Calabrese, V., Scuto, M., Salinaro, A. T., Dionisio, G., Modafferi, S., Ontario, M. L., Greco, V., Sciuto, S., Schmitt, C. P., Calabrese, E. J. & Peters, V. (2020). Hydrogen sulfide and carnosine: Modulation of oxidative stress and inflammation in kidney and brain axis. Antioxidants, 9(12), 1-35. Artikel 1303. https://doi.org/10.3390/antiox9121303

Bordbar, F., Jensen, J., Du, M., Abied, A., Guo, W., Xu, L., Gao, H., Zhang, L. & Li, J. (2020). Identification and validation of a novel candidate gene regulating net meat weight in Simmental beef cattle based on imputed next-generation sequencing. Cell Proliferation, 53(9), Artikel e12870. https://doi.org/10.1111/cpr.12870

Su, G., Sørensen, A. C., Chu, T. T., Meier, K., Nielsen, T. & Lund, M. S. (2020). Impact of phenotypic information and composition of reference population on genomic prediction in fish under the presence of genotype by environment interaction. Aquaculture, 526, Artikel 735358. https://doi.org/10.1016/j.aquaculture.2020.735358

Li, J., Gao, H., Madsen, P., Li, R., Liu, W., Bao, P., Xue, G., Gao, Y., Di, X. & Su, G. (2020). Impact of the Order of Legendre Polynomials in Random Regression Model on Genetic Evaluation for Milk Yield in Dairy Cattle Population. Frontiers in Genetics, 11, Artikel 586155. https://doi.org/10.3389/fgene.2020.586155

Liu, A., Lund, M. S., Boichard, D. A., Karaman, E., Fritz, S., Aamand, G. P., Nielsen, U. S., Wang, Y. & Su, G. (2020). Improvement of genomic prediction by integrating additional single nucleotide polymorphisms selected from imputed whole genome sequencing data. Heredity, 124(1), 37-49. https://doi.org/10.1038/s41437-019-0246-7

Liu, A., Lund, M. S., Boichard, D., Mao, X., Karaman, E., Fritz, S., Aamand, G. P., Wang, Y. & Su, G. (2020). Imputation for sequencing variants preselected to a customized low-density chip. Scientific Reports, 10, Artikel 9524. https://doi.org/10.1038/s41598-020-66523-7

Zhang, Q., Cai, Z., Lhomme, M., Sahana, G., Lesnik, P., Guerin, M., Fredholm, M. & Karlskov-Mortensen, P. (2020). Inclusion of endophenotypes in a standard GWAS facilitate a detailed mechanistic understanding of genetic elements that control blood lipid levels. Scientific Reports, 10(1), Artikel 18434. https://doi.org/10.1038/s41598-020-75612-6

Kamal, N., Mun, T., Reid, D., Lin, J.-S., Akyol, T. Y., Sandal, N., Asp, T., Hirakawa, H., Stougaard, J., Mayer, K. F. X., Sato, S. & Andersen, S. U. (2020). Insights into the evolution of symbiosis gene copy number and distribution from a chromosome-scale Lotus japonicus Gifu genome sequence. D N A Research, 27(3), Artikel dsaa015. https://doi.org/10.1093/dnares/dsaa015

Morgante, F., Huang, W., Sørensen, P., Maltecca, C. & Mackay, T. F. C. (2020). Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits. G3 (Bethesda, Md.), 10(12), 4599-4613. https://doi.org/10.1534/g3.120.401847

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, Section A - Animal Science, 69(1-2), 131-135. https://doi.org/10.1080/09064702.2019.1674914

Ba, H., Qin, T., Cai, Z., Liu, W. & Li, C. (2020). Molecular evidence for adaptive evolution of olfactory-related genes in cervids. Genes and Genomics, 42(4), 355-360. https://doi.org/10.1007/s13258-019-00911-w

Manzanilla-Pech, C. I. V., Gordo, D., Difford, G. F., Løvendahl, P. & Lassen, J. (2020). Multitrait genomic prediction of methane emissions in Danish Holstein cattle. Journal of Dairy Science, 103(10), 9195-9206. https://doi.org/10.3168/jds.2019-17857

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

Schonherz, A. A., Bødker, J. S., Schmitz, A., Brøndum, R. F., Jakobsen, L. H., Roug, A. S., Severinsen, M. T., El-Galaly, T. C., Jensen, P., Johnsen, H. E., Bøgsted, M. & Dybkær, K. (2020). Normal myeloid progenitor cell subsetassociated gene signatures for acute myeloid leukaemia subtyping with prognostic impact. PLOS ONE, 15(4), Artikel e0229593. https://doi.org/10.1371/journal.pone.0229593

Gebreyesus, G., Sahana, G., Christian Sørensen, A., Lund, M. S. & Su, G. (2020). Novel approach to incorporate information about recessive lethal genes increases the accuracy of genomic prediction for mortality traits. Heredity, 125, 155-166. https://doi.org/10.1038/s41437-020-0329-5

Wu, X., Mesbah-Uddin, M., Guldbrandtsen, B., Lund, M. S. & Sahana, G. (2020). Novel haplotypes responsible for prenatal death in Nordic Red and Danish Jersey cattle. Journal of Dairy Science, 103(5), 4570-4578. https://doi.org/10.3168/jds.2019-17831

Czech, B., Guldbrandtsen, B. & Szyda, J. (2020). Patterns of DNA variation between the autosomes, the X chromosome and the Y chromosome in Bos taurus genome. Scientific Reports, 10(1), Artikel 13641. https://doi.org/10.1038/s41598-020-70380-9

Chu, T. T., Sørensen, A. C., Lund, M. S., Meier, K., Nielsen, T. & Su, G. (2020). Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions. Frontiers in Genetics, 11, Artikel 866. https://doi.org/10.3389/fgene.2020.00866

Schonherz, A. A., Szekeres, B. D., Nielsen, V. H. & Guldbrandtsen, B. (2020). Population structure and genetic characterization of two native Danish sheep breeds. Acta Agriculturae Scandinavica, Section A - Animal Science, 69(1-2), 53-67. https://doi.org/10.1080/09064702.2019.1639804

Slagboom, M., Hjortø, L., Sørensen, A. C., Mulder, H. A., Thomasen, J. R. & Kargo, M. (2020). Possibilities for a specific breeding program for organic dairy production. Journal of Dairy Science, 103(7), 6332-6345. https://doi.org/10.3168/jds.2019-16900

Holmqvist, V., Roos, L. K. S., Kjartansdottir, K. R., Dunø, M., Petersen, M. R., Hnida, C., Pedersen, I. S., Ernst, A., Toft, C. L. F., Diemer, T., Ingerslev, H. J., Pinborg, A. & Løssl, K. (2020). Præimplantationsgenetisk testning for aneuploidi. Ugeskrift for Læger, 181(4), Artikel V12180849.

Poulsen, B. G., Ask, B., Nielsen, H. M., Ostersen, T. & Christensen, O. F. (2020). Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information. Genetics, selection, evolution : GSE, 52(1), Artikel 58. https://doi.org/10.1186/s12711-020-00578-y

Gebreyesus, G., Difford, G. F., Buitenhuis, B., Lassen, J., Noel, S. J., Højberg, O., Plichta, D. R., Zhu, Z., Poulsen, N. A., Sundekilde, U. K., Løvendahl, P. & Sahana, G. (2020). Predictive ability of host genetics and rumen microbiome for subclinical ketosis. Journal of Dairy Science, 103(5), 4557-4569. https://doi.org/10.3168/jds.2019-17824

Larsen, F., Guldbrandtsen, B., Christensen, D., Pitcovski, J., Kjærup, R. B. & Dalgaard, T. S. (2020). Pustulan activates chicken bone marrow-derived dendritic cells in vitro and promotes ex vivo CD4⁺ T cell recall response to infectious bronchitis virus. Vaccines, 8(2), Artikel 226. https://doi.org/10.3390/vaccines8020226

Rohde, P. D., Fourie Sørensen, I. & Sørensen, P. (2020). qgg: an R package for large-scale quantitative genetic analyses. Bioinformatics (Oxford, England), 36(8), 2614-2615. https://doi.org/10.1093/bioinformatics/btz955

Poulsen, N. A., Hein, L., Kargo, M. & Buitenhuis, A. J. (2020). Realization of breeding values for milk fatty acids in relation to seasonal variation in organic milk. Journal of Dairy Science, 103(3), 2434-2441. https://doi.org/10.3168/jds.2019-17065

Gautason, E., Schonherz, A. A., Sahana, G. & Guldbrandtsen, B. (2020). Relationship of Icelandic cattle with Northern and Western European cattle breeds, admixture and population structure. Acta Agriculturae Scandinavica, Section A - Animal Science, 69(1-2), 25-38. https://doi.org/10.1080/09064702.2019.1699951

Hozé, C., Escouflaire, C., Mesbah-Uddin, M., Barbat, A., Boussaha, M., Deloche, M.-C., Boichard, D., Fritz, S. & Capitan, A. (2020). Short communication: A splice site mutation in CENPU is associated with recessive embryonic lethality in Holstein cattle. Journal of Dairy Science, 103(1), 607-612. https://doi.org/10.3168/jds.2019-17056

Tessema, B. B., Liu, H., Sørensen, A. C., Andersen, J. R. & Jensen, J. (2020). Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat. Frontiers in Genetics, 11, Artikel 578123. https://doi.org/10.3389/fgene.2020.578123

Zhang, Y., Ran, Y., Nagy, I., Lenk, I., Qiu, J. L., Asp, T., Jensen, C. S. & Gao, C. (2020). Targeted mutagenesis in ryegrass (Lolium spp.) using the CRISPR/Cas9 system. Plant Biotechnology Journal, 18(9), 1854-1856. https://doi.org/10.1111/pbi.13359

Malinowska, M., Nagy, I., Wagemaker, C. A. M., Ruud, A. K., Svane, S. F., Thorup-Kristensen, K., Jensen, C. S., Eriksen, B., Krusell, L., Jahoor, A., Jensen, J., Eriksen, L. B. & Asp, T. (2020). The cytosine methylation landscape of spring barley revealed by a new reduced representation bisulfite sequencing pipeline, WellMeth. The Plant Genome, 13(3), e20049. Artikel e20049. https://doi.org/10.1002/tpg2.20049

Ask, B., Christensen, O. F., Heidaritabar, M., Madsen, P. & Nielsen, H. M. (2020). The predictive ability of indirect genetic models is reduced when culled animals are omitted from the data. Genetics, selection, evolution : GSE, 52(1), Artikel 8. https://doi.org/10.1186/s12711-020-0527-x

Tsai, H.-Y., Cericola, F., Edriss, V., Andersen, J. R., Orabi, J., Jensen, J. D., Jahoor, A., Janss, L. & Jensen, J. (2020). Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data. PLOS ONE, 15(5), Artikel e0232665. https://doi.org/10.1371/journal.pone.0232665

Liu, A., Lund, M. S., Boichard, D., Karaman, E., Guldbrandtsen, B., Fritz, S., Aamand, G. P., Nielsen, U. S., Sahana, G., Wang, Y. & Su, G. (2020). Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses. Genetics Selection Evolution, 52(1), Artikel 48. https://doi.org/10.1186/s12711-020-00568-0

Harwood, S. L., Nielsen, N. S., Jensen, K. T., Nielsen, P. K., Thøgersen, I. B. & Enghild, J. J. (2020). α₂-macroglobulin-like protein 1 can conjugate and inhibit proteases through their hydroxyl groups, because of an enhanced reactivity of its thiol ester. Journal of Biological Chemistry, 295(49), 16732-16742. https://doi.org/10.1074/jbc.RA120.015694

Christensen, O. F., Nielsen, B., Su, G., Xiang, T., Madsen, P., Ostersen, T., Velander, I. & Strathe, A. B. (2019). A bivariate genomic model with additive, dominance and inbreeding depression effects for sire line and three-way crossbred pigs. Genetics Selection Evolution, 51(1), Artikel 45. https://doi.org/10.1186/s12711-019-0486-2

Liu, H., Tessema, B. B., Jensen, J., Cericola, F., Andersen, J. R. & Sørensen, A. C. (2019). ADAM-Plant: A Software for Stochastic Simulations of Plant Breeding From Molecular to Phenotypic Level and From Simple Selection to Complex Speed Breeding Programs. Frontiers in Plant Science, 9, 1926. Artikel 1926. https://doi.org/10.3389/fpls.2018.01926

Poulsen, N. A., Robinson, R. C., Barile, D., Larsen, L. B. & Buitenhuis, B. (2019). A genome-wide association study reveals specific transferases as candidate loci for bovine milk oligosaccharides synthesis. BMC Genomics, 20, Artikel 404. https://doi.org/10.1186/s12864-019-5786-y

Mesbah-Uddin, M., Hoze, C., Michot, P., Barbat, A., Lefebvre, R., Boussaha, M., Sahana, G., Fritz, S., Boichard, D. & Capitan, A. (2019). A missense mutation (p.Tyr452Cys) in the CAD gene compromises reproductive success in French Normande cattle. Journal of Dairy Science, 102(7), 6340-6356. https://doi.org/10.3168/jds.2018-16100

Iso-Touru, T., Wurmser, C., Venhoranta, H., Hiltpold, M., Savolainen, T., Sironen, A., Fischer, K., Flisikowski, K., Fries, R., Vicente-Carrillo, A., Alvarez-Rodriguez, M., Nagy, S., Mutikainen, M., Peippo, J., Taponen, J., Sahana, G., Guldbrandtsen, B., Simonen, H., Rodriguez-Martinez, H. ... Pausch, H. (2019). A splice donor variant in CCDC189 is associated with asthenospermia in Nordic Red dairy cattle. BMC Genomics, 20(1), Artikel 286. https://doi.org/10.1186/s12864-019-5628-y

Bornhofen, E., Ramires, T. G., Bergonci, T., Nakamura, L. R. & Righetto, A. J. (2019). Associations between global indices of risk management and agricultural development. Agricultural Systems, 173, 281-288. https://doi.org/10.1016/j.agsy.2019.03.006

Fan, H., Hou, Y., Sahana, G., Gao, H., Zhu, C., Du, L., Zhao, F. & Wang, L. (2019). A Transcriptomic Study of the Tail Fat Deposition in Two Types of Hulun Buir Sheep According to Tail Size and Sex. Animals, 9(9), Artikel 655. https://doi.org/10.3390/ani9090655

Gao, H., Madsen, P., Aamand, G. P., Thomasen, J. R., Sørensen, A. C. & Jensen, J. (2019). Bias in estimates of variance components in populations undergoing genomic selection: a simulation study. BMC Genomics, 20(1), Artikel 956. https://doi.org/10.1186/s12864-019-6323-8

Malik, P. L., Janss, L., Nielsen, L. K., Borum, F., Jørgensen, H., Eriksen, B., Schjoerring, J. K. & Rasmussen, S. K. (2019). Breeding for dual-purpose wheat varieties using marker–trait associations for biomass yield and quality traits. Theoretical and Applied Genetics, 132(12), 3375-3398. https://doi.org/10.1007/s00122-019-03431-z

Clasen, J. B., Lehmann, J. O., Thomasen, J. R., Østergaard, S. & Kargo, M. (2019). Combining extended lactation with sexed semen in a dairy cattle herd: Effect on genetic and total economic return. Livestock Science, 223, 176-183. https://doi.org/10.1016/j.livsci.2019.03.001

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). Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits. Journal of Dairy Science, 102(12), 11124-11141. https://doi.org/10.3168/jds.2019-16676

Garnsworthy, P. C., Difford, G. F., Bell, M. J., Bayat, A. R., Huhtanen, P., Kuhla, B., Lassen, J., Peiren, N., Pszczola, M., Sorg, D., Visker, M. H. P. W. & Yan, T. (2019). Comparison of methods to measure methane for use in genetic evaluation of dairy cattle. Animals, 9(10), Artikel 837. https://doi.org/10.3390/ani9100837

Chu, T. T., Bastiaansen, J. W. M., Berg, P., Romé, H., Marois, D., Henshall, J. & Jensen, J. (2019). Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments. Genetics Selection Evolution, 51(1), Artikel 68. https://doi.org/10.1186/s12711-019-0513-3

Wang, X., Gao, H., Gebremedhin, K. G., Bjerg, B. S., Van Os, J., Tucker, C. B. & Zhang, G. (2019). Corrigendum to “A predictive model of equivalent temperature index for dairy cattle (ETIC)” [J. Therm. Biol. (2018) 165–170]. Journal of Thermal Biology, 82, 252-253. https://doi.org/10.1016/j.jtherbio.2018.12.012

Warner, D., Dettmann, F., Buitenhuis, A. J., Kargo, M., Hostrup Kjeldsen, A. M., Nielsen, N. H., Lefebvre, D. M. & Santschi, D. E. (2019). Development of a prediction equation for body weight change in early-lactating dairy cows by Fourier-transform infrared predicted fatty acid profiles in milk. Journal of Dairy Science, 102(Supplement 1), 109-110. Artikel 95. https://www.adsa.org/Portals/0/SiteContent/Docs/Meetings/2019ADSA/2019ADSA_Abstract_Book.pdf?v20190715

Clasen, J. B., Fogh, A. & Kargo, M. (2019). Differences between performance of F1 crossbreds and Holsteins at different production levels. Journal of Dairy Science, 102(1), 436-441. https://doi.org/10.3168/jds.2018-14975

Cai, Z., Guldbrandtsen, B., Lund, M. S. & Sahana, G. (2019). Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle. BMC Genetics, 20, Artikel 15. https://doi.org/10.1186/s12863-019-0717-0

Pedersen, M. L. M., Velander, I. H., Nielsen, M. B. F., Lundeheim, N. & Nielsen, B. (2019). Duroc boars have lower progeny mortality and lower fertility than Pietrain boars1. Translational Animal Science, 3(2), 885-892. https://doi.org/10.1093/tas/txz036

Olijhoek, D., Hellwing, A. L. F., Grevsen, K., Haveman, L., Chowdhury, M. R., Løvendahl, P., Weisbjerg, M. R., Noel, S. J., Hojberg, O., Wiking, L. & Lund, P. (2019). Effect of dried oregano (Origanum vulgare L.) plant material in feed on methane production, rumen fermentation, nutrient digestibility, and milk fatty acid composition in dairy cows. Journal of Dairy Science, 102(11), 9902–9918. https://doi.org/10.3168/jds.2019-16329

Steenfeldt, S., Sørensen, P. & Nielsen, B. L. (2019). Effects of choice feeding and lower ambient temperature on feed intake, growth, foot health, and panting of fast- and slow-growing broiler strains. Poultry Science, 98(2), 503-513. https://doi.org/10.3382/ps/pey323

Lee, A., Krawczel, P., Ternman, E. M., Schneider, L., Løvendahl, P. & Munksgaard, L. (2019). Effects of mean occupation rate on hourly automatic milking system use and total milk production in Holstein cows. Journal of Dairy Science, 102(Suppl. 1), 250-250. Artikel 262. https://www.adsa.org/Portals/0/SiteContent/Docs/Meetings/2019ADSA/2019ADSA_Abstract_Book.pdf?v20190715

Szalanski, M., Kristensen, T., Difford, G., Lassen, J., Buitenhuis, A. J., Pszczola, M. & Løvendahl, P. (2019). Enteric methane emission from Jersey cows during the spring transition from indoor feeding to grazing. Journal of Dairy Science, 102(7), 6319-6329. https://doi.org/10.3168/jds.2018-15984

Knorst, V., Yates, S., Byrne, S., Asp, T., Widmer, F., Studer, B. & Kölliker, R. (2019). First assembly of the gene-space of Lolium multiflorum and comparison to other Poaceae genomes. Grassland Science, 65(2), 125-134. https://doi.org/10.1111/grs.12225

Zaalberg, R. M., Shetty, N., Janss, L. & Buitenhuis, A. J. (2019). Genetic analysis of Fourier transform infrared milk spectra in Danish Holstein and Danish Jersey. Journal of Dairy Science, 102(1), 503-510. https://doi.org/10.3168/jds.2018-14464

Zhang, H., Liu, A., Li, X., Xu, W., Shi, R., Luo, H., Su, G., Dong, G., Guo, G. & Wang, Y. (2019). Genetic analysis of skinfold thickness and its association with body condition score and milk production traits in Chinese Holstein population. Journal of Dairy Science, 102(3), 2347-2352. https://doi.org/10.3168/jds.2018-15180

Mebratie, W., Madsen, P., Hawken, R., Romé, H., Marois, D., Henshall, J., Bovenhuis, H. & Jensen, J. (2019). Genetic parameters for body weight and different definitions of residual feed intake in broiler chickens. Genetics Selection Evolution, 51(1), Artikel 53. https://doi.org/10.1186/s12711-019-0494-2

Rohde, P. D., Jensen, I. R., Sarup, P. M., Ørsted, M., Demontis, D., Sørensen, P. & Kristensen, T. N. (2019). Genetic Signatures of Drug Response Variability in Drosophila melanogaster. Genetics (Print), 213(2), 633-650. https://doi.org/10.1534/genetics.119.302381

Aliakbari, A., Ehsani, A., Vaez Torshizi, R., Løvendahl, P., Esfandyari, H., Jensen, J. & Sarup, P. (2019). Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle. Journal of Animal Science, 97(9), 3832-3844. https://doi.org/10.1093/jas/skz228

Zhang, T., Gao, H., Sahana, G., Zan, Y., Fan, H., Liu, J., Shi, L., Liu, J., Du, L., Wang, L. & Zhao, F. (2019). Genome-wide association studies revealed candidate genes for tail fat deposition and body size in the Hulun Buir sheep. Journal of Animal Breeding and Genetics, 136(5), 362-370. https://doi.org/10.1111/jbg.12402

Gan, Q. F., Li, Y. R., Lund, M., Su, G. S. & Liang, X. W. (2019). Genome-wide association study identifies loci linked to serum electrolyte traits in Chinese Holstein cattle. Animal Genetics, 50(6), 744-748. https://doi.org/10.1111/age.12851

Mebratie, W., Reyer, H., Wimmers, K., Bovenhuis, H. & Jensen, J. (2019). Genome wide association study of body weight and feed efficiency traits in a commercial broiler chicken population, a re-visitation. Scientific Reports, 9, Artikel 922. https://doi.org/10.1038/s41598-018-37216-z

Kristensen, P. S., Jensen, J., Andersen, J. R., Guzmán, C., Jihad, O. & Jahoor, A. (2019). Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material. Genes, 10(9), Artikel 669. https://doi.org/10.3390/genes10090669

Slagboom, M., Kargo, M., Sørensen, A. C., Thomasen, J. R. & Mulder, H. A. (2019). Genomic selection improves the possibility of applying multiple breeding programs in different environments. Journal of Dairy Science, 102(9), 8197-8209. https://doi.org/10.3168/jds.2018-15939

Robertsen, C. D., Hjortshøj, R. L. & Janss, L. L. (2019). Genomic selection in cereal breeding. Agronomy, 9(2), Artikel 95. https://doi.org/10.3390/agronomy9020095

Liu, A., Su, G., Höglund, J., Zhang, Z., Thomasen, J. R., Christiansen, I. A., Wang, Y. & Kargo, M. (2019). Genotype by environment interaction for female fertility traits under conventional and organic production systems in Danish Holsteins. Journal of Dairy Science, 102(9), 8134-8147. https://doi.org/10.3168/jds.2018-15482

Zhang, Z., Kargo, M., Liu, A., Thomasen, J. R., Pan, Y. & Su, G. (2019). Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model. Heredity, 123(2), 202-214. https://doi.org/10.1038/s41437-019-0192-4