Persichilli, C., Karaman, E., Finocchiaro, R., Pilla, F., Fabris, A., Visentin, G. & Cassandro, M. (2023). Study of fertility traits in Italian Jersey Cattle. Italian Journal of Animal Science, 22(sup1), 41. Artikel 0563. https://aspacongress2023.com/wp-content/uploads/2023/06/ASPA-25th-Congress-Book-of-Abstract_compressed.pdf

Wang, X., Li, W., Feng, X., Li, J., Liu, G. E., Fang, L. & Yu, Y. (2023). Correction: Harnessing male germline epigenomics for the genetic improvement in cattle . Journal of Animal Science and Biotechnology, 14(1), Artikel 110. https://doi.org/10.1186/s40104-023-00917-1

Forte, F. P., Malinowska, M., Nagy, I., Schmid, J., Dijkwel, P., Hume, D. E., Johnson, R. D., Simpson, W. R. & Asp, T. (2023). Corrigendum: Methylome changes in Lolium perenne associated with long-term colonisation by the endophytic fungus Epichloë sp. LpTG-3 strain AR37 (Frontiers in Plant Science, (2023), 14, (1258100), 10.3389/fpls.2023.1258100). Frontiers in Plant Science, 14, Artikel 1332690. https://doi.org/10.3389/fpls.2023.1332690

Zhao, P., Peng, C., Fang, L., Wang, Z. & Liu, G. E. (2023). Taming transposable elements in livestock and poultry: a review of their roles and applications. Genetics Selection Evolution, 55(1), Artikel 50. https://doi.org/10.1186/s12711-023-00821-2

Wang, X., Li, W., Feng, X., Li, J., Liu, G. E., Fang, L. & Yu, Y. (2023). Harnessing male germline epigenomics for the genetic improvement in cattle. Journal of Animal Science and Biotechnology, 14(1), Artikel 76. https://doi.org/10.1186/s40104-023-00874-9

Sahu, S. K., Mokhade, A. & Bokde, N. D. (2023). An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges. Applied Sciences (Switzerland), 13(3), Artikel 1956. https://doi.org/10.3390/app13031956

Sahana, G., Cai, Z., Sanchez, M. P., Bouwman, A. C. & Boichard, D. (2023). Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle. Journal of Dairy Science, 106(8), 5218-5241. https://doi.org/10.3168/jds.2022-22694

Raffo, M. A. & Jensen, J. (2023). Gene × gene and genotype × environment interactions in wheat. Crop Science, 63(4), 1779-1793. https://doi.org/10.1002/csc2.20986

Legarra, A. & Christensen, O. F. (2023). Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes. JDS Communications , 4(1), 55-60. https://doi.org/10.3168/jdsc.2022-0276

Zira, S., Bouquet, A. E. R., Rydhmer, L., Kargo, M. & Puillet, L. (2023). Carbon footprint based on lifetime productivity for future cows selected for resilience to climate-related disturbances. Journal of Dairy Science, 106(12), 8953-8968. https://doi.org/10.3168/jds.2023-23492

Zhou, R., Yu, X., Rosenqvist, E. & Ottosen, C. O. (2023). Physiological and genetic studies inspired insights into tomato responding to multiple abiotic stresses. Acta Horticulturae, (1372), 1-7. https://doi.org/10.17660/ActaHortic.2023.1372.1

Zhao, P., Gu, L., Gao, Y., Pan, Z., Liu, L., Li, X., Zhou, H., Yu, D., Han, X., Qian, L., Liu, G. E., Fang, L. & Wang, Z. (2023). Young SINEs in pig genomes impact gene regulation, genetic diversity, and complex traits. Communications Biology, 6(1), Artikel 894. https://doi.org/10.1038/s42003-023-05234-x

Zhang, Z., Shi, S., Zhang, Q., Aamand, G. P., Lund, M. S., Su, G. & Ding, X. (2023). Improving Genomic Prediction Accuracy in the Chinese Holstein Population by Combining with the Nordic Holstein Reference Population. Animals, 13(4), Artikel 636. https://doi.org/10.3390/ani13040636

Zaalberg, R. M., Chu, T. T., Bovbjerg, H., Jensen, J. & Villumsen, T. M. (2023). Genetic parameters for early piglet weight, litter traits and number of functional teats in organic pigs. Animal, 17(3), Artikel 100717. https://doi.org/10.1016/j.animal.2023.100717

Xiang, R., Fang, L., Liu, S., Macleod, I. M., Liu, Z., Breen, E. J., Gao, Y., Liu, G. E., Tenesa, A., CattleGTEx Consortium, Mason, B. A., Chamberlain, A. J., Wray, N. R. & Goddard, M. E. (2023). Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle. Cell Genomics, 3(10), Artikel 100385. https://doi.org/10.1016/j.xgen.2023.100385

Wang, X., Gao, Y., Li, C. J., Fang, L., Liu, G. E., Zhao, X., Zhang, Y., Cai, G., Xue, G., Liu, Y., Wang, L., Zhang, F., Wang, K., Zhang, M., Li, R., Gao, Y. & Li, J. (2023). The single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells in Chinese holstein cattle. BMC Genomic Data, 24(1), Artikel 39. https://doi.org/10.1186/s12863-023-01139-0

Vardhan, B. V. S., Khedkar, M., Srivastava, I., Thakre, P. & Bokde, N. D. (2023). A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator. Energies, 16(3), Artikel 1243. https://doi.org/10.3390/en16031243

Tubin, S., Vozenin, M. C., Prezado, Y., Durante, M., Prise, K. M., Lara, P. C., Greco, C., Massaccesi, M., Guha, C., Wu, X., Mohiuddin, M. M., Vestergaard, A., Bassler, N., Gupta, S., Stock, M. & Timmerman, R. (2023). Novel unconventional radiotherapy techniques: Current status and future perspectives – Report from the 2nd international radiation oncology online seminar. Clinical and Translational Radiation Oncology, 40, Artikel 100605. https://doi.org/10.1016/j.ctro.2023.100605

Tiwari, S., Sengupta, J. & Bokde, N. D. (2023). Analysis of Individual User Data Rate in a TDMA-RIS-NOMA Downlink System: Beyond the Limitation of Conventional NOMA. Electronics, 12(3), Artikel 618. https://doi.org/10.3390/electronics12030618

Thorup, V. M., Munksgaard, L., Terré, M., Henriksen, J. C. S., Weisbjerg, M. R. & Lovendahl, P. (2023). The relationship between feed efficiency and behaviour differs between lactating Holstein and Jersey cows. Journal of Dairy Research, 90(3), 257-260. https://doi.org/10.1017/S0022029923000420

Therkildsen, M., Vestergaard, M., Kargo, M., Keto, L., Ertbjerg, P., Thorkelsson, G., Gudjónsdóttir, M., Kjetså, M., Honkatukia, M., Egelandsdal, B., Svartedal, N., Røe, M., Fikse, F. W., Karlssom, A. H. & Hessle, A. (2023). Carcass characteristics of Nordic native cattle breeds. Genetic Resources, 4(7), 1-19. https://doi.org/10.46265/genresj.LWUP7415

Stephansen, R. B., Martin, P., Manzanilla-Pech, C. I. V., Gredler-Grandl, B., Sahana, G., Madsen, P., Weigel, K., Tempelman, R. J., Peñagaricano, F., Gaddis, K. L. P., White, H. M., Santos, J. E. P., Koltes, J. E., Schenkel, F., Hailemariam, D., Plastow, G., Abdalla, E., VandeHaar, M., Veerkamp, R. F. ... Lassen, J. (2023). Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe. Journal of Dairy Science, 106(12), 9078-9094. https://doi.org/10.3168/jds.2023-23330, https://doi.org/10.3168/jds.2023-23330

Stephansen, R. B., Manzanilla Pech, C. I. V., Gebreyesus, G., Sahana, G. & Lassen, J. (2023). Prediction of body condition in Jersey dairy cattle from 3D-images using machine learning techniques. Journal of Animal Science, 101, Artikel skad376. https://doi.org/10.1093/jas/skad376

Skovbjerg, C. K., Angra, D., Robertson-Shersby-Harvie, T., Kreplak, J., Keeble-Gagnère, G., Kaur, S., Ecke, W., Windhorst, A., Nielsen, L. K., Schiemann, A., Knudsen, J., Gutierrez, N., Tagkouli, V., Fechete, L. I., Janss, L., Stougaard, J., Warsame, A., Alves, S., Khazaei, H. ... Andersen, S. U. (2023). Genetic analysis of global faba bean diversity, agronomic traits and selection signatures. Theoretical and Applied Genetics, 136(5), Artikel 114. https://doi.org/10.1007/s00122-023-04360-8

Si, J., Dai, D., Li, K., Fang, L. & Zhang, Y. (2023). A Multi-Tissue Gene Expression Atlas of Water Buffalo (Bubalus bubalis) Reveals Transcriptome Conservation between Buffalo and Cattle. Genes, 14(4), Artikel 890. https://doi.org/10.3390/genes14040890

Shi, R., Chen, Z., Su, G., Luo, H., Liu, L., Guo, G. & Wang, Y. (2023). Genomic prediction of service sire effect on female reproductive performance in Holstein cattle: A comparison between different methods, validation population and marker densities. Journal of Animal Breeding and Genetics, 140(4), 355-365. https://doi.org/10.1111/jbg.12763

Schoeler, T., Speed, D., Porcu, E., Pirastu, N., Pingault, J. B. & Kutalik, Z. (2023). Participation bias in the UK Biobank distorts genetic associations and downstream analyses. Nature Human Behaviour, 7(7), 1216-1227. https://doi.org/10.1038/s41562-023-01579-9

Salunke, S., Shrivastava, A. K., Hashmi, M. F., Ahuja, B. & Bokde, N. D. (2023). Quad Key-Secured 3D Gauss Encryption Compression System with Lyapunov Exponent Validation for Digital Images. Applied Sciences (Switzerland), 13(3), Artikel 1616. https://doi.org/10.3390/app13031616

Salimiyekta, Y., Jensen, J., Su, G. & Gebreyesus, G. (2023). Age-dependent genetic and environmental variance of semen quality in Nordic Holstein bulls. Journal of Dairy Science, 106(4), 2598-2612. https://doi.org/10.3168/jds.2022-22442

Sahana, J., Cortés-Sánchez, J. L., Sandt, V., Melnik, D., Corydon, T. J., Schulz, H., Cai, Z., Evert, K., Grimm, D. & Wehland, M. (2023). Long-Term Simulation of Microgravity Induces Changes in Gene Expression in Breast Cancer Cells. International Journal of Molecular Sciences , 24(2), Artikel 1181. https://doi.org/10.3390/ijms24021181

Romé, H., Chu, T. T., Marois, D., Huang, C. H., Madsen, P. & Jensen, J. (2023). Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers. Genetics Selection Evolution, 55(1), Artikel 58. https://doi.org/10.1186/s12711-023-00829-8

Rohner, M., Manzanares, C., Yates, S., Thorogood, D., Copetti, D., Lübberstedt, T., Asp, T. & Studer, B. (2023). Fine-Mapping and Comparative Genomic Analysis Reveal the Gene Composition at the S and Z Self-incompatibility Loci in Grasses. Molecular Biology and Evolution, 40(1), Artikel msac259. https://doi.org/10.1093/molbev/msac259

Rohde, P. D., Fourie Sørensen, I. & Sørensen, P. (2023). Expanded utility of the R package, qgg, with applications within genomic medicine. Bioinformatics (Oxford, England), 39(11), Artikel btad656. https://doi.org/10.1093/bioinformatics/btad656

Raffo, M. A., Cuyabano, B. C. D., Rincent, R., Sarup, P., Moreau, L., Mary-Huard, T. & Jensen, J. (2023). Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat. Frontiers in Plant Science, 13, Artikel 1075077. https://doi.org/10.3389/fpls.2022.1075077

Pan, Z., Wang, Y., Wang, M., Wang, Y., Zhu, X., Gu, S., Zhong, C., An, L., Shan, M., Damas, J., Halstead, M. M., Guan, D., Trakooljul, N., Wimmers, K., Bi, Y., Wu, S., Delany, M. E., Bai, X., Cheng, H. H. ... Zhou, H. (2023). An atlas of regulatory elements in chicken: A resource for chicken genetics and genomics. Science Advances, 9(18), Artikel eade1204. https://doi.org/10.1126/sciadv.ade1204

Ning, M., Zhao, Y., Dai, D., Yao, C., Liu, H., Fang, L., Wang, B., Zhang, Y. & Cao, J. (2023). Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows. Journal of Dairy Science, 106(7), 5018-5028. https://doi.org/10.3168/jds.2022-22941

Nielsen, B., Horndrup, L. V., Turner, S. P., Christensen, O. F., Nielsen, H. M. & Ask, B. (2023). Selection for social genetic effects in purebred pigs improves behaviour and handling of their crossbred progeny. Genetics, selection, evolution : GSE, 55(1), 54. Artikel 54. https://doi.org/10.1186/s12711-023-00828-9

Mei, Q., Liu, H., Zhao, S., Xiang, T. & Christensen, O. F. (2023). Genomic evaluation for two-way crossbred performance in cattle. Genetics Selection Evolution, 55(1), Artikel 17. https://doi.org/10.1186/s12711-023-00792-4

Manzanilla-Pech, C. I. V., Stephansen, R. B. & Lassen, J. (2023). Genetic parameters for feed intake and body weight in dairy cattle using high-throughput 3-dimensional cameras in Danish commercial farms. Journal of Dairy Science, 106(12), 9006-9015. https://doi.org/10.3168/jds.2023-23405

Long, E. M., Romay, M. C., Ramstein, G., Buckler, E. S. & Robbins, K. R. (2023). Utilizing evolutionary conservation to detect deleterious mutations and improve genomic prediction in cassava. Frontiers in Plant Science, 13, Artikel 1041925. https://doi.org/10.3389/fpls.2022.1041925

Londhe, D. S., Katpatal, Y. B., Singh, C. K. & Bokde, N. D. (2023). Assessment of vegetation variation and its response to ENSO and IOD in the semi-arid ecosystem of Western India. Journal of Water and Climate Change, 14(8), 2642-2662. https://doi.org/10.2166/wcc.2023.010

Londhe, D. S., Katpatal, Y. B. & Bokde, N. D. (2023). Performance Assessment of Bias Correction Methods for Precipitation and Temperature from CMIP5 Model Simulation. Applied Sciences (Switzerland), 13(16), Artikel 9142. https://doi.org/10.3390/app13169142

Liu, C., Huang, R., Su, G., Hou, L., Zhou, W., Liu, Q., Qiu, Z., Zhao, Q. & Li, P. (2023). Introgression of pigs in Taihu Lake region possibly contributed to the improvement of fertility in Danish Large White pigs. BMC Genomics, 24(1), Artikel 733. https://doi.org/10.1186/s12864-023-09860-x

Liu, T., Nielsen, B., Christensen, O. F., Lund, M. S. & Su, G. (2023). The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs. Journal of Animal Science and Biotechnology, 14(1), Artikel 1. https://doi.org/10.1186/s40104-022-00800-5

Li, J., Zhao, T., Guan, D., Pan, Z., Bai, Z., Teng, J., Zhang, Z., Zheng, Z., Zeng, J., Zhou, H., Fang, L. & Cheng, H. (2023). Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits. Cell Genomics, 3(10), Artikel 100390. https://doi.org/10.1016/j.xgen.2023.100390

Kristensen, P. S., Sarup, P., Fé, D., Orabi, J., Snell, P., Ripa, L., Mohlfeld, M., Chu, T. T., Herrström, J., Jahoor, A. & Jensen, J. (2023). Prediction of additive, epistatic, and dominance effects using models accounting for incomplete inbreeding in parental lines of hybrid rye and sugar beet. Frontiers in Plant Science, 14, Artikel 1193433. https://doi.org/10.3389/fpls.2023.1193433

Khaipho-Burch, M., Ferebee, T., Giri, A., Ramstein, G., Monier, B., Yi, E., Cinta Romay, M. & Buckler, E. S. (2023). Elucidating the patterns of pleiotropy and its biological relevance in maize. PLOS Genetics, 19(3), Artikel e1010664. https://doi.org/10.1371/journal.pgen.1010664

Jiang, Y. F., Wang, S., Wang, C. L., Xu, R. H., Wang, W. W., Jiang, Y., Wang, M. S., Jiang, L., Dai, L. H., Wang, J. R., Chu, X. H., Zeng, Y. Q., Fang, L. Z., Wu, D. D., Zhang, Q. & Ding, X. D. (2023). Pangenome obtained by long-read sequencing of 11 genomes reveal hidden functional structural variants in pigs. iScience, 26(3), Artikel 106119. https://doi.org/10.1016/j.isci.2023.106119

Jayakodi, M., Golicz, A. A., Kreplak, J., Fechete, L. I., Angra, D., Bednář, P., Bornhofen, E., Zhang, H., Boussageon, R., Kaur, S., Cheung, K., Čížková, J., Gundlach, H., Hallab, A., Imbert, B., Keeble-Gagnère, G., Koblížková, A., Kobrlová, L., Krejčí, P. ... Andersen, S. U. (2023). The giant diploid faba genome unlocks variation in a global protein crop. Nature, 615(7953), 652-659. https://doi.org/10.1038/s41586-023-05791-5

Jain, S. K., Bhatia, R., Shrivastava, N., Salunke, S., Hashmi, M. F. & Bokde, N. D. (2023). Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks. Future Internet, 15(8), Artikel 259. https://doi.org/10.3390/fi15080259

Hsu, Y. H. H., Pintacuda, G., Liu, R., Nacu, E., Kim, A., Tsafou, K., Petrossian, N., Crotty, W., Suh, J. M., Riseman, J., Martin, J. M., Biagini, J. C., Mena, D., Ching, J. K. T., Malolepsza, E., Li, T., Singh, T., Ge, T., Egri, S. B. ... Wellcome Trust Case Control Consortium (2023). Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia. iScience, 26(5), Artikel 106701. https://doi.org/10.1016/j.isci.2023.106701

Guo, X., Sarup, P., Jahoor, A., Jensen, J. & Christensen, O. F. (2023). Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley. Genetics Selection Evolution, 55(1), Artikel 61. https://doi.org/10.1186/s12711-023-00835-w

Guillenea, A., Lund, M. S., Evans, R., Boerner, V. & Karaman, E. (2023). A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population. Genetics, selection, evolution : GSE, 55(1), Artikel 34. https://doi.org/10.1186/s12711-023-00806-1

Gebreyesus, G., Milkevych, V., Lassen, J. & Sahana, G. (2023). Supervised learning techniques for dairy cattle body weight prediction from 3D digital images. Frontiers in Genetics, 13, Artikel 947176. https://doi.org/10.3389/fgene.2022.947176

Gautason, E., Sahana, G., Guldbrandtsen, B. & Berg, P. (2023). Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program. Genetics Selection Evolution, 55(1), Artikel 48. https://doi.org/10.1186/s12711-023-00826-x

Forte, F. P., Malinowska, M., Nagy, I., Schmid, J., Dijkwel, P., Hume, D. E., Johnson, R. D., Simpson, W. R. & Asp, T. (2023). Methylome changes in Lolium perenne associated with long-term colonisation by the endophytic fungus Epichloë sp. LpTG-3 strain AR37. Frontiers in Plant Science, 14, Artikel 1258100. https://doi.org/10.3389/fpls.2023.1258100, https://doi.org/10.3389/fpls.2023.1258100

Eiríksson, J. H., Su, G., Strandén, I. & Christensen, O. F. (2023). Segregation between breeds and local breed proportions in genetic and genomic models for crossbreds. Genetics Selection Evolution, 55(1), Artikel 45. https://doi.org/10.1186/s12711-023-00810-5

Clasen, J. B., Fikse, W. F., Su, G. & Karaman, E. (2023). Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach. Heredity, 131(1), 33-42. https://doi.org/10.1038/s41437-023-00619-4

Christensen, B., Buitenhuis, A. J., Jacobsen, L. N., Ostenfeld, M. S. & Sørensen, E. S. (2023). The Effect of Human and Bovine Milk Osteopontin on Intestinal Caco-2 Cells: A Transcriptome Comparison. Nutrients, 15(5), Artikel 1166. https://doi.org/10.3390/nu15051166

Cai, Z., Wu, X., Thomsen, B., Lund, M. S. & Sahana, G. (2023). Genome-wide association study identifies functional genomic variants associated with young stock survival in Nordic Red Dairy Cattle. Journal of Dairy Science, 106(11), 7832-7845. https://doi.org/10.3168/jds.2023-23252

Byg, L. M., Speed, M., Speed, D. & Østergaard, S. D. (2023). Genetic liability to bipolar disorder and body mass index: A bidirectional two-sample mendelian randomization study. Bipolar Disorders, 25(1), 25-31. https://doi.org/10.1111/bdi.13267

Buitenhuis, A. J., Hein, L., Sørensen, L. P. & Kargo, M. (2023). Correlation between breeding values for milk fatty acids and Nordic Total Merit index traits for Danish Holstein and Danish Jersey. Journal of Dairy Science, 106(8), 5554-5561. https://doi.org/10.3168/jds.2022-22575, https://doi.org/10.3168/jds.2022-22575

Buitenhuis, A. J. & Poulsen, N. A. (2023). Estimation of heritability for milk urea and genetic correlations with milk production traits in 3 Danish dairy breeds. Journal of Dairy Science, 106(8), 5562-5569. https://doi.org/10.3168/jds.2022-22798

Bornhofen, E., Fé, D., Nagy, I., Lenk, I., Greve, M., Didion, T., Jensen, C. S., Asp, T. & Janss, L. (2023). Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data. BMC Genomics, 24(1), Artikel 213. https://doi.org/10.1186/s12864-023-09292-7

Bolormaa, S., Haile-Mariam, M., Marett, L., Miglior, F., Baes, C. F., Schenkel, F. S., Connor, E. E., Manzanilla Pech, C. I. V., Wall, E., Coffey, M., Goddard, M., MacLeod, I. & Pryce, J. E. (2023). Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle. Animal Production Science, 63(11), 1113-1125. Artikel AN23022. https://doi.org/10.1071/AN23022

Bokde, N. D., Milkevych, V., Nielsen, R. K., Villumsen, T. M. & Sahana, G. (2023). A novel approach for anomaly detection in dairy cow gas emission records. Computers and Electronics in Agriculture, 214, Artikel 108286. https://doi.org/10.1016/j.compag.2023.108286

Boggio, G. M., Christensen, O. F., Legarra, A., Meynadier, A. & Marie-Etancelin, C. (2023). Microbiability of milk composition and genetic control of microbiota effects in sheep. Journal of Dairy Science, 106(9), 6288-6298. https://doi.org/10.3168/jds.2022-22948

Boerner, V., Nguyen, T. T. T. & Nieuwhof, G. J. (2023). Integration of Interbull's multiple across-country evaluation approach breeding values into the multiple-trait single-step random regression test-day genetic evaluation for yield traits of Australian Red breeds. Journal of Dairy Science, 106(2), 1159-1167. https://doi.org/10.3168/jds.2022-21816

DBDS Genetic Consortium (2023). Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura. Nature Genetics, 55(11), 1843-1853. https://doi.org/10.1038/s41588-023-01538-0

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical classification of insects with multitask learning and anomaly detection. Ecological Informatics, 77, Artikel 102278. https://doi.org/10.1016/j.ecoinf.2023.102278

Berrandou, T.-E., Balding, D. & Speed, D. (2023). LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics. American Journal of Human Genetics, 110(1), 23-29. https://doi.org/10.1016/j.ajhg.2022.11.010

for EuroSIDA and SHCS (2023). Hepatitis delta infection among persons living with HIV in Europe. Liver International, 43(4), 819-828. https://doi.org/10.1111/liv.15519

Balbuena, L., Peters, E. & Speed, D. (2023). Using polygenic risk scores to investigate the evolution of smoking and mental health outcomes in UK biobank participants. Acta Psychiatrica Scandinavica, 148(5), 447-456. https://doi.org/10.1111/acps.13601, https://doi.org/10.1111/acps.13601

Ahuja, B., Doriya, R., Salunke, S., Hashmi, M. F., Gupta, A. & Bokde, N. D. (2023). HDIEA: high dimensional color image encryption architecture using five-dimensional Gauss-logistic and Lorenz system. Connection Science, 35(1), Artikel 2175792. https://doi.org/10.1080/09540091.2023.2175792

Ahmad, S., Drag, M. H., Mohamad Salleh, S., Cai, Z. & Nielsen, M. O. (2023). Gene coexpression network analysis reveals perirenal adipose tissue as an important target of prenatal malnutrition in sheep. Physiological Genomics, 55(9), 392-413. https://doi.org/10.1152/physiolgenomics.00128.2022

Adlam, D., Berrandou, T. E., Georges, A., Nelson, C. P., Giannoulatou, E., Henry, J., Ma, L., Blencowe, M., Turley, T. N., Yang, M. L., Chopade, S., Finan, C., Braund, P. S., Sadeg-Sayoud, I., Iismaa, S. E., Kosel, M. L., Zhou, X., Hamby, S. E., Cheng, J. ... DISCO register (2023). Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation. Nature Genetics, 55(6), 964-972. https://doi.org/10.1038/s41588-023-01410-1