Daniel Gianola

22.5k total citations · 5 hit papers
353 papers, 16.6k citations indexed

About

Daniel Gianola is a scholar working on Genetics, Plant Science and Agronomy and Crop Science. According to data from OpenAlex, Daniel Gianola has authored 353 papers receiving a total of 16.6k indexed citations (citations by other indexed papers that have themselves been cited), including 304 papers in Genetics, 134 papers in Plant Science and 88 papers in Agronomy and Crop Science. Recurrent topics in Daniel Gianola's work include Genetic and phenotypic traits in livestock (296 papers), Genetic Mapping and Diversity in Plants and Animals (177 papers) and Genetics and Plant Breeding (121 papers). Daniel Gianola is often cited by papers focused on Genetic and phenotypic traits in livestock (296 papers), Genetic Mapping and Diversity in Plants and Animals (177 papers) and Genetics and Plant Breeding (121 papers). Daniel Gianola collaborates with scholars based in United States, Norway and Germany. Daniel Gianola's co-authors include K.A. Weigel, Guilherme J. M. Rosa, Daniel Sørensen, Rohan L. Fernando, Gustavo de los Campos, José Crossa, JL Foulley, B. Heringstad, Paulino Pérez‐Rodríguez and I. Misztal and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and PLoS ONE.

In The Last Decade

Daniel Gianola

346 papers receiving 15.9k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

2002 699 citations Daniel Sørensen, Daniel Gianola profile →
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

2010 574 citations José Crossa, Daniel Gianola et al. Genetics profile →
Sire evaluation for ordered categorical data with a threshold model

1983 510 citations Daniel Gianola et al. Genetics Selection Evolution profile →
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree

2009 446 citations Hugo Naya, Daniel Gianola et al. Genetics profile →
Theory and Analysis of Threshold Characters

1982 412 citations Daniel Gianola profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Gianola United States	66	13.8k	7.1k	3.7k	2.5k	1.6k	353	16.6k
Guilherme J. M. Rosa United States	51	5.6k 0.4×	2.2k 0.3×	2.4k 0.6×	1.7k 0.7×	795 0.5×	337	8.8k
Rohan L. Fernando United States	50	9.3k 0.7×	5.3k 0.7×	1.2k 0.3×	1.6k 0.6×	610 0.4×	211	10.7k
I. Misztal United States	65	13.9k 1.0×	4.7k 0.7×	4.2k 1.1×	5.9k 2.3×	2.4k 1.5×	389	16.3k
T.H.E. Meuwissen Norway	52	14.0k 1.0×	6.8k 1.0×	2.1k 0.6×	2.1k 0.8×	633 0.4×	270	16.2k
K.A. Weigel United States	54	7.7k 0.6×	2.7k 0.4×	3.9k 1.0×	2.0k 0.8×	817 0.5×	254	9.3k
Ben J. Hayes Australia	79	22.9k 1.7×	11.1k 1.6×	4.6k 1.2×	2.9k 1.2×	901 0.5×	369	28.0k
Chris Haley United Kingdom	59	11.9k 0.9×	4.5k 0.6×	940 0.3×	2.3k 0.9×	754 0.5×	348	15.8k
Karin Meyer Australia	50	6.3k 0.5×	2.2k 0.3×	2.6k 0.7×	2.1k 0.8×	867 0.5×	167	8.1k
Jack C. M. Dekkers United States	65	11.0k 0.8×	3.9k 0.5×	3.7k 1.0×	5.1k 2.0×	1.8k 1.1×	423	15.3k
L.D. Van Vleck United States	56	8.7k 0.6×	2.1k 0.3×	4.8k 1.3×	2.9k 1.1×	1.2k 0.7×	407	10.9k

Countries citing papers authored by Daniel Gianola

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Gianola

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Gianola

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Pegolo, Sara, Mehdi Momen, Gota Morota, et al.. (2020). Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle. Scientific Reports. 10(1). 7751–7751. 25 indexed citations

Gianola, Daniel & Rohan L. Fernando. (2019). A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits. Genetics. 214(2). 305–331. 19 indexed citations

Gianola, Daniel, Rohan L. Fernando, & Dorian J. Garrick. (2019). A certain invariance property of BLUE in a whole‐genome regression context. Journal of Animal Breeding and Genetics. 136(2). 113–117. 1 indexed citations

Fernando, Rohan L. & Daniel Gianola. (2018). Bayesian inference of genomic similarity among individuals from mark- ers and phenotypes. Proceedings of the World Congress on Genetics Applied to Livestock Production. 942. 1 indexed citations

Valente, B.D., Gota Morota, Francisco Peñagaricano, et al.. (2015). The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models. Genetics. 200(2). 483–494. 14 indexed citations

Gianola, Daniel, Gota Morota, & José Crossa. (2014). Genome-enabled Prediction of Complex Traits with Kernel Methods: What Have We Learned?. Proceedings of the World Congress on Genetics Applied to Livestock Production. 212. 14 indexed citations

Morota, Gota, Masanori Koyama, Guilherme J. M. Rosa, K.A. Weigel, & Daniel Gianola. (2013). Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data. Genetics Selection Evolution. 45(1). 17–17. 32 indexed citations

Weigel, K.A., G. de los Campos, Óscar González-Recio, et al.. (2009). Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science. 92(10). 5248–5257. 98 indexed citations

König, S., YM Chang, Uta König von Borstel, Daniel Gianola, & Henner Simianer. (2008). Genetic and Phenotypic Relationships Among Milk Urea Nitrogen, Fertility, and Milk Yield in Holstein Cows. Journal of Dairy Science. 91(11). 4372–4382. 55 indexed citations

10.

Heringstad, B., YM Chang, Mathias Tiedemann Svendsen, & Daniel Gianola. (2007). Genetic Analysis of Calving Difficulty and Stillbirth in Norwegian Red Cows. Journal of Dairy Science. 90(7). 3500–3507. 44 indexed citations

11.

Caraviello, D.Z., K.A. Weigel, Melanie Craven, et al.. (2006). Analysis of Reproductive Performance of Lactating Cows on Large Dairy Farms Using Machine Learning Algorithms. Journal of Dairy Science. 89(12). 4703–4722. 67 indexed citations

12.

Campos, G. de los, Daniel Gianola, & B. Heringstad. (2006). A Structural Equation Model for Describing Relationships Between Somatic Cell Score and Milk Yield in First-Lactation Dairy Cows. Journal of Dairy Science. 89(11). 4445–4455. 56 indexed citations

13.

Naya, Hugo, Daniel Gianola, Héctor Romero, J. I. Urioste, & Héctor Musto. (2005). Inferring Parameters Shaping Amino Acid Usage in Prokaryotic Genomes via Bayesian MCMC Methods. Molecular Biology and Evolution. 23(1). 203–211. 16 indexed citations

14.

Korsgaard, Inge Riis, Mogens Sandø Lund, Daniel Sørensen, et al.. (2003). Multivariate Bayesian analysis of Gaussian, right censored Gaussian,\nordered categorical and binary traits using Gibbs sampling. SHILAP Revista de lepidopterología. 61 indexed citations

15.

Ødegård, Jørgen, Just Jensen, Per Madsen, et al.. (2003). Detection of Mastitis in Dairy Cattle by Use of Mixture Models for Repeated Somatic Cell Scores: A Bayesian Approach via Gibbs Sampling. Journal of Dairy Science. 86(11). 3694–3703. 30 indexed citations

16.

Heringstad, B., Romdhane Rekaya, Daniel Gianola, G. Klemetsdal, & K.A. Weigel. (2003). Bivariate Analysis of Liability to Clinical Mastitis and to Culling in First-Lactation Cows. Journal of Dairy Science. 86(2). 653–660. 16 indexed citations

17.

Weigel, K.A., et al.. (2000). Preliminary report on international dairy sire evaluation using individual performance records. Bulletin - International Bull Evaluation Service/Interbull bulletin. 3. 2 indexed citations

18.

Jensen, Just, et al.. (1994). Bayesian Inference on Variance and Covariance Components for Traits Influenced by Maternal and Direct Genetic Effects, Using the Gibbs Sampler. Acta Agriculturae Scandinavica Section A – Animal Science. 44(4). 193–201. 48 indexed citations

19.

Foulley, J. L., et al.. (1987). Sire evaluation with uncertain paternity. 19(1). 83–102. 2 indexed citations

20.

Meijering, A. & Daniel Gianola. (1985). Linear versus nonlinear methods of sire evaluation for categorical traits: a simulation study. Genetics Selection Evolution. 17(1). 115–115. 122 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.

Explore authors with similar magnitude of impact