| Improving imputation quality in BEAGLE for crop and livestock data T Pook, M Mayer, J Geibel, S Weigend, D Cavero, CC Schoen, ... G3: Genes, Genomes, Genetics 10 (1), 177-188, 2020 | 53 | 2020 |
| How array design creates SNP ascertainment bias J Geibel, C Reimer, S Weigend, A Weigend, T Pook, H Simianer PLoS One 16 (3), e0245178, 2021 | 44 | 2021 |
| MoBPS-modular breeding program simulator T Pook, M Schlather, H Simianer G3: Genes, Genomes, Genetics 10 (6), 1915-1918, 2020 | 37 | 2020 |
| HaploBlocker: creation of subgroup-specific haplotype blocks and libraries T Pook, M Schlather, G de Los Campos, M Mayer, CC Schoen, ... Genetics 212 (4), 1045-1061, 2019 | 36 | 2019 |
| Using local convolutional neural networks for genomic prediction T Pook, J Freudenthal, A Korte, H Simianer Frontiers in genetics 11, 561497, 2020 | 25 | 2020 |
| Newly developed MAGIC population allows identification of strong associations and candidate genes for anthocyanin pigmentation in eggplant G Mangino, A Arrones, M Plazas, T Pook, J Prohens, P Gramazio, ... Frontiers in Plant Science 13, 847789, 2022 | 14 | 2022 |
| How imputation can mitigate SNP ascertainment Bias J Geibel, C Reimer, T Pook, S Weigend, A Weigend, H Simianer BMC genomics 22 (1), 340, 2021 | 11 | 2021 |
| A unifying concept of animal breeding programmes H Simianer, L Büttgen, A Ganesan, NT Ha, T Pook Journal of Animal Breeding and Genetics 138 (2), 137-150, 2021 | 8 | 2021 |
| Turning the PAGE – the potential of genome editing in breeding for complex traits revisited H Simianer, T Pook, M Schlather Proc. 11th World Congress on Genetics Applied to Livestock Production …, 2018 | 8 | 2018 |
| Simulation study on the integration of health traits in horse breeding programs L Büttgen, J Geibel, H Simianer, T Pook Animals 10 (7), 1153, 2020 | 7 | 2020 |
| Increasing calling accuracy, coverage, and read-depth in sequence data by the use of haplotype blocks T Pook, A Nemri, EG Gonzalez Segovia, D Valle Torres, H Simianer, ... PLoS Genetics 17 (12), e1009944, 2021 | 5 | 2021 |
| Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments E Vojgani, T Pook, JWR Martini, AC Hölker, M Mayer, CC Schön, ... Theoretical and Applied Genetics 134, 2913-2930, 2021 | 5 | 2021 |
| MoBPSweb: A web-based framework to simulate and compare breeding programs T Pook, L Büttgen, A Ganesan, NT Ha, H Simianer G3 11 (2), jkab023, 2021 | 5 | 2021 |
| Phenotype Prediction under Epistasis E Vojgani, T Pook, H Simianer Epistasis: Methods and Protocols, 105-120, 2021 | 4 | 2021 |
| Imputation of low‐density marker chip data in plant breeding: Evaluation of methods based on sugar beet T Niehoff, T Pook, M Gholami, T Beissinger The Plant Genome 15 (4), e20257, 2022 | 3 | 2022 |
| The Modular Breeding Program Simulator (MoBPS) allows efficient simulation of complex breeding programs T Pook, C Reimer, A Freudenberg, L Büttgen, J Geibel, A Ganesan, ... Animal Production Science, 2021 | 3 | 2021 |
| How Array Design Affects SNP Ascertainment Bias J Geibel, C Reimer, S Weigend, A Weigend, T Pook, H Simianer bioRxiv, 833541, 2019 | 3 | 2019 |
| Methods and software to enhance statistical analysis in large scale problems in breeding and quantitative genetics T Pook Dissertation, Göttingen, Georg-August Universität, 2019, 2019 | 3 | 2019 |
| How economic weights translate into genetic and phenotypic progress, and vice versa H Simianer, J Heise, S Rensing, T Pook, J Geibel, C Reimer Genetics Selection Evolution 55 (1), 1-12, 2023 | 2 | 2023 |
| Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction A Freudenberg, J Vandenplas, M Schlather, T Pook, R Evans, ... Frontiers in Genetics 14, 1220408, 2023 | 2 | 2023 |
Henner SimianerProfessor of Animal Breeding and Genetics, Georg-August University GoettingenBestätigte E-Mail-Adresse bei gwdg.de
