Publications

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Larsen, A., Bøggild, H., Mortensen, J. T., Foldager, L., Hansen, J., Christensen, A., Arendt, M., Rosenberg, N. & Munk-Jørgensen, P. (2010). Psychopathology, defence mechanisms, and the psychosocial work environment. International Journal of Social Psychiatry, 56(6), 563-77. https://doi.org/10.1177/0020764008099555

Abdallah, M., Greaves-Lord, K., Grove, J., Nørgaard-Pedersen, B., Hougaard, D. M. & Mortensen, E. L. (2011). Psychiatric Comorbidities in Autism Spectrum Disorders: Findings from a Danish Historic Birth Cohort. European Child & Adolescent Psychiatry, 20(11-12), 599-601. https://doi.org/10.1007/s00787-011-0220-2

Østergaard, S. D., Foldager, L., Allgulander, C., Dahl, A. A., Huuhtanen, M.-T., Rasmussen, I. & Munk-Jørgensen, P. (2010). Psychiatric caseness is a marker of major depressive episode in general practice. Scandinavian Journal of Primary Health Care, 28(4), 211-5. https://doi.org/10.3109/02813432.2010.501235

Christensen, O. F. (2006). Pseudo-likelihood for non-reversible nucleotide substitution models with neighbour dependent rates. Statistical Applications in Genetics and Molecular Biology, 5(18).

Christensen, O. F., Hobolth, A. & Jensen, J. L. (2005). Pseudo-likelihood analysis of codon substitution models with neighbor dependent rates. Journal of Computational Biology, 12, 1166-1182.

Musgaard, M., Thøgersen, L. & Schiøtt, B. (2011). Protonation States of Important Acidic Residues in the Central Ca²⁺ Ion Binding Sites of the Ca²⁺-ATPase: A Molecular Modeling Study. Biochemistry, 50(51), 11109-11120. https://doi.org/10.1021/bi201164b

Ludvigsen, M., Bjerregård Pedersen, M., Lystlund Lauridsen, K., Svenstrup Poulsen, T., Hamilton-Dutoit, S. J., Besenbacher, S., Bendix, K., Møller, M. B., Nørgaard, P., d'Amore, F. & Honoré, B. (2018). Proteomic profiling identifies outcome-predictive markers in patients with peripheral T-cell lymphoma, not otherwise specified. Blood Advances, 2(19), 2533-2542. https://doi.org/10.1182/bloodadvances.2018019893

Dam, S., Dyrlund, T. F., Ussatjuk, A., Jochimsen, B., Nielsen, K., Goffard, N., Ventosa, M. A. L., Lorentzen, A. M., Gupta, V., Andersen, S. U., Enghild, J. J., Ronson, C. W., Roepstorff, P. & Stougaard, J. (2014). Proteome reference maps of the Lotus japonicus nodule and root. Proteomics, 14(2-3), 230-240. https://doi.org/10.1002/pmic.201300353

Thøgersen, L. (2009). Protein Domain Model: A preliminary exploration. Poster session presented at Faraday Discussion: Multiscale Modelling of Soft Matter, Groningen, Netherlands.

Kringelbach, T., Højgaard, M., Rohrberg, K., Spanggaard, I., Laursen, B. E., Ladekarl, M., Haslund, C. A., Harsløf, L., Belcaid, L., Gehl, J., Søndergaard, L., Eefsen, R. L., Hansen, K. H., Kodahl, A. R., Jensen, L. H., Holt, M. I., Oellegaard, T. H., Yde, C. W., Ahlborn, L. B. & Lassen, U. (2023). ProTarget: a Danish Nationwide Clinical Trial on Targeted Cancer Treatment based on genomic profiling – a national, phase 2, prospective, multi-drug, non-randomized, open-label basket trial. BMC Cancer, 23(1), Article 182. https://doi.org/10.1186/s12885-023-10632-9

Cheng, Z., McLaughlin, D. L., Little, M. W., Ferris, C., Salavati, M., Ingvartsen, K. L., Crowe, M. A., Wathes, D. C. & GplusE Consortium (2023). Proportion of Concentrate in the Diet of Early Lactation Dairy Cows Has Contrasting Effects on Circulating Leukocyte Global Transcriptomic Profiles, Health and Fertility According to Parity. International Journal of Molecular Sciences , 24(1), Article 39. https://doi.org/10.3390/ijms24010039

Blanquart, F., Achaz, G., Bataillon, T. & Tenaillon, O. (2014). Properties of selected mutations and genotypic landscapes under Fisher's geometric model. Evolution, 68(12), 3537-54. https://doi.org/10.1111/evo.12545

Villesen, P. & Stride Nielsen, L. (2017). Profiling of cocaine using ratios of GC-MS peaks. Scientific Reports, 7(1), 11646. Article 11646. https://doi.org/10.1038/s41598-017-12042-x

Fredslund, J. & Lange, M. (2007). primique: automatic design of specific PCR primers for each sequence in a family. B M C Bioinformatics, 8(369). https://doi.org/10.1186/1471-2105-8-369

Fredslund, J., Schauser, L., Madsen, L. H., Sandal, N. N. & Stougaard, J. (2005). PriFi - Using a Multiple Alignment of Related Sequences to Find Primers for Amplification of Homologs. Nucleic Acids Research, 33(W), 516-520.

Larsen, J. K., Foldager, L., Bendsen, B. B. & Munk-Jørgensen, P. (2011). Prematurity and low birth weight as risk factors for the development of affective disorder, especially depression and schizophrenia: a register study. Poster session presented at American Psychiatric Association 164th Annual Meeting, Honolulu, Hawaii, United States.

Winters, J., Kobek-Kjeldager, C., Foldager, L. & Pedersen, L. J. (2021). Preliminary results on the effect of keeping pigs in home environment post weaning. In E. Strandberg, L. Pinotti, S. Messori, D. Kenny, M. Lee, J. F. Hocquette, V. A. P. Cadavez, S. Millet, R. Evans, T. Veldkamp, M. Pastell & G. Pollott (Eds.), Book of Abstracts of the 72nd Annual Meeting of the European Federation of Animal Science (pp. 356-356). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-918-3

Kjældgaard, A. L., Pilely, K., Olsen, K. S., Jessen, A. H., Lauritsen, A. Ø., Pedersen, S. W., Svenstrup, K., Karlsborg, M., Thagesen, H., Blaabjerg, M., Theódórsdóttir, Á., Elmo, E. G., Møller, A. T., Bonefeld, L., Berg, M., Garred, P. & Møller, K. (2021). Prediction of survival in amyotrophic lateral sclerosis: a nationwide, Danish cohort study. BMC Neurology, 21(1), Article 164. https://doi.org/10.1186/s12883-021-02187-8

Søndergaard, D., Nielsen, S., Pedersen, C. N. S. & Besenbacher, S. (2017). Prediction of Primary Tumors in Cancers of Unknown Primary. Journal of integrative bioinformatics, 14(2). https://doi.org/10.1515/jib-2017-0013

Foldager, L., Thorup, V. M., Pastell, M., Jensen, B. B. & Krogh, M. A. (2024). Prediction of mortality in land-based recirculating aquaculture system (RAS) trout farms. In P. Linde (Ed.), Symposium i Anvendt Statistik 2024 (pp. 17-24). Økonomisk Institut, Københavns Universitet & Symposium i Anvendt Statistik.

De Koster, J., Salavati, M., Grelet, C., Crowe, M. A., Matthews, E., O'Flaherty, R., Opsomer, G., Foldager, L., GplusE Consortium & Hostens, M. (2019). Prediction of metabolic clusters in early lactation dairy cows using models based on milk biomarkers. Journal of Dairy Science, 102(3), 2631-2644. https://doi.org/10.3168/jds.2018-15533

Grelet, C., Vanlierde, A., Dehareng, F., Froidmont, E. & GplusE Consortium (2017). Prediction of energy status of dairy cows using MIR milk spectra. In Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science (Vol. 23, pp. 403-403). Wageningen Academic Publishers.

Grelet, C., Vanlierde, A., Salavati, M., Hostens, M., Foldager, L., Dehareng, F. & GplusE Consortium (2017). Prediction of energy status of dairy cows using MIR milk spectra. Abstract from ICAR 2017, United Kingdom. http://www.icar.org/wp-content/uploads/2017/06/1.-Clement-Grelet.pdf

Foldager, L., Gaillard, C., Sørensen, M. T., Larsen, T., Matthews, E., O'Flaherty, R., Carter, F., Crowe, M., Grelet, C., Salavati, M., Hostens, M., GplusE Consortium, Ingvartsen, K. L. & Krogh, M. A. (2020). Predicting physiological imbalance in Holstein dairy cows by three different sets of milk biomarkers. Preventive Veterinary Medicine, 179, Article 105006. https://doi.org/10.1016/j.prevetmed.2020.105006

Foldager, L., Munksgaard, L., Trénel, P. & Thomsen, P. T. (2015). Predicting daily eating activity of dairy cows from 3D accelerometer data and RFID signals by use of a random forests model. In C. H. Knight (Ed.), Proceedings of the Third DairyCare Conference 2015 (pp. 31-31)

Cavassim, M. I. A., Baker, Z., Hoge, C., Schierup, M. H., Schumer, M. & Przeworski, M. (2022). PRDM9 losses in vertebrates are coupled to those of paralogs ZCWPW1 and ZCWPW2. Proceedings of the National Academy of Sciences (PNAS), 119(9), Article e2114401119. https://doi.org/10.1073/pnas.2114401119

Sükösd, Z., Knudsen, B., Kjems, J. & Pedersen, C. N. S. (2012). PPfold 3.0: fast RNA secondary structure prediction using phylogeny and auxiliary data. Bioinformatics, 28(20), 2691-2692. https://doi.org/10.1093/bioinformatics/bts488

Grelet, C., Vanlierde, A., Hostens, M., Foldager, L., Salavati, M., Ingvartsen, K. L., Crowe, M., Sorensen, M. T., Froidmont, E., Ferris, C. P., Marchitelli, C., Becker, F., Larsen, T., Carter, F., GplusE Consortium & Dehareng, F. (2019). Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. Animal, 13(3), 649-658. https://doi.org/10.1017/S1751731118001751

Grelet, C., Froidmont, E., Foldager, L., Salavati, M., Hostens, M., Ferris, C. P., Ingvartsen, K. L., Crowe, M., Sørensen, M. T., Fernandez Pierna, J. A., Vanlierde, A., Gengler, N., GplusE Consortium & Dehareng, F. (2020). Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation. Journal of Dairy Science, 103(5), 4435-4445. https://doi.org/10.3168/jds.2019-17910

Ghouse, J., Ahlberg, G., Rand, S. A., Olesen, M. S., Vilhjalmsson, B., Stender, S. & Bundgaard, H. (2024). Potential Influence of Risk Factor Control on the Association Between Lipoprotein(a) and Atherosclerotic Cardiovascular Disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 44(6), 1455-1457. https://doi.org/10.1161/ATVBAHA.124.320990

Höglund, J., Sørensen, M. T., Larsen, T., Foldager, L., Ferris, C., Bell, M., Carter, F., Santoro, A., Crowe, M. & Ingvartsen, K. L. (2016). Potential for novel metabolite measurements in milk as biomarker phenotypes for dairy traits. In Book of abstracts of the 67th Annual Meeting of the European Federation of Animal Science (pp. 299). Wageningen Academic Publishers, The Netherlands. https://doi.org/10.3920/978-90-8686-830-8

Winters, J. F. M., Foldager, L. & Pedersen, L. J. (2023). Postweaning growth and feeding behaviour in pigs: effect of heavier hybrid and weaning intact litters in farrowing pens for loose-housed sows. Animal, 17(1), Article 100688. https://doi.org/10.1016/j.animal.2022.100688

Riber, A. B., Herskin, M. S., Foldager, L., Sandercock, D. A., Murrell, J. & Tahamtani, F. (2021). Post-mortem examination of fast-growing broilers with different degrees of identifiable gait defects. Veterinary Record, 189(7), Article e454. https://doi.org/10.1002/vetr.454

Privé, F., Aschard, H., Carmi, S., Folkersen, L., Hoggart, C., O'Reilly, P. F. & Vilhjálmsson, B. J. (2022). Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. American Journal of Human Genetics, 109(1), 12-23. https://doi.org/10.1016/j.ajhg.2021.11.008

Feldman, M. W., Otto, S. P. & Christiansen, F. (1996). Population genetic perspectives on the evolution of recombination. Annual Review of Genetics, 49, 261-295.

Raveane, A., Aneli, S., Montinaro, F., Athanasiadis, G., Barlera, S., Birolo, G., Boncoraglio, G., Di Blasio, A. M., Di Gaetano, C., Pagani, L., Parolo, S., Paschou, P., Piazza, A., Stamatoyannopoulos, G., Angius, A., Brucato, N., Cucca, F., Hellenthal, G., Mulas, A. ... Capelli, C. (2019). Population structure of modern-day Italians reveals patterns of ancient and archaic ancestries in Southern Europe. Science Advances, 5(9), Article eaaw3492. https://doi.org/10.1126/sciadv.aaw3492

Als, T. D., Lescai, F., Dahl, H. A., Grove, J., Demontis, D., Wang, A. G., Andorsdottir, G., Biskopstø, M., Johansen, O., Nyegaard, M., Bolund, L., Mors, O., Wang, J. & Børglum, A. (2014). Population Structure & Cryptic Relatedness of the isolated Population of the Faroe Islands. Poster session presented at XXII World Congress of Psychiatric Genetics, København, Denmark.

Anciaux, Y., Lambert, A., Ronce, O., Roques, L. & Martin, G. (2019). Population persistence under high mutation rate: From evolutionary rescue to lethal mutagenesis. Evolution, 73(8), 1517-1532. https://doi.org/10.1111/evo.13771

Castellano, D. & Munch, K. (2020). Population Genomics in the Great Apes. In J. Y. Dutheil (Ed.), Methods in Molecular Biology (pp. 453-463). Springer. https://doi.org/10.1007/978-1-0716-0199-0_19

Feldman, M. & Christiansen, F. (1984). Population genetic theory of the cost of inbreeding. American Naturalist, 123, 642–653.

Christiansen, FB. (2006). Population Genetics with a view towards Bioinformatics. Lecture Notes in Population Genetics. Eget.

Christiansen, F. B. (2005). Population Genetics with a view towards Bioinformatics. Lecture Notes in Molecular Population Genetics. Eget.

Christiansen, F. (1990). Population consequences of genetic design in sexually reproducing organisms. In H. Mooney & G. Bernardi (Eds.), Introduction of Genetically Modified Organisms into the Environment (pp. 43-55). John Wiley & Sons Ltd.

Chalmer, M. A., Kogelman, L. J. A., Ullum, H., Sørensen, E., Didriksen, M., Mikkelsen, S., Dinh, K. M., Brodersen, T., Nielsen, K. R., Bruun, M. T., Banasik, K., Brunak, S., Erikstrup, C., Pedersen, O. B., Ostrowski, S. R., Olesen, J. & Hansen, T. F. (2023). Population-Based Characterization of Menstrual Migraine and Proposed Diagnostic Criteria. JAMA network open, 6(5), e2313235. https://doi.org/10.1001/jamanetworkopen.2023.13235

Qian, Y., Kehr, B. & Halldorsson, B. V. (2015). PopAlu: population-scale detection of Alu polymorphisms. PeerJ, 3, Article 1269. https://doi.org/10.7717/peerj.1269

Weiner, D. J., Wigdor, E. M., Ripke, S., Walters, R. K., Kosmicki, J. A., Grove, J., Samocha, K. E., Goldstein, J. I., Okbay, A., Bybjerg-Grauholm, J., Werge, T., Hougaard, D. M., Taylor, J., Skuse, D., Devlin, B., Anney, R., Sanders, S. J., Bishop, S., Mortensen, P. B. ... iPSYCH-Broad Autism Group (2017). Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nature Genetics, 49(7), 978-985. https://doi.org/10.1038/ng.3863

Ding, Y., Hou, K., Xu, Z., Pimplaskar, A., Petter, E., Boulier, K., Privé, F., Vilhjálmsson, B. J., Olde Loohuis, L. M. & Pasaniuc, B. (2023). Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature, 618, 774-781. https://doi.org/10.1038/s41586-023-06079-4

Hayeck, T. J., Busby, G. B., Chun, S., Lewis, A. C. F., Roberts, M. C. & Vilhjálmsson, B. J. (2023). Polygenic Risk Scores: Genomes to Risk Prediction. Clinical Chemistry, 69(6), 551–557. https://doi.org/10.1093/clinchem/hvad049

Agerbo, E., Sullivan, P. F., Vilhjálmsson, B. J., Pedersen, C. B., Mors, O., Børglum, A. D., Hougaard, D. M., Hollegaard, M. V., Meier, S., Mattheisen, M., Ripke, S., Wray, N. R. & Mortensen, P. B. (2015). Polygenic Risk Score, Parental Socioeconomic Status, Family History of Psychiatric Disorders, and the Risk for Schizophrenia: A Danish Population-Based Study and Meta-analysis. J A M A Psychiatry. https://doi.org/10.1001/jamapsychiatry.2015.0346

Wang, X., Walker, A., Revez, J. A., Ni, G., Adams, M. J., McIntosh, A. M. & Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2023). Polygenic risk prediction: why and when out-of-sample prediction R² can exceed SNP-based heritability. American Journal of Human Genetics, 110(7), 1207-1215. https://doi.org/10.1016/j.ajhg.2023.06.006

Displaying results 351 to 400 out of 1317

Revised 21.08.2024

Palle Villesen