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Unit information: Genomic Data Science in 2016/17

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Unit name Genomic Data Science
Unit code SSCM30005
Credit points 20
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Davis
Open unit status Not open
Pre-requisites

This is part of an intercalated BSc for Medical, Veterinary or Dental students

Co-requisites

None

School/department Bristol Medical School
Faculty Faculty of Health Sciences

Description

Understanding human genetics through essential statistical and computational skills that will equip you to interpret genomic data in the laboratory and clinic. Topics include genome-wide association and sequence analysis, genetic risk prediction, big data and bioinformatics, twin and family studies and practical genomics coding skills. We only assume knowledge of topics encountered in the first two years of a medical degree, and the teaching is specifically tailored to intercalating medical students.

Intended learning outcomes

After this component of the course, students will be able to:

1) perform association analyses to assess the correlation between genotype and phenotype

2) discuss approaches to assessing heritability, including twin studies and SNP-based heritability

3) predict genetic risk on the basis of genetic variants

4) critically evaluate approaches to the analysis of DNA sequences

interrogate genomic databases and understand the information retrieved

Teaching details

Methods of Teaching

This unit will be delivered in the form of workshops using a variety of methods including interactive lectures, presentations, debates, practical workshops and seminars. Links to e-learning resources and pre-course reading will be provided before the course to help students revise their previous foundational knowledge of statistics.

Contact Hours Per Week 2

Student Input

20 contact hours, 20 hours coursework, half of a 3 hour exam, 150 hours independent study

Assessment Details

This part of the course will be assessed as one third of written Paper 1 and through 2 coursework exercises during the unit and 1 oral presentation. Written paper 1 will be composed of both multiple choice questions and short answer questions and one third of this three-hour exam will be based on this unit. The two in-unit essays will follow the format of analysis reports which will be graded according to university guidelines and the oral presentation will be delivered as either a short seminar or debate. Overall, paper 1 will form 35% of the course mark, with 15% coming from the oral presentation for the short units.

Method of assessment: written 70% practical 30%

Reading and References

Lewin – Genes

Strachan and Read – Human Molecular Genetics – 4th Edition - 2010

HAPMAP (2005) A haplotype map of the human genome. Nature 437: 1299-1320.

HAPMAP (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851-861.

1000 genomes. A global reference for human genetic variation. Nature 526 68-74 2015

1000 genomes. An integrated map of structural variation in 2,504 human genomes Nature 526 75-81 2015

1000 genomes. An integrated map of genetic variation from 1,092 human genomes” Nature 491 56-65 2012

1000 genomes. A map of human genome variation from population-scale sequencing” Nature 467 1061-1073 2010

UK10K Consortium. The UK10K project identifies rare variants in health and disease. Nature. 2015 Oct 1;526(7571):82-90. doi: 10.1038/nature14962. Epub 2015 Sep 14.

Marchini J1, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010

Jul;11(7):499-511. doi: 10.1038/nrg2796.

Collins, Rory (2011): UK biobank: the need for large prospective epidemiological studies. In: Journal of Epidemiology and Community Health,65 (1), pp. A37, 2011.

Collins, Rory (2012): What makes UK Biobank special? In: The Lancet , 379(9822), pp. 1173 - 1174, 2012.

Tyler-Smith C, Yang H, Landweber LF, Dunham I, Knoppers BM, et al. (2015) Where Next for Genetics and Genomics? PLoS Biol 13(7): e1002216. doi: 10.1371/journal.pbio.1002216

Balding, D. J. (2006) A tutorial on statistical methods for population association studies. Nat Rev Genet 7, 781–791.

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