Jan Graffelman

1.5k total citations
39 papers, 811 citations indexed

About

Jan Graffelman is a scholar working on Genetics, Molecular Biology and Statistics and Probability. According to data from OpenAlex, Jan Graffelman has authored 39 papers receiving a total of 811 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Genetics, 7 papers in Molecular Biology and 6 papers in Statistics and Probability. Recurrent topics in Jan Graffelman's work include Genetic Associations and Epidemiology (17 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Genetic and phenotypic traits in livestock (8 papers). Jan Graffelman is often cited by papers focused on Genetic Associations and Epidemiology (17 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Genetic and phenotypic traits in livestock (8 papers). Jan Graffelman collaborates with scholars based in Spain, United States and Italy. Jan Graffelman's co-authors include Bruce S. Weir, Rolf F. Hoekstra, Vı́ctor Moreno, Deepti Jain, Fred van Eeuwijk, Jaume Bertranpetit, David J. Balding, Anna González‐Neira, Joseph D. Schulman and Edward F. Fugger and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Journal of Dairy Science.

In The Last Decade

Jan Graffelman

38 papers receiving 790 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan Graffelman Spain 14 334 170 104 100 78 39 811
Darryl J. Holman United States 18 280 0.8× 167 1.0× 130 1.3× 52 0.5× 37 0.5× 37 1.3k
Carlo Giovanni Camarda France 13 125 0.4× 99 0.6× 49 0.5× 19 0.2× 27 0.3× 50 1.2k
Wai Yee Wong Netherlands 15 250 0.7× 331 1.9× 209 2.0× 60 0.6× 99 1.3× 20 1.4k
M. Muksitul Haque United States 13 327 1.0× 769 4.5× 355 3.4× 11 0.1× 68 0.9× 17 1.3k
Emmanuel Milot Canada 18 415 1.2× 193 1.1× 61 0.6× 20 0.2× 22 0.3× 35 1.4k
José Edgardo Dipierri Argentina 17 449 1.3× 72 0.4× 164 1.6× 26 0.3× 18 0.2× 154 1.1k
Bennett Dyke United States 19 571 1.7× 181 1.1× 62 0.6× 63 0.6× 62 0.8× 61 1.5k
M.K. Bhasin India 14 232 0.7× 72 0.4× 108 1.0× 50 0.5× 38 0.5× 114 718
Andrés Moreno‐Estrada United States 15 895 2.7× 313 1.8× 24 0.2× 8 0.1× 70 0.9× 35 1.4k
Pascale Gerbault United Kingdom 14 577 1.7× 180 1.1× 32 0.3× 8 0.1× 28 0.4× 22 1.1k

Countries citing papers authored by Jan Graffelman

Since Specialization
Citations

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

Fields of papers citing papers by Jan Graffelman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jan Graffelman. 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 Jan Graffelman. The network helps show where Jan Graffelman may publish in the future.

Co-authorship network of co-authors of Jan Graffelman

This figure shows the co-authorship network connecting the top 25 collaborators of Jan Graffelman. A scholar is included among the top collaborators of Jan Graffelman 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 Jan Graffelman. Jan Graffelman 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.
Graffelman, Jan, Bruce S. Weir, & Jérôme Goudet. (2024). Estimation of Jacquard’s genetic identity coefficients with bi-allelic variants by constrained least-squares. Heredity. 134(1). 10–20.
2.
Graffelman, Jan & Jan de Leeuw. (2023). Improved Approximation and Visualization of the Correlation Matrix. The American Statistician. 77(4). 432–442. 11 indexed citations
3.
Graffelman, Jan, et al.. (2021). A network algorithm for the X chromosomal exact test for Hardy–Weinberg equilibrium with multiple alleles. Molecular Ecology Resources. 21(5). 1547–1557. 4 indexed citations
4.
Galván‐Femenía, Iván, C. Barceló‐Vidal, Lauro Sumoy, et al.. (2021). A likelihood ratio approach for identifying three-quarter siblings in genetic databases. Heredity. 126(3). 537–547. 4 indexed citations
5.
Terré, M., et al.. (2021). Using compositional mixed-effects models to evaluate responses to amino acid supplementation in milk replacers for calves. Journal of Dairy Science. 104(7). 7808–7819. 2 indexed citations
6.
Egozcue, Juan José, Jan Graffelman, M. I. Ortego, & Vera Pawlowsky‐Glahn. (2020). Some thoughts on counts in sequencing studies. NAR Genomics and Bioinformatics. 2(4). lqaa094–lqaa094. 9 indexed citations
7.
Graffelman, Jan. (2019). Goodness-of-fit filtering in classical metric multidimensional scaling with large datasets. Journal of Applied Statistics. 47(11). 2011–2024. 6 indexed citations
8.
Blay, Natàlia, Eduard Casas, Iván Galván‐Femenía, et al.. (2019). Assessment of kinship detection using RNA-seq data. Nucleic Acids Research. 47(21). e136–e136. 6 indexed citations
9.
Graffelman, Jan & Bruce S. Weir. (2017). Multi‐allelic exact tests for Hardy–Weinberg equilibrium that account for gender. Molecular Ecology Resources. 18(3). 461–473. 20 indexed citations
10.
Puig, Xavier, Josep Ginebra, & Jan Graffelman. (2017). A Bayesian test for Hardy–Weinberg equilibrium of biallelic X-chromosomal markers. Heredity. 119(4). 226–236. 9 indexed citations
11.
Graffelman, Jan, Deepti Jain, & Bruce S. Weir. (2017). A genome-wide study of Hardy–Weinberg equilibrium with next generation sequence data. Human Genetics. 136(6). 727–741. 59 indexed citations
12.
Graffelman, Jan & Bruce S. Weir. (2016). Testing for Hardy–Weinberg equilibrium at biallelic genetic markers on the X chromosome. Heredity. 116(6). 558–568. 60 indexed citations
13.
Graffelman, Jan, et al.. (2013). Statistical Inference for Hardy-Weinberg Proportions in the Presence of Missing Genotype Information. PLoS ONE. 8(12). e83316–e83316. 11 indexed citations
14.
Graffelman, Jan & Vı́ctor Moreno. (2013). The mid p-value in exact tests for Hardy-Weinberg equilibrium. Statistical Applications in Genetics and Molecular Biology. 12(4). 433–48. 57 indexed citations
15.
Laayouni, Hafid, Ludovica Montanucci, Martin Sikora, et al.. (2011). Similarity in Recombination Rate Estimates Highly Correlates with Genetic Differentiation in Humans. PLoS ONE. 6(3). e17913–e17913. 12 indexed citations
16.
Graffelman, Jan. (2010). Book review: Biplots in Practice. Michael Greenacre. BBVA Foundation, Rubes Editorial.. RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya). 34(2). 4 indexed citations
17.
Bosch, Elena, Hafid Laayouni, Carlos Morcillo-Suárez, et al.. (2009). Decay of linkage disequilibrium within genes across HGDP-CEPH human samples: most population isolates do not show increased LD. BMC Genomics. 10(1). 338–338. 14 indexed citations
18.
Graffelman, Jan, David J. Balding, Anna González‐Neira, & Jaume Bertranpetit. (2007). Variation in estimated recombination rates across human populations. Human Genetics. 122(3-4). 301–310. 34 indexed citations
19.
Graffelman, Jan, et al.. (2007). Graphical Tests for Hardy-Weinberg Equilibrium Based on the Ternary Plot. Human Heredity. 65(2). 77–84. 63 indexed citations
20.
Graffelman, Jan, Edward F. Fugger, Keyvan Keyvanfar, & Joseph D. Schulman. (1999). Human live birth and sperm–sex ratios compared. Human Reproduction. 14(11). 2917–2920. 30 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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