Statistical Methods in Genetic Epidemiology

The Centre for Biostatistics at The University of Manchester and the Royal Statistical Society Manchester Local Group are organising the following seminar, which all are welcome to attend.

Further details are at: http://www.statslife.org.uk/events/events-calendar/icalrepeat.detail/2013/10/16/53/-/statistical-methods-in-genetic-epidemiology

Date: Wednesday 16th October 2013

Time: 14.00-17.00

Venue: Manchester Dental Education Centre (MANDEC), Higher Cambridge Street, Manchester, M15 6FH

Theme: Statistical Methods in Genetic Epidemiology

Programme:

14:00-14:50: Dr Frank Dudbridge, London School of Hygiene and Tropical Medicine

“Power and predictive accuracy of polygenic risk scores”
Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that are not individually significant.  Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values.  Here I derive the statistical properties of the polygenic score from a quantitative genetics model in terms of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and method for weighting effect sizes in the score.  A novel approach to estimating the variance explained by a marker panel is also proposed.  I show that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size.  I also show that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available.

14:50-15:40: Professor John Thompson, University of Leicester

“Beyond Mendelian Randomization”
Mendelian randomization (MR) is a form of instrumental variable analysis that seeks to establish causal associations between exposures and outcomes using non-randomized data. In such an analysis, genes are used as the instruments and so the recent growth in data from genomewide association studies the technique has made MR very popular. However, MR requires the assumption that the chosen genes do not have a pleiotropic effect on the outcome and this assumption is rarely justified. We will argue that, because of the likelihood of pleiotropy, a MR does not provide convincing evidence of causality between exposure and outcome, but that it does still provide useful information about common causal pathways shared by the exposure and the outcome. We will suggest that genes have an important role in the search for causation within epidemiological data, but that MR is a very special case of this general analysis that is currently being over-used.

15:40-16:00: Refreshments

16:00-16:50: Dr Vincent Plagnol, University College London

“Bayesian test for co-localisation between pairs of genetic association studies using summary statistics”
Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases. The next challenge consists of understanding the molecular basis of these associations. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets. We demonstrate the value of this approach by applying the methodology to several cardiovascular and autoimmune association studies.

Registration: For refreshment purposes, to register for this free event please contact Wendy Lamb on wendy.j.lamb@manchester.ac.uk  or +44 (0)161 275 5764