Teaching Plan for Population Genetics & Molecular

TEACHING PLAN FOR POPULATION GENETICS & MOLECULAR EVOLUTION

1. Basic description

Name of the course: Population Genetics and Molecular Evolution Academic year: 2018/2019 Year: 2nd Term: 3rd Code: 52322 Number of credits: 4 credits Total number of hours committed: 40 hours Teaching language: English Lecturer: Antonio Barbadilla / Julio Rozas / Alejandro Sánchez-Gracia Timetable: See official calendar

2. Presentation of the course

This course covers the theoretical concepts and practical skills that are required to analyse and understand DNA and protein sequence evolution both at the population genetics (within species) and divergence (between species) levels. It includes central subjects in these areas, such as measuring molecular variation, the Hardy–Weinberg equilibrium, the factors promoting evolution, the notions of genetic linkage and gene mapping, the coalescent theory, the neutral theory of molecular evolution, the DNA and amino acid substitution models or the essential ideas of molecular phylogenetic tree thinking. The course also introduce most popular computer algorithms and software to simulate and analyse DNA and protein sequence evolution and provide some guidelines for the correct interpretation of the results generated by these programs.

3. Competencies to be worked in the course

The above competencies interrelate with the basic competencies set out in Royal Decree 1393/2007, namely: a. competence to comprehend knowledge, on the basis of general secondary education b. competence to apply knowledge to day-to-day work in international management or marketing, in particular, ability to develop and defend arguments and to solve problems c. competence to gather and interpret relevant data, enabling the development of critical judgements on the economic and social reality d. competence to communicate and transmit information (ideas, problems, solutions) to a specialised and non-specialised public e. competence to develop learning activities in a relatively autonomous manner.

In order to establish a correspondence between the basic competencies and those developed in the degree, these are grouped according to two criteria. Thus, the competencies developed in the subject are structured into those that are seen as a development or specification of basic competencies and those that define the professional profile of the graduate, with respect to general and specific competencies.

Basic competence: understanding of knowledge

I. General competencies

● CB4. That the students can convey information, ideas, problems and solutions to both specialist and non-specialist audiences.

● CB5. That the students have developed those skills needed to undertake further studies with a high degree of autonomy.

● CG1. That the students will acquire an intra- and interdisciplinary training in both computational and scientific subjects with a solid basic training in biology.

II. Transversal competencies

● CT1. Mastering oral and written communication in English.

III. Specific competencies

● CE2. To manage and exploit all kinds of biological and biomedical information to transform it into knowledge.

● CE4. To integrate clinical and omics data for a greater understanding of biological phenomena.

● CE6. To computationally analyze DNA sequences, RNA and proteins, as well as to carry out comparative analyzes of genomes.

● CE8. To identify meaningful and reliable sources of scientific information to substantiate the state of arts of a bioinformatic problem and to address its resolution.

Learning outcomes

● RA2.1. Visualize, manipulate and extract biological data.

● RA4.1. Process, manage and interpret basic genomics data.

● RA4.2. Objectively analyze genotypes and/or sequences.

● RA6.1. Understand how similar sequences are identified in a database.

● RA6.2. Use genomic databases to extract sequences and functional information.

● RA8.1. Use efficiently specific search tools and resources from databases and information related to biomedicine and bioinformatics.

● RA8.2. Quote valid sources of scientific information to support the state of the arts of a bioinformatic problem.

4. Contents

• Basic description of contents outlined for the curriculum

Part A. POPULATION GENETICS (10h)

Session 1. Introduction to Population Genetics (2h) Darwinian evolution and population thinking. Levels of genetic variation. Gene and genotype frequencies (DNA and Haplotype). Estimation of genetic variation. Data and relevant databases.

Session 2. Population dynamics of genetic variation I (2h) Random mating and Hardy-Weinberg Equilibrium. Genetic Drift. Mutation. The neutral theory of molecular evolution.

Session 3. Population dynamics of genetic variation II (2h) The coalescent process. Population subdivision. Migration. Population history and demography.

Session 4. Population dynamics of genetic variation III (2h) Natural selection. Single selection models. Fixation probabilities of new mutations. Linkage disequilibrium. Genetic hitchhiking. Gene mapping. GWAs.

Session 5. Evolutionary inference (2h) Inferring natural selection from sequence data. Neutrality-based tests: Tajima’s D. Inferring population history and demography: Fst statistics.

Part B. MOLECULAR EVOLUTION (10H)

Session 6. The neutral theory of molecular evolution (2h) The molecular clock. Theoretical basis of the neutral theory. Evolutionary consequences of the neutral theory.

Session 7. Models of DNA and protein sequence evolution (2h) Scoring matrices (PAM, BLOSUM). Markov models of DNA evolution (JC, GTR). Empirical amino acid substitution models.

Session 8. Molecular phylogenetics (2h) The tree thinking, and basic phylogenetic concepts. How to interpret a phylogenetic tree. Sequence homology. Phylogenetic tree file formats (newick).

Session 9. Molecular adaptation and functional divergence (2h) Neutrality tests combining polymorphism and divergence data. Codon substitution models. Evolutionary rate changes after gene duplication.

Session 10. Population genetics of divergence (2h) Gene trees. Populations and species trees. Incomplete linage sorting. Divergence with gene flow.

PRACTICAL WORK –COMPUTER CLASSES (18-20h)

PR1- Estimation of Genetic Variation Estimation of genetic variation from Mendelian polymorphism and nucleotide data. Testing HW equilibrium.

PR2- Computer simulation I Simulation of genetic drift and mutation with R.

PR3- Computer simulation II Simulation of coalescent trees with R.

PR4- Computer simulation III Simulation of gene frequency trajectories in finite populations under selection with R.

PR5- Inferring natural selection I Inferring natural selection from intraspecific data.

PR6- Interspecific Variation Analysis I Estimation of the nucleotide and amino acid divergence across different functional regions.

PR7- Interspecific Variation Analysis II Neutrality tests comparing the amount of variation within a species (polymorphism) to the divergence between species (substitutions): MK and HKA tests.

PR8- Inferring natural selection II Inferring natural selection from divergence data. PAML software, and Datamonkey web server.

PR9- Detecting functional divergence after gene duplication Evolutionary rate shifts. Type I and II functional divergence. DIVERGE software.

PR10- Population genetics of divergence analyses Isolation and Isolation with migration models. IM and ABC-based models.

5. Assessment

Several exams will be carried out to quantify the fulfilment of the course learning objectives. All exams are compulsory. In order to successfully complete this course, the student must pass at least 50% of the final examination. The course grading will be partitioned as follows: from 10 points, 6 points correspond to the evaluation of the theoretical contents (final exam), 1 point to the mid-term exam, plus 3 points to the evaluation of practical contents. 1 point. Evaluation of theoretical-practical concepts (mid-term exam). 6 points. Evaluation of theoretical concepts (final exam). It will consist of several multiple-choice questions. 3 points. Evaluation of theoretical-practical contents by a practical exercise. Student’s assistance to practical class sessions is mandatory. Recuperation Information

Only the students that after the evaluation have not passed the course can retake the final theoretical exam in July. .

Assessment Time period Type of Assessment agent Type of Grouping Weight elements assessment activity (%) Comp Opt Lecturer Self- Co- Indiv Group assess assess (#) Theoretical 10% mid-term exam

Theoretical 60% final exam

Practical work 30%

Working competencies and assessment of learning outcomes:

Learning outcomes

6. Bibliography and teaching resources

• Basic bibliography

Higgs, P. G. and Attwood, T. K. 2005. Bioinformatics and Molecular Evolution. Blackwell Publishing. Victoria.

Haddock, S. H. D. and Dunn, C. W. 2011. Practical computing for biologists. Sinauer Associates. Sunderland.

Knowles, L. L. and Kubatko, L. S. (eds.). 2010. Estimating Species Trees: Practical and Theoretical Aspects. Wiley-Blackwell. Hoboken.

Nielsen, R. and Slatkin, M. 2013. An Introduction to Population Genetics: Theory and Applications. Sinauer Associates. Sunderland.

Yang, Z. 2014. Molecular Evolution. A Statistical Approach. Oxford University Press. New York.

• Supplementary bibliography

Casillas, S. and Barbadilla, A. 2017. Molecular Population Genetics. FlyBook. Genetics 205: 1003-1035; DOI: https://doi.org/10.1534/genetics.116.196493

Posada, D. 2014. Reconstruction of Phylogenetic Trees. In Vargas, P. and Zardoya, R. (eds.). The tree of life (Chapter 54; pp: 651-661). Sinauer Associates.

Rozas, J. and Sánchez-Gracia, A. 2014. Nucleotide Variability Analysis and Intraespecific phylogenies. In Vargas, P. and Zardoya, R. (eds.). The tree of life (Chapter 55; pp: 663-673). Sinauer Associates.

• Teaching resources MKT and other concepts. https://www.youtube.com/watch?v=aQXjpVkE-s4

7. Methodology

Theoretical classes and practical lessons: Face-to-face

8. Scheduling activities

Each student will receive 40 hours of class: 20h theoretical 20h practical (computer lab) exercises (2 groups; 1 teacher/group)

Week Activity in the classroom Activity outside the classroom Grouping/type of activity Grouping/type of activity Week 1 T: 2 hours P: 2 hours

Week 2 T: 2 hours P: 2 hours

Week 3 T: 2 hours P: 2 hours

Week 4 T: 2 hours P: 2 hours

Week 5 T: 2 hours P: 2 hours

Week 6 T: 2 hours P: 2 hours

Week 7 T: 2 hours P: 2 hours

Week 8 T: 2 hours P: 2 hours

Week 9 T: 2 hours P: 2 hours

Week 10 T: 2 hours P: 2 hours

Week final exams Juny 27, 2018 (3h) July 18, 2018 (3h)

La còpia i/o plagi total o parcial als treballs i/o exàmens comportarà suspendre l'assignatura amb una qualificació de zero sense dret a recuperació, sense perjudici de l'aplicació de les altres sancions previstes al Reglament de Règim disciplinari dels estudiants de la Universitat Pompeu Fabra en funció de la gravetat de la infracció.

La copia y/o plagio total o parcial en los Trabajos y/o exámenes comportará suspender la asignatura con una calificación de cero sin derecho a recuperación, sin perjuicio de la aplicación de las otras sanciones previstas en el Reglamento de Régimen disciplinario de los estudiantes de la Universitat Pompeu Fabra en función de la gravedad de la infracción.

Total or partial copy and/or plagiarism will imply a failure in the subject with a final grade of zero points and no access to the make-up exam. According to the academic regulations specified in the Disciplinary rules for students of Universitat Pompeu Fabra, other additional sanctions may apply depending on the seriousness of the offence.