Bioinformatics and Statistical Genetics

Credits
6
Types
  • MDS: Elective
  • MIRI: Elective
  • MEI: Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
EIO
Statistical Genetics and Epidemiology

Teachers

Person in charge

Others

Weekly hours

Theory
1
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
7

Competences

Transversal Competences

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

    • Third language

  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

    • Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.

    • Generic Technical Competences

    Generic

  • CG4 - Design and implement data science projects in specific domains and in an innovative way

    • Technical Competences

    Especifics

  • CE1 - Develop efficient algorithms based on the knowledge and understanding of the computational complexity theory and considering the main data structures within the scope of data science
  • CE2 - Apply the fundamentals of data management and processing to a data science problem
  • CE5 - Model, design, and implement complex data systems, including data visualization
  • CE6 - Design the Data Science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from both structured and unstructured data and potentially stored in heterogeneous formats.
  • CE9 - Apply appropriate methods for the analysis of non-traditional data formats, such as processes and graphs, within the scope of data science

    • Objectives

    1. Introduce the student to the algorithmic, computational, and statistical problems that arise in the analysis of biological data.
      Related competences: CB10, CB6, CB7, CT4, CT5, CE5, CE6, CE9, CG4,
    2. Reinforce the knowledge of discrete structures, algorithmic techniques, and statistical techniques that the student may have from previous courses.
      Related competences: CT5, CE1, CE2, CE9,

    Contents

    1. Introduction to statistical genetics
      Basic terminology, haplotype definition, SNP, STN, descriptive statistics
    2. Hardy-Weinberg equilibrium
      Hardy-Weinberg law. Hardy-Weinberg assumptions. Multiple alleles. Statistical tests for Hardy-Weinberg equilibrium: chi-square, exact and likelihood-ratio tests. Graphical representations. Disequilibrium coefficients: the inbreeding coefficient, Weir's D. R-package HardyWeinberg.
    3. Linkage disequilibrium and Phase estimation
      Definition of linkage disequilibrium (LD). Measures for LD. Estimation of LD by maximum likelihood. Haplotypes. The HapMap project. Graphics for LD. The LD heatmap. Phase ambiguity for double heterozygotes. Phase estimation with the EM algorithm. Estimation of haplotype frequencies. R-package haplo.stats.
    4. Population substructure
      Definition of population substructure. Population substructure and Hardy-Weinberg equilibrium. Population substructure and LD. Statistical methods for detecting substructure. Multidimensional scaling. Metric and non-metric multidimensional scaling. Euclidean distance matrices. Stress. Graphical representations.
    5. Family relationships and allele sharing
      Identity by state (IBS) and Identity by descent (IBD). Kinship coefficients. Allele sharing. Detection of family relationships. Graphical representations.
    6. Genetic association analysis
      Disease-marker association studies. Genetic models: dominant, co-dominant and recessive models. Testing models with chi-square tests. The alleles test and the Cochran-Armitage trend test. Genome-wide assocation tests.
    7. Introduction to Epidemiology
      To define epidemiology, understand its core principles, and appreciate its relevance in public health.
    8. Measures of Disease Frequency
      To understand and calculate various measures used to quantify disease occurrence in populations.
    9. Analytical Study Designs and Their Core Measures I
      To understand the major analytical study designs and the primary measures of association and effect derived from them.
    10. Analytical Study Designs and Their Core Measures II
      To understand the major analytical study designs and the primary measures of association and effect derived from them.
    11. Bias, Confounding, and Causality
      To understand potential threats to validity in epidemiological studies and the criteria for establishing causality.
    12. Introduction to Risk Assessment
      To define risk assessment, understand its framework, and appreciate its role in public health decision-making
    13. Applications and Future Directions
      To review practical applications of epidemiology and risk assessment and discuss emerging challenges

    Activities

    Activity Evaluation act


    Development of syllabus topics


    Objectives: 1 2
    Contents:
    Theory
    15h
    Problems
    0h
    Laboratory
    24h
    Guided learning
    0h
    Autonomous learning
    75h

    Final exam Epidemiology


    Objectives: 1 2
    Week: 18 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final exam Statistical Genetics


    Objectives: 1 2
    Week: 9 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    All classes consist of a theoretical session (a lecture in which the professor introduces new concepts or techniques and detailed examples illustrating them) followed by a practical session (in which the students work on the examples and exercises proposed in the lecture). On the average, two hours a week are dedicated to theory and one hour a week to practice, and the professor allocates them according to the subject matter. Students are required to take an active part in class and to submit the exercises at the end of each class.

    Evaluation methodology

    The assessment of the course is structured into two parts.

    In the first half of the lecture (Statistical Genetics), students are evaluated through continuous assessment, consisting of weekly exercises, and a mid-term exam. The grade for this part is composed of 30% from the continuous assessment and 70% from the mid-term exam.

    In the second half of the lecture (Epidemiology), students are evaluated through continuous in-class assessment and a final exam. The grade for this part is composed of 30% from the in-class assessment and 70% from the final exam.

    The final grade for the lecture is calculated as a weighted average of the two parts, with 50% corresponding to Statistical Genetics and 50% to Epidemiology. In order to pass the lecture, students must obtain a passing grade in both parts independently.

    Bibliography

    Basic

    Complementary

    Previous capacities

    Basic knowledge of algorithms and data structures.
    Basic knowledge of statistics.
    Basic knowledge of the Python programming language.
    Basic knowledge of the R programming language.