Aim: To apply a range of statistical methods involving the aggregtion of genetic variant data. We intend to undertake this with the aid f pathway analysis and weighing the analysis with prior biological information.

Hypothesis: Pathway analysis and prior biological information can improve the performance of analytical methods involving the aggrevation of rare genetic variants.

Exposure variables: We would like to carry out the project with as much data as possible as this will give us the maximum amount of statistical power for our analysis. Therefore we would like all available genotype data from the ALSPAC cohort for genetics vriants with MAFless than = 0.05 if possible.

Outcome variables: We would also like access to the phenotype data mentioned in Section 8. If possible we would also like to perform a hypothesis free analysis using a wide range of phenotypes. It would therefore be ideal to have direct access to the R drive which I've had experience with on a previous project.

Confounding variables: As we will be looking a the effects of aggregated genetics variant scores on individual phenotypes on interest we will not have to consider possible confounding phenotype variables.

Project Background:

Currently there are few analytical approche that provide the necessary power to efficiently analyse rare variant data (ie variants with MAFless than 0.05) and therefore there needs to be an emphasis on the development of new methods. Our aim is to conduct an overview of the existing methods which aggragate rare variant data together and then to apply more novels measures to develop improved methods with greater statistical power,. In order to achieve this we owuld like to request access to all the ALSPAC genotype data for genetic variants with a minor allele frequency of 0.05 or les. Although we have some phenotypes i particular that we are interested in, we would also like to be able to undertake a hypothesis free analysis of cardiovascular phenotypes if possible.