Degree | Type | Year | Semester |
---|---|---|---|
2500890 Genetics | OB | 3 | 1 |
Quantitative genetics addresses the genetic analysis of quantitative or complex traits, such as some of the involved in the evolution of species, the ones determining most of the productive traits in plants and animals, and also the predisposition to diseases. Its most applicative side has been, and continues to be, the genetic selection, that is made to improve plant and animal production.
There is a phenomenon clearly observable, the resemblance between relatives, for which Quantitative Genetics has developed a model based upon previous concepts of Population genetics. This model assumes that complex traits are determined by a large number of genes. By applying several statistical concepts it is possible to estimate a number of genetic parameters that will inform if the trait is susceptible to be selected. All this theory is based in classic papers from Fisher, Wright, Lush and others.
In addition to selection that is done within populations, crossing among populations is a strategy widely used to improve production traits. This course covers both the theoretical and applicative aspects of both methodological approaches, and also the population structures that make the genetic improvement more efficient.
On the other hand, in the last years there has been a growing interest regarding the identification of particular genes that determine quantitative or complex traits. Currently we have different molecular genetics tools that allow us to genotype a high number of SNP (single nucleotide polymorphism) loci that are markers for quantitative trait loci (QTL) dispersed across the genome. This strategy of identification is known as “Genome wide association studies”. This course covers the explanation of this strategy and another related strategy such as genomic prediction.
The specific learning objectives are:
*Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents.
The teaching methodology used along the learning process will be based upon the student’s work, and the teacher will be in charge of helping the student in acquiring and interpreting the information related to the contents of the subject.
According to this methodology, the activities that are the following:
Lectures: Lectures will address the fundamental knowledge of the subject. Lectures will be interactive and will foment the dialog with and among students. They will be based on audiovisuals (mainly ppt presentations) that will be uploaded into Campus Virtual before the lectures. This activity will be complemented with classes devoted to solving problems, tutorial sessions and practical sessions in the computer laboratory.
Problems seminars: There will be 2 groups of about 30 students each. The seminars will consist of solving problems in an active manner. This will help the reasoning in the context of Quantitative Genetics and the understanding of the fundamental concepts of the subject. There will be problems solved in the classroom and others that the student will have to solve by him/her self. It is assumed that all the problems will be worked out by the students prior to the seminar.
Self learning and study group: In addition to solving problems in the classroom, groups of 2 different students will have to solve 6 exercises representing the most important items of the course.
Computer practices: Formally they are included in the course “Laboratori Integrat V”, but they are a great support to better understand what has been taught in the lectures.
Tutorials: Concerted sessions to solve doubts discuss specific contents of the course.
*The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lectures | 30 | 1.2 | 2, 1, 3, 7, 8, 4, 9, 5, 6 |
Problems lab | 15 | 0.6 | 2, 4, 9, 5, 6 |
Type: Supervised | |||
Tutorials | 5 | 0.2 | 2, 1, 3, 7, 8, 4, 9, 5, 6 |
Type: Autonomous | |||
Individual study and self learning | 55 | 2.2 | |
Problem solving | 42 | 1.68 | 2, 1, 4, 5, 6 |
There will be a continuous assessment on a personal basis in the context of the programmed learning activities.
Two mid-term exams of 40 questions with 4 alternative answers will be made. The exams will contain questions on theory (32) and problems (8) and will last 90 minutes. The scores obtained in the 2 mid-term exams will represent 80% of the final mark (40% each).
In addition to that, aiming at verifying if the students make progresses on the understanding of the subject problems, the obligatory resolution of 6 exercises will be requested. These exercises will be made in groups of 2 different students and after assessment will represent 20% of the final mark.
The minimum mark to pass the course is 5 (on a maximum of 10).This final mark will be calculated weighing the scores of the two mid-term exams and the exercises. To compute this weighted average, each score must be at least 4. In the test exams, the students will have to attain a score of at least 4 in each of the questions of theory and problems.
In the case the student does not pass the subject with this continuous assessment, the students will be able to make a retake examination of one or two of the mid-term exams. To be eligible for the retake process, the student should have been previously evaluated in a set of activities equaling at least two thirds of the final score of the course or module. Thus, the student will be graded as "No Avaluable" if the weighthin of all conducted evaluation activities is less than 67% of the final score. This retake examination, similar to that described for the mid-term exams, will contain 40 questions if the two mid-term exams are recovered or 25 questions if only one mid-term exam needs to be recovered. For students that want to improve their marks, thesame applies. In this case, the score that will be taken into account for the final mark will be the one obtained in the last exam.
*Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Mid-term exam 1 | 40% | 1.5 | 0.06 | 2, 1, 3, 7, 8, 4, 9, 5 |
Mid-term exam 2 | 40% | 1.5 | 0.06 | 2, 1, 4, 5, 6 |
Problem solving in groups | 20% | 0 | 0 | 2, 1, 3, 7, 8, 4, 9, 5, 6 |
Recovery exam (theory and problems) | 0 | 0 | 0 | 2, 1, 3, 8, 5, 6 |
General
Caballero A. 2017. Genética Cuantitativa. Síntesis, Madrid.
Falconer D.S., Mackay T.F.C. 2001. Introducción a la Genética Cuantitativa. Acribia, Zaragoza.
Lynch M., Walsh B. 1997. Genetics and analysis of quantitative traits. Sinauer, Sunderland.
Animals
Minvielle F. 1990. Principes d'amélioration génétique des animaux domestiques. INRA, Paris.
Nicholas F.W. 2003. An introduction to Veterinary Genetics. Blackwell Publishing, Oxford.
Piedrafita J. 1998. Notas sobre teoría de mejora genética. Col·lecció Materials, 49. Servei de Publicacions UAB, Bellaterra.
Plants
Acquaah G. 2012. Principles of Plant Genetics and Breeding, 2nd Ed. Wiley, Wiley-Blackwell. (on-line in UAB)
Allard R.W. 1999. Principles of plant breeding. Wiley, New York.
Cubero J.I. 2013. Introducción a la Mejora Genética Vegetal. 3a ed. Mundi-Prensa, Madrid.
Kang M.S. 2002. Quantitative genetics, genomics and plant breeding. CABI, Wallingford. (on-line in UAB; there is a new edition from 2020)
Rodríguez Borruezo A. 2009. A primer of Genetics and Plant Breeding. UPV, Servicio de Publicación, Valencia.
Web sites (for Lab Integrat V)
Genup: http://www-personal.une.edu.au/~bkinghor/genup.htm
PQGen: https://sites.google.com/a/unizar.es/pqgen/