Biostatistics
Code: 101965 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2500890 Genetics | FB | 2 | 1 |
Contact
- Name:
- Mauro Santos Maroño
- Email:
- Mauro.Santos@uab.cat
Use of Languages
- Principal working language:
- spanish (spa)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- No
- Some groups entirely in Spanish:
- Yes
Prerequisites
It is very convenient to have some basic mathematical knowledge for a good development of this subject.
Objectives and Contextualisation
Statistical tools are very important in the field of Biology. Nowadays, the development of Genetics and Genomics require great expertise in Statistics.
The objectives of the course are (1) to master the basic concepts of statistics; (2) to develop the ability to apply these concepts correctly, especially in the problems originated in the life sciences and Genetics; (3) to learn how to communicate effectively the results of a statistical analysis; and (4) to obtain basic skills with some statistical computing programs.
Competències
Apply the scientific method to problem solving. Apply the theoretical knowledge in practice. Know, apply and interpret the basic procedures of mathematical calculation, statistical analysis and basic computer applications, which is essential in genetics and genomics. Learn the basic principles of experimental design and interpretation of results. Design and interpret association studies between genetic polymorphisms and phenotypic characters for the identification of genetic variants that affect such characters, including associations to genetic pathologies and those that confer susceptibility to diseases. Have the ability to analyse and synthesize large amounts of data.
Resultats d'aprenentatge
1. Apply the scientific method to problem solving.
2. Apply theoretical knowledge to practice.
3. Describe the problems associated with multiple statistical comparisons.
4. Design experiments and interpret the results.
5. Prepare a report on the results of genetic research.
6. List the basic statistical principles of quantitative genetics.
7. Explain the logic of statistical reasoning and the importance of the randomization of causes.
8. Pose a genetic research problem.
9. Make decisions.
10. Reason critically.
11. Capacity for analysis and synthesis.
12. Use of statistical packages.
Competences
- Apply knowledge of theory to practice.
- Apply scientific method to problem solving.
- Be able to analyse and synthesise.
- Design and interpret studies associating genetic polymorphisms and phenotypical characters to identify genetic variants that affect the phenotype, including those associated to pathologies and those that confer susceptibility to human illnesses or those of other species of interest.
- Design experiments and interpret the results.
- Know, apply and interpret the basic procedures of mathematical calculation, statistical analysis and IT, the use of which is indispensable in genetics and genomics.
- Make decisions.
- Reason critically.
Learning Outcomes
- Apply knowledge of theory to practice.
- Apply scientific method to problem solving.
- Be able to analyse and synthesise.
- Describe the problems associated to multiple statistical comparisons.
- Design experiments and interpret the results.
- Enumerate the basic statistical principles of quantitative genetics.
- Explain the logic of statistical reasoning and the importance of the randomisation of causes.
- Make decisions.
- Pose a genetic research problem.
- Produce a report on the results of genetic research.
- Reason critically.
- Use statistical packages.
Content
Topic 1: Introduction. Experimental design and statistical inference. Sampling: Biological population, statistical population.
Topic 2: Hyphotesis testing. Elements of a statistical test.
Topic 3: Statistical analysis of one and two samples: Student's t test. Comparison of means. Paired data.
Topic 4: Analysis of variance. I. Model of fixed effects of a factor. Analysis of variance procedure. Tests a posteriori.
Topic 5: Analysis of variance. II. Fixed effects model for two or more factors.
Topic 6: Principles of experimental design. Experimental unit and treatment. Experimental variation (error) and its control. Repetitions. Statistical power and effect size.
Topic 7: Statistical analysis in regression.
Topic 8: Multiple regression.
Topic 9: Analysis of covariance.
Topic 10: Analysis of enumerative data.
Topic 11: Introduction to the design of experiments in genetic epidemiology: Methods of detection of genes involved in diseases: linkage and association studies.
Topic 12: Non-parametric statistics.
Topic 13: Introduction to Bayesian statistics.
“*Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents.”
Methodology
General introduction to the basic concepts of statistics to understand statistical reasoning and the proper use of statistical tools in the design and analysis of experiments.
Theory classes: The student acquires the scientific knowledge of the subject by attending theory classes.
Classes of problems: The knowledge acquired in the classes of theory are applied to the resolution of practical problems.
Classes of practices: Essential to understand and put into practice the knowledge acquired in theory. The concepts and methods studied are reviewed using statistical packages.
“*The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.”
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Clases de teoria | 30 | 1.2 | 4, 5, 6, 7, 9 |
| Seminarios de problemas | 11 | 0.44 | 2, 1, 5, 10, 9, 8, 11, 3 |
| Seminarios de pràcticas | 12 | 0.48 | 2, 1, 5, 10, 9, 8, 11, 3, 12 |
| Type: Supervised | |||
| Tutorias de grupo | 4 | 0.16 | 2, 1, 5, 10, 9 |
| Type: Autonomous | |||
| Elaboracion de trabajos | 20 | 0.8 | 2, 1, 10, 11, 3 |
| Horas de estudio | 60 | 2.4 | 2, 1, 4, 5, 10, 6, 7, 9, 8, 11, 3, 12 |
Assessment
Competences of this subject will be evaluated through continuous assessment, which includes written, practical exams and individual works. The evaluation system is organized into 3 modules, each which a specific weight in the final grade.
Theory
Evaluation through two partial tests. The first partial will have a weight of 30% and the second partial will have a weight of 40%. The final test is aimed at students who have not passed some of the partial tests and their weight in the final grade will be the same as that of each partial test.
Problems
This is carried out by performing short problems in class. The students have to solve and discuss the problems in front of their classmates. This section represents 15% of the final grade of the subject.
Practices
This is carried out through a practical test in the computer room in sessions of one hour for each group. In this practical session, the student must use the appropriate statistical program, enter the data of a study, propose an analysis of it, and answer specific questions. The weight of this test is 15% of the final grade for the subject.
Attendance to practical sessions is mandatory.
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 evaluationactivities is less than 67% of the final score.
“*Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.”
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Pruebas escritas. Primer parcial | 30% | 2 | 0.08 | 2, 4, 5, 6, 7, 9, 3 |
| Pruebas escritas. Segundo parcial | 40% | 3 | 0.12 | 2, 4, 5, 6, 7, 9, 3 |
| Pruebas prácticas | 15% | 1 | 0.04 | 8, 11, 12 |
| Seminarios de problemas | 15% | 7 | 0.28 | 1, 10, 11, 3 |
Bibliography
Books
- Alan Grafen, Rosie Hails. Modern statistics for the life sciences. Oxford University Press, 2002.
- Martínez-González MA, Sánchez-Villegas, Faulín Fajardos FJ. Bioestadística amigable. 2ª Edición. Ediciones Díaz de Santos, 2006.
- Robert R. Sokal, F. James Rohlf. Biometry: The principles and practice of statistics in biological research. W.H. Freeman and Company, New York. 2013.
- David C. Howell. Statistical Methods for Psychology, 8th ed. Wadsworth, Cengage Learning ALL. 2013.
- StatSoft Electronic Statistics Textbook (http://www.statsoft.com/Textbook)