Advanced genetic improvement

A.Y. 2019/2020
8
Max ECTS
80
Overall hours
SSD
AGR/17
Language
Italian
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
Students will be able to use at basic level the genomic information provided to breeders and artificial insemination centers that are the fundamental pillar of the genomic selection. They will be able to interpret the genomic reproductive value (GEBV) and to implement, on farms, innovative selection programs. They will have the basic knowledge of the bioinformatic tools and usage skills for the management of genomic data.
Course syllabus and organization

Unique edition

Responsible
Lesson period
Second semester
Course syllabus
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.

'H53-49-A' - 'Didactic unit: Quantitative Genetics and Selection'.
OBJECTIVES OF THE MODULE:
The module aims to provide the knowledge for the interpretation of the relationship between phenotype and genotype, central to quantitative genetics and the genomic selection of livestock. The genomic information available today is a basic component for modern quantitative genetics.

MODULE ARTICULATION:
Frontal teaching
1. Sequencing and genotyping;
2. The genotyping chips for SNP markers;
3. Genetic markers and their use in population genomic management;
4. Genomic and phenotypic variability in species in livestock production;
5. Genetic structures of populations: F1, F2, Backcross, populations in outbreeding, commercial hybrids;
6. Quantitative Trait Loci and Markers;
7 Genomic and genomic inbreeding;
8. The infinitesimal genetic model in a genomic key;
6. Concept of Breeding Value and its estimate starting from one or more sources of information;
7. Accuracy of the index and SEP. Genetic basis;
8. Correlation between characters and related response;
9. Economic selection indices;
10. The breeder selection schemes;
11. Response to the selection;
12. Management of genomic variability;

Practical labs
The labs will be developed in the computer room on specific software for the selection of reproducers and for the management of genomic data. Students will have to use the environment "The software for statistical computing" (https://www.r-project.org/) (https://www.rstudio.com/).

'H53-49-B' - 'Didactic unit: Mixed model and genomic selection'
OBJECTIVES OF THE MODULE:
The module aims to provide the basics of genomic evaluation methods in livestock production species and its application in selection programs.

MODULE ARTICULATION:
Frontal teaching
1. Genomic selection: instruments, populations, prediction equations;
2. The estimate of gene substitution effect in the one model;
3. The estimation of the prediction equations from the "training population";
4. Application of the prediction equations in the "application population"
5. Genomic Expected Breeding Value (GEBV);
6. Accuracy of the GEBV;
7. The "One Step" method;
8. Genomic selection schemes;
9. Management farm reproduction using the genomic information;

Practical labs
The labs will be developed in the computer room on specific software for the selection of reproducers and for the management of genomic data. Students will have to use the environment "The software for statistical computing" (https://www.r-project.org/) (https://www.rstudio.com/). They will also have to use Winthor.
Prerequisites for admission
No prerequisite
Teaching methods
The course is based on class frontal lectures and computer practice sessions. For the computer sessions, public domain software will be used. The software allows the management and use of data useful for understanding the course topics.
Bibliography
The subject matter is recent and in constant evolution. Therefore, the teaching material will be provided by the teacher. No textbook is needed.
Calculation environment R "The R software for statistical computing" (https://www.r-project.org/) and Rstudio (https://www.rstudio.com/).
Assessement methods and criteria
The exam will consist into 1 written test.
Brief description of the test procedures:
The exam consists in the presentation of report that the student will prepare with the tools that will be provided in the course which will have as its content the utilization and management of genomic data and their use for genomic selection. The report will be presented by the student to the oral exam to allow the verification of the knowledge of the subjects covered in the course by the student.
Mixed model selection and genomics
AGR/17 - LIVESTOCK SYSTEMS, ANIMAL BREEDING AND GENETICS - University credits: 3
Practicals: 16 hours
Lessons: 16 hours
Professor: Bagnato Alessandro
Quantitative genetics and selection
AGR/17 - LIVESTOCK SYSTEMS, ANIMAL BREEDING AND GENETICS - University credits: 5
Practicals: 16 hours
Lessons: 32 hours
Professor: Bagnato Alessandro
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Professor(s)