Publikationer

Peer-reviewed publikationer ved QGG

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Sørensen, I. F., Edwards, S. M., Rohde, P. D. & Sørensen, P. (2017). Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster /631/208/721 /631/208/721 /631/208/729/743 /631/208/729/743 /119 /45 /45/43 article. Scientific Reports, 7(1), [2413]. https://doi.org/10.1038/s41598-017-02281-3

Østergaard, S., Ettema, J. F., Hjortø, L., Pedersen, J. & Kargo, M. (2014). Multiple Regression and Mediator Variables can be used to Avoid Double Counting when Economic Values are Derived using Stochastic Herd Simulation. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada. https://event.crowdcompass.com/wcgalp2014/activity/C3Ek3J3FXl

Vilkki, J., Dolezal, M. A., Sahana, G., Iso-Touru, T., Panitz, F., Fasold, M., Bagnato , A. & Soller, M. (2014). Multiple Breed Validation of Five QTL Affecting Mastitis Resistance. Abstract fra Plant and Animal Genome XXII, San Diego, USA. http://pag14.mapyourshow.com/5_0/sessions/sessiondetails.cfm?ScheduledSessionID=11AECD

Vilkki, J., Dolezal, M. A., Sahana, G., Iso-Touru, T., Panitz, F., Fasold, M., Bagnato , A. & Soller, M. (2014). Multiple Breed Validation of Five QTL Affecting Mastitis Resistance. Poster session præsenteret ved Plant and Animal Genome XXII, San Diego, USA.

Pedersen, G. A., Madsen, P. & Jensen, J. (1995). Multibreed evaluation of bulls performance tested for growth and feed intake based on full and part test period records. I Book of Abstract No. 1 of the 46th Annual Meeting of EAAP (s. 15)

Karaman, E., Lund, M. S. & Su, G. (2020). Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome. Heredity, 124(2), 274-287. https://doi.org/10.1038/s41437-019-0273-4

Mebratie, W., Madsen, P., Hawken, R. & Jensen, J. (2018). Multi-trait estimation of genetic parameters for body weight in a commercial broiler chicken population. Livestock Science, 217, 15-18. https://doi.org/10.1016/j.livsci.2018.09.007

Lund, M. S., Sørensen, P., Guldbrandtsen, B. & Sorensen, D. (2003). Multi-trait Fine Mapping of Quantitative Trait Loci Using Combined Linkage Disequilibria and Linkage Analysis. Genetics, 163(1), 405-410. http://www.genetics.org/cgi/content/abstract/163/1/405

Gebreyesus, G., Buitenhuis, A. J., Poulsen, N. A., Visker, M. H. P. W., Zhang, Q., van Valenberg, H. J. F., Sun, D. & Bovenhuis, H. (2017). Multi-population GWAS for the de novo synthesis of milk fatty acids. Poster session præsenteret ved 5th Annual GenSAP Meeting, 15-16 November 2017, Billund, Danmark.

Gebreyesus, G., Buitenhuis, A. J., Poulsen, N. A., Visker, M. H. P. W., Zhang, Q., van Valenberg, H. J. F., Sun, D. & Bovenhuis, H. (2019). Multi-population GWAS and enrichment analyses reveal novel genomic regions and promising candidate genes underlying bovine milk fatty acid composition. BMC Genomics, 20, [178]. https://doi.org/10.1186/s12864-019-5573-9

van den Berg, I., Boichard, D. & Lund, M. S. (2015). Multi-breed GWAS and meta-analysis using sequences of five dairy cattle breeds improve QTL mapping. I Book of Abstract of the 66th Annual Meeting of the European Federation of Animal Science (Bind 21, s. 347-347). Wageningen Academic Publishers. EAAP Book of Abstracts http://www.wageningenacademic.com/doi/book/10.3920/978-90-8686-816-2

Kristensen, P. S., Jahoor, A., Andersen, J. R., Jihad, O., Janss, L. & Jensen, J. (2019). Multi-Trait and Trait-Assisted Genomic Prediction of Winter Wheat Quality Traits Using Advanced Lines from Four Breeding Cycles. Hapres CBGG; Crop, Breeding, Genetics, Genomics, [:e190010]. https://doi.org/10.20900/cbgg20190010

Rohde, P. D., Nyegaard, M., Kjolby, M. & Sørensen, P. (2021). Multi-Trait Genomic Risk Stratification for Type 2 Diabetes. Frontiers in Medicine, 8, [711208]. https://doi.org/10.3389/fmed.2021.711208

Lund, M. S., Sørensen, P., Guldbrandtsen, B. & Sorensen, D. (2001). Multi trait QTL fine mapping using combined linkage disequilibria and linkage analysis. I Book of Abstract of the 52nd. Annual Meeting of EAAP (Bind 7, s. 4)

Friggens, N. & Løvendahl, P. (2007). Muligheder skabt ved hjælp af on-farm frugtbarhedsprofiler. I K. M. Pedersen (red.), Ikke angivet (s. 49-50). Department of Basic Animal and Veterinary Sciences, University of Copenhagen.

Berg, P. & Møller, S. H. (2010). Mulighed for at selektere for reduceret aggression i gruppeindhusning. I Faglig Årsberetning 2009 (s. 17-22). Pelsdyrerhvervets Forsøgs- og ForskningsCenter. Faglig Årsberetning Nr. 2009

Sørensen, A. C., (2015). Mulige strategier for og udfordringer ved udnyttelse af alternativt avlsmål for frilands- og økologisk produktion af svin, Nr. 1457554, 5 s., jun. 04, 2015.

Henryon, M., Ostersen, T., Ask, B., Sørensen, A. C. & Berg, P. (2015). Most of the long-term genetic gain from optimum-contribution selection can be realised with restrictions imposed during optimisation. Genetics Selection Evolution, 47(21), 1-14. https://doi.org/10.1186/s12711-015-0107-7

Henryon, M., Berg, P., Sørensen, A. C., Ostersen, T. & Nielsen, B. (2011). Most of the benefits from genomic selection can be realised by genotyping a proportion of selection candidates. I Book of Abstracts of the 62nd Annual Meeting of the European Federation of Animal Science (Bind 17, s. 391-391). Wageningen Academic Publishers.

Henryon, M., Berg, P., Sørensen, A. C., Ostersen, T. & Nielsen, B. (2012). Most of the benefits from genomic selection can be realised by genotyping a proportion of selection candidates. Journal of Animal Science, 90(13), 4681-4689. https://doi.org/10.2527/jas.2012-5158

Henryon, M., Berg, P., Sørensen, A. C., Ostersen, T. & Nielsen, B. (2013). Most of the benefits from genomic selection can be realised by genotyping a proportion of selection candidates. Poster session præsenteret ved 62nd Annual Meeting of the European Federation of Animal Science, EAAP, Stavanger, Norge.

Henryon, M., Ostersen, T., Ask, B., Sørensen, A. C. & Berg, P. (2014). Most of the Long-Term Genetic Gains from Optimum-Contribution Selection can be Realised with Restrictions Imposed. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada. https://event.crowdcompass.com/wcgalp2014/activity/f2dgTqAW6h

Løhmann, D., Sørensen, P., Jørgensen, J. & Rosthøj, S. (2014). Most central nervous system tumours in children are diagnosed with little delay after admission. Danish Medical Journal, 61(8), [A4886].

Sørensen, L. P., Bjerring, M. & Løvendahl, P. (2016). Monitoring individual cow udder health in automated milking systems using online somatic cell counts. Journal of Dairy Science, 99(1), 608-620. https://doi.org/10.3168/jds.2014-8823

Rokouei, M., Torshizi, R. V., Shahrbabak, M. M., Sargolzaei, M. & Sørensen, A. C. (2010). Monitoring inbreeding trends and inbreeding depression for economically important traits of Holstein cattle in Iran. Journal of Dairy Science, 93(7), 3294-3302. https://doi.org/10.3168/jds.2009-2748

Korsgaard, I. R., Løvendahl, P. & Sloth, K. H. (2003). Monitoring af daglige målinger af celletal (SCC) - automatisk og on-line detektion af mastitis. I Bilag til Temamøde: Malkekoens Energioptagelse, Mobilisering og sundhed (MEMO) (s. 19-32). DJF intern rapport Bind 176

Kristensen, T. N., Berg, P., Løvendahl, P. & Loeschcke, V. (2002). Molekylær chaperoner. Jordbrugsforskning, 6, 1-3.

Bjerre, A. E., Guldbrandtsen, B., Nielsen, V. H. & Christiansen, F. B. (1994). Molecular marker response to selection on a quantitative character. Yichuan, 121(1), 214-215.

Hedegaard, J., Skovgaard, K., Mortensen, S., Sorensen, P., Jensen, T. K., Hornshøj, H., Bendixen, C. & Heegaard, P. M. H. (2007). Molecular characterisation of the early response in pigs to experimental infection with Actinobacillus pleuropneumoniae using cDNA microarrays. Acta Veterinaria Scandinavica (Online), 49(1), 11. https://doi.org/10.1186/1751-0147-49-11

Kargo Sørensen, M. (2002). Modern Breeding Plans: Screening plans and nuclear breeding - application of ET and Ovum pick up. I Proc. 16th International WJCB Conference: Session IV

Sørensen, P. & Su, G. (2003). Moderate heritablility for Tibial Dyschondroplasia (TD) and weakly positive correlation between TD and body weight. I Proc. 3rd European Poultry Genetics Symposium (s. 82-83)

Heidaritabar, M., Bijma, P., Janss, L., Bortoluzzi, C., Nielsen, H. M., Madsen, P., Ask, B. & Christensen, O. F. (2019). Models with indirect genetic effects depending on group sizes: a simulation study assessing the precision of the estimates of the dilution parameter. Genetics Selection Evolution, 51(1), 24. [24]. https://doi.org/10.1186/s12711-019-0466-6

Jönsson, L., Madsen, P. & Mark, T. (2016). Modelling repeated competition records in genetic evaluations of Danish sport horses. Journal of Animal Breeding and Genetics (Online), 133(4), 291-302. https://doi.org/10.1111/jbg.12190

Jensen, J., Jamrozik, J. & Schaeffer, L. R. (2001). Modelling production in all lactations in dairy cattle using random regression test day models. I Book of Abstract of the 52nd Annual Meeting EAAP (Bind 7, s. 2)

Shirali, M., Nielsen, B. J., Ostersen, T., Christensen, O. F., Madsen, P., Su, G. & Jensen, J. (2018). Modelling of grouped recorded data for different population structures in pig breeding. Abstract fra 69th Annual Meeting of the European Federation of Animal Science EAAP 2018, Dubrovnik, Kroatien.

Berg, P. & Hansen, B. K. (2008). Modelling genetic effects of feed conversion ratio in group housed mink. I Proceeding of the IX International Scientific Congress in Fur Animal Production (s. 71-72). Scientifur Bind 32 Nr. 4

Anantharama Ankinakatte, S. & Edwards, D. (2015). Modelling discrete longitudinal data using acyclic probabilistic finite automata: (Submitted December 2013, forventer accept i 2015). Computational Statistics & Data Analysis, 88, 46-52. https://doi.org/10.1016/j.csda.2015.02.009

Jeon, G. J., Madsen, P., Lovendahl, P., Jensen, J. & Norberg, E. (2003). Modelling daily somatic cell score and milk yield. I Book of Abstracts (s. 266)

Labouriau, R. & Madsen, P. (2009). Modelling censored discrete time data with a view towards applications in animal quantitative genetics. EAAP Book of Abstracts, 604. http://www.wageningenacademic.com/_clientfiles/EAAP/eaap2009-e.pdf

Labouriau, R. & Madsen, P. (2009). Modelling censored discrete time data with a view towards applications in animal quantitative genetics. Poster session præsenteret ved Annual Meeting of of the European Association for Animal Production, EAAP, Barcelona, Spanien.

Edwards, D. (2013). Modelling and visualizing fine-scale linkage disequilibrium structure. B M C Bioinformatics, 14(179). https://doi.org/10.1186/1471-2105-14-179

Lund, M. S. (2003). Modellering af QTL-effekter over tid samt deres vekselvirkning med miljøfaktorer øger statistisk styrke og biologisk forståelse. I Bilag til Temamøde om Biometrianvendelse i planteforskningen (s. 43-49). DJF intern rapport Bind 184

Berg, P. (1988). Modeller vedr. tærskelegenskaber. I Rapport fra Nordisk Seminar. Avlsværdivurdering af kvægsygdomme: Med diskussion (s. 10-15). Landsudvalget for Kvæg.

Rome, H. J. S., Marois, D., Huang, C-H. & Jensen, J. (2020). Modeling maternal effects in later traits in broilers. Abstract fra 71st Annual Meeting of European Federation of Animal Science.

Gebreyesus, G., Lund, M. S., Janss, L., Bovenhuis, H. & Buitenhuis, A. J. (2016). Modeling heterogeneous co-variancas for genomic regions in prediction for milk protein compositions. I Book of Abstracts of the 67th Annual Meeting of the European Association for Animal Production Wageningen Academic Publishers. Book of Abstracts of the Annual EAAP Meeting Bind 22 https://doi.org/10.3920/978-90-8686-830-8

Gebreyesus, G., Lund, M. S., Buitenhuis, A. J., Bovenhuis, H., Poulsen, N. A. & Janss, L. (2017). Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits. Genetics Selection Evolution, 49, [89]. https://doi.org/10.1186/s12711-017-0364-8

Berg, P. & Hansen, B. K. (2006). Modeling feed conversion efficiency in mink. I NJF Seminar, NJF's Subsection for Fur Animals Nordic Association of Agricultural Scientists (NJF).

Sorensen, D. & Waagepetersen, R. (2002). Model selection for prediction of breeding values. I Proc. 7th WCGALP, 32: CD-rom Communication No 20-06 (s. 513-520). Editions Quae.

Berg, P. & Hansen, B. K. (2006). Model for individuel foderkonvertering hos mink. I S. H. Møller (red.), Store mink - store udfordringer. Produktion af højtydende mink uden uønskede følgevirkninger (s. 40-44). Aarhus Universitet, Det Jordbrugsvidenskabelige Fakultet. Intern rapport - Husdyrbrug Nr. 2

Gao, H., Su, G., Janss, L., Yuan, Z. & Lund, M. S. (2013). Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population: (submitted 2012). Journal of Dairy Science, 96(7), 4678-4687. https://doi.org/10.3168/jds.2012-6406

Guo, X., Christensen, O. F., Ostersen, T., Sorensen, D. A., Wang, Y., Lund, M. S. & Su, G. (2014). Model comparison based on genomic predictions of litter size and piglet mortality. EAAP scientific series, 20, 214-214. http://www.wageningenacademic.com/eaap2014

Gianola, D., Sorensen, D. & Waagepetersen, R. (2003). Model choice in animal breeding: A case study of litter size in pigs. I Abstract book, International Biometrics Society and Statistical Society of Canada (s. 309)

Ødegård, J., Madsen, P. & Klementsdal, G. (2006). Mixture models in animal breeding. I Book of abstracts: CD communication 26-11, 7 pp (s. 242). WCGALP. http://www.wcgalp8.org.br/wcgalp8/articles/paper/26_783-1536.pdf

Ødegård, J., Klemetsdal, G., Jensen, J. & Madsen, P. (2004). Mixture models - use and applications within the field of animal breeding. I Book of Abstracts (s. 89)

Gianola, D., Ødegaard, J., Heringstad, B., Klemetsdal, G., Sorensen, D., Madsen, P., Jensen, J. & Detilleux, J. (2004). Mixture model for inferring susceptibility to mastitis in dairy cattle: a procedure for likelihood-based inference. Genetics Selection Evolution, 36(1), 3-27. https://doi.org/10.1051/gse:2003048

Sorensen, D. (2003). Mixed model, likelihood and Bayesian methods in Quantitative Genetics.

Pedersen, H. S., Løvendahl, P., Nikolaisen, N., Holm, P., Hyttel, P., Nyengaard, J. R., Chen, F. & Callesen, H. (2014). Mitochondrial dynamics in pre- and postpubertal pig oocytes before and after in vitro maturation. Reproduction, Fertility and Development, 26(1), 189-190. [152]. https://doi.org/10.1071/RDv26n1abs

Berg, P., Hansen, B. K., Malmkvist, J. & Hansen, S. W. (2003). Minkens adfærd, effekt af egen, mors og burkammeratens genetiske dispositiond. I Faglig årsberetning 2002 (s. 13-16). Faglig Årsberetning Bind 2002

Hansen, B. K. & Berg, P. (1997). Mink kit growth performance in the suckling period II. Estimations of sources of variation. Acta Agriculturae Scandinavica, Section A - Animal Science, 47, 240-246.

Hansen, B. K. & Berg, P. (1996). Mink kit growth performance in the suckling period - estimates of sources of variation. I A. Frindt & M. Brzozowski (red.), Progress in Fur Animal Science, Applied Science Reports (Bind 27, s. 183-189)

Berg, P. & Hansen, B. K. (1996). Mink kit growth performance - Estimates of sources of variation. Poster session præsenteret ved Annual Meeting of the EAAP, Lillehammer, Norge.

Hansen, B. K. & Berg, P. (1998). Mink dam weight changes during the lactation period I. Genetic and environmental effects. Acta Agriculturae Scandinavica, Section A - Animal Science, 48, 49-57.

Sahana, G. (2000). Minimum number of individuals required in preservation programme of domestic animal population - some views. Indian Dairyman, 52(9), 29-34.

Sahana, G. (2000). Minimum number of individuals in preserved domestic animal population. I R. Sahai & R. K. Vijh (red.), Domestic Animal Diversity - Conservation and Sustainable Development (s. 92-100). SI Publication.

Poulsen, N. A., Szekeres, B. D., Kargo, M. & Larsen, L. B. (2020). Mineral and fatty acid composition of milk from native Jutland and Danish Red 1970 cattle breeds. Acta Agriculturae Scandinavica A: Animal Sciences, 69(1-2), 131-135. https://doi.org/10.1080/09064702.2019.1674914

Løvendahl, P., Lassen, J. & Chagunda, M. G. G. (2014). Milking Efficiency – A Milkability Trait for Automatically Milked Cows. Paper præsenteret ved 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada. https://event.crowdcompass.com/wcgalp2014/activity/2MTb6GCN3n

Sahana, G. & Sadana, D. K. (1999). Milk yield efficiency traits in Murrah buffaloes. I Proceeding of National Seminar on Sustainable Development of Buffaloes for Milk, Meat and Draft (s. 154)

Kucerova, J., Matejicek, A., Jandurova, O. M., Sørensen, P., Nemcova, E., Stipkova, M., Kott, T., Bouska, J. & Frelich, J. (2006). Milk protein genes CSN1S1, CSN2, CSN3, LGB and their relation to genetic values of milk production parameters in Czech Fleckvieh. Czech Journal of Animal Science, 6, 241-247. http://www.cazv.cz/default.asp?ch=54&typ=1&val=48905&ids=3054

Sundekilde, U., Poulsen, N. A., Buitenhuis, A. J., Gustavsson, F., Paulsson, M., Larsen, L. B. & Bertram, H. C. S. (2012). Milk metabolite composition and its relation to nutritional and technological quality – a NMR metabolomics approach. I Proceedings from 9th International Symposium on Milk Genomics and Human Health (s. 5)

Sundekilde, U., Poulsen, N. A., Buitenhuis, A. J., Gustavsson, F., Paulsson, M., Larsen, L. B. & Bertram, H. C. S. (2012). Milk metabolite composition and its relation to nutritional and technological quality – a NMR metabolomics approach. Poster session præsenteret ved 9th International Symposium on Milk Genomics & Human Health, Wageningen, Holland.

Paulsson, M., Andrén, A., Stålhammar, H., Larsen, L. B., Lund, M. S., Bendixen, C., Bertram, H. C., Poulsen, N. A., Buitenhuis, B. & Månsson, H. L. (2009). Milk genomics - svensk-danskt forskningsprojekt i tiden. Pejling fraan Svensk Mjoelk, 14, 1-2.

Poulsen, N. A., Buitenhuis, A. J., Le, T. T. T., Gregersen, V. R., Lindahl, I. E. I., Christensen, B. S., Sørensen, E. S. & Larsen, L. B. (2018). Milk Genomics: Variation in Danish Dairy milk and its possible exploitation in the dairy chain. Abstract fra 15th International Symposium on Milk Genomics and Human Health, Sacramento, California, USA.

Larsen, L. B., Poulsen, N. A., Jensen, H. B., Sundekilde, U. K., Bertram, H. C. S., Gregersen, V. R., Gustavsson, F., Glantz, M., Bertelsen, H. P., de Moura Maciel, G., S. Hansen, M., Bakman, M., Stålhammar, H., Andrén, A., Fikse, W. F., Lindmark-Månsson, H., Buitenhuis, A. J., Bendixen, C. & Paulsson, M. (2014). Milk Genomics from a Technological Perspective. Abstract fra 11th International Symposium on Milk Genomics and Human Health, Aarhus, Danmark. http://milkgenomics.org/11th-international-symposium-milk-genomics-human-health/

Abete, P., Calabrese, E., Ji, L. L., Kristensen, T. N., Le Bourg, E., Loeschcke, V., Morris, B., Rengo, F., Rattan, S., Safwat, A., Sarup, P., Sørensen, J. G. & Vaiserman, A. (2008). Mild stress and healthy aging: perspectives for human beings. I Mild stress and healthy aging: applying hormesis in aging research and interventions (s. 171-183). Springer.

Sarup, P., Petersen, S. M. M., Nielsen, N. C., Loeschcke, V. & Malmendal, A. (2016). Mild heat treatments induce long-term changes in metabolites associated with energy metabolism in Drosophila melanogaster. Biogerontology, 17(5), 873-882. [873]. https://doi.org/10.1007/s10522-016-9657-5

Difford, G. F., Løvendahl, P., Lassen, J., Guldbrandtsen, B. & Sahana, G. (2018). Microbiability – new insights into (genetic) modelling methane emissions of cattle. I Proceedings of the World Congress on Genetics Applied to Livestock Production, 2018: Volume Challenges - Environmental (Bind 11). [405] http://www.wcgalp.org/proceedings/2018/microbiability%E2%80%93-new-insights-genetic-modelling-methane-emissions-cattle

Hornshøj, H., Conley, L. N., Hedegaard, J., Sørensen, P., Panitz, F. & Bendixen, C. (2007). Microarray Expression Profiles of 20.000 Genes across 23 Healthy Porcine Tissues. P L o S One, 2(11), 1-10. https://doi.org/10.1371/journal.pone.0001203

Fang, L., Sørensen, P., Sahana, G., Panitz, F., Su, G., Zhang, S., Yu, Y., Li, B., Ma, L., Liu, G., Lund, M. S. & Thomsen, B. (2018). MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle. Scientific Reports, 8, [9345]. https://doi.org/10.1038/s41598-018-27729-y

Wang, H-M., Shi, L., Liang, T., Wang, B., Wu, W. J., Su, G., Wei, J., Li, P. & Huang, R. (2017). MiR-696 regulates C2C12 cell proliferation and differentiation by targeting CNTFRα. International Journal of Biological Sciences, 13(4), 413-425. https://doi.org/10.7150/ijbs.17508

Jensen, J. (1993). Methods for prediction breeding values. I NJF Workshop on Present aspects and future plans in research on genetics, breeding, and reproduction in fur animal

Hedegaard, J., Arce, C., Bicciato, S., Bonnet, A., Buitenhuis, B., Collado-Romero, M., Conley, L. N., SanCristobal, M., Ferrari, F., Garrido, J. J., Groenen, M. AM., Hornshøj, H., Hulsegge, I., Jiang, L., Jiménez-Marín, Á., Kommadath, A., Lagarrigue, S., Leunissen, J. AM., Liaubet, L. ... Watson, M. (2009). Methods for interpreting lists of affected genes obtained in a DNA microarray experiment. BMC Proceedings, (4:S5). https://doi.org/10.1186/1753-6561-3-S4-S5

Kadri, N. K., Sahana, G., Sørensen, P. & Guldbrandtsen, B. (2012). Methods for genome wide association study in structured population. I MBG Annual PhD-days 2012 (s. 19-19).

Olijhoek, D. W., Løvendahl, P., Lassen, J., Hellwing, A. L. F., Höglund, J. K., Weisbjerg, M. R., Noel, S. J., McLean, F., Højberg, O. & Lund, P. (2018). Methane production, rumen fermentation, and diet digestibility of Holstein and Jersey dairy cows being divergent in residual feed intake and fed at 2 forage-to-concentrate ratios. Journal of Dairy Science, 101, 9926-9940. https://doi.org/10.3168/jds.2017-14278

Szalanski, M., Kristensen, T., Difford, G., Løvendahl, P. & Lassen, J. (2017). Methane emission in Jersey cows during spring transition from in-door feeding to grazing. I Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science (2017 udg., Bind 23, s. 148-148). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-859-9

Lassen, J. & Madsen, J. (2010). Metan-målinger på KFC viser gunstige resultater. Ny KvægForskning, 4, 2-3.

Garcia-Baccino, C. A., Legarra, A., Christensen, O. F., Misztal, I., Pocrnic, I., Vitezica, Z. G. & Cantet, R. J. C. (2017). Metafounders are related to F_st fixation indices and reduce bias in single-step genomic evaluations. Genetics Selection Evolution, 49(1), 1-14. [34]. https://doi.org/10.1186/s12711-017-0309-2

Guo, X., Jahoor, A., Jensen, J. & Sarup, P. (2022). Metabolomic spectra for phenotypic prediction of malting quality in spring barley. Scientific Reports, 12, [7881]. https://doi.org/10.1038/s41598-022-12028-4

Hayhurst, C., Kargo Sørensen, M., Royal, M. D. & Løvendahl, P. (2007). Metabolic Regulation in Danish Bull Calves and the Relationship to the Fertility of their Female Offspring. Journal of Dairy Science, 90, 3909-3916. https://doi.org/10.3168/jds.2006-731

Bouwman, A. C., Daetwyler, H. D., Chamberlain, A. J., Ponce, C. H., Sargolzaei, M., Schenkel, F. S., Sahana, G., Govignon-Gion, A., Boitard, S., Dolezal, M., Pausch, H., Brøndum, R. F., Bowman, P. J., Thomsen, B., Guldbrandtsen, B., Lund, M. S., Servin, B., Garrick, D. J., Reecy, J. ... Hayes, B. J. (2018). Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature Genetics, 50, 362-372. https://doi.org/10.1038/s41588-018-0056-5

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