have been finished.
Efficiency of BLUP genomic prediction models that use pre-computed SNP variances
Veerkamp, R.F. ; Calus, M.P.L. ; Schrooten, C. - \ 2013
In: Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013. - Wageningen Academic Publishers - ISBN 9789086862283 - p. 453 - 453.
Genome-wide association study to identify chromosomal regions associated with antibody response to Mycobacterium avium subspecies paratuberculosis in milk of Dutch Holstein-Friesians
Hulzen, K.J.E. van; Schopen, G.C.B. ; Arendonk, J.A.M. van; Nielen, M. ; Koets, A.P. ; Schrooten, C. ; Heuven, H.C.M. - \ 2012
Journal of Dairy Science 95 (2012)5. - ISSN 0022-0302 - p. 2740 - 2748.
single nucleotide polymorphisms - estimated breeding values - quantitative trait loci - genetic-variation - johnes-disease - linkage disequilibrium - short-communication - us holsteins - infection - cattle
Heritability of susceptibility to Johne's disease in cattle has been shown to vary from 0.041 to 0.159. Although the presence of genetic variation involved in susceptibility to Johne's disease has been demonstrated, the understanding of genes contributing to the genetic variance is far from complete. The objective of this study was to contribute to further understanding of genetic variation involved in susceptibility to Johne's disease by identifying associated chromosomal regions using a genome-wide association approach. Log-transformed ELISA test results of 265,290 individual Holstein-Friesian cows from 3,927 herds from the Netherlands were analyzed to obtain sire estimated breeding values for Mycobacterium avium subspecies paratuberculosis (MAP)-specific antibody response in milk using a sire-maternal grandsire model with fixed effects for parity, year of birth, lactation stage, and herd; a covariate for milk yield on test day; and random effects for sire, maternal grandsire, and error. For 192 sires with estimated breeding values with a minimum reliability of 70%, single nucleotide polymorphism (SNP) typing was conducted by a multiple SNP analysis with a random polygenic effect fitting 37,869 SNP simultaneously. Five SNP associated with MAP-specific antibody response in milk were identified distributed over 4 chromosomal regions (chromosome 4, 15, 18, and 28). Thirteen putative SNP associated with MAP-specific antibody response in milk were identified distributed over 10 chromosomes (chromosome 4, 14, 16, 18, 19, 20, 21, 26, 27, and 29). This knowledge contributes to the current understanding of genetic variation involved in Johne's disease susceptibility and facilitates control of Johne's disease and improvement of health status by breeding.
Imputation from lower density marker panels to BovineHD in a multi-breed dataset
Schrooten, C. ; Binsbergen, R. van; Beatson, P. ; Bovenhuis, H. - \ 2012
In: Book of Abstracts of the 63rd Annual Meeting of the European Association for Animal Production. - Wageningen Academic Publishers - ISBN 9789086867615 - p. 307 - 307.
Imputation of genotypes with low-density chips and its effect on reliabilty of direct genomic values in Dutch Holstein cattle
Mulder, H.A. ; Calus, M.P.L. ; Druet, T. ; Schrooten, C. - \ 2012
Journal of Dairy Science 95 (2012)2. - ISSN 0022-0302 - p. 876 - 889.
nucleotide polymorphism genotypes - haplotype-phase inference - dairy-cattle - unrelated individuals - wide association - breeding values - jersey cattle - marker panels - gene content - accuracy
Genomic selection using 50,000 single nucleotide polymorphism (50k SNP) chips has been implemented in many dairy cattle breeding programs. Cheap, low-density chips make genotyping of a larger number of animals cost effective. A commonly proposed strategy is to impute low-density genotypes up to 50,000 genotypes before predicting direct genomic values (DGV). The objectives of this study were to investigate the accuracy of imputation for animals genotyped with a low-density chip and to investigate the effect of imputation on reliability of DGV. Low-density chips contained 384, 3,000, or 6,000 SNP. The SNP were selected based either on the highest minor allele frequency in a bin or the middle SNP in a bin, and DAGPHASE, CHROMIBD, and multivariate BLUP were used for imputation. Genotypes of 9,378 animals were used, from which approximately 2,350 animals had deregressed proofs. Bayesian stochastic search variable selection was used for estimating SNP effects of the 50k chip. Imputation accuracies and imputation error rates were poor for low-density chips with 384 SNP. Imputation accuracies were higher with 3,000 and 6,000 SNP. Performance of DAGPHASE and CHROMIBD was very similar and much better than that of multivariate BLUP for both imputation accuracy and reliability of DGV. With 3,000 SNP and using CHROMIBD or DAGPHASE for imputation, 84 to 90% of the increase in DGV reliability using the 50k chip, compared with a pedigree index, was obtained. With multivariate BLUP, the increase in reliability was only 40%. With 384 SNP, the reliability of DGV was lower than for a pedigree index, whereas with 6,000 SNP, about 93% of the increase in reliability of DGV based on the 50k chip was obtained when using DAGPHASE for imputation. Using genotype probabilities to predict gene content increased imputation accuracy and the reliability of DGV and is therefore recommended for applications of imputation for genomic prediction. A deterministic equation was derived to predict accuracy of DGV based on imputation accuracy, which fitted closely with the observed relationship. The deterministic equation can be used to evaluate the effect of differences in imputation accuracy on accuracy and reliability of DGV.
Reliability of direct genomic values based on imputed 50k genotypes using low density chips in Dutch Holstein cattle
Mulder, H.A. ; Calus, M.P.L. ; Druet, T. ; Schrooten, C. - \ 2011
In: Book of Abstracts of the 62nd Annual Meeting of the European Federation of Animal Science, Stavanger, Norway, 29 August - 2 September, 2011. - Wageningen Academic Publishers - ISBN 9789086861774 - p. 25 - 25.
Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls
Roos, A.P.W. de; Schrooten, C. ; Veerkamp, R.F. ; Arendonk, J.A.M. van - \ 2011
Journal of Dairy Science 94 (2011)3. - ISSN 0022-0302 - p. 1559 - 1567.
marker-assisted selection - cattle breeding schemes - dairy-cattle - wide selection - populations - prediction - progress - gain
Genomic selection has the potential to revolutionize dairy cattle breeding because young animals can be accurately selected as parents, leading to a much shorter generation interval and higher rates of genetic gain. The aims of this study were to assess the effects of genomic selection and reduction of the generation interval on the rate of genetic gain and rate of inbreeding. Furthermore, the merit of proven bulls relative to young bulls was studied. This is important for breeding organizations as it determines the relative importance of progeny testing. A closed nucleus breeding scheme was simulated in which 1,000 males and 1,000 females were born annually, 200 bulls were progeny tested, and 20 sires and 200 dams were selected to produce the next generation. In the "proven" (PROV) scenario, only cows with own performance records and progeny-tested bulls were selected as parents. The proportion of the genetic variance that was explained by simulated marker information (M) was varied from 0 to 100%. When M increased from 0 to 100%, the rate of genetic gain increased from 0.238 to 0.309 genetic standard deviations (s) per year (+30%), whereas the rate of inbreeding reduced from 1.00 to 0.42% per generation. Alternatively, when young cows and bulls were selected as parents (YNG scenario), the rate of genetic gain for M=0% was 0.292 s/yr but the corresponding rate of inbreeding increased substantially to 3.15% per generation. A realistic genomic selection scheme (YNG with M=40%) gave 108% higher rate of genetic gain (0.495 s/yr) and approximately the same rate of inbreeding per generation as the conventional system without genomic selection (PROV with M=0%). The rate of inbreeding per year, however, increased from 0.18 to 0.52% because the generation interval in the YNG scheme was much shorter. Progeny-testing fewer bulls reduced the rate of genetic gain and increased the rate of inbreeding for PROV, but had negligible effects for YNG because almost all sires were young bulls. In scenario YNG with M=40%, the best young bulls were superior to the best proven bulls by 1.27 s difference in genomic estimated breeding value. This superiority increased even further when fewer bulls were progeny tested. This stochastic simulation study shows that genomic selection in combination with a severe reduction in the generation interval can double the rate of genetic gain at the same rate of inbreeding per generation, but with a higher rate of inbreeding per year. The number of progeny-tested bulls can be greatly reduced, although this will slightly affect the quality of the proven bull team. Therefore, it is important for breeding organizations to predict the future demand for proven bull semen in light of the increasing superiority of young bulls
The impact of genomic selection and short generation interval on dairy cattle breeding programms
Roos, S. de; Schrooten, C. ; Veerkamp, R.F. ; Arendonk, J.A.M. van - \ 2010
Genotyping panels available in cattle and their properties
Druet, T. ; Zhang, Z. ; Coppieters, W. ; Mulder, H.A. ; Calus, M.P.L. ; Mullaart, E. ; Schrooten, C. ; Roos, S. de; Georges, M. - \ 2010
In: Book of Abstracts 61th Annual Meeting of the European Association for Animal Production, Heraklion, Crete, Greece, 23-27 August 2010. - Wageningen : Wageningen Academic Publishers - ISBN 9789086861521 - p. 356 - 356.
Breeding for a global dairy market using genomic selection
Roos, S. de; Schrooten, C. ; Veerkamp, R.F. ; Arendonk, J.A.M. van - \ 2009
In: Book of Abstracts of the 60th Annual Meeting of the European Association for Animal Production, Barcelona, Spain, 24 - 27 August, 2009. - Wageningen : Wageningen Academic Publishers - ISBN 9789086861217 - p. 29 - 29.
From the simulation of a closed nucleus breeding program for dairy cattle it was concluded that the introduction of genomic selection and the use of young animals as parents increased the rate of genetic gain by a factor 2.4 when genetic markers explained 50% of the genetic variance. In this situation, all bulls in the top 100 EBV list were young bulls. While genomic selection reduced the rate of inbreeding, the actual rate of inbreeding per year was increased by a factor 1.6 because of the use of young animals as parents. When a reference population was available in environment A but not in environment B, selection based on the average EBV in environment A and B was the most effective strategy when the genetic correlation between A and B was ¿0.90. When the genetic correlation between A and B was ¿0.75 the rate of genetic gain was lower across all strategies. Splitting the population gave the highest rate of genetic gain but also the highest rate of inbreeding.
Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values
Calus, M.P.L. ; Meuwissen, T.H.E. ; Windig, J.J. ; Knol, E.F. ; Schrooten, C. ; Vereijken, A.L.J. ; Veerkamp, R.F. - \ 2009
Genetics, Selection, Evolution 41 (2009). - ISSN 0999-193X - 10 p.
quantitative trait loci - linkage disequilibrium - assisted selection - identity - descent - information - parameters
The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD) probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r2 value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction
Heritability estimates of behavioural and physiological responses of Holstein Friesian heifer calves to a behavioural test
Reenen, C.G. van; Werf, J.T.N. van der; Engel, B. ; Campion, J. ; Schrooten, C. ; Calus, M.P.L. - \ 2009
In: Proceedings of the 42nd congress of the ISAE, Dublin, Ireland, 5-9 August, 2008. - - p. 110 - 110.
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