2026 Rohan L. Fernando: Genetics Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Genetics	59	3211	3037	1397	1311	260	17122

Rohan L. Fernando publication distribution in Genetics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Genetics in 2026. The highlighted bar marks where Rohan L. Fernando sits on this spectrum.

45 publications 703+

This scientist: 260 publications — 68th percentile

68% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 703 publications or more.

Rohan L. Fernando D-index placement in Genetics in 2026

The chart shows the D-index (discipline H-index) distribution of Genetics scientists ranked by Research.com in 2026. The highlighted bar marks where Rohan L. Fernando sits on this spectrum.

40 D-Index 160+

This scientist: 59 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 160 D-Index or more.

Research.com Recognitions

2012 - Rockefeller Prentice Award in Animal Breeding and Genetics, American Society of Animal Science

Overview

Rohan L. Fernando is affiliated with Iowa State University in the United States. Their research spans the fields of Biochemistry, Genetics and Molecular Biology, with additional focus in Agricultural and Biological Sciences.

The main topics of their work include:

Genetic and phenotypic traits in livestock
Genetic Mapping and Diversity in Plants and Animals
Genetics and Plant Breeding
Animal Virus Infections Studies
Animal Disease Management and Epidemiology
Animal Nutrition and Physiology
Genetic Associations and Epidemiology

Frequent publication venues for their work include:

Journal of Animal Science
Genetics Selection Evolution
Frontiers in Genetics
G3 Genes Genomes Genetics
Genetics in Medicine Open

Among recent papers authored or co-authored by Rohan L. Fernando are:

"Cross-validation of best linear unbiased predictions of breeding values using an efficient leave-one-out strategy" (2021), Journal of Animal Breeding and Genetics
"Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies" (2021), G3 Genes Genomes Genetics
"Genetic Analysis of Antibody Response to Porcine Reproductive and Respiratory Syndrome Vaccination as an Indicator Trait for Reproductive Performance in Commercial Sows" (2020), Frontiers in Genetics
"Genome-wide association study of disease resilience traits from a natural polymicrobial disease challenge model in pigs identifies the importance of the major histocompatibility complex region" (2021), G3 Genes Genomes Genetics
"XSim version 2: simulation of modern breeding programs" (2022), G3 Genes Genomes Genetics

Frequent co-authors collaborating with Fernando include:

Jack C. M. Dekkers
Hao Cheng
Nick V. L. Serão
Leticia P. Sanglard
Kent A. Gray

The scientist's scholarly contributions cover various subfields such as Genetics, Animal Science and Zoology, Plant Science, Agronomy and Crop Science, and Small Animals.

Rohan L. Fernando received the Rockefeller Prentice Award in Animal Breeding and Genetics from the American Society of Animal Science in 2012.

Best Publications

The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values

D. Habier;R. L. Fernando;J. C. M. Dekkers

1507
Citations
Extension of the bayesian alphabet for genomic selection

David Habier;Rohan L Fernando;Kadir Kizilkaya;Kadir Kizilkaya;Dorian J Garrick;Dorian J Garrick

1384
Citations
Marker assisted selection using best linear unbiased prediction

R.L. Fernando;M. Grossman

774
Citations
Deregressing estimated breeding values and weighting information for genomic regression analyses

Dorian J Garrick;Dorian J Garrick;Jeremy F Taylor;Rohan L Fernando

743
Citations
Additive Genetic Variability and the Bayesian Alphabet

Daniel Gianola;Daniel Gianola;Daniel Gianola;Gustavo A. de los Campos;William G. Hill;Eduardo Manfredi

572
Citations
Genomic-assisted prediction of genetic value with semiparametric procedures.

Daniel Gianola;Daniel Gianola;Daniel Gianola;Rohan L. Fernando;Alessandra Stella

507
Citations
Factors Affecting Accuracy From Genomic Selection in Populations Derived From Multiple Inbred Lines: A Barley Case Study

Shengqiang Zhong;Jack C.M. Dekkers;Rohan Luigi Fernando;Jean-Luc Jannink

496
Citations
Bayesian Methods in Animal Breeding Theory

Daniel Gianola;Rohan L. Fernando

493
Citations
Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine ( Pinus taeda L.)

Márcio F. R. Resende;Patricio Muñoz;Marcos D. V. Resende;Marcos D. V. Resende;Dorian J. Garrick

488
Citations
Genomic Selection Using Low-Density Marker Panels

David Habier;David Habier;Rohan L. Fernando;Jack C. M. Dekkers

436
Citations
Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

Gustavo de los Campos;Ana I. Vazquez;Rohan Fernando;Yann C. Klimentidis

375
Citations
Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction

David Habier;David Habier;Rohan L. Fernando;Dorian J. Garrick

372
Citations
Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes.

K. Kizilkaya;K. Kizilkaya;R. L. Fernando;D. J. Garrick;D. J. Garrick

336
Citations
Influence of slaughter weight on growth and carcass characteristics, commercial cutting and curing yields, and meat quality of barrows and gilts from two genotypes.

F. Cisneros;Michael Ellis;F. K. McKeith;J. McCaw

293
Citations
Genomic selection in admixed and crossbred populations.

A. Toosi;R. L. Fernando;J. C. M. Dekkers

276
Citations
Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

Mahdi Saatchi;Mathew C. McClure;Mathew C. McClure;Stephanie D. McKay;Megan M. Rolf

275
Citations
Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens.

Huiyu Wang;Ignacy Misztal;Ignacio Aguilar;Andres Legarra

251
Citations
A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses

Rohan L Fernando;Jack Cm Dekkers;Dorian J Garrick;Dorian J Garrick

238
Citations
Genomic selection of purebreds for crossbred performance.

Noelia Ibánẽz-Escriche;Rohan L Fernando;Ali Toosi;Jack Cm Dekkers

217
Citations
Genetic analyses of growth, real-time ultrasound, carcass, and pork quality traits in Duroc and Landrace pigs: II. Heritabilities and correlations.

L. L. Lo;D. G. McLaren;F. K. McKeith;R. L. Fernando

200
Citations

Frequent Co-Authors

Dorian J. Garrick Massey University

Jack C. M. Dekkers Iowa State University

Anna Wolc Iowa State University

Daniel Gianola University of Wisconsin–Madison

Max F. Rothschild Iowa State University

James M. Reecy Iowa State University

Susan J. Lamont Iowa State University

Bernt Guldbrandtsen Aarhus University

George C. Fahey University of Illinois at Urbana-Champaign

Floyd K. Mckeith University of Illinois at Urbana-Champaign

External Links

Wikipedia of Rohan L. Fernando Personal website of Rohan L. Fernando

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Studying Genetics can open doors to a wide range of professional options, especially with the growing availability of online degrees. Those interested in healthcare administration may explore online medical coding programs to gain skills in handling patient data and billing, which is essential for many roles in medical facilities.

Many schools now offer fast track programs that allow motivated students to complete their genetics or related degrees more quickly, helping them enter the workforce sooner. Flexible learning has also become more common, letting students balance studies with other commitments through self paced university options.

Additionally, finding the right institution has never been easier. Accessible online applications, like the majuba college online application, help students get started without added expenses. Exploring these related online pathways can provide a strong foundation for a variety of careers in genetics and beyond.