Dag Inge Våge

5.3k total citations
70 papers, 3.0k citations indexed

About

Dag Inge Våge is a scholar working on Genetics, Cell Biology and Molecular Biology. According to data from OpenAlex, Dag Inge Våge has authored 70 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Genetics, 17 papers in Cell Biology and 15 papers in Molecular Biology. Recurrent topics in Dag Inge Våge's work include Genetic and phenotypic traits in livestock (24 papers), Genetic Mapping and Diversity in Plants and Animals (19 papers) and melanin and skin pigmentation (17 papers). Dag Inge Våge is often cited by papers focused on Genetic and phenotypic traits in livestock (24 papers), Genetic Mapping and Diversity in Plants and Animals (19 papers) and melanin and skin pigmentation (17 papers). Dag Inge Våge collaborates with scholars based in Norway, United States and United Kingdom. Dag Inge Våge's co-authors include Helge Klungland, Sigbjørn Lien, I.A. Boman, Roger D. Cone, Dongsi Lu, S. Adalsteinsson, G. Klemetsdal, Luis Gomez‐Raya, Matthew Kent and Bruce A. Boston and has published in prestigious journals such as Nature Genetics, Scientific Reports and Genetics.

In The Last Decade

Dag Inge Våge

69 papers receiving 2.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dag Inge Våge Norway 29 1.1k 1.1k 1.0k 668 408 70 3.0k
Helge Klungland Norway 22 952 0.9× 744 0.7× 858 0.9× 380 0.6× 375 0.9× 48 2.2k
Luca Fontanesi Italy 40 814 0.7× 3.3k 3.0× 429 0.4× 1.5k 2.2× 63 0.2× 273 5.3k
M. Guéguen France 31 402 0.4× 707 0.7× 287 0.3× 1.0k 1.6× 96 0.2× 54 3.1k
Francis Minvielle France 26 397 0.4× 1.0k 0.9× 304 0.3× 436 0.7× 32 0.1× 86 2.3k
Alycia A. Truett United States 8 160 0.1× 383 0.4× 802 0.8× 772 1.2× 88 0.2× 9 2.3k
Bertram Brenig Germany 32 261 0.2× 1.7k 1.6× 334 0.3× 1.9k 2.8× 35 0.1× 316 4.1k
Eyal Seroussi Israel 27 124 0.1× 1.5k 1.4× 166 0.2× 911 1.4× 176 0.4× 104 2.8k
Luis Gomez‐Raya Spain 20 243 0.2× 981 0.9× 204 0.2× 239 0.4× 24 0.1× 74 1.7k
Rafael A. Fissore United States 45 426 0.4× 887 0.8× 143 0.1× 2.1k 3.1× 59 0.1× 116 6.0k
W. Barendse Australia 38 211 0.2× 3.9k 3.6× 148 0.1× 852 1.3× 143 0.4× 153 5.0k

Countries citing papers authored by Dag Inge Våge

Since Specialization
Citations

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

Fields of papers citing papers by Dag Inge Våge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dag Inge Våge. 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 Dag Inge Våge. The network helps show where Dag Inge Våge may publish in the future.

Co-authorship network of co-authors of Dag Inge Våge

This figure shows the co-authorship network connecting the top 25 collaborators of Dag Inge Våge. A scholar is included among the top collaborators of Dag Inge Våge 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 Dag Inge Våge. Dag Inge Våge 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.
2.
Dolvik, N. I., et al.. (2020). Segment-based coancestry, additive relationship and genetic variance within and between the Norwegian and the Swedish Fjord horse populations. Acta Agriculturae Scandinavica Section A – Animal Science. 69(1-2). 118–126. 2 indexed citations
3.
Son, Maren van, et al.. (2019). Association between single-nucleotide polymorphisms within candidate genes and fertility in Landrace and Duroc pigs. Acta veterinaria Scandinavica. 61(1). 58–58. 4 indexed citations
4.
Nome, Torfinn, Thu‐Hien To, Manu Kumar Gundappa, et al.. (2019). SalMotifDB: a tool for analyzing putative transcription factor binding sites in salmonid genomes. BMC Genomics. 20(1). 694–694. 10 indexed citations
5.
Andersen-Ranberg, I.M., et al.. (2018). Relationship between sperm motility characteristics and ATP concentrations, and association with fertility in two different pig breeds. Animal Reproduction Science. 193. 226–234. 30 indexed citations
6.
Son, Maren van, et al.. (2017). RNA sequencing reveals candidate genes and polymorphisms related to sperm DNA integrity in testis tissue from boars. BMC Veterinary Research. 13(1). 362–362. 24 indexed citations
7.
Våge, Dag Inge, et al.. (2013). A missense mutation in growth differentiation factor 9 (GDF9) is strongly associated with litter size in sheep. BMC Genetics. 14(1). 1–1. 205 indexed citations
8.
Boman, I.A., et al.. (2010). Selection based on progeny testing induces rapid changes in myostatin allele frequencies - a case study in sheep. Journal of Animal Breeding and Genetics. 128(1). 52–55. 9 indexed citations
9.
Boman, I.A., et al.. (2010). Impact of two myostatin (MSTN) mutations on weight gain and lamb carcass classification in Norwegian White Sheep (Ovis aries). Genetics Selection Evolution. 42(1). 4–4. 61 indexed citations
10.
Boman, I.A. & Dag Inge Våge. (2009). An insertion in the coding region of the myostatin (MSTN) gene affects carcass conformation and fatness in the Norwegian Spælsau (Ovis aries). BMC Research Notes. 2(1). 98–98. 34 indexed citations
11.
Lien, Sigbjørn, et al.. (2008). Association analysis of the constructed linkage maps covering TLR2 and TLR4 with clinical mastitis in Norwegian Red cattle. Journal of Animal Breeding and Genetics. 125(2). 110–118. 17 indexed citations
12.
13.
Klungland, Helge, et al.. (2004). The Melanocyte-Stimulating Hormone Receptor (Mci-R) Gene as a Tool in Evolutionary Studies of Artiodactyles. Hereditas. 131(1). 39–46. 11 indexed citations
14.
Olsen, Hanne Gro, Luis Gomez‐Raya, Dag Inge Våge, et al.. (2002). A Genome Scan for Quantitative Trait Loci Affecting Milk Production in Norwegian Dairy Cattle. Journal of Dairy Science. 85(11). 3124–3130. 76 indexed citations
15.
Øyehaug, Leiv, Erik Plahte, Dag Inge Våge, & Stig W. Omholt. (2002). The Regulatory Basis of Melanogenic Switching. Journal of Theoretical Biology. 215(4). 449–468. 28 indexed citations
16.
Klungland, Helge, Ayman Sabry, B. Heringstad, et al.. (2001). Quantitative trait loci affecting clinical mastitis and somatic cell count in dairy cattle. Mammalian Genome. 12(11). 837–842. 99 indexed citations
17.
Olsen, Hanne Gro, Dag Inge Våge, Sigbjørn Lien, & Helge Klungland. (2000). A DNA polymorphism in the bovine c‐kit gene. Animal Genetics. 31(1). 71–71. 8 indexed citations
18.
Lien, Sigbjørn, Astrid Karlsen, G. Klemetsdal, et al.. (2000). A primary screen of the bovine genome for quantitative trait loci affecting twinning rate. Mammalian Genome. 11(10). 877–882. 63 indexed citations
19.
Klungland, Helge, Dag Inge Våge, & Sigbjørn Lien. (1997). Linkage mapping of the Fcγ2 receptor gene to bovine Chromosome 18. Mammalian Genome. 8(4). 300–301. 6 indexed citations
20.
Cone, Roger D., D Lu, Dag Inge Våge, et al.. (1996). The melanocortin receptors: agonists, antagonists, and the hormonal control of pigmentation: Recent Prog Horm Res. 51. 39 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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