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Unit information: Cracking Causality in 2016/17

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Unit name Cracking Causality
Unit code SSCM30009
Credit points 10
Level of study H/6
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Timpson
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

Using genetic data to discover the environmental causes of disease. Topics include Mendelian randomisation, twin and family designs for causal inference, and translation from genetics to new drugs and new policies.

Intended learning outcomes

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

1) critically evaluate the limitations of observational epidemiology

2) perform Mendelian Randomisation analyses to make causal inferences

3) discuss twin, family and cohort designs for inferring causality

4) discuss the gene-environment correlations and interactions

describe applications of causal analysis methods, and how they have led to new drugs and policies

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 and seminars.

Contact Hours Per Week 2

Student Input

10 contact hours, 5 hours coursework, half of a 3-hour exam, 75 hours independent study

Assessment Details

This part of the course will be assessed as half of written Paper 2 and through an oral presentation. Written paper 2 will be a three-hour exam composed of four essay questions (two on each short unit). In the same way that the 20-credit units are balanced by essay writing in the unit and short questions and multiple choice questions in the exam, the 10-credit units have less coursework and more long-form writing in the exam. The last requirement for assessment in the 10-credit units will be an oral presentation that will be delivered as either a short seminar or debate. Paper 2 will form 12% of the course mark, with 5% coming from the oral presentation for the short units.

Method of assessment: written 70%, practical 30%

Reading and References

Lewin – Genes XI - 2013

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

Davey Smith G, Ebrahim S (2004) Mendelian Randomisation: prospects, potentials and limitations. International Jounal of Epidemiology 33: 30-42.

Davey Smith G, Lawlor DA, Harbord R, Timpson N, Day I, Ebrahim S (2007) Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med 4: e352.

Freathy RM, Timpson NJ, Lawlor DA, Pouta A, Ben-Shlomo Y, Ruokonen A, Ebrahim S, Shields B, Zeggini E, Weedon MN, et al. (2008) Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes 57: 1419-1426.

Lawlor DA, Harbord RM, Sterne JAC, Timpson NJ, Davey Smith G (2008) Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine 27: 1133-1163.

Davey Smith G (2010) Mendelian randomization for strengthening causal inference in observational studies: application to gene by environment interaction. Perspectives

Davey Smith G (2011) Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in

population health research and practice. International Journal of Epidemiology 40: 537-562.

Nordestgaard BG, Palmer TM, Benn M, Zacho J, Tybjaerg-Hansen A, Davey Smith G, Timpson NJ (2012) The Effect of Elevated Body Mass Index on Ischemic Heart Disease Risk: Causal Estimates from a Mendelian Randomisation Approach. PLoS Med 9: e1001212.

Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, Davey Smith G, Sterne JAC (2012) Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res 21: 223-242.

Brion M-JA, Shakhbazov K, Visscher PM (2013) Calculating statistical power in Mendelian randomization studies. International Journal of Epidemiology 42: 1497-1501.

Evans DM, Brion MJA, Paternoster L, Kemp JP, McMahon G, Munafò M, Whitfield JB, Medland SE, Montgomery GW, Timpson NJ, et al. (2013) Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates. PLoS Genetics 9: e1003919.

Fall T, Hägg S, Mägi R, Ploner A, Fischer K, Horikoshi M, Sarin A-P, Thorleifsson G, Ladenvall C, Kals M, et al. (2013) The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis. PLoS Med 10: e1001474.

Palmer TM, Nordestgaard BG, Benn B, Tybjærg-Hansen A, Davey Smith G, Lawlor DA, Timpson NJ (2013) Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts. BMJ 347.

Pierce BL, Burgess S (2013) Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators. American Journal of Epidemiology 178: 1177-1184.

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