Research Tools

Below are some of the tools that we have been developed within the group to aid the wider research community.

1) Recall by genotype study planner

The Recall by Genotype (RbG) study planner helps researchers estimate power and needed sample sizes for RbG studies.

Recall by Genotype (RbG) is a study design in which a sub-set of participants are recruited from an existing study on the basis of previously measured genotypic variation. Analysis of their biosamples or collection of new data is then undertaken. By exploiting the key properties of genetic variants that arise from the random allocation of alleles at conception (i.e, “Mendelian randomization (MR)”), RbG studies enhance the ability to make cause-and-effect inferences and avoid problems faced by observational studies such as confounding, reverse causation and various other biases that can generate spurious associations.

RbG studies have the potential to maximize the utility of large population-based studies where the collection of genetic data has become routine, but where detailed biological measurement is impractical and random sampling is inefficient. In contrast to other designs, recall (of samples, data or participants) on the basis of genotypic variation has the potential to yield manageable groups for precise measurement in any collection with genetic data.

2) MR Dictionary

The definitive list of terms for Mendelian randomization research: MR Dictionary

As Mendelian randomization (MR) becomes more commonplace in clinical guidelines and drug development, the MR Dictionary aims to provide useful definitions and descriptions for undertaking, understanding and interpreting MR studies to a wide, inter-disciplinary audience – both those new to MR and those who are experienced in its use but who want to remain up to date.

The MR Dictionary was borne from discussions with course and conference attendees, colleagues, and collaborators, all of whom were keen to have a publically available and easy-to-use platform that provided an overview of MR theory, methodology and interpretation.

3) metaboprep R package

The Metabolite quality control package metaboprep

  1. Reads in and process (un)targeted metabolite data from Metabolon and Nightingale platforms, saving datasets in tab-delimited format for use elsewhere
  2. Povides useful summary data in the form of tab-delmited text file and a PDF report.
  3. Performs QC on the data using a standardized pipeline and according to user-defined thresholds.

4) iPVs R package

The identification of principal variables R package iPVs aids in data reduction by:

  1. identifying representative (or independent) principal variables in an inter-correlated data set
  2. estimating the number of independent variables (Me) in your data set