J. J. Arranz

2.9k total citations
92 papers, 1.5k citations indexed

About

J. J. Arranz is a scholar working on Genetics, Molecular Biology and Cancer Research. According to data from OpenAlex, J. J. Arranz has authored 92 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Genetics, 26 papers in Molecular Biology and 23 papers in Cancer Research. Recurrent topics in J. J. Arranz's work include Genetic and phenotypic traits in livestock (58 papers), Genetic Mapping and Diversity in Plants and Animals (34 papers) and Cancer-related molecular mechanisms research (23 papers). J. J. Arranz is often cited by papers focused on Genetic and phenotypic traits in livestock (58 papers), Genetic Mapping and Diversity in Plants and Animals (34 papers) and Cancer-related molecular mechanisms research (23 papers). J. J. Arranz collaborates with scholars based in Spain, France and Australia. J. J. Arranz's co-authors include Beatriz Gutiérrez‐Gil, F. San Primitivo, Aroa Suárez‐Vega, Yolanda Bayón, E. García‐Gámez, Pamela Wiener, F. Goyache, I. Fernández, I. Álvarez and L. J. Royo and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

J. J. Arranz

90 papers receiving 1.4k citations

Peers

J. J. Arranz

GENET

ACS

ASZ

Beatriz Gutiérrez‐Gil Spain

C. A. Gill United States

Rowan J. Bunch Australia

Rosemarie Weikard Germany

Yves Amigues France

Stanisław Kamiński Poland

Paola Crepaldi Italy

Giuseppina Schiavo Italy

Elisabeth Jonas Sweden

Hermann Schwarzenbacher Austria

Beatriz Gutiérrez‐Gil Spain

J. J. Arranz

1.3k ×1.2

GENET

381 ×1.2

387 ×1.4

331 ×1.2

ACS

276 ×1.4

ASZ

Citations per year, relative to J. J. Arranz J. J. Arranz (= 1×) peers Beatriz Gutiérrez‐Gil

Countries citing papers authored by J. J. Arranz

Since Specialization

Citations

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

Fields of papers citing papers by J. J. Arranz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

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

All Works

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20 of 20 papers shown

Cozzi, Paolo, Arianna Manunza, Anna M. Johansson, et al.. (2024). SMARTER-database: a tool to integrate SNP array datasets for sheep and goat breeds. SHILAP Revista de lepidopterología. 2024. 4 indexed citations

Arranz, J. J., Aroa Suárez‐Vega, Beatriz Gutiérrez‐Gil, et al.. (2024). Assessment of milk metabolites as biomarkers for predicting feed efficiency in dairy sheep. Journal of Dairy Science. 107(7). 4743–4757. 7 indexed citations

Gutiérrez‐Gil, Beatriz, et al.. (2024). Unraveling dynamic transcriptomic changes in sheep's lactating mammary gland following Escherichia coli lipopolysaccharide exposure. Journal of Dairy Science. 107(12). 11269–11282. 1 indexed citations

Mateo, Javier, et al.. (2023). Relationships between growth-related traits with carcass and meat quality traits in Spanish Assaf suckling lambs. Livestock Science. 269. 105173–105173. 2 indexed citations

Gutiérrez‐Gil, Beatriz, et al.. (2022). Low-density SNP panel for efficient imputation and genomic selection of milk production and technological traits in dairy sheep. Journal of Dairy Science. 105(10). 8199–8217. 7 indexed citations

Suárez‐Vega, Aroa, et al.. (2021). Accuracy of Imputation of Microsatellite Markers from a 50K SNP Chip in Spanish Assaf Sheep. Animals. 11(1). 86–86. 8 indexed citations

Suárez‐Vega, Aroa, et al.. (2021). Study on the concordance between different SNP‐genotyping platforms in sheep. Animal Genetics. 52(6). 868–880. 4 indexed citations

Gutiérrez‐Gil, Beatriz, et al.. (2021). Estimation of genetic parameters for cheese-making traits in Spanish Churra sheep. Journal of Dairy Science. 104(3). 3250–3260. 11 indexed citations

Suárez‐Vega, Aroa, et al.. (2021). Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds. Journal of Dairy Science. 104(11). 11850–11866. 15 indexed citations

10.

Reverter, Antônio, et al.. (2020). A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep. Journal of Animal Science. 98(12). 1 indexed citations

11.

Arranz, J. J., et al.. (2020). Identification of potential functional variants underlying ovine resistance to gastrointestinal nematode infection by using RNA‐Seq. Animal Genetics. 51(2). 266–277. 5 indexed citations

12.

Gutiérrez‐Gil, Beatriz, et al.. (2020). Analysis of Whole Genome Resequencing Datasets from a Worldwide Sample of Sheep Breeds to Identify Potential Causal Mutations Influencing Milk Composition Traits. Animals. 10(9). 1542–1542. 9 indexed citations

13.

Gutiérrez‐Gil, Beatriz, et al.. (2019). Microbiota characterization of sheep milk and its association with somatic cell count using 16s rRNA gene sequencing. Journal of Animal Breeding and Genetics. 137(1). 73–83. 36 indexed citations

14.

Suárez‐Vega, Aroa, Beatriz Gutiérrez‐Gil, Pablo G. Toral, et al.. (2019). Conjugated linoleic acid (CLA)-induced milk fat depression: application of RNA-Seq technology to elucidate mammary gene regulation in dairy ewes. Scientific Reports. 9(1). 12 indexed citations

15.

Suárez‐Vega, Aroa, J. J. Arranz, Valentín Pérez Pérez, et al.. (2018). Early adipose deposits in sheep: comparative analysis of the perirenal fat transcriptome of Assaf and Churra suckling lambs. Animal Genetics. 49(6). 605–617. 14 indexed citations

16.

Arranz, J. J., et al.. (2017). Estimations of linkage disequilibrium, effective population size and ROH‐based inbreeding coefficients in Spanish Churra sheep using imputed high‐density SNP genotypes. Animal Genetics. 48(4). 436–446. 26 indexed citations

17.

Gutiérrez‐Gil, Beatriz, et al.. (2011). The role of bovine causal genes underlying dairy traits in Spanish Churra sheep. Animal Genetics. 42(4). 415–420. 18 indexed citations

18.

García, Javier, et al.. (2010). Can Iberian Water Rail Rallus aquaticus be sexed reliably using simple morphometrics?. Ringing & Migration. 25(1). 42–46. 7 indexed citations

19.

Primitivo, F. San, Susana Pedrosa, J. J. Arranz, et al.. (2007). Variabilidad del DNA mitocondrial en razas ovinas ibéricas. Archivos de Zootecnia. 56(1). 455–460. 2 indexed citations

20.

Ugarte, E., et al.. (1996). Genetic Parameters and Trends for Milk Production of Blond-Faced Latxa Sheep Using Bayesian Analysis. Journal of Dairy Science. 79(12). 2268–2277. 21 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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