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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    Meta-analysis of genome wide association studies for the stature of cattle reveals numerous common genes that regulate size in mammals
    Hayes, B. ; Bouwman, A.C. ; Daetwyler, H.D. ; Chamberlain, Amanda - \ 2018
    In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. -
    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.
    Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle
    Binsbergen, R. van; Bink, M.C.A.M. ; Calus, M.P.L. ; Hayes, B. ; Eeuwijk, F.A. van; Veerkamp, R.F. - \ 2013
    Consensus methods for breeding low methane emitting : Breeding ruminants that emit less methane - development of consensus methods for measurements of methane (white paper)
    Pickering, N.K. ; Haas, Y. de; Basarab, J. ; Cammack, K. ; Hayes, B. ; Hegarty, R. ; Lassen, J. ; McEwan, J. ; Miller, S. ; Pinares-Patino, C. ; Shackell, G. ; Vercoe, P. ; Oddy, Hutton - \ 2013
    ASGGN - 57
    animal breeding - selection - animal genetics - genomics - methane - sheep - cattle - agriculture and environment - dierveredeling - selectie - diergenetica - genomica - methaan - schapen - rundvee - landbouw en milieu
    This report was prepared by a working group of the Animal Selection, Genetics and Genomics Network (ASGGN) of the Global Research Alliance for reducing greenhouse gases from agriculture. It is a summary of published and yet to be published work. The purpose is to evaluate methods that are potentially useful for measuring CH4 emissions in individual animals so as to estimate genetic parameters and subsequently screen animals for use in selective breeding programs including its use in the development of genomic selection.
    1000 Bull Genomes - Toward genomic Selectionf from whole genome sequence Data in Dairy and Beef Cattle
    Hayes, B. ; Daetwyler, H.D. ; Fries, R. ; Guldbrandtsen, B. ; Mogens Sando Lund, M. ; Didier A. Boichard, D.A. ; Stothard, P. ; Veerkamp, R.F. ; Hulsegge, B. ; Rocha, D. ; Tassell, C. ; Mullaart, E. ; Gredler, B. ; Druet, T. ; Bagnato, A. ; Goddard, M.E. ; Chamberlain, H.L. - \ 2013
    Genomic prediction of breeding values is now used as the basis for selection of dairy cattle, and in some cases beef cattle, in a number of countries. When genomic prediction was introduced most of the information was to thought to be derived from linkage disequilibrium between markers and causative variants. It has become clear that much of the predictive power, based on 50,000 DNA markers, in fact derives from prediction of the effect of large chromosome segments that segregate within fairly closely related animals. This has lead to problems with across breed prediction, rapid decay of predictive power over generations and insufficient accuracy in some situations. Using full genome sequence data in genomic prediction should overcome these problems. If linkage disequilibrium between SNP on standard arrays and causative mutations affecting the quantitative trait is incomplete, accuracy of prediction should be improved as a result of including the actual causative mutations affecting the trait of interest in the data set. Secondly, persistence of accuracy of genomic predictions across generations will be improved with full sequence data, as the genomic predictions no longer depend on associations between SNP and causative mutations which currently erode quite rapidly with recombination. Thirdly, if genomic predictions are made across breeds, using full sequence data is likely to be particularly advantageous, as there is no longer a need to rely on marker- associations which may not persist across breeds. However, the cost of sequencing is such that the very large numbers of animals required for genomic prediction will not be sequenced An alternative strategy is to sequence key ancestors of the population, then impute the genotypes for the sequence variants into much larger reference sets with phenotypes and SNP panel genotypes. The 1000 Bull Genomes Project aims at building such a resource of sequenced key ancestor bulls for the bovine research community. The most recent run of the project included 238 full genome sequences of 130 Holstein, 43 Fleckvieh, 48 Angus and 15 Jersey bulls, sequenced at an average of 10.5 fold coverage. There were 25.2 million filtered sequence variants detected in the sequences, including 23.5 million SNP and 1.7 million insertion-deletions. Agreement of sequence genotypes to genotypes from an 800K SNP array in the sequenced Holstein bulls, where there was most data, was excellent at 98.8%. This increased to 99.7% when the genotypes were imputed based on all sequences. Concordance was slightly lower in other breeds. This project will provide an excellent opportunity to identify the most important causative variants, leading to greater understanding of biology underlying quantitative traits. Examples are given of genomic predictions for fertility, health and production traits using imputed sequence data.
    Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle
    Binsbergen, R. van; Bink, M.C.A.M. ; Calus, M.P.L. ; Hayes, B. ; Eeuwijk, F.A. van; Veerkamp, R.F. - \ 2013
    Accuracy of Imputation to Whole-Genome Sequence Data in Holstein Friesian Cattle
    Binsbergen, R. van; Bink, M.C.A.M. ; Calus, M.P.L. ; Hayes, B. ; Eeuwijk, F.A. van; Veerkamp, R.F. - \ 2013
    The use of whole-genome sequence data can lead to more accurate genomic predictions in animal and plants. Despite the fact that costs of sequencing are falling, sequencing a high number of individuals is still far too expensive. A promising approach is to sequence the genomes of a core set of individuals and impute the missing genotypes for the remaining individuals that are genotyped with currently available marker arrays. Relevant questions are how many animals do we need to sequence and what SNP arrays can we impute from for accurate imputation? Sequence data of 124 Holstein Friesian bulls from different countries were provided by the 1000 bull genomes project consortium (www.1000bullgenomes.com). Two chromosomes with distinct sizes (1 and 29) were selected for this study. The Beagle software was used for imputation and accuracy was assessed via cross validation. The 124 bulls were randomly divided into five sets: four sets were merged into a reference set (n_ref=100), and the remaining set in turn as the validation set. For the validation individuals all markers were set to missing, except for markers that occur on two commonly used arrays that include 777k and 54k SNP across the genome. In a second scenario the same was done, except half of the reference individuals were randomly removed (n_ref=50). Accuracy of imputation was calculated by the correlation between true and imputed genotypes per locus. The results will be presented and the impact of the size of the reference set and the marker density will be discussed.
    Toward genomic prediction from genome sequence data and the 1000 bull genomes project
    Hayes, B. ; Anderson, C.L. ; Daetwyler, H.D. ; Fries, R. ; Guldbrandtsen, B. ; Lund, M. ; Boichard, D.A. ; Stothard, P. ; Veerkamp, R.F. ; Hulsegge, B. ; Rocha, D. ; Tassell, C. ; Coote, D. ; Goddard, M.E. ; The 1000 Bull Genomes Consortium, - \ 2012
    In: 4th International Conference of Quantative Genetics. - - p. 55 - 55.
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