Kathryn E. Kemper

12.8k total citations · 3 hit papers
39 papers, 4.4k citations indexed

About

Kathryn E. Kemper is a scholar working on Genetics, Plant Science and Small Animals. According to data from OpenAlex, Kathryn E. Kemper has authored 39 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Genetics, 11 papers in Plant Science and 4 papers in Small Animals. Recurrent topics in Kathryn E. Kemper's work include Genetic and phenotypic traits in livestock (31 papers), Genetic Mapping and Diversity in Plants and Animals (29 papers) and Genetic Associations and Epidemiology (16 papers). Kathryn E. Kemper is often cited by papers focused on Genetic and phenotypic traits in livestock (31 papers), Genetic Mapping and Diversity in Plants and Animals (29 papers) and Genetic Associations and Epidemiology (16 papers). Kathryn E. Kemper collaborates with scholars based in Australia, United Kingdom and United States. Kathryn E. Kemper's co-authors include Peter M. Visscher, Michael E. Goddard, Jian Yang, Ben J. Hayes, Zhili Zheng, Loïc Yengo, Julia Sidorenko, Naomi R. Wray, Timothy M. Frayling and Andrew R. Wood and has published in prestigious journals such as Nature Communications, Nature Genetics and Genetics.

In The Last Decade

Kathryn E. Kemper

39 papers receiving 4.3k citations

Hit Papers

Meta-analysis of genome-wide association studies for heig... 2018 2026 2020 2023 2018 2019 2019 400 800 1.2k

Peers

Kathryn E. Kemper
Christopher Chang United States
Shashaank Vattikuti United States
Yurii S. Aulchenko Netherlands
Daniel Pomp United States
Benjamin F. Voight United States
Xiaoquan Wen United States
Brian P. McEvoy Australia
Paul Scheet United States
Noah Zaitlen United States
Christopher Chang United States
Kathryn E. Kemper
Citations per year, relative to Kathryn E. Kemper Kathryn E. Kemper (= 1×) peers Christopher Chang

Countries citing papers authored by Kathryn E. Kemper

Since Specialization
Citations

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

Fields of papers citing papers by Kathryn E. Kemper

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kathryn E. Kemper. 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 Kathryn E. Kemper. The network helps show where Kathryn E. Kemper may publish in the future.

Co-authorship network of co-authors of Kathryn E. Kemper

This figure shows the co-authorship network connecting the top 25 collaborators of Kathryn E. Kemper. A scholar is included among the top collaborators of Kathryn E. Kemper 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 Kathryn E. Kemper. Kathryn E. Kemper 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.
Kemper, Kathryn E., Julia Sidorenko, Huanwei Wang, et al.. (2024). Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank. Nature Communications. 15(1). 3776–3776. 6 indexed citations
2.
Wu, Yeda, Enda M. Byrne, Zhili Zheng, et al.. (2019). Genome-wide association study of medication-use and associated disease in the UK Biobank. Nature Communications. 10(1). 1891–1891. 138 indexed citations
3.
Lloyd‐Jones, Luke R., Jian Zeng, Julia Sidorenko, et al.. (2019). Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nature Communications. 10(1). 5086–5086. 263 indexed citations breakdown →
4.
Jiang, Longda, Zhili Zheng, Ting Qi, et al.. (2019). A resource-efficient tool for mixed model association analysis of large-scale data. Nature Genetics. 51(12). 1749–1755. 286 indexed citations breakdown →
5.
Abdellaoui, Abdel, David Hugh-Jones, Loïc Yengo, et al.. (2019). Genetic correlates of social stratification in Great Britain. Nature Human Behaviour. 3(12). 1332–1342. 137 indexed citations
6.
Kemper, Kathryn E., et al.. (2018). Comparison of Genotypic and Phenotypic Correlations: Cheverud’s Conjecture in Humans. Genetics. 209(3). 941–948. 91 indexed citations
7.
Kemper, Kathryn E., P.J. Bowman, Ben J. Hayes, Peter M. Visscher, & Michael E. Goddard. (2018). A multi-trait Bayesian method for mapping QTL and genomic prediction. Genetics Selection Evolution. 50(1). 10–10. 30 indexed citations
8.
Yengo, Loïc, Matthew R. Robinson, Matthew C. Keller, et al.. (2018). Imprint of assortative mating on the human genome. Nature Human Behaviour. 2(12). 948–954. 71 indexed citations
9.
Yengo, Loïc, Julia Sidorenko, Kathryn E. Kemper, et al.. (2018). Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Human Molecular Genetics. 27(20). 3641–3649. 1296 indexed citations breakdown →
10.
Yap, Chloe X., Julia Sidorenko, Yang Wu, et al.. (2018). Dissection of genetic variation and evidence for pleiotropy in male pattern baldness. Nature Communications. 9(1). 5407–5407. 65 indexed citations
11.
Kemper, Kathryn E., Mathew D. Littlejohn, Thomas Lopdell, et al.. (2016). Leveraging genetically simple traits to identify small-effect variants for complex phenotypes. BMC Genomics. 17(1). 858–858. 39 indexed citations
12.
MacLeod, Iona M., P.J. Bowman, Christy J. Vander Jagt, et al.. (2016). Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics. 17(1). 144–144. 222 indexed citations
13.
Moore, S.G., J.E. Pryce, Ben J. Hayes, et al.. (2015). Differentially Expressed Genes in Endometrium and Corpus Luteum of Holstein Cows Selected for High and Low Fertility Are Enriched for Sequence Variants Associated with Fertility1. Biology of Reproduction. 94(1). 19–19. 57 indexed citations
14.
Kemper, Kathryn E., Coralie M. Reich, P.J. Bowman, et al.. (2015). Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions. Genetics Selection Evolution. 47(1). 29–29. 101 indexed citations
15.
Kemper, Kathryn E., et al.. (2014). Selection for complex traits leaves little or no classic signatures of selection. BMC Genomics. 15(1). 246–246. 99 indexed citations
16.
Kemper, Kathryn E., Michael E. Goddard, & Stephen Bishop. (2013). Adaptation of gastrointestinal nematode parasites to host genotype: single locus simulation models. Genetics Selection Evolution. 45(1). 14–14. 15 indexed citations
17.
Bolormaa, Sunduimijid, J.E. Pryce, Kathryn E. Kemper, et al.. (2013). Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies. Genetics Selection Evolution. 45(1). 43–43. 47 indexed citations
18.
Kemper, Kathryn E., P.J. Bowman, J.E. Pryce, Ben J. Hayes, & Michael E. Goddard. (2012). Long-term selection strategies for complex traits using high-density genetic markers. Journal of Dairy Science. 95(8). 4646–4656. 29 indexed citations
19.
Kemper, Kathryn E., Hans D. Daetwyler, Peter M. Visscher, & Michael E. Goddard. (2012). Comparing linkage and association analyses in sheep points to a better way of doing GWAS. Genetics Research. 94(4). 191–203. 18 indexed citations
20.
Kemper, Kathryn E. & Michael E. Goddard. (2012). Understanding and predicting complex traits: knowledge from cattle. Human Molecular Genetics. 21(R1). R45–R51. 65 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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