Markus Neuditschko

2.7k total citations
56 papers, 1.0k citations indexed

About

Markus Neuditschko is a scholar working on Genetics, Equine and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Markus Neuditschko has authored 56 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Genetics, 17 papers in Equine and 14 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Markus Neuditschko's work include Genetic and phenotypic traits in livestock (36 papers), Genetic Mapping and Diversity in Plants and Animals (22 papers) and Veterinary Equine Medical Research (17 papers). Markus Neuditschko is often cited by papers focused on Genetic and phenotypic traits in livestock (36 papers), Genetic Mapping and Diversity in Plants and Animals (22 papers) and Veterinary Equine Medical Research (17 papers). Markus Neuditschko collaborates with scholars based in Switzerland, Austria and France. Markus Neuditschko's co-authors include Herman W. Raadsma, Mehar S. Khatkar, Thomas Druml, Г. Брем, Gertrud Grilz-Seger, Mirjam Frischknecht, Benjamin Dainat, Christine Flury, Heidi Signer‐Hasler and Tosso Leeb and has published in prestigious journals such as PLoS ONE, Scientific Reports and Molecular Ecology.

In The Last Decade

Markus Neuditschko

55 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Markus Neuditschko Switzerland 18 877 215 214 165 118 56 1.0k
J. Jordana Spain 25 1.5k 1.7× 57 0.3× 88 0.4× 259 1.6× 271 2.3× 105 1.9k
E.W. Brascamp Netherlands 25 1.8k 2.0× 316 1.5× 324 1.5× 101 0.6× 187 1.6× 76 2.2k
J. L. Vega‐Pla Spain 17 849 1.0× 38 0.2× 56 0.3× 92 0.6× 187 1.6× 72 1.1k
A. T. Bowling United States 19 880 1.0× 74 0.3× 37 0.2× 187 1.1× 332 2.8× 62 1.2k
Javier Cañón Spain 23 1.6k 1.8× 63 0.3× 72 0.3× 60 0.4× 311 2.6× 98 2.0k
Kari Elo Finland 19 1.1k 1.2× 66 0.3× 86 0.4× 34 0.2× 224 1.9× 44 1.5k
G Leroy France 17 770 0.9× 60 0.3× 38 0.2× 31 0.2× 98 0.8× 24 958
Tetsuro Nomura Japan 13 634 0.7× 140 0.7× 91 0.4× 10 0.1× 65 0.6× 77 744
Miika Tapio Finland 17 1.2k 1.4× 80 0.4× 74 0.3× 12 0.1× 183 1.6× 45 1.5k
Amparo Martínez Spain 21 1.3k 1.5× 44 0.2× 74 0.3× 20 0.1× 229 1.9× 156 1.7k

Countries citing papers authored by Markus Neuditschko

Since Specialization
Citations

This map shows the geographic impact of Markus Neuditschko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Markus Neuditschko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Neuditschko more than expected).

Fields of papers citing papers by Markus Neuditschko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus Neuditschko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Markus Neuditschko. The network helps show where Markus Neuditschko may publish in the future.

Co-authorship network of co-authors of Markus Neuditschko

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Neuditschko. A scholar is included among the top collaborators of Markus Neuditschko based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Markus Neuditschko. Markus Neuditschko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Neuditschko, Markus, et al.. (2025). Evaluation of genomic and phenomic prediction for application in apple breeding. BMC Plant Biology. 25(1). 103–103.
2.
Ricard, Anne, et al.. (2024). Population structure and genomic diversity of the Einsiedler horse. Animal Genetics. 55(3). 475–479. 1 indexed citations
3.
Mikko, Sofia, Anne Ricard, Brandon D. Velie, et al.. (2024). Using high-density SNP data to unravel the origin of the Franches-Montagnes horse breed. Genetics Selection Evolution. 56(1). 53–53. 2 indexed citations
4.
Stefaniuk‐Szmukier, Monika, et al.. (2024). Inertial sensor data of horses from four breeds at walk and trot in hand on a straight line. Data in Brief. 55. 110764–110764. 2 indexed citations
5.
Eynard, Sonia E, Fanny Mondet, Olivier Bouchez, et al.. (2024). Sequence‐Based Multi Ancestry Association Study Reveals the Polygenic Architecture of Varroa destructor Resistance in the Honeybee Apis mellifera. Molecular Ecology. 34(3). e17637–e17637. 3 indexed citations
6.
Lindtke, Dorothea, Sylvain Lerch, I. Morel, & Markus Neuditschko. (2024). Assessment of genome complementarity in three beef-on-dairy crossbreds reveals sire-specific effects on production traits with comparable rates of genomic inbreeding reduction. BMC Genomics. 25(1). 1118–1118. 1 indexed citations
7.
Phocas, Florence, et al.. (2023). An Overview of Selection Concepts Applied to Honey Bees. Bee World. 100(1). 2–8. 3 indexed citations
8.
Lindtke, Dorothea, Franz R. Seefried, Cord Drögemüller, & Markus Neuditschko. (2023). Increased heterozygosity in low‐pass sequencing data allows identification of blood chimeras in cattle. Animal Genetics. 54(5). 613–618. 3 indexed citations
9.
Dainat, Benjamin, Geoffrey R. Williams, Sonia E Eynard, et al.. (2023). Identification of runs of homozygosity in Western honey bees (Apis mellifera) using whole‐genome sequencing data. Ecology and Evolution. 13(1). e9723–e9723. 4 indexed citations
10.
11.
Bragança, Filipe M. Serra, et al.. (2022). Determining Objective Parameters to Assess Gait Quality in Franches-Montagnes Horses for Ground Coverage and Over-Tracking - Part 2: At Trot. Journal of Equine Veterinary Science. 120. 104166–104166. 2 indexed citations
12.
Brascamp, E.W., et al.. (2021). Exploring Two Honey Bee Traits for Improving Resistance Against Varroa destructor: Development and Genetic Evaluation. Insects. 12(3). 216–216. 11 indexed citations
13.
Dainat, Benjamin, et al.. (2021). Two quantitative trait loci are associated with recapping of Varroa destructor‐infested brood cells in Apis mellifera mellifera. Animal Genetics. 53(1). 156–160. 4 indexed citations
14.
Dainat, Benjamin, et al.. (2021). Identification of quantitative trait loci associated with calmness and gentleness in honey bees using whole‐genome sequences. Animal Genetics. 52(4). 472–481. 12 indexed citations
15.
16.
Neuditschko, Markus, Mehar S. Khatkar, & Herman W. Raadsma. (2014). Fine Scale Population Structure of Global Cattle Breeds using Dense Haplotype Data. Proceedings of the World Congress on Genetics Applied to Livestock Production. 166. 1 indexed citations
17.
Neuditschko, Markus, Mehar S. Khatkar, & Herman W. Raadsma. (2012). NetView: A High-Definition Network-Visualization Approach to Detect Fine-Scale Population Structures from Genome-Wide Patterns of Variation. PLoS ONE. 7(10). e48375–e48375. 95 indexed citations
18.
Neuditschko, Markus, et al.. (2012). Genome-wide association mapping of milk production traits in Braunvieh cattle. Journal of Dairy Science. 95(9). 5357–5364. 29 indexed citations
19.
Neuditschko, Markus, et al.. (2010). SpinNet: A New Tool To Study The Population Structure With A Genome-Wide SNP Survey. Proceedings of the World Congress on Genetics Applied to Livestock Production. 364. 2 indexed citations
20.
Khatkar, Mehar S., Matthew Hobbs, Markus Neuditschko, et al.. (2010). Assignment of chromosomal locations for unassigned SNPs/scaffolds based on pair-wise linkage disequilibrium estimates. BMC Bioinformatics. 11(1). 171–171. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by J. Jordana Breakdown of academic impact, for papers by E.W. Brascamp Breakdown of academic impact, for papers by J. L. Vega‐Pla Breakdown of academic impact, for papers by A. T. Bowling Breakdown of academic impact, for papers by Javier Cañón Breakdown of academic impact, for papers by Kari Elo Breakdown of academic impact, for papers by G Leroy Breakdown of academic impact, for papers by Tetsuro Nomura Breakdown of academic impact, for papers by Miika Tapio Breakdown of academic impact, for papers by Amparo Martínez
Rankless by CCL
2026