Bioinformática y Genética Estadística

Créditos
6
Tipos
  • MDS: Optativa
  • MIRI: Optativa
  • MEI: Optativa
Requisitos
Esta asignatura no tiene requisitos , pero tiene capacidades previas
Departamento
EIO
Statistical Genetics and Epidemiology

Profesorado

Responsable

Otros

Horas semanales

Teoría
1
Problemas
0
Laboratorio
2
Aprendizaje dirigido
0
Aprendizaje autónomo
7

Competencias

Competencias Transversales

Uso solvente de los recursos de información

  • CT4 - Gestionar la adquisicion, la estructuracion, el analisis y la visualizacion de datos e informacion en el ambito de la especialidad y valorar de forma critica los resultados de esta gestion.

    • Lengua extranjera

  • CT5 - Conocer una tercera lengua, preferentemente el inglés, con un nivel adecuado oral y escrito y en consonancia con las necesidades que tendrán los titulados y tituladas.

    • Básicas

  • CB6 - Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios (o multidisciplinares) relacionados con su área de estudio.
  • CB7 - Que los estudiantes sean capaces de integrar conocimientos y enfrentarse a la complejidad de formular juicios a partir de una información que, siendo incompleta o limitada, incluya reflexiones sobre las responsabilidades sociales y éticas vinculadas a la aplicación de sus conocimientos y juicios.
  • CB10 - Poseer y comprender conocimientos que aporten una base u oportunidad de ser originales en el desarrollo y/o aplicación de ideas, a menudo en un contexto de investigación.

    • Competencias Técnicas Genéricas

    Genéricas

  • CG4 - Diseñar y poner en marcha proyectos de ciencia de datos en dominios específicos de forma innovadora

    • Competencias Técnicas

    Específicas

  • CE1 - Desarrollar algoritmos eficientes basados en el conocimiento y comprensión de la teoría de la complejidad computacional y las principales estructuras de datos dentro del ámbito de ciencia de datos
  • CE2 - Aplicar los fundamentos de la gestión y procesamiento de datos en un problema de ciencia de datos
  • CE5 - Modelar, diseñar e implementar sistemas complejos de datos, incluyendo la visualización de datos
  • CE6 - Diseñar el proceso de Ciencia de Datos y aplicar metodologías científicas para obtener conclusiones sobre poblaciones y tomar decisiones en consecuencia, a partir de datos estructurados o no estructurados y potencialmente almacenados en formatos heterogéneos.
  • CE9 - Aplicar métodos adecuados para el análisis de otro tipo de formatos, tales como procesos y grafos, dentro del ámbito de ciencia de datos

    • Objetivos

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

    Contenidos

    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

    Actividades

    Actividad Acto evaluativo


    Development of syllabus topics


    Objetivos: 1 2
    Contenidos:
    Teoría
    15h
    Problemas
    0h
    Laboratorio
    24h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    75h

    Final exam Epidemiology


    Objetivos: 1 2
    Semana: 18 (Fuera de horario lectivo)
    Teoría
    0h
    Problemas
    0h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    0h

    Final exam Statistical Genetics


    Objetivos: 1 2
    Semana: 9 (Fuera de horario lectivo)
    Teoría
    0h
    Problemas
    0h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    0h

    Metodología docente

    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.

    Método de evaluación

    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.

    Bibliografía

    Básico

    Complementario

    Capacidades previas

    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.