Torsten Pook
Zitiert von
Zitiert von
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
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
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
MoBPS-modular breeding program simulator
T Pook, M Schlather, H Simianer
G3: Genes, Genomes, Genetics 10 (6), 1915-1918, 2020
Using local convolutional neural networks for genomic prediction
T Pook, J Freudenthal, A Korte, H Simianer
Frontiers in genetics 11, 561497, 2020
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
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, 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
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
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
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
Phenotype Prediction under Epistasis
E Vojgani, T Pook, H Simianer
Epistasis: Methods and Protocols, 105-120, 2021
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
How Array Design Affects SNP Ascertainment Bias
J Geibel, C Reimer, S Weigend, A Weigend, T Pook, H Simianer
bioRxiv, 833541, 2019
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
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, e20257, 2022
ANOVA-HD: Analysis of variance when both input and output layers are high-dimensional
G de Los Campos, T Pook, A Gonzalez-Reymundez, H Simianer, G Mias, ...
Plos one 15 (12), e0243251, 2020
Methods and software to enhance statistical analysis in large scale problems in breeding and quantitative genetics
T Pook
Georg-August-Universität Göttingen, 2019
Development and validation of a horse reference panel for genotype imputation
P Reich, C Falker-Gieske, T Pook, J Tetens
Genetics Selection Evolution 54 (1), 49, 2022
Comparison of breeding strategies for the creation of a synthetic pig line
A Ganteil, T Pook, ST Rodriguez-Ramilo, B Ligonesche, C Larzul
bioRxiv, 2021.09. 22.461330, 2021
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