Master computer modeling for agricultural breeding programs using AlphaSimR. Learn to simulate and evaluate genetic improvements in crops and livestock.
Master computer modeling for agricultural breeding programs using AlphaSimR. Learn to simulate and evaluate genetic improvements in crops and livestock.
This specialized course teaches the fundamentals of breeding program modeling using the AlphaSimR package in R. Students learn to design, simulate, and analyze breeding scenarios for various agricultural species. The curriculum covers DNA-trait relationships, selection response, and complex breeding program modeling, providing practical skills for optimizing genetic improvements in agriculture through computer simulation.
4.6
(17 ratings)
Instructors:
English
English
What you'll learn
Design and setup computer simulated breeding programs
Implement breeding scenarios using AlphaSimR package
Analyze genetic improvement outcomes across populations
Evaluate breeding strategies for different species
Understand DNA trait relationships in breeding programs
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This course provides comprehensive training in breeding program modeling using AlphaSimR. Students learn to design and simulate breeding programs, analyze genetic improvements, and evaluate different breeding scenarios. The curriculum covers key concepts including DNA-trait relationships, genetic lottery simulation, selection response analysis, and complex breeding program modeling. Through practical exercises and case studies, participants gain hands-on experience in optimizing breeding programs for various agricultural species.
Introduction to breeding programme modelling
Module 1
Relationship between DNA & traits
Module 2
DNA Lottery
Module 3
Response to selection
Module 4
Modelling complex breeding programmes
Module 5
Fee Structure
Instructors

1 Course
Distinguished Expert in Selective Breeding and Population Genetics
Gregor Gorjanc is a Professor of Selective Breeding at The Roslin Institute, University of Edinburgh, where he leads the HighlanderLab focusing on population improvement through data science, genetics, and breeding. His academic journey began in Slovenia, earning an engineering degree in Animal Science (2003) and a PhD in Genetics (2010) from the University of Ljubljana, where he subsequently held a teaching position before moving to The Roslin Institute in 2013. His research encompasses three main areas: developing methods for genetics and breeding, designing and optimizing breeding programs, and analyzing phenotypic and genetic data to understand biology and improve populations. Gorjanc's expertise uniquely spans multiple domains, including data science, genetics, and breeding, with applications across diverse agricultural populations including animals, plants, and insects. His work at the HighlanderLab demonstrates his commitment to advancing selective breeding techniques through innovative approaches to population management and improvement.

1 Course
Distinguished Expert in Statistical Genetics and Animal Breeding
Ivan Pocrnic is a Career Track Fellow at The Roslin Institute, University of Edinburgh, where he specializes in developing novel breeding methodologies and tools at the interface of theoretical quantitative genetics and practical animal breeding. His academic journey began at the University of Zagreb, Croatia, where he earned a BSc in Agricultural Economics and an MSc in Animal Genetics and Breeding. After gaining practical experience at the Croatian Agricultural Agency, he completed his PhD in Animal Breeding and Genetics at the University of Georgia under distinguished scientist Professor Ignacy Misztal. Pocrnic's expertise lies in applying genomic selection to mega-scale datasets across various species including pigs, chickens, and cattle, with a particular focus on single-step genomic prediction. His significant contributions include developing algorithms for mega-scale evaluations now implemented by major livestock breeding companies. At Roslin, he works with diverse data types, from genomic and pedigree information to productivity and environmental records, developing statistical models to predict optimal breeding strategies. His current research focuses on cross-breeding programs and the development of statistical models for analyzing genetic variability in relation to climate challenges.
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4.6 course rating
17 ratings
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