Milkevych, V., Karaman, E., Sahana, G., Janss, L., Cai, Z. & Lund, M. S. (2021).
MeSCoT: The tool for quantitative trait simulation through the mechanistic modeling of genes' regulatory interactions.
G3 (Bethesda, Md.),
11(7), Article jkab133.
https://doi.org/10.1093/g3journal/jkab133
Hao, D., Bai, J., Du, J.
, Wu, X., Thomsen, B., Gao, H., Su, G. & Wang, X. (2021).
Overview of metabolomic analysis and the integration with multi-omics for economic traits in cattle.
Metabolites,
11(11), Article 753.
https://doi.org/10.3390/metabo11110753
Pan, Z., Yao, Y., Yin, H.
, Cai, Z., Wang, Y., Bai, L., Kern, C., Halstead, M., Chanthavixay, G., Trakooljul, N., Wimmers, K.
, Sahana, G., Su, G., Lund, M. S., Fredholm, M., Karlskov-Mortensen, P., Ernst, C. W., Ross, P., Tuggle, C. K. ... Zhou, H. (2021).
Pig genome functional annotation enhances the biological interpretation of complex traits and human disease.
Nature Communications,
12(1), Article 5848.
https://doi.org/10.1038/s41467-021-26153-7Washburn, J. D., Cimen, E.
, Ramstein, G., Reeves, T., O’Briant, P., McLean, G., Cooper, M., Hammer, G. & Buckler, E. S. (2021).
Predicting phenotypes from genetic, environment, management, and historical data using CNNs.
Theoretical and Applied Genetics,
134(12), 3997-4011.
https://doi.org/10.1007/s00122-021-03943-7Ma, X., Christensen, O. F., Gao, H., Huang, R.
, Nielsen, B., Madsen, P., Jensen, J., Ostersen, T., Li, P.
, Shirali, M. & Su, G. (2021).
Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait.
Heredity,
126(1), 206-217.
https://doi.org/10.1038/s41437-020-0339-3
Hjortø, L., Henryon, M.
, Liu, H., Berg, P., Thomasen, J. R. & Sørensen, A. C. (2021).
Pre-selection against a lethal recessive allele in breeding schemes with optimum-contribution selection or truncation selection.
Genetics Selection Evolution,
53, Article 75.
https://doi.org/10.1186/s12711-021-00669-4
Kramer, L. M., Wolc, A.
, Esfandyari, H., Thekkoot, D. M., Zhang, C., Kemp, R. A., Plastow, G. & Dekkers, J. C. M. (2021).
Purebred-crossbred genetic parameters for reproductive traits in swine.
Journal of Animal Science,
99(10), Article skab270.
https://doi.org/10.1093/jas/skab270Salimiyekta, Y., Vaez‐torshizi, R., Abbasi, M. A., Emmamjome‐kashan, N., Amin‐afshar, M.
, Guo, X. & Jensen, J. (2021).
Random regression model for genetic evaluation and early selection in the iranian holstein population.
Animals,
11(12), Article 3492.
https://doi.org/10.3390/ani11123492
Li, Y., Pu, L., Shi, L.
, Gao, H., Zhang, P., Wang, L. & Zhao, F. (2021).
Revealing New Candidate Genes for Teat Number Relevant Traits in Duroc Pigs Using Genome-Wide Association Studies.
Animals,
11(3), Article 806.
https://doi.org/10.3390/ani11030806Ask, B., Pedersen, L. V.
, Christensen, O. F., Nielsen, H. M., Turner, S. P.
& Nielsen, B. (2021).
Selection for social genetic effects in purebreds increases growth in crossbreds.
Genetics, selection, evolution : GSE,
53(1), Article 15.
https://doi.org/10.1186/s12711-021-00609-2Gautason, E., Sahana, G., Su, G., Benjamínsson, B. H., Jóhannesson, G.
& Guldbrandtsen, B. (2021).
Short Communication: Investigation of the feasibility of genomic selection in Icelandic Cattle.
Journal of Animal Science,
99(7), Article skab139.
https://doi.org/10.1093/jas/skab139
De, T., Goncalves, A.
, Speed, D., Froguel, P., NFBC consortium, Gaffney, D. J., Johnson, M. R., Jarvelin, M.-R. & Coin, L. J. (2021).
Signatures of TSPAN8 variants associated with human metabolic regulation and diseases.
iScience,
24(8), Article 102893.
https://doi.org/10.1016/j.isci.2021.102893Zhu, Z., Difford, G., Noel, S. J., Lassen, J., Løvendahl, P. & Hojberg, O. (2021).
Stability Assessment of the Rumen Bacterial and Archaeal Communities in Dairy Cows Within a Single Lactation and Its Association With Host Phenotype.
Frontiers in Microbiology,
12, Article 636223.
https://doi.org/10.3389/fmicb.2021.636223
Chu, T. T., Henryon, M.
, Jensen, J., Ask, B.
& Christensen, O. F. (2021).
Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects.
Genetics Selection Evolution,
53(1), Article 1.
https://doi.org/10.1186/s12711-020-00598-8
Ahmad, S., Drag, M. H., Salleh, S. M.
, Cai, Z. & Nielsen, M. O. (2021).
Transcriptomics analysis of differentially expressed genes in subcutaneous and perirenal adipose tissue of sheep as affected by their pre- and early postnatal malnutrition histories.
BMC Genomics,
22(1), Article 338.
https://doi.org/10.1186/s12864-021-07672-5
Bauer, M., Glenn, T., Achtyes, E. D., Alda, M., Agaoglu, E., Altınbaş, K., Andreassen, O. A., Angelopoulos, E., Ardau, R., Vares, E. A., Aydin, M., Ayhan, Y., Baethge, C., Bauer, R., Baune, B. T., Balaban, C., Becerra-Palars, C., Behere, A. P., Behere, P. B. ... Whybrow, P. C. (2021).
Variations in seasonal solar insolation are associated with a history of suicide attempts in bipolar I disorder.
International Journal of Bipolar Disorders,
9(1), Article 26.
https://doi.org/10.1186/s40345-021-00231-7Gebreyesus, G., Poulsen, N. A., Larsen, M. K., Larsen, L. B., Sørensen, E. S., Würtz Heegaard, C. & Buitenhuis, A. J. (2021).
Vitamin B12 and transcobalamin in bovine milk: Genetic variation and genome-wide association with loci along the genome.
JDS Communications ,
2(3), 127-131.
https://doi.org/10.3168/jdsc.2020-0048
Islam, M. S., Jensen, J., Løvendahl, P., Karlskov-Mortensen, P.
& Shirali, M. (2020).
Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle.
Journal of Dairy Science,
103(10), 9150-9166.
https://doi.org/10.3168/jds.2019-17137
Zhang, Q., Difford, G., Sahana, G., Løvendahl, P., Lassen, J., Lund, M. S., Guldbrandtsen, B. & Janss, L. (2020).
Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows.
The ISME Journal,
14(8), 2019-2033.
https://doi.org/10.1038/s41396-020-0663-x
Macedo, F. L.
, Christensen, O. F., Astruc, J.-M., Aguilar, I., Masuda, Y. & Legarra, A. (2020).
Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups.
Genetics, selection, evolution : GSE,
52(1), Article 47.
https://doi.org/10.1186/s12711-020-00567-1Difford, G. F., Løvendahl, P., Veerkamp, R. F., Bovenhuis, H., Visker, M. H. P. W., Lassen, J. & de Haas, Y. (2020).
Can greenhouse gases in breath be used to genetically improve feed efficiency of dairy cows? Journal of Dairy Science,
103(3), 2442-2459.
https://doi.org/10.3168/jds.2019-16966
Ba, H.
, Cai, Z., Gao, H., Qin, T., Liu, W., Xie, L., Zhang, Y., Jing, B., Wang, D. & Li, C. (2020).
Chromosome-level genome assembly of Tarim red deer, Cervus elaphus yarkandensis.
Scientific Data,
7(1), Article 187.
https://doi.org/10.1038/s41597-020-0537-0Madsen, M. D., Villumsen, T. M., Hansen, B. K.
, Møller, S. H., Jensen, J. & Shirali, M. (2020).
Combined analysis of group recorded feed intake and individually recorded body weight and litter size in mink.
Animal : an international journal of animal bioscience,
14(9), 1793-1801.
https://doi.org/10.1017/S1751731120000762
Wang, X., Su, G., Hao, D., Lund, M. S. & Kadarmideen, H. N. (2020).
Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations.
Journal of Animal Science and Biotechnology,
11(1), Article 3.
https://doi.org/10.1186/s40104-019-0407-9
Cui, X., Zhang, S., Zhang, Q.
, Guo, X., Wu, C., Yao, M. & Sun, D. (2020).
Comprehensive MicroRNA Expression Profile of the Mammary Gland in Lactating Dairy Cows With Extremely Different Milk Protein and Fat Percentages.
Frontiers in Genetics,
11, Article 548268.
https://doi.org/10.3389/fgene.2020.548268Christensen, O. F., Nielsen, B., Su, G., Xiang, T., Madsen, P., Ostersen, T., Velander, I. & Strathe, A. B. (2020).
Correction to: A bivariate genomic model with additive, dominance and inbreeding depression effects for sire line and three-way crossbred pigs.
Genetics, selection, evolution : GSE,
52(1), Article 23.
https://doi.org/10.1186/s12711-020-00541-x
Cagnano, G., Vázquez-De-Aldana, B. R.
, Asp, T., Roulund, N., Jensen, C. S. & Soto-Barajas, M. C. (2020).
Determination of loline alkaloids and mycelial biomass in endophyte-infected schedonorus pratensis by near-infrared spectroscopy and chemometrics.
Microorganisms,
8(5), Article 776.
https://doi.org/10.3390/microorganisms8050776
Cai, Z., Dusza, M.
, Guldbrandtsen, B., Lund, M. S. & Sahana, G. (2020).
Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle.
Genetics, selection, evolution : GSE,
52(1), Article 19.
https://doi.org/10.1186/s12711-020-00538-6
Wang, L., Janss, L. L., Madsen, P., Henshall, J., Huang, C.-H., Marois, D.
, Alemu, S., Sørensen, A. C. & Jensen, J. (2020).
Effect of genomic selection and genotyping strategy on estimation of variance components in animal models using different relationship matrices.
Genetics, selection, evolution : GSE,
52(1), Article 31.
https://doi.org/10.1186/s12711-020-00550-w
Song, J.
, Chen, W., Zhang, J., Huang, K., Hou, B.
& Prishchepov, A. V. (2020).
Effects of building density on land surface temperature in China: Spatial patterns and determinants.
Landscape and Urban Planning,
198.
https://doi.org/10.1016/j.landurbplan.2020.103794Esfandyari, H., Fè, D., Tessema, B. B., Janss, L. L. & Jensen, J. (2020).
Effects of different strategies for exploiting genomic selection in perennial ryegrass breeding programs.
G3: Genes, Genomes, Genetics,
10(10), 3783-3795.
https://doi.org/10.1534/g3.120.401382
Skovbjerg, C. K., Knudsen, J. N., Füchtbauer, W.
, Stougaard, J., Stoddard, F. L.
, Janss, L. & Andersen, S. U. (2020).
Evaluation of yield, yield stability, and yield–protein relationship in 17 commercial faba bean cultivars.
Legume Science,
2(3), Article e39.
https://doi.org/10.1002/leg3.39
Dettmann, F., Warner, D.
, Buitenhuis, B., Kargo, M., Kjeldsen, A. M. H., Nielsen, N. H., Lefebvre, D. M. & Santschi, D. E. (2020).
Fatty Acid Profiles from Routine Milk Recording as a Decision Tool for Body Weight Change of Dairy Cows after Calving.
Animals,
10(11), 1-14. Article 1958.
https://doi.org/10.3390/ani10111958Forte, F. P., Schmid, J., Dijkwel, P. P.
, Nagy, I., Hume, D. E., Johnson, R. D., Simpson, W. R., Monk, S. M., Zhang, N., Sehrish, T.
& Asp, T. (2020).
Fungal Endophyte Colonization Patterns Alter Over Time in the Novel Association Between Lolium perenne and Epichloë Endophyte AR37.
Frontiers in Plant Science,
11, Article 570026.
https://doi.org/10.3389/fpls.2020.570026
Zaalberg, R. M., Buitenhuis, A. J., Sundekilde, U. K., Poulsen, N. A. & Bovenhuis, H. (2020).
Genetic analysis of orotic acid predicted with Fourier transform infrared milk spectra.
Journal of Dairy Science,
103(4), 3334-3348.
https://doi.org/10.3168/jds.2018-16057
Chu, T. T., Madsen, P., Norberg, E., Wang, L., Marois, D., Henshall, J.
& Jensen, J. (2020).
Genetic analysis on body weight at different ages in broiler chicken raised in commercial environment.
Journal of Animal Breeding and Genetics (Online),
137(2), 245-259.
https://doi.org/10.1111/jbg.12448
Zaalberg, R. M., Bovenhuis, H.
, Poulsen, N. A., Larsen, L. B., Sehested, J. & Buitenhuis, A. J. (2020).
Genetic analysis on minerals predicted with Fourier transform infrared milk spectra for two Danish dairy cattle breeds. Abstract from 71st Annual Meeting of European Federation of Animal Science.
https://www.wageningenacademic.com/doi/book/10.3920/978-90-8686-900-8
Eydivandi, S., Sahana, G., Momen, M., Moradi, M. H.
& Schönherz, A. A. (2020).
Genetic diversity in Iranian indigenous sheep vis-à-vis selected exogenous sheep breeds and wild mouflon.
Animal Genetics,
51(5), 772-787.
https://doi.org/10.1111/age.12985
Guo, X., Sarup, P. M., Jensen, J. D., Jihad, O., Kristensen, N. H.
, Mulder, F. A. A., Jahoor, A.
& Jensen, J. (2020).
Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley.
Frontiers in Plant Science,
11, Article 575467.
https://doi.org/10.3389/fpls.2020.575467
Mao, X., Sahana, G., Johansson, A. M.
, Liu, A., Ismael, A., Løvendahl, P., De Koning, D.-J.
& Guldbrandtsen, B. (2020).
Genome-wide association mapping for dominance effects in female fertility using real and simulated data from Danish Holstein cattle.
Scientific Reports,
10(1), Article 2953.
https://doi.org/10.1038/s41598-020-59788-5
Gan, Q. F., Li, Y. R., Liu, Q. H.
, Lund, M., Su, G. S. & Liang, X. W. (2020).
Genome-wide association studies for the concentrations of insulin, triiodothyronine, and thyroxine in Chinese Holstein cattle.
Tropical Animal Health and Production,
52(4), 1655-1660.
https://doi.org/10.1007/s11250-019-02170-zCao, L., Liu, H., Mulder, H. A., Henryon, M.
, Thomasen, J. R., Kargo, M. & Sørensen, A. C. (2020).
Genomic Breeding Programs Realize Larger Benefits by Cooperation in the Presence of Genotype × Environment Interaction Than Conventional Breeding Programs.
Frontiers in Genetics,
11, Article 251.
https://doi.org/10.3389/fgene.2020.00251
Cai, Z., Sarup, P., Ostersen, T.
, Nielsen, B., Fredholm, M., Karlskov-Mortensen, P.
, Sørensen, P., Jensen, J., Guldbrandtsen, B., Lund, M. S., Christensen, O. F. & Sahana, G. (2020).
Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds.
Journal of Animal Science,
98(7), Article 229.
https://doi.org/10.1093/jas/skaa229
Tsai, H.-Y.
, Janss, L. L., Andersen, J. R., Orabi, J., Jensen, J. D., Jahoor, A.
& Jensen, J. (2020).
Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat.
Scientific Reports,
10(1), Article 3347.
https://doi.org/10.1038/s41598-020-60203-2Guo, X., Svane, S. F., Füchtbauer, W. S., Andersen, J. R.
, Jensen, J. & Thorup-Kristensen, K. (2020).
Genomic prediction of yield and root development in wheat under changing water availability.
Plant Methods,
16, Article 90.
https://doi.org/10.1186/s13007-020-00634-0
Ankamah-Yeboah, T.
, Janss, L. L., Jensen, J. D., Hjortshøj, R. L. & Rasmussen, S. K. (2020).
Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.
Frontiers in Plant Science,
11, Article 539.
https://doi.org/10.3389/fpls.2020.00539Tausen, M., Clausen, M. M., Moeskjær, S., Shihavuddin, ASM., Dahl, A. B.
, Janss, L. & Andersen, S. U. (2020).
Greenotyper: Image-based plant phenotyping using distributed computing and deep learning.
Frontiers in Plant Science,
11, Article 1181.
https://doi.org/10.3389/fpls.2020.01181
Liu, T., Luo, C., Ma, J., Wang, Y., Shu, D.
, Su, G. & Qu, H. (2020).
High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers.
Frontiers in Genetics,
11, Article 108.
https://doi.org/10.3389/fgene.2020.00108Bordbar, 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), Article e12870.
https://doi.org/10.1111/cpr.12870Su, 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, Article 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, Article 586155.
https://doi.org/10.3389/fgene.2020.586155Liu, 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, Article 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), Article 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), Article dsaa015.
https://doi.org/10.1093/dnares/dsaa015Morgante, 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/g3.120.401847Ba, 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-wManzanilla-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
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), Article 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
Hutchings, N., Lærke, P. E., Munkholm, L. J., Elsgaard, L., Kristensen, T., Rasmussen, J., Lund, P., Børsting, C. F., Løvendahl, P., Mikkelsen, M. H., Albrektsen, R., Gyldenkærne, S., Møller, H. B., Hansen, M. J., Feilberg, A. & Adamsen, A. P. S., (2020).
Opdatering af effekter og potentialer af klimavirkemidler til anvendelse i landbrug, No. 2019-0035910, 23 p., Mar 02, 2020.
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, Article 866.
https://doi.org/10.3389/fgene.2020.00866
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
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), Article 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
