General information on the Unit
Contact hours: 60 (23 lectures, 27 practicals)
Personal work hours: 90
Venue: Organized by the Mediterranean Agronomic Institute of Zaragoza (CIHEAM Zaragoza)
- Developed during the first academic year of the Master, at the second part of the first semester.
- The assessment of this Unit consists of a written exam and the evaluation of practical exercise during the first semester.
Requisites and permanence
Knowledge on basic statistics and R software is required. Propaedeutic on basic statistics and R software are organized for students who need them.
Combination of theoretical and practical classes, individual study and work.
Lectures are delivered in English and the documents supplied by the lecturers are also in English. The exams can be taken either in English, Spanish or French.
Presentation of the Unit and context within the syllabus
This unit reviews different tools for linkage mapping and GWAS. First, the Unit studies different genetic concepts relate with alleles inheritance such as Identity by descent (IBD) and Identity by state (IBS) and their application in QTL mapping. Genetic distance and population structure is being introduced as a commonly caused by selection, mutation and drift in quantitative genetics. Secondly, of the unit is devoted to QTL (Quantitative Trait Loci) detection and QTL validation, QTL mapping and GWAS. The last part of the unit is about evaluation of selection strategies based on different types of selection in plant breeding. This unit treats also genomic prediction and selection as extensions of QTL mapping and marker assisted selection. The initial part of the unit deals with these background principles for genomic prediction as well as with QTL mapping, GWAS, multiple regression and selecting genetic predictors. The main body of the Unit is then consist in theory and practice of genomic prediction and selection with special attention to different genomic prediction models (ridge, GBLUP, Lasso) and the construction of training and test set for genome enable prediction.
- SC1 Familiarize with applications of IBD and IBS – founder inference, imputation, GRM.
- SC2 Realize the consequence of population structure and how to work with it.
- SC3 Having a good basics in quantitative genetics for QTL analysis and meta-analysis.
- SC4 Assessing the relevant methods and techniques in the evaluation of selection strategies
- SC5 Familiarize with genomic prediction and selection.
- SC6 Having a good basics genomic prediction models.
- SC7 Assessing the relevant methods and techniques in training and test set for genomic enable prediction.
- GC1 Integrating scientific and technical knowledge and applying them discerningly.
- GC2 Performing scientific and/or technical information searches and processing them selectively.
- GC3 Analyzing results or strategies and elaborating conclusions which contribute to clarify the problems and to find possible solutions.
- GC4 Integrating scientific and technical knowledge and applying them discerningly.
- GC5 Performing scientific and/or technical information searches and processing them selectively.
- GC6 Analyzing results or strategies and elaborating conclusions, which contribute to clarify the problems and to find possible solutions.
The student, at the end of the learning of this Unit:
- Knows the differences between IBD and IBS.
- Knows the different measures of genetic distance.
- Understands the possible causes of genetic differentiations and Wahlund effect.
- Learns about Fst and its uses.
- Knows how to construct PCA plots and isolate groups from the plots.
- Knows the basics of quantitative genetics for QTL analysis and meta-analysis.
- Learns the methods for QLT interval mapping and association mapping.
- Understands the problems of population structure in association mapping.
- Is familiar with the evaluation of selection strategies: types and multi-traits selection in plant breeding.
- Define genomic prediction models
- Calculates genome estimated breeding values and assess their accuracy.
- Designs and apply genomic selection strategies for a range of crops and breeding schemes.
- Evaluates the efficiency of phenotypic, marker assisted and genomic selection strategies.
- IBD, IBS, genetic distance, population structure
- QTL mapping and GWAS (estimation of positions and allelic effects)
- Evaluation of selection strategies
- Cross validation, prediction error, training-test set construction
- Penalized regression, ridge, GBLUP, Lasso
- Dimension reduction, PCR, PLS
- GxE, QTLxE, factorial regression, selecting environmental covariables for predicting phenotypes
- Genomic prediction and GxE, genomic prediction for genotypic intercepts and sensitivities, Jarquin approach (double ridge) ; environmental classification, subdividing TPE
Learning activity 1: Lectures combined with applied examples
Percentage of contact: 26%
Learning activity 2: Tutored individual work
(a) IBD, IBS, genetic distance, population structure
(b) QTL mapping and GWAS using specific software
Hours: (a, b): 25
Percentage of contact: (a,b): 48%
Learning activity 3: Tutored group work
Students, guided by the tutor, work in groups of 5-6 persons. They receive a set of papers on genomic prediction and each group has to select three of them. Every group must draft a synthesis document for public presentation and joint discussion.
Percentage of contact: 71%
Assessment system 1: Written exams composed of questions provided by all lecturers of the Unit.
The questions are multiple choice, calculations and long questions. The exam assesses the content of lectures and, the practical work, in addition to the home work exercises presented by the students.
Weighting: 40% of the final score of the Unit
Assessment system 2: Global assessment by the tutors of the individual work related to learning activities 2 (a, b) based on the reports submitted by each student about the exercises performed. Understanding of the methodology and quality of the results will be assessed.
Weighting: 20% of the final score of the Unit
Assessment system 3: Presentation a group work related with the interpretation of three papers on genomic prediction. The score is based both on the powerpoint document and on the presentation and defence of the work. The score is the same for all members of the group.
Weighting: 40% of the final score of the Unit
Daniela BUSTOS, WUR (The Netherlands)
Fred van EEUWIJK, WUR (The Netherlands)
Emilie MILLET, WUR (The Netherlands)
Carel PEETERS, WUR (The Netherlands)
Julio ISIDRO, CBGP-UPM, Madrid (Spain)
Bart-Jan van ROSSUM, WUR (The Netherlands)
Silvio SALVI, Univ. Bologna (Italy)
Chin Jian YANG, SRUC, Edinburg (UK)