Books and Software
Professor Bernardo has written two textbooks published by Stemma Press as well as software that can be downloaded below:
Essentials of Plant Breeding (Hardbound, 260 pages, ISBN 978-0-9720724-2-7)
This textbook is for a first-semester course in plant breeding. It describes how modes of reproduction affect the choice of breeding methods, and outlines the breeding methods appropriate for self-pollinated, cross-pollinated, and asexually propagated species. Essentials of Plant Breeding includes a review of basic genetics; an introduction to the use of DNA markers in plant breeding; and a description of general features of breeding programs for field crops, vegetables and fruits, forages, turfgrasses, flowers and ornamentals, and tree and palm species.
Breeding for Quantitative Traits in Plants (3rd ed., hardbound, 422 pages, ISBN 978-0-9720724-3-4)
Most of the economically important traits in crops are quantitative and are controlled by many genes. Breeding for Quantitative Traits in Plants investigates the application of quantitative genetics to plant breeding. This book is an ideal text for a graduate-level course and a useful reference for practicing plant breeders.
Gaming software in which players allocate resources and make breeding decisions to develop a barley cultivar that meets standards for yield, protein, and disease resistance, all within a fixed budget.
Software for teaching
Simulation programs for QTL mapping, association mapping, and and recurrent genomewide selection.
- Download zip file for Windows and macOS
GModel and GModel2
No-frills software for linkage mapping of quantitative trait loci (QTL) in a biparental cross, or for association mapping in a germplasm collection.
RR-BLUP and RR-BLUP2
No-frills software for genomewide selection by ridge regression-best linear unbiased prediction. The software calculates genomewide marker effects, estimates the predictive ability by cross-validation, and (if desired) predicts the performance of a test population.
No-frills software that imputes missing marker data and identifies SNP loci that are monomorphic, have too many missing values, have minor allele frequencies that are too low, or that are redundant with other SNP loci.