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.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Assessing keel bone damage in laying hens by palpation : effects of assessor experience on accuracy, inter-rater agreement and intra-rater consistency
Buijs, S. ; Heerkens, J.L.T. ; Ampe, B. ; Delezie, E. ; Rodenburg, T.B. ; Tuyttens, F.A.M. - \ 2019
Poultry Science 98 (2019)2. - ISSN 0032-5791 - p. 514 - 521.
accuracy - deviation - fracture - keel bone - palpation

Accurate assessment is essential when evaluating keel bone damage. Palpation is commonly used to assess keel bone damage in living hens. However, there is little information on the accuracy of assessment of deviations and fractures on different parts of the keel, and on the consistency within, and agreement between, assessors. Crucially, although the importance of experience is commonly emphasized, knowledge on its effect is scarce. Ten assessors with or without prior experience palpated the same 50 75-wk-old hens for deviations, medial fractures, and caudal fractures (scored as present/absent). Accuracy, sensitivity, specificity, precision, and negative predictive value were determined by comparing palpation scores to post-dissection assessment, and then compared between experienced and inexperienced assessors. To determine the effect of the experience gained during the experiment, hens were subsequently re-assessed. Consistency within, and agreement between, assessors were also determined. Assessors with prior experience were more accurate (proportion of accurately assessed deviations: experienced 0.83 vs. inexperienced 0.79±0.01, P = 0.04; medial fractures: 0.82 vs. 0.68±0.03 in session 1 only, P = 0.04; caudal fractures: 0.41 vs. 0.29±0.03, P = 0.03), and inexperienced assessors classified medial fractures more accurately in session 2 (session 1: 0.68 vs. session 2: 0.77±0.04, P = 0.04). However, effect sizes were small for deviations and even experienced assessors lacked accuracy when assessing caudal fractures. Unexpectedly, deviations tended to be assessed more accurately in session 1 than in session 2, regardless of assessor status (1: 0.83 vs. 2: 0.79±0.01, P = 0.06), suggesting that prolonged assessment contributes to errors. Prior experience decreased specificity and precision of fracture assessment (more unfractured keels were classified as fractured) even though overall accuracy was greater. Intra-rater consistency was fair to good (0.55 to 0.67) for deviations and medial fractures, but poor to fair (0.36 to 0.44) for caudal fractures, and unaffected by prior experience (P = 0.49 to 0.89). In conclusion, experience improves accuracy to a limited extent but does not guarantee high accuracy for all types of damage. Future research should determine if other training methods (e.g., comparison to post-dissection scores or to radiographs) improve accuracy.

Improving accuracy of bulls' predicted genomic breeding values for fertility using daughters' milk progesterone profiles
Tenghe, A.M.M. ; Bouwman, A.C. ; Berglund, B. ; Koning, D.J. de; Veerkamp, R.F. - \ 2018
Journal of Dairy Science 101 (2018)6. - ISSN 0022-0302 - p. 5177 - 5193.
accuracy - dairy cattle - milk progesterone - multitrait genomic prediction
The main objective of this study was to investigate the benefit of accuracy of genomic prediction when combining records for an intermediate physiological phenotype in a training population with records for a traditional phenotype. Fertility was used as a case study, where commencement of luteal activity (C-LA) was the physiological phenotype, whereas the interval from calving to first service and calving interval were the traditional phenotypes. The potential accuracy of across-country genomic prediction and optimal recording strategies of C-LA were also investigated in terms of the number of farms and number of repeated records for C-LA. Predicted accuracy was obtained by estimating population parameters for the traits in a data set of 3,136 Holstein Friesian cows with 8,080 lactations and using a deterministic prediction equation. The effect of genetic correlation, heritability, and reliability of C-LA on the accuracy of genomic prediction were investigated. When the existing training population was 10,000 bulls with reliable estimated breeding value for the traditional trait, predicted accuracy for the physiological trait increased from 0.22 to 0.57 when 15,000 cows with C-LA records were added to the bull training population; but, when the interest was in predicting the traditional trait, we found no benefit from the additional recording. When the genetic correlation was higher between the physiological and traditional traits (0.7 instead of 0.3), accuracy increased less when adding the 15.000 cows with C-LA (from 0.51 to 0.63). In across-country predictions, we observed little to no increase in accuracy of the intermediate physiological phenotype when the training population from Sweden was large, but when accuracy increased the training population was small (200 cows), from 0.19 to 0.31 when 15,000 cows were added from the Netherlands (genetic correlation of 0.5 between countries), and from 0.19 to 0.48 for genetic correlation of 0.9. The predicted accuracy initially increased substantially when recording on the same farm was extended and multiple C-LA records per cow were used in prediction compared with single records; that is, accuracy increased from 0.33 with single records to 0.38 with multiple records (on average 1.6 records per cow) from 2 yr of recording C-LA. But, when the number C-LA per cow increased beyond 2 yr of recording, we noted no substantial benefit in accuracy from multiple records. For example, for 5 yr of recording (on average 2.5 records per cow), accuracy was 0.47; on doubling the recording period to 10 yr (on average 3.1 records per cow), accuracy increased by 0.07 units, whereas when C-LA was recorded for 15 yr (on average 3.3 records per cow) accuracy increased only by 0.05 units. Therefore, for genomic prediction using expensive equipment to record traits for training populations, it is important to optimize the recording strategy. The focus should be on recording more cows rather than continuous recording on the same cows.
The effect of acquisition error and level of detail on the accuracy of spatial analyses
Biljecki, Filip ; Heuvelink, Gerard B.M. ; Ledoux, Hugo ; Stoter, Jantien - \ 2018
Cartography and Geographic Information Science 45 (2018)2. - ISSN 1523-0406 - p. 156 - 176.
3D city model - accuracy - CityGML - error - level of detail - Scale
There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data.
Data from: Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP
Berg, S. van den; Calus, M.P.L. ; Meuwissen, T.H.E. ; Wientjes, Y.C.J. - \ 2015
genomic prediction - across population - Bayesian variable selection - GBLUP - accuracy - number of independent chromosome segments
Background: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). Results: The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (M e ) across the populations. Conclusion: Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than M e . Across populations, M e is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than M e across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.
Data from: An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments
Wientjes, Y.C.J. ; Bijma, P. ; Veerkamp, R.F. ; Calus, M.P.L. - \ 2015
genomic prediction - multi-population - accuracy - prediction equation
Predicting the accuracy of estimated genomic values using genome-wide marker information is an important step in designing training populations. Currently, different deterministic equations are available to predict accuracy within populations, but not for multipopulation scenarios where data from multiple breeds, lines or environments are combined. Therefore, our objective was to develop and validate a deterministic equation to predict the accuracy of genomic values when different populations are combined in one training population. The input parameters of the derived prediction equation are the number of individuals and the heritability from each of the populations in the training population; the genetic correlations between the populations, i.e., the correlation between allele substitution effects of quantitative trait loci; the effective number of chromosome segments across predicted and training populations; and the proportion of the genetic variance in the predicted population captured by the markers in each of the training populations. Validation was performed based on real genotype information of 1033 Holstein–Friesian cows that were divided into three different populations by combining half-sib families in the same population. Phenotypes were simulated for multiple scenarios, differing in heritability within populations and in genetic correlations between the populations. Results showed that the derived equation can accurately predict the accuracy of estimating genomic values for different scenarios of multipopulation genomic prediction. Therefore, the derived equation can be used to investigate the potential accuracy of different multipopulation genomic prediction scenarios and to decide on the most optimal design of training populations.
Using selection index theory to estimate consistency of multi-locus linkage disequilibrium across populations
Wientjes, Y.C.J. ; Veerkamp, R.F. ; Calus, M.P.L. - \ 2015
BMC Genetics 16 (2015). - ISSN 1471-2156
genomic breeding values - genetic-relationship information - quantitative trait loci - dairy-cattle breeds - prediction - accuracy - haplotype - markers - impact - lines
The potential of combining multiple populations in genomic prediction is depending on the consistency of linkage disequilibrium (LD) between SNPs and QTL across populations. We investigated consistency of multi-locus LD across populations using selection index theory and investigated the relationship between consistency of multi-locus LD and accuracy of genomic prediction across different simulated scenarios. In the selection index, QTL genotypes were considered as breeding goal traits and SNP genotypes as index traits, based on LD among SNPs and between SNPs and QTL. The consistency of multi-locus LD across populations was computed as the accuracy of predicting QTL genotypes in selection candidates using a selection index derived in the reference population. Different scenarios of within and across population genomic prediction were evaluated, using all SNPs or only the four neighboring SNPs of a simulated QTL. Phenotypes were simulated using different numbers of QTL underlying the trait. The relationship between the calculated consistency of multi-locus LD and accuracy of genomic prediction using a GBLUP type of model was investigated.
The accuracy of predicting QTL genotypes, i.e. the measure describing consistency of multi-locus LD, was much lower for across population scenarios compared to within population scenarios, and was lower when QTL had a low MAF compared to QTL randomly selected from the SNPs. Consistency of multi-locus LD was highly correlated with the realized accuracy of genomic prediction across different scenarios and the correlation was higher when QTL were weighted according to their effects in the selection index instead of weighting QTL equally. By only considering neighboring SNPs of QTL, accuracy of predicting QTL genotypes within population decreased, but it substantially increased the accuracy across populations.
Consistency of multi-locus LD across populations is a characteristic of the properties of the QTL in the investigated populations and can provide more insight in underlying reasons for a low empirical accuracy of across population genomic prediction. By focusing in genomic prediction models only on neighboring SNPs of QTL, multi-locus LD is more consistent across populations since only short-range LD is considered, and accuracy of predicting QTL genotypes of individuals from another population is increased.
Animal board Invited Review: Genetic possibilities to reduce enteric methane emissions from ruminants
Pickering, N.K. ; Oddy, V.H. ; Basarab, J. ; Cammack, K. ; Hayes, B. ; Hegarty, R. ; Lassen, J. ; McEwan, J. ; Miller, S. ; Pinares-Patino, C. ; Haas, Y. de - \ 2015
Animal 9 (2015)9. - ISSN 1751-7311 - p. 1431 - 1440.
special topics-mitigation - nitrous-oxide emissions - dairy-cows - genomic selection - sheep - rumen - fermentation - accuracy - cattle - livestock
Measuring and mitigating methane (CH4) emissions from livestock is of increasing importance for the environment and for policy making. Potentially, the most sustainable way of reducing enteric CH4 emission from ruminants is through the estimation of genomic breeding values to facilitate genetic selection. There is potential for adopting genetic selection and in the future genomic selection, for reduced CH4 emissions from ruminants. From this review it has been observed that both CH4 emissions and production (g/day) are a heritable and repeatable trait. CH4 emissions are strongly related to feed intake both in the short term (minutes to several hours) and over the medium term (days). When measured over the medium term, CH4 yield (MY, g CH4/kg dry matter intake) is a heritable and repeatable trait albeit with less genetic variation than for CH4 emissions. CH4 emissions of individual animals are moderately repeatable across diets, and across feeding levels, when measured in respiration chambers. Repeatability is lower when short term measurements are used, possibly due to variation in time and amount of feed ingested prior to the measurement. However, while repeated measurements add value; it is preferable the measures be separated by at least 3 to 14 days. This temporal separation of measurements needs to be investigated further. Given the above issue can be resolved, short term (over minutes to hours) measurements of CH4 emissions show promise, especially on systems where animals are fed ad libitum and frequency of meals is high. However, we believe that for short-term measurements to be useful for genetic evaluation, a number (between 3 and 20) of measurements will be required over an extended period of time (weeks to months). There are opportunities for using short-term measurements in standardised feeding situations such as breath 'sniffers' attached to milking parlours or total mixed ration feeding bins, to measure CH4. Genomic selection has the potential to reduce both CH4 emissions and MY, but measurements on thousands of individuals will be required. This includes the need for combined resources across countries in an international effort, emphasising the need to acknowledge the impact of animal and production systems on measurement of the CH4 trait during design of experiments.
The effect of rare alleles on estimated genomic relationships from whole genome sequence data
Eynard, S.E. ; Windig, J.J. ; Leroy, G. ; Binsbergen, R. van; Calus, M.P.L. - \ 2015
BMC Genetics 16 (2015). - ISSN 1471-2156
information - pedigree - conservation - populations - prediction - accuracy - cattle - coefficients - improvement - challenges
Relationships between individuals and inbreeding coefficients are commonly used for breeding decisions, but may be affected by the type of data used for their estimation. The proportion of variants with low Minor Allele Frequency (MAF) is larger in whole genome sequence (WGS) data compared to Single Nucleotide Polymorphism (SNP) chips. Therefore, WGS data provide true relationships between individuals and may influence breeding decisions and prioritisation for conservation of genetic diversity in livestock. This study identifies differences between relationships and inbreeding coefficients estimated using pedigree, SNP or WGS data for 118 Holstein bulls from the 1000 Bull genomes project. To determine the impact of rare alleles on the estimates we compared three scenarios of MAF restrictions: variants with a MAF higher than 5%, variants with a MAF higher than 1% and variants with a MAF between 1% and 5%. Results We observed significant differences between estimated relationships and, although less significantly, inbreeding coefficients from pedigree, SNP or WGS data, and between MAF restriction scenarios. Computed correlations between pedigree and genomic relationships, within groups with similar relationships, ranged from negative to moderate for both estimated relationships and inbreeding coefficients, but were high between estimates from SNP and WGS (0.49 to 0.99). Estimated relationships from genomic information exhibited higher variation than from pedigree. Inbreeding coefficients analysis showed that more complete pedigree records lead to higher correlation between inbreeding coefficients from pedigree and genomic data. Finally, estimates and correlations between additive genetic (A) and genomic (G) relationship matrices were lower, and variances of the relationships were larger when accounting for allele frequencies than without accounting for allele frequencies. Conclusions Using pedigree data or genomic information, and including or excluding variants with a MAF below 5% showed significant differences in relationship and inbreeding coefficient estimates. Estimated relationships and inbreeding coefficients are the basis for selection decisions. Therefore, it can be expected that using WGS instead of SNP can affect selection decision. Inclusion of rare variants will give access to the variation they carry, which is of interest for conservation of genetic diversity.
Design of reference populations for genomic selection in crossbreeding programs
Grevenhof, E.M. van; Werf, J.H.J. van der - \ 2015
Genetics, Selection, Evolution 47 (2015). - ISSN 0999-193X - 9 p.
relationship matrix - genetic evaluation - information - accuracy - performance - prediction - livestock
Background In crossbreeding programs, genomic selection offers the opportunity to make efficient use of information on crossbred (CB) individuals in the selection of purebred (PB) candidates. In such programs, reference populations often contain genotyped PB animals, although the breeding objective is usually more focused on CB performance. The question is what would be the benefit of including a larger proportion of CB individuals in the reference population. MethodsIn a deterministic simulation study, we evaluated the benefit of including various proportions of CB animals in a reference population for genomic selection of PB animals in a crossbreeding program. We used a pig breeding scheme with selection for a moderately heritable trait and a size of 6000 for the reference population. ResultsApplying genomic selection to improve the performance of CB individuals, with a genetic correlation between PB and CB performance (rPC) of 0.7, selection accuracy of PB candidates increased from 0.49 to 0.52 if the reference population consisted of PB individuals, it increased to 0.55 if the reference population consisted of the same number of CB individuals, and to 0.60 if the size of the CB reference population was twice that of the reference population for each PB line. The advantage of using CB rather than PB individuals increased linearly with the proportion of CB individuals in the reference population. This advantage disappeared quickly if rPC was higher or if the breeding objective put some emphasis on PB performance. The benefit of adding CB individuals to an existing PB reference population was limited for high rPC. ConclusionsUsing CB rather than PB individuals in a reference population for genomic selection can provide substantial advantages, but only when correlations between PB and CB performances are not high and PB performance is not part of the breeding objective.
Grondwaterstand handmatig meten blijkt best nauwkeurig
Knotters, M. ; Meij, T. de; Pleijter, M. - \ 2014
H2O : tijdschrift voor watervoorziening en afvalwaterbehandeling 47 (2014)1. - ISSN 0166-8439 - p. 28 - 28.
grondwaterstand - nauwkeurigheid - meting - experimenteel veldonderzoek - overijssel - groundwater level - accuracy - measurement - field experimentation
Het valt reuze mee met de nauwkeurigheid van handmatig gemeten grondwaterstanden. Wie de grondwaterstand op een zeer traditionele manier meet (via meetlint in de peilbuis) of met een elektronisch peilapparaat met geluidssignaal, maakt meetfouten van hooguit enkele centimeters. Een experiment bij het Boetelerveld wijst dat uit.
Genomic prediction based on data from three layer lines using non-linear regression models
Huang, H. ; Windig, J.J. ; Vereijken, A. ; Calus, M.P.L. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 11 p.
dairy-cattle breeds - dimensionality reduction - gaussian kernel - accuracy - traits - values - validation - selection - pedigree - plant
Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. Results - When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Conclusions - Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional occurrence of large negative accuracies when the evaluated line was not included in the training dataset. Furthermore, when using a multi-line training dataset, non-linear models provided information on the genotype data that was complementary to the linear models, which indicates that the underlying data distributions of the three studied lines were indeed heterogeneous.
A comparison of principal component regression and genomic REML for genomic prediction across populations
Dadousis, C. ; Veerkamp, R.F. ; Heringstad, B. ; Pszczola, M.J. ; Calus, M.P.L. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 14 p.
breeding values - multi-breed - short communication - variable selection - wide association - genetic value - dairy-cattle - data sets - accuracy - information
Background Genomic prediction faces two main statistical problems: multicollinearity and n¿«¿p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction. Methods The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK. Results In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC. Conclusions On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.
Linkage disequilibrium patterns and persistence of phase in purebred and crossbred pig (Sus scrofa) populations
Veroneze, R. ; Bastiaansen, J.W.M. ; Knol, E.F. ; Guimaraes, S.E.F. ; Silva, F.F. ; Harlizius, B. ; Lopes, M.S. ; Lopes, P.S. - \ 2014
BMC Genetics 15 (2014). - ISSN 1471-2156 - 9 p.
haplotype block structure - genomic selection - accuracy - breeds - cattle - wide - prediction - diversity - genetics - extent
Background Genomic selection and genomic wide association studies are widely used methods that aim to exploit the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL). Securing a sufficiently large set of genotypes and phenotypes can be a limiting factor that may be overcome by combining data from multiple breeds or using crossbred information. However, the estimated effect of a marker in one breed or a crossbred can only be useful for the selection of animals in another breed if there is a correspondence of the phase between the marker and the QTL across breeds. Using data of five pure pig (Sus scrofa) lines (SL1, SL2, SL3, DL1, DL2), one F1 cross (DLF1) and two commercial finishing crosses (TER1 and TER2), the objectives of this study were: (i) to compare the equality of LD decay curves of different pig populations; and (ii) to evaluate the persistence of the LD phase across lines or final crosses. Results Almost all of the lines presented different extents of LD, except for the SL2 and DL3, both of which exhibited the same extent of LD. Similar levels of LD over large distances were found in crossbred and pure lines. The crossbred animals (DLF1, TER1 and TER2) presented a high persistence of phase with their parental lines, suggesting that the available porcine single nucleotide polymorphism (SNP) chip should be dense enough to include markers that have the same LD phase with QTL across crossbred and parental pure lines. The persistence of phase across pure lines varied considerably between the different line comparisons; however, correlations were above 0.8 for all line comparisons when marker distances were smaller than 50 kb. Conclusions This study showed that crossbred populations could be very useful as a reference for the selection of pure lines by means of the available SNP chip panel. Here, we also pinpoint pure lines that could be combined in a multiline training population. However, if multiline reference populations are used for genomic selection, the required density of SNP panels should be higher compared with a single breed reference population.
Strategic test-day recording regimes to estimate lactation yield in tropical dairy animals
McGill, D.M. ; Thomson, P.C. ; Mulder, H.A. ; Lievaart, J.J. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 13 p.
day milk-yield - genetic-parameters - sahiwal cattle - production traits - curves - cows - accuracy - models
Background In developing dairy sectors, genetic improvement programs have limited resources and recording of herds is minimal. This study evaluated different methods to estimate lactation yield and sampling schedules with fewer test-day records per lactation to determine recording regimes that (1) estimate lactation yield with a minimal impact on the accuracy of selection and (2) optimise the available resources. Methods Using Sahiwal cattle as a tropical dairy breed example, weekly milk records from 464 cows were used in a simulation study to generate different shaped lactation curves. The daily milk yields from these simulated lactation curves were subset to equally spaced (weekly, monthly and quarterly) and unequally spaced (with four, five or six records per lactation) test-day intervals. Lactation yield estimates were calculated from these subsets using two methods: the test-interval method and Wood’s (Nature 216:164-165, 1967) lactation curve model. Using the resulting lactation yields, breeding values were predicted and comparisons were made between the sampling regimes and estimation methods. Results The results show that, based on the mean square error of prediction, use of Wood’s lactation curve model to estimate total yield was more accurate than use of the test-interval method. However, the differences in the ranking of animals were small, i.e. a 1 to 5% difference in accuracy. Comparisons between the different test-day sampling regimes showed that, with the same number of records per lactation (for example, quarterly and four test-days), strategically timed test-days can result in more accurate estimates of lactation yield than test-days at equal intervals. Conclusions An important outcome of these results is that combining Wood’s model for lactation yield estimation and as few as four, five or six strategically placed test-day records can produce estimates of lactation yield that are comparable with estimates based on monthly test-day records using the test-interval method. Furthermore, calculations show that although using fewer test-days results in a decrease in the accuracy of selection, it does provide an opportunity to progeny-test more sires. Thus, using strategically timed test-days and Wood’s model to estimate lactation yield, can lead to a more efficient use of the allocated resources.
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 based on data from three layer lines: a comparison between linear methods
Calus, M.P.L. ; Huang, H. ; Vereijken, J. ; Visscher, J. ; Napel, J. ten; Windig, J.J. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 13 p.
principal component approach - support vector regression - dairy-cattle breeds - linkage disequilibrium - prior-knowledge - discriminant-analysis - values - selection - accuracy - traits
Background The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. Methods Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we evaluated the following genomic prediction models: genome-enabled BLUP (GBLUP), ridge regression BLUP (RRBLUP), principal component analysis followed by ridge regression (RRPCA), BayesC and Bayesian stochastic search variable selection. Prediction accuracy was measured as the correlation between predicted breeding values and observed phenotypes divided by the square root of the heritability. The data used concerned laying hens with phenotypes for number of eggs in the first production period and known genotypes. The hens were from two closely-related brown layer lines (B1 and B2), and a third distantly-related white layer line (W1). Lines had 1004 to 1023 training animals and 238 to 240 validation animals. Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms. Results Genomic prediction models yielded 0.13 to 0.16 higher accuracies than pedigree-based BLUP. When excluding the line itself from the training dataset, genomic predictions were generally inaccurate. Use of multiple lines marginally improved prediction accuracy for B2 but did not affect or slightly decreased prediction accuracy for B1 and W1. Differences between models were generally small except for RRPCA which gave considerably higher accuracies for B2. Correlations between genomic predictions from different methods were higher than 0.96 for W1 and higher than 0.88 for B1 and B2. The greater differences between methods for B1 and B2 were probably due to the lower accuracy of predictions for B1 (~0.45) and B2 (~0.40) compared to W1 (~0.76). Conclusions Multi-line genomic prediction did not affect or slightly improved prediction accuracy for closely-related lines. For distantly-related lines, multi-line genomic prediction yielded similar or slightly lower accuracies than single-line genomic prediction. Bayesian variable selection and GBLUP generally gave similar accuracies. Overall, RRPCA yielded the greatest accuracies for two lines, suggesting that using PCA helps to alleviate the “n¿«¿p” problem in genomic prediction.
Evaluating the performance of commonly used gas analysers for methane eddy covariance flux measurements: the InGOS inter-comparison field experiment
Peltola, O. ; Hensen, A. ; Helfter, C. ; Belelli Marchesini, L. ; Bosveld, F.C. ; Bulk, W.C.M. van de; Elbers, J.A. ; Haapanala, S. ; Holst, J. ; Laurila, T. ; Lindroth, A. ; Nemitz, E. ; Röckmann, T. ; Vermeulen, A.T. ; Mammarella, I. - \ 2014
Biogeosciences 11 (2014). - ISSN 1726-4170 - p. 3163 - 3186.
water-vapor - atmospheric methane - mixing-ratio - wpl terms - path - ch4 - attenuation - accuracy - strategy - quality
The performance of eight fast-response methane (CH4) gas analysers suitable for eddy covariance flux measurements were tested at a grassland site near the Cabauw tall tower (Netherlands) during June 2012. The instruments were positioned close to each other in order to minimise the effect of varying turbulent conditions. The moderate CH4 fluxes observed at the location, of the order of 25 nmol m-2 s-1, provided a suitable signal for testing the instruments' performance. Generally, all analysers tested were able to quantify the concentration fluctuations at the frequency range relevant for turbulent exchange and were able to deliver high-quality data. The tested cavity ringdown spectrometer (CRDS) instruments from Picarro, models G2311-f and G1301-f, were superior to other CH4 analysers with respect to instrumental noise. As an open-path instrument susceptible to the effects of rain, the LI-COR LI-7700 achieved lower data coverage and also required larger density corrections; however, the system is especially useful for remote sites that are restricted in power availability. In this study the open-path LI-7700 results were compromised due to a data acquisition problem in our data-logging setup. Some of the older closed-path analysers tested do not measure H2O concentrations alongside CH4 (i.e. FMA1 and DLT-100 by Los Gatos Research) and this complicates data processing since the required corrections for dilution and spectroscopic interactions have to be based on external information. To overcome this issue, we used H2O mole fractions measured by other gas analysers, adjusted them with different methods and then applied them to correct the CH4 fluxes. Following this procedure we estimated a bias of the order of 0.1 g (CH4) m-2 (8% of the measured mean flux) in the processed and corrected CH4 fluxes on a monthly scale due to missing H2O concentration measurements. Finally, cumulative CH4 fluxes over 14 days from three closed-path gas analysers, G2311-f (Picarro Inc.), FGGA (Los Gatos Research) and FMA2 (Los Gatos Research), which were measuring H2O concentrations in addition to CH4, agreed within 3% (355–367 mg (CH4) m-2) and were not clearly different from each other, whereas the other instruments derived total fluxes which showed small but distinct differences (±10%, 330–399 mg (CH4) m-2).
Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications
Calus, M.P.L. ; Bouwman, A.C. ; Hickey, J.M. ; Veerkamp, R.F. ; Mulder, H.A. - \ 2014
Animal 8 (2014)11. - ISSN 1751-7311 - p. 1743 - 1753.
density marker panels - nucleotide polymorphism genotypes - dutch holstein cattle - dairy-cattle - snp genotypes - jersey cattle - beef-cattle - accuracy - populations - selection
In livestock, many studies have reported the results of imputation to 50k single nucleotide polymorphism (SNP) genotypes for animals that are genotyped with low-density SNP panels. The objective of this paper is to review different measures of correctness of imputation, and to evaluate their utility depending on the purpose of the imputed genotypes. Across studies, imputation accuracy, computed as the correlation between true and imputed genotypes, and imputation error rates, that counts the number of incorrectly imputed alleles, are commonly used measures of imputation correctness. Based on the nature of both measures and results reported in the literature, imputation accuracy appears to be a more useful measure of the correctness of imputation than imputation error rates, because imputation accuracy does not depend on minor allele frequency (MAF), whereas imputation error rate depends on MAF. Therefore imputation accuracy can be better compared across loci with different MAF. Imputation accuracy depends on the ability of identifying the correct haplotype of a SNP, but many other factors have been identified as well, including the number of genotyped immediate ancestors, the number of animals with genotypes at the high-density panel, the SNP density on the low- and high-density panel, the MAF of the imputed SNP and whether imputed SNP are located at the end of a chromosome or not. Some of these factors directly contribute to the linkage disequilibrium between imputed SNP and SNP on the low-density panel. When imputation accuracy is assessed as a predictor for the accuracy of subsequent genomic prediction, we recommend that: (1) individual-specific imputation accuracies should be used that are computed after centring and scaling both true and imputed genotypes; and (2) imputation of gene dosage is preferred over imputation of the most likely genotype, as this increases accuracy and reduces bias of the imputed genotypes and the subsequent genomic predictions.
Short communication : Validation of genomic breeding value predictions for feed intake and feed efficiency traits
Pryce, J.E. ; Wales, W.J. ; Haas, Y. de; Veerkamp, R.F. ; Hayes, B.J. ; Coffey, M.P. ; Marett, L.C. ; Bornhill, J.B. ; Gonzalez-Recio, O. - \ 2014
Journal of Dairy Science 97 (2014)1. - ISSN 0022-0302 - p. 537 - 542.
dairy-cattle - data sets - accuracy
Validating genomic prediction equations in independent populations is an important part of evaluating genomic selection. Published genomic predictions from 2 studies on (1) residual feed intake and (2) dry matter intake (DMI) were validated in a cohort of 78 multiparous Holsteins from Australia. The mean realized accuracy of genomic prediction for residual feed intake was 0.27 when the reference population included phenotypes from 939 New Zealand and 843 Australian growing heifers (aged 5–8 mo) genotyped on high density (770k) single nucleotide polymorphism chips. The 90% bootstrapped confidence interval of this estimate was between 0.16 and 0.36. The mean realized accuracy was slightly lower (0.25) when the reference population comprised only Australian growing heifers. Higher realized accuracies were achieved for DMI in the same validation population and using a multicountry model that included 958 lactating cows from the Netherlands and United Kingdom in addition to 843 growing heifers from Australia. The multicountry analysis for DMI generated 3 sets of genomic predictions for validation animals, one on each country scale. The highest mean accuracy (0.72) was obtained when the genomic breeding values were expressed on the Dutch scale. Although the validation population used in this study was small (n=78), the results illustrate that genomic selection for DMI and residual feed intake is feasible. Multicountry collaboration in the area of dairy cow feed efficiency is the evident pathway to achieving reasonable genomic prediction accuracies for these valuable traits.
Right-hand-side updating for fast computing of genomic breeding values
Calus, M.P.L. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 24 p.
genetic value - prediction - selection - accuracy - reliability - algorithm - cattle
Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.
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