Statistical methods for genetics

Course objectives

Learning goals. The aim of this course is to provide the basic knowledge for a statistical approach to the analysis of genetic data. Students have to be able to formalize problems in the genetic field by using appropriate statistical tools, selecting the most convenient statistical models and interpreting the obtained results. Knowledge and understanding. After attending the course, students understand the problems related to the analysis of genetic data and know the most appropriate tools to deal with them. Applying knowledge and understanding. At the end of the course, students are able to use basic statistical models for the analysis of genetic data, also seen as multivariate data with complex structure. Students are able to interpret the results obtained by applying such models on real data. Making judgements. Students develop critical skills through the application and discussion of statistical tools aiming at the analysis of real genetic data, during the practical exercises in software laboratory. Communication skills Students, through practical exercises, applications on benchmark data close to real experimental situations, group activities, acquire a substantial technical-scientific ability to efficaciously communicate the obtained results. Learning skills. After attending the course, students have a broader knowledge of statistical models for analyzing genetic data, have learned son basic notions on the studies of this context, know how to apply the methods discussed, and critically comment the obtained results, through a massive use of the most known software in this field.

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FRANCESCA MARTELLA Lecturers' profile

Program - Frequency - Exams

Course program
The lectures are divided into the following thematic areas: Part 1 (about 18 hours) - Introduction to genetics - Basic methods for the statistical analysis of genetic data Part 2 (about 30 hours) - Clustering and dimensional reduction for genetic data - Latent variable models for genetic data - Application on real data
Prerequisites
To deal with the course content, a basic knowledge of matrix algebra, theory of statistical inference, linear regression model, mathematical analysis is required.
Books
- Lecture notes and scientific papers given by the Professor during the course - Statistics in Human Genetics. Pak Sham (1998) - Bioinformatics and Computational Biology solutions using R and Bioconductor. Gentleman et al. (2005) - Statistical Analysis of Next Generation Sequencing Data. Datta S. and Dan Nettleton (2014) - The fundamentals of Modern Statistical Genetics. Laird and Lange (2011)
Teaching mode
The frontal lectures (unless health emergencies) are organized by alternating theoretical lectures, use of software for applications on real data sets and working groups.
Frequency
Participation at the lectures is encouraged.
Exam mode
In order to pass the exam, students have to make - a lab exam to evaluate the ability to use the statistical tools for the analysis of genetic data; - an oral exam to evaluate the knowledge and understanding of the discussed methods; - a team work (planned with the Professor): critical presentation of a scientific paper given by the Professor regarding analyses of genetic data. If a student is not attending the frontal lectures, it is recommended to contact the Professor. Each part of the exam is worth 1/3 of the final vote.
Bibliography
- Gentleman R., Carey V.J., Huber W., Irizarry R.A, Dudoit S. Bioinformatics and Computational Biology Solutions Using R and Bioconductor, New York, Springer. - Laid N.M. and Lange C. (2011) The Fundamentals of Modern Statistical Genetics (Statistics for Biology and Health), New York, Springer. - G. McLachlan, D. Peel, (2000). Finite Mixture Models, Wiley Series in Probability and Statistics. - P. Sham (1998) Statistics in Human Genetics, Arnold, London. - A.C. Rencher, (2002). Methods of Multivariate Analysis, Wiley Series in Probability and Statistics; 2nd edition.
Lesson mode
The frontal lectures (unless health emergencies) are organized by alternating theoretical lectures, use of software for applications on real data sets and working groups.
  • Lesson code10589782
  • Academic year2024/2025
  • CourseStatistical Sciences
  • CurriculumBiostatistica
  • Year2nd year
  • Semester1st semester
  • SSDSECS-S/01
  • CFU6
  • Subject areaAttività formative affini o integrative