Population Genetics and Molecular Evolution

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
UB;UAB
This course provides a comprehensive introduction to the fundamental principles governing the evolution of DNA and protein sequences, both within and among populations. It covers key topics such as genetic variation, linkage disequilibrium, the effects of evolutionary forces on genetic change, the neutral theory of molecular evolution, and the role of adaptation in population divergence and speciation.

The course also places particular emphasis on computational approaches to molecular evolution, including the main algorithms and bioinformatics tools used to analyze genes and genomes. Teaching combines in-person lectures with problem-solving sessions and hands-on computational exercises, complemented by short activities designed to reinforce key concepts and promote active learning.

Teachers

Person in charge

  • Alejandro Sánchez Gracia (elsanchez@ub.edu)
  • Julio Rozas Liras (jrozas@ub.edu)
  • Marta Puig Font (marta.puig@uab.cat)

Others

  • Albert Alegret Garcia (albert.alegret@upc.edu)
  • Sara Guirao Rico (sguirao@ub.edu)

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Competences

Learning Outcomes

Knowledge

  • K1 - Recognize the basic principles of biology, from cellular to organism scale, and how these are related to current knowledge in the fields of bioinformatics, data analysis, and machine learning; thus achieving an interdisciplinary vision with special emphasis on biomedical applications.
  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K3 - Identify the mathematical foundations, computational theories, algorithmic schemes and information organization principles applicable to the modeling of biological systems and to the efficient solution of bioinformatics problems through the design of computational tools.
  • K7 - Analyze the sources of scientific information, valid and reliable, to justify the state of the art of a bioinformatics problem and to be able to address its resolution.

    • Skills

  • S1 - Integrate omics and clinical data to gain a greater understanding and a better analysis of biological phenomena.
  • S2 - Computationally analyze DNA, RNA and protein sequences, including comparative genome analyses, using computation, mathematics and statistics as basic tools of bioinformatics.
  • S3 - Solve problems in the fields of molecular biology, genomics, medical research and population genetics by applying statistical and computational methods and mathematical models.
  • S5 - Disseminate information, ideas, problems and solutions from bioinformatics and computational biology to a general audience.
  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.

    • Competences

  • C2 - Identify the complexity of the economic and social phenomena typical of the welfare society and relate welfare to globalization, sustainability and climate change in order to use technique, technology, economy and sustainability in a balanced and compatible way.
  • C3 - Communicate orally and in writing with others in the English language about learning, thinking and decision making outcomes.
  • C4 - Work as a member of an interdisciplinary team, either as an additional member or performing managerial tasks, in order to contribute to the development of projects (including business or research) with pragmatism and a sense of responsibility and ethical principles, assuming commitments taking into account the available resources.

    • Objectives

    1. Acquire a foundational understanding of the evolution of biological sequences.
      Related competences: K1, S1, S3, S8, C2, C3, C4,
    2. Acquire practical skills in applying computational tools to analyze molecular population genetics and divergence data.
      Related competences: K3, K7, S2, S5, S7, C3,
    3. Gain a basic understanding of the theoretical, mathematical, and algorithmic principles involved in population genetics and molecular evolution.
      Related competences: K2, K3, S3, C3,

    Contents

    1. Genetic variation
      Types of genetic variation. Allele and genotype frequencies. Hardy-Weinberg equilibrium.
    2. Genetic drift and mutation
      Genetic drift. Mutation. Neutral genetic variation.
    3. Natural selection
      Basic model of natural selection. Fitness and selection coefficient. Balancing selection.
    4. Migration and population structure
      Continent-island model. Fixation indices.
    5. Extension of population genetics: molecular population genetics
      Measuring DNA polymorphism. Linkage disequilibrium. Genetic hitchhiking. Gene mapping. GWAS.
    6. Molecular clocks and the neutral theory of molecular evolution
      Theoretical basis and key concepts. Predicted consequences and examples from biological data
    7. Modelling sequence evolution
      Estimation of sequence divergence and evolutionary rates. Application of computational simulations in the study of molecular evolution. Backward- and forward-time simulations.
    8. Neutrality tests
      Neutrality tests based on sequence data: Tajima's D, HKA and MK tests
    9. Molecular adaptation and functional divergence
      Inferring natural selection from divergence data. Codon substitution models. Changes in amino acid substitution rates after gene duplication and speciation.

    Activities

    Activity Evaluation act





    Mid-term exam


    Objectives: 1
    Week: 9
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final exam


    Objectives: 1 3
    Week: 17
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    Classroom teaching will include a combination of theoretical lectures, interactive seminars, and practical sessions in the computer lab.

    Theoretical lectures will provide the core knowledge and key concepts of the course, offering students the opportunity to ask questions and engage in discussions to deepen their understanding.

    Seminars will focus on active learning, where students will analyze real research studies in greater depth.

    Practical sessions in the computer lab will offer hands-on experience in specialized software and tools to analyze data, run simulations, and apply concepts in real-research data scenarios.

    Evaluation methodology

    In order to successfully complete the course, the student must participate in all evaluated activities and obtain a final grade greater than 5/10.

    The final grade will be calculated as follows (maximum final grade is 10):

    4 points: Final exam
    4 point: Midterm exam
    2 points: Evaluation of practical sessions

    Re-evaluation Information:
    Students who do not reach a final grade of 5.0 (regardless of the grade obtained in the midterm or final examination), must take the re-evaluation exam.
    Only the theoretical part of the course can be retaken in this exam. Practical work and assignments will not be re-evaluated.

    If deemed appropriate by the teaching staff, an oral examination may be conducted to verify the authorship of any assessment activity.

    Bibliography

    Basic

    Complementary

    Web links