Hans D. Daetwyler

12.3k total citations · 5 hit papers
141 papers, 6.6k citations indexed

About

Hans D. Daetwyler is a scholar working on Genetics, Plant Science and Molecular Biology. According to data from OpenAlex, Hans D. Daetwyler has authored 141 papers receiving a total of 6.6k indexed citations (citations by other indexed papers that have themselves been cited), including 118 papers in Genetics, 78 papers in Plant Science and 20 papers in Molecular Biology. Recurrent topics in Hans D. Daetwyler's work include Genetic and phenotypic traits in livestock (105 papers), Genetic Mapping and Diversity in Plants and Animals (88 papers) and Genetics and Plant Breeding (47 papers). Hans D. Daetwyler is often cited by papers focused on Genetic and phenotypic traits in livestock (105 papers), Genetic Mapping and Diversity in Plants and Animals (88 papers) and Genetics and Plant Breeding (47 papers). Hans D. Daetwyler collaborates with scholars based in Australia, United States and United Kingdom. Hans D. Daetwyler's co-authors include Ben J. Hayes, Beatriz Villanueva, Ricardo Pong‐Wong, John Woolliams, J. H. J. van der Werf, John M. Hickey, M.P.L. Calus, Gustavo de los Campos, Michael E. Goddard and Matthew Hayden and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Hans D. Daetwyler

139 papers receiving 6.5k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

2012 623 citations Hans D. Daetwyler et al. profile →
The Impact of Genetic Architecture on Genome-Wide Evaluation Methods

2010 607 citations Hans D. Daetwyler et al. profile →
Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

2008 510 citations Hans D. Daetwyler et al. profile →
1000 Bull Genomes Project to Map Simple and Complex Genetic Traits in Cattle: Applications and Outcomes

2018 252 citations Ben J. Hayes, Hans D. Daetwyler profile →
Genes and spacers of cloned sea urchin histone DNA analyzed by sequencing

1978 209 citations Hans D. Daetwyler et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hans D. Daetwyler Australia	39	5.3k	3.3k	832	829	644	141	6.6k
J.I. Weller Israel	40	4.4k 0.8×	1.7k 0.5×	1.2k 1.4×	580 0.7×	533 0.8×	152	5.2k
M.P.L. Calus Netherlands	43	6.2k 1.2×	3.1k 0.9×	1.5k 1.8×	382 0.5×	427 0.7×	239	7.1k
Henner Simianer Germany	43	4.9k 0.9×	1.5k 0.4×	1.1k 1.3×	544 0.7×	515 0.8×	212	6.1k
Lakshmi K. Matukumalli United States	25	3.4k 0.6×	2.1k 0.6×	581 0.7×	1.2k 1.4×	812 1.3×	39	4.8k
John M. Hickey United Kingdom	37	4.2k 0.8×	3.1k 0.9×	434 0.5×	490 0.6×	186 0.3×	96	5.1k
Dirk‐Jan de Koning Sweden	39	4.0k 0.8×	1.2k 0.4×	472 0.6×	800 1.0×	533 0.8×	172	4.9k
Wouter Coppieters Belgium	37	4.2k 0.8×	1.3k 0.4×	595 0.7×	1.4k 1.6×	992 1.5×	111	5.5k
Georg Thaller Germany	38	4.4k 0.8×	1.2k 0.4×	1.6k 1.9×	565 0.7×	605 0.9×	195	5.6k
Miguel Pérez‐Enciso Spain	40	3.5k 0.7×	1.1k 0.3×	340 0.4×	1.3k 1.5×	563 0.9×	140	5.0k
Rohan L. Fernando United States	50	9.3k 1.7×	5.3k 1.6×	1.2k 1.5×	702 0.8×	590 0.9×	211	10.7k

Countries citing papers authored by Hans D. Daetwyler

Since Specialization

Citations

This map shows the geographic impact of Hans D. Daetwyler'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 Hans D. Daetwyler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hans D. Daetwyler more than expected).

Fields of papers citing papers by Hans D. Daetwyler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hans D. Daetwyler. 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 Hans D. Daetwyler. The network helps show where Hans D. Daetwyler may publish in the future.

Co-authorship network of co-authors of Hans D. Daetwyler

This figure shows the co-authorship network connecting the top 25 collaborators of Hans D. Daetwyler. A scholar is included among the top collaborators of Hans D. Daetwyler 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 Hans D. Daetwyler. Hans D. Daetwyler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Sharma, Neelesh, et al.. (2024). Phenotyping for heat stress tolerance in wheat population using physiological traits, multispectral imagery, and machine learning approaches. Plant Stress. 14. 100593–100593. 3 indexed citations

Joukhadar, Reem & Hans D. Daetwyler. (2022). Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies. Methods in molecular biology. 2481. 173–183. 4 indexed citations

Vincent, Delphine, Vilnis Ezernieks, Saleh Shahinfar, et al.. (2022). A community resource to mass explore the wheat grain proteome and its application to the late-maturity alpha-amylase (LMA) problem. GigaScience. 12. 3 indexed citations

Vincent, Delphine, Vilnis Ezernieks, Frank Bedon, et al.. (2022). Mining the Wheat Grain Proteome. International Journal of Molecular Sciences. 23(2). 713–713. 8 indexed citations

Buggiotti, Laura, Andrey A. Yurchenko, N. S. Yudin, et al.. (2021). Demographic History, Adaptation, and NRAP Convergent Evolution at Amino Acid Residue 100 in the World Northernmost Cattle from Siberia. Molecular Biology and Evolution. 38(8). 3093–3110. 40 indexed citations

Keeble‐Gagnère, Gabriel, Raj Pasam, Kerrie Forrest, et al.. (2021). Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization. Frontiers in Plant Science. 12. 756877–756877. 15 indexed citations

Li, Yongjun, et al.. (2020). Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection. The Plant Genome. 14(1). e20064–e20064. 13 indexed citations

MacLeod, Iona M., Amanda J. Chamberlain, Christy J. Vander Jagt, et al.. (2020). Mitochondrial protein gene expression and the oxidative phosphorylation pathway associated with feed efficiency and energy balance in dairy cattle. Journal of Dairy Science. 104(1). 575–587. 19 indexed citations

Barbulescu, Denise M., Pankaj Maharjan, P. A. Salisbury, et al.. (2020). Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.). Plants. 9(6). 719–719. 18 indexed citations

10.

Xiang, Ruidong, Irene van den Berg, Iona M. MacLeod, et al.. (2019). Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits. Proceedings of the National Academy of Sciences. 116(39). 19398–19408. 102 indexed citations

11.

Chamberlain, Amanda J., Ben J. Hayes, Ruidong Xiang, et al.. (2018). Identification of regulatory variation in dairy cattle with RNA sequence data. Proceedings of the World Congress on Genetics Applied to Livestock Production. 254. 8 indexed citations

12.

Jagt, Christy Vander, Amanda J. Chamberlain, Robert D. Schnabel, Ben J. Hayes, & Hans D. Daetwyler. (2018). Which is the best variant caller for large whole-genome sequencing datasets?. Proceedings of the World Congress on Genetics Applied to Livestock Production. 128. 1 indexed citations

13.

Khansefid, Majid, Sunduimijid Bolormaa, Andrew Swan, et al.. (2018). Exploiting sequence variants for genomic prediction in Australian sheep using Bayesian models. RUNE (Research UNE). 253. 2 indexed citations

14.

Moghaddar, Nasir, Iona M. MacLeod, Naomi Duijvesteijn, et al.. (2018). Genomic evaluation based on selected variants from imputed whole-genome sequence data in Australian sheep populations. RUNE (Research UNE). 456. 4 indexed citations

15.

Bolormaa, Sunduimijid, Amanda J. Chamberlain, J. H. J. van der Werf, Hans D. Daetwyler, & Iona M. MacLeod. (2018). Evaluating the accuracy of imputed whole genome sequence in sheep. Proceedings of the World Congress on Genetics Applied to Livestock Production. 263. 1 indexed citations

16.

Goddard, Michael E., Iona M. MacLeod, Kathryn E. Kemper, et al.. (2018). The use of multi-breed reference populations and multi-omic data to maximize accuracy of genomic prediction. Queensland's institutional digital repository (The University of Queensland). 115. 2 indexed citations

17.

Duijvesteijn, Naomi, Sunduimijid Bolormaa, Cedric Gondro, et al.. (2018). Genome-wide association study of meat quality traits using whole-genome sequence data in a multi-breed sheep population. Proceedings of the World Congress on Genetics Applied to Livestock Production. 257. 4 indexed citations

18.

Lin, Zibei, Noel O. I. Cogan, Luke W. Pembleton, et al.. (2016). Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass. The Plant Genome. 9(1). 68 indexed citations

19.

Daetwyler, Hans D., Urmil Bansal, Harbans Bariana, Matthew Hayden, & Ben J. Hayes. (2014). Genomic prediction for rust resistance in diverse wheat landraces. Theoretical and Applied Genetics. 127(8). 1795–1803. 83 indexed citations

20.

Daetwyler, Hans D.. (2013). Genomic Selection for Disease and Morphological Traits in Diverse Wheat Landraces. 2 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.

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