Advanced Genetic Improvement
A.Y. 2025/2026
Learning objectives
The objective of this course is to provide the operational tools used in livestock to select and reproduce animals, tolls that are nowadays based on genomic technologies. Advancement in molecular technologies has in fact generated new selection processes based on the genomic information available on each animal. Students will learn the basics for interpreting the available genomic information and tools for their use in genomic selection and breeding management.
Expected learning outcomes
1. Knowledge and Understanding:
By the end of the course, the student should demonstrate knowledge of the fundamental principles of genomics and genetic improvement applied to animal production, the most common analytical methodologies, and the tools used to study and solve problems related to the genetic improvement of domestic animals. Additionally, the student should understand the theoretical and practical concepts governing data registration processes on farms, data analysis, genetic value calculations, and the use of genomics for reproductive management of animals in breeding.
2. Ability to Apply Knowledge and Understanding:
The student should demonstrate the ability to apply knowledge and concepts to solve practical problems related to the application of genetics in animal improvement. The student should show the ability to use specific technical and methodological tools (e.g., data analysis, laboratory techniques, calculation software) to address case studies, analyze real data, and propose concrete solutions in the fields of livestock population management and herd management.
3. Critical Thinking and Judgment:
The student should demonstrate the ability to critically argue the information acquired by evaluating sources, data, and proposed models. Specific activities aimed at developing this ability include practical exercises on calculating EBVs (Estimated Breeding Values), genomic data analysis, group work on complex case studies, and writing reports that require critical analysis and proposing well-founded interpretations.
4. Ability to Communicate What Has Been Learned:
The student should demonstrate the ability to express themselves using scientifically appropriate terminology, particularly regarding animal genetics and genomics applied to genetic improvement in animals. Exercises, oral presentations, and participation in group discussions are intended to stimulate the ability to communicate correctly, defend their ideas, and engage in scientific discussions with peers and instructors.
5. Lifelong learning skills:
The student should demonstrate the ability to use the knowledge acquired to interpret new phenomena and address complex issues. They should be able to rely on available knowledge sources (e.g., scientific databases, publications, learning platforms) and organize their own study autonomously, developing a critical and informed approach to emerging topics in the field.
By the end of the course, the student should demonstrate knowledge of the fundamental principles of genomics and genetic improvement applied to animal production, the most common analytical methodologies, and the tools used to study and solve problems related to the genetic improvement of domestic animals. Additionally, the student should understand the theoretical and practical concepts governing data registration processes on farms, data analysis, genetic value calculations, and the use of genomics for reproductive management of animals in breeding.
2. Ability to Apply Knowledge and Understanding:
The student should demonstrate the ability to apply knowledge and concepts to solve practical problems related to the application of genetics in animal improvement. The student should show the ability to use specific technical and methodological tools (e.g., data analysis, laboratory techniques, calculation software) to address case studies, analyze real data, and propose concrete solutions in the fields of livestock population management and herd management.
3. Critical Thinking and Judgment:
The student should demonstrate the ability to critically argue the information acquired by evaluating sources, data, and proposed models. Specific activities aimed at developing this ability include practical exercises on calculating EBVs (Estimated Breeding Values), genomic data analysis, group work on complex case studies, and writing reports that require critical analysis and proposing well-founded interpretations.
4. Ability to Communicate What Has Been Learned:
The student should demonstrate the ability to express themselves using scientifically appropriate terminology, particularly regarding animal genetics and genomics applied to genetic improvement in animals. Exercises, oral presentations, and participation in group discussions are intended to stimulate the ability to communicate correctly, defend their ideas, and engage in scientific discussions with peers and instructors.
5. Lifelong learning skills:
The student should demonstrate the ability to use the knowledge acquired to interpret new phenomena and address complex issues. They should be able to rely on available knowledge sources (e.g., scientific databases, publications, learning platforms) and organize their own study autonomously, developing a critical and informed approach to emerging topics in the field.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
The program is developed in two units. The first provides the basics of quantitative genetics and selection through the genomic approach that has revolutionized the selection process and is changing the management approach of animals in livestock production. In the second unit, the part relating to the estimation of the genetic / genomic value of animals is developed with specific references to genomic selection in populations.
'H53-49-A' - 'Teaching unit: Quantitative genetics and selection'.
OBJECTIVES OF THE UNIT:
The unit aims to provide the knowledge for the interpretation of the relationship between phenotype and genotype, central to quantitative genetics and the selection of breeding stock. The genomic information available today is a basic component of modern quantitative genetics.
ARTICULATION OF THE MODULE:
Frontal teaching
1) Sequencing and genotyping;
2) Genotyping chips for SNP markers;
3) Genetic markers and their use in the genomic management of populations;
4) Genomic and phenotypic variability in livestock production populations;
5) Genetic structures of populations: F1, F2, Backcross, Outbreeding populations, commercial hybrids;
6) Quantitative Trait Loci and markers - linkage;
7) Genomic kinship and genomic inbreeding;
8) The infinitesimal genetic model in a genomic context;
9) Concept of Breeding Value and its estimate starting from one or more sources of information;
10) Accuracy of reproductive value and SEP. Genetic basis;
11) Correlation between characters and correlated response;
12) Complex economic indices;
13) The population selection schemes;
14) Response to the selection;
15) Management of genomic variability;
Exercises
The exercises will be developed in the computer classroom on specific software for the selection of reproducers and for the management of genomic data. Students must use the R environment "The R software for statistical computing" (https://www.r-project.org/) (https://www.rstudio.com/).
'H53-49-B' - 'Teaching unit: Mixed model and genomic selection'
OBJECTIVES OF THE MODULE:
The module aims to provide the basics of genomic evaluation in livestock production species and its application in breeding programs.
ARTICULATION OF THE MODULE:
Frontal teaching
1. The contribution of information to reproductive value
2) The genetic evaluation model
3) Systems of equations in matrix algebra (OLS / GLS)
3) The mixed model and EVB estimation with relation to the genetic model
4) Genetic variance and environmental variance;
5) The sire model
6) Genomic Selection: tools, populations, prediction equations;
7) The estimation of the gene substitution effect in the one-locus model;
8) The estimation of the prediction equations from the "training population";
9) Application of prediction equations in the "application population"
10) Genomic Reproductive Value (GEBV);
Exercises
The exercises will be developed in the classroom on Microsoft Excel and specific software for the selection of reproducers and for the management of genomic data. Students must use the R environment "The R software for statistical computing" (https://www.r-project.org/) (https://www.rstudio.com/).
'H53-49-A' - 'Teaching unit: Quantitative genetics and selection'.
OBJECTIVES OF THE UNIT:
The unit aims to provide the knowledge for the interpretation of the relationship between phenotype and genotype, central to quantitative genetics and the selection of breeding stock. The genomic information available today is a basic component of modern quantitative genetics.
ARTICULATION OF THE MODULE:
Frontal teaching
1) Sequencing and genotyping;
2) Genotyping chips for SNP markers;
3) Genetic markers and their use in the genomic management of populations;
4) Genomic and phenotypic variability in livestock production populations;
5) Genetic structures of populations: F1, F2, Backcross, Outbreeding populations, commercial hybrids;
6) Quantitative Trait Loci and markers - linkage;
7) Genomic kinship and genomic inbreeding;
8) The infinitesimal genetic model in a genomic context;
9) Concept of Breeding Value and its estimate starting from one or more sources of information;
10) Accuracy of reproductive value and SEP. Genetic basis;
11) Correlation between characters and correlated response;
12) Complex economic indices;
13) The population selection schemes;
14) Response to the selection;
15) Management of genomic variability;
Exercises
The exercises will be developed in the computer classroom on specific software for the selection of reproducers and for the management of genomic data. Students must use the R environment "The R software for statistical computing" (https://www.r-project.org/) (https://www.rstudio.com/).
'H53-49-B' - 'Teaching unit: Mixed model and genomic selection'
OBJECTIVES OF THE MODULE:
The module aims to provide the basics of genomic evaluation in livestock production species and its application in breeding programs.
ARTICULATION OF THE MODULE:
Frontal teaching
1. The contribution of information to reproductive value
2) The genetic evaluation model
3) Systems of equations in matrix algebra (OLS / GLS)
3) The mixed model and EVB estimation with relation to the genetic model
4) Genetic variance and environmental variance;
5) The sire model
6) Genomic Selection: tools, populations, prediction equations;
7) The estimation of the gene substitution effect in the one-locus model;
8) The estimation of the prediction equations from the "training population";
9) Application of prediction equations in the "application population"
10) Genomic Reproductive Value (GEBV);
Exercises
The exercises will be developed in the classroom on Microsoft Excel and specific software for the selection of reproducers and for the management of genomic data. Students must use the R environment "The R software for statistical computing" (https://www.r-project.org/) (https://www.rstudio.com/).
Prerequisites for admission
No prerequisite
Teaching methods
The course is based on class frontal lectures and computer practice sessions. For the computer sessions, Microsoft Excel and public domain software (R and ChatGPT) will be used. The software allows the management and use of data useful for understanding the course topics.
Teaching Resources
Class notes provided by the teacher
-) Genetic Improvement of Farmed Animals (2021). Geoff Simm, Geoff Pollot, Raphael Mrode, Ross Houston and Karen Marshall, CABI International. Available for students on CABI:
https://www.cabidigitallibrary.org/doi/epdf/10.1079/9781789241723.0000
Calculation environment R "The R software for statistical computing" (https://www.r-project.org/) and Rstudio (https://www.rstudio.com/).
-) Genetic Improvement of Farmed Animals (2021). Geoff Simm, Geoff Pollot, Raphael Mrode, Ross Houston and Karen Marshall, CABI International. Available for students on CABI:
https://www.cabidigitallibrary.org/doi/epdf/10.1079/9781789241723.0000
Calculation environment R "The R software for statistical computing" (https://www.r-project.org/) and Rstudio (https://www.rstudio.com/).
Assessment methods and Criteria
The exam will consist into 1 oral test.
Brief description of the test procedures:
The student will be asked to deepen specific topics of the course program.
The use of the computer (excel / R) will be allowed for the part related to the module "Mixed model and genomic selection".
Brief description of the test procedures:
The student will be asked to deepen specific topics of the course program.
The use of the computer (excel / R) will be allowed for the part related to the module "Mixed model and genomic selection".
AGR/17 - LIVESTOCK SYSTEMS, ANIMAL BREEDING AND GENETICS - University credits: 8
Practicals: 32 hours
Lessons: 48 hours
Lessons: 48 hours
Professor:
Bagnato Alessandro
Professor(s)