J. Casellas

2.4k total citations
136 papers, 1.7k citations indexed

About

J. Casellas is a scholar working on Genetics, Animal Science and Zoology and Small Animals. According to data from OpenAlex, J. Casellas has authored 136 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 114 papers in Genetics, 38 papers in Animal Science and Zoology and 29 papers in Small Animals. Recurrent topics in J. Casellas's work include Genetic and phenotypic traits in livestock (109 papers), Genetic Mapping and Diversity in Plants and Animals (71 papers) and Animal Behavior and Welfare Studies (27 papers). J. Casellas is often cited by papers focused on Genetic and phenotypic traits in livestock (109 papers), Genetic Mapping and Diversity in Plants and Animals (71 papers) and Animal Behavior and Welfare Studies (27 papers). J. Casellas collaborates with scholars based in Spain, Canada and United States. J. Casellas's co-authors include J. Piedrafita, L. Varona, G. Caja, Juan F. Medrano, Giovanni Bittante, Alessio Cecchinato, J. L. Noguera, Armand Sánchez, Raquel Quintanilla and Noelia Ibáñez‐Escriche and has published in prestigious journals such as PLoS ONE, Scientific Reports and Genetics.

In The Last Decade

J. Casellas

132 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Casellas Spain 24 1.2k 486 399 276 251 136 1.7k
Nick V. L. Serão United States 22 541 0.5× 585 1.2× 327 0.8× 180 0.7× 386 1.5× 96 1.5k
Noelia Ibáñez‐Escriche Spain 22 1.2k 1.0× 650 1.3× 190 0.5× 225 0.8× 226 0.9× 101 1.6k
T.H. Short United States 16 1.3k 1.1× 591 1.2× 363 0.9× 280 1.0× 192 0.8× 28 1.6k
Beatriz Gutiérrez‐Gil Spain 23 1.3k 1.1× 276 0.6× 331 0.8× 186 0.7× 381 1.5× 93 1.8k
C. A. Gill United States 18 891 0.7× 406 0.8× 257 0.6× 84 0.3× 234 0.9× 66 1.4k
Raquel Quintanilla Spain 25 1.2k 1.0× 630 1.3× 179 0.4× 125 0.5× 408 1.6× 91 1.8k
Gilles Renand France 23 931 0.8× 833 1.7× 605 1.5× 132 0.5× 356 1.4× 87 1.7k
R. M. Thallman United States 25 1.5k 1.2× 642 1.3× 583 1.5× 103 0.4× 202 0.8× 79 1.9k
Yoshinobu Uemoto Japan 22 977 0.8× 521 1.1× 163 0.4× 131 0.5× 196 0.8× 95 1.3k
J. L. Noguera Spain 31 1.6k 1.3× 1.0k 2.1× 141 0.4× 313 1.1× 398 1.6× 90 2.2k

Countries citing papers authored by J. Casellas

Since Specialization
Citations

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

Fields of papers citing papers by J. Casellas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Casellas

This figure shows the co-authorship network connecting the top 25 collaborators of J. Casellas. A scholar is included among the top collaborators of J. Casellas 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 J. Casellas. J. Casellas 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.
Ibáñez‐Escriche, Noelia, et al.. (2025). Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms. Animal Microbiome. 7(1). 13–13. 2 indexed citations
2.
Casellas, J., et al.. (2024). Applicability of machine learning methods for classifying lightweight pigs in commercial conditions. Translational Animal Science. 8. txae171–txae171. 1 indexed citations
3.
Cánovas, Ángela, et al.. (2023). Transcriptome Profile in Dairy Cows Resistant or Sensitive to Milk Fat Depression. Animals. 13(7). 1199–1199. 2 indexed citations
4.
Sastre, Natalia, Anna Mercadé, & J. Casellas. (2023). SNP+ to predict dropout rates in SNP arrays. Conservation Genetics Resources. 15(3). 113–116. 1 indexed citations
5.
Ibáñez‐Escriche, Noelia, et al.. (2023). A Bayesian Multivariate Gametic Model in a Reciprocal Cross with Genomic Information: An Example with Two Iberian Varieties. Animals. 13(10). 1648–1648. 2 indexed citations
6.
Ibáñez‐Escriche, Noelia, et al.. (2023). A multivariate gametic model for the analysis of purebred and crossbred data. An example between two populations of Iberian pigs. Journal of Animal Breeding and Genetics. 141(2). 153–162. 1 indexed citations
7.
Casellas, J., et al.. (2023). Parent-offspring genotyped trios unravelling genomic regions with gametic and genotypic epistatic transmission bias on the cattle genome. Frontiers in Genetics. 14. 1132796–1132796. 1 indexed citations
8.
Noguera, José Luís, et al.. (2021). Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs. Genes. 13(1). 12–12. 5 indexed citations
9.
Sánchez, María, et al.. (2020). Genetic inbreeding depression load for morphological traits and defects in the Pura Raza Española horse. Genetics Selection Evolution. 52(1). 62–62. 23 indexed citations
10.
Noce, Antonia, Tainã Figueiredo Cardoso, Arianna Manunza, et al.. (2018). Expression patterns and genetic variation of the ovine skeletal muscle transcriptome of sheep from five Spanish meat breeds. Scientific Reports. 8(1). 10486–10486. 9 indexed citations
11.
Segarra, Sergi, Marta Artieda, Olga Francino, et al.. (2015). A Genetic Predictive Model for Canine Hip Dysplasia: Integration of Genome Wide Association Study (GWAS) and Candidate Gene Approaches. PLoS ONE. 10(4). e0122558–e0122558. 31 indexed citations
12.
Varona, L., et al.. (2014). Linkage Disequilibrium and Persistence of Phase in Five Spanish Local Beef Cattle Breeds. Proceedings of the World Congress on Genetics Applied to Livestock Production. 502. 2 indexed citations
13.
Manunza, Arianna, J. Casellas, Raquel Quintanilla, et al.. (2014). A genome-wide association analysis for porcine serum lipid traits reveals the existence of age-specific genetic determinants. BMC Genomics. 15(1). 758–758. 24 indexed citations
14.
Casellas, J., et al.. (2013). Short communication: Accounting for new mutations in genomic prediction models. Journal of Dairy Science. 96(8). 5398–5402. 3 indexed citations
15.
Casellas, J., et al.. (2011). Comparison of linear, skewed-linear, and proportional hazard models for the analysis of lambing interval in Ripollesa ewes1. Journal of Animal Science. 90(6). 1788–1797. 2 indexed citations
16.
Medrano, Juan F., Abbas Ahmadi, & J. Casellas. (2010). Dairy Cattle Breeding Simulation Program: A simulation program to teach animal breeding principles and practices. Journal of Dairy Science. 93(6). 2816–2826. 3 indexed citations
17.
Fàbrega, Emma, et al.. (2009). Genetic background and phenotypic characterization over two farrowings of leg conformation defects in Landrace and Large White sows1. Journal of Animal Science. 87(5). 1606–1612. 9 indexed citations
18.
Casellas, J. & Juan F. Medrano. (2008). Within-Generation Mutation Variance for Litter Size in Inbred Mice. Genetics. 179(4). 2147–2155. 20 indexed citations
19.
Varona, L., Noelia Ibáñez‐Escriche, Rodrigo A. Quintanilla, J. L. Noguera, & J. Casellas. (2008). Bayesian analysis of quantitative traits using skewed distributions. Genetics Research. 90(2). 179–190. 10 indexed citations
20.
Casellas, J., J. Piedrafita, & L. Varona. (2007). Bayes factor for testing between different structures of random genetic groups: A case study using weaning weight in Bruna dels Pirineus beef cattle. Genetics Selection Evolution. 39(1). 39–53. 7 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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