Molecular epidemiology

 
15
2016
Method course
Spεr
40
2016-09-14
English
Tove Fall and Marcel den Hoed
Dept of Medical Sciences
BMC, Navet, 4th floor (E10:4), Husargatan 3, Uppsala
Box 1115, 751 41 Uppsala, SWEDEN
October 31st - Nov 12th (2 weeks)
BMC, Uppsala
Two weeks of full-time studies
 

Molecular epidemiology aims to identify genetic and environmental risk factors at the molecular level to study the etiology, distribution and prevention of disease within families and across populations. The research field has arisen through the merging of molecular biology and epidemiology, and aims to improve understanding of the pathogenesis of disease by identifying genetic variants, metabolites, proteins and other molecules that influence the risk of developing disease.
This course is intended for PhD and master students interested in molecular epidemiology and clinical '-omics'. It aims to give the students a broad methodological background in methods used in molecular epidemiology at an elementary to intermediate level. It will cover a range of methods used in molecular epidemiology and clinical -omics, and will also give some hands-on training in performing such studies. Experience with basic epidemiological and genetic concepts is an advantage.

After successfully completing this course you as a student are expected to be able to:

• Describe the main techniques by which genetic and other -omics data are acquired from biological samples (S2)

• Explain the concepts, advantages and disadvantages of genome-wide association studies, and differentiate between the candidate gene, genome-wide association and next-generation sequencing approaches (S4)

• Plan, undertake and independently analyse a genome-wide association study, including quality control and data management of results (S4)

• Perform and interpret the results of a meta-analysis of genome-wide association studies (S3)

• Be familiar with and able to describe the use of some main tools that are usually incorporated in the dissemination of genome-wide association studies, beyond the actual main analyses (S2)

• Describe different approaches to transcriptomic and epigenetic studies with a focus on methylation (S2)

• Describe different approaches to proteomics and metabolomics (S2)

• Understand how to evaluate the potential usefulness of a clinical biomarker (S3)

• Explain how the Mendelian randomization design can be used in a causal context (S3)

Solo taxonomy
S1. Pre-structural - The task is not attacked appropriately; the student hasn’t really understood the point and uses too simple a way of going about it.
S2. Uni-structural - The student's response only focuses on one relevant aspect.
S3. Multi-structural - The student's response focuses on several relevant aspects but they are treated independently and additively. Assessment of this level is primarily quantitative.
S4. Relational - The different aspects have become integrated into a coherent whole. This level is what is normally meant by an adequate understanding of some topic.
S5. Extended abstract - The previous integrated whole may be conceptualised at a higher level of abstraction and generalised to a new topic or area.

The main topics covered in this course are:
• Genetic epidemiology with a focus on genomics of common diseases

• The use of biomarkers in epidemiological studies: analytical and clinical validity, clinical utility

• Clinical -omics: transcriptomics (RNA sequencing and microarray data), metabolomics and proteomics

• Mendelian randomization

• Practical training on genetic association studies with the aim that you as a student should be able to perform analyses for such studies independently

• Overview of useful bioinformatics tools and databases

The course will be designed using several kinds of teaching methods:

• Lectures: The main concepts of the curriculum will be discussed in regular lectures.

• Seminar: The purpose of the seminar is to review the course content covered in lectures based on discussion around a published paper distributed before the seminar.

• Computer labs: The exercises and hands-on work with statistical analyses will be performed in practical sessions on your own computer. Tutors will be available to answer questions.

• Lab visit: We will schedule a lab visit to the SciLifeLab SNP&SEQ platform for the students to see machines producing molecular data.

Written home examination.

A collection of scientific articles provided at the start of the course.

Tove Fall, Marcel den Hoed, and Stefan Gustafsson, plus additional tutors and teachers that are specialists in relevant fields.

Tove Fall (tove.fall@medsci.uu.se)
Marcel den Hoed (marcel.den_hoed@medsci.uu.se)

Please include your written motivation to participate in the course with your application. This motivation will be used to select participants if the number of applicants exceeds the number of available spaces. PhD students will be prioritized.

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