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Q-Genie tool: Assessment of risk of bias for genetic association studies in meta-analyses

Completion of the human genome project along with rapid advances in genotyping technology has resulted in an increase in the number of published genetic association studies. In addition to the challenges common to classical epidemiological designs, genetic association studies face additional threats to validity. These include, among others, i) appropriately powered analyses as a vast majority of genotype-phenotype associations have modest effect sizes, ii) quality of genotyping, iii) batch related differences in genotyping, which can manifest as false associations if all cases are in one batch and controls are in the other, iv) choice of inheritance model, and v) genotype-phenotype relationships confounded by gene-gene and gene-environment interactions. Ultimately, inferences from genetic association studies require careful assessment of risk of bias.

The Q-Genie tool is an ongoing initiative by McMaster University. The goal of this project is to develop an instrument that facilitates an easy and convenient evaluation of risk of bias in genetic association studies to be used in systematic reviews.

The tool contains 11 items assessing the following dimensions: scientific basis for development of the research question, ascertainment of comparison groups (i.e. cases and controls), technical and non-technical classification of genetic variant tested, classification of the outcome, discussion of sources of bias, appropriateness of sample size, description of planned statistical analyses, statistical methods used, test of assumptions in the genetic studies (e.g. agreement with the Hardy Weinberg equilibrium), and appropriate interpretation of results.

The reliability and construct validity of the tool has been established based on a pilot study and critical review of the items (Sohani et al BMC Genetic 2015). The pilot study shows excellent performance characteristics. Further evaluation of the Q-Genie is currently under progress. A large-scale systematic review is being undertaken to assess its utility in identifying bias, improving precision, and reducing heterogeneity in meta-analyses.

Q-Genie team: Zahra Sohani, Sonia Anand, David Meyre, Shohinee Sarma, Russell de Souza, Sebastien Robiou-du-Pont, Aihua Li, Alexandra Mayhew, Fereshteh T. Yazdi, Hudson Reddon, Amel Lamri, Akram Alyass, Carolina Stryjecki, Adeola Ishola, Yvonne Lee, and Neeti Vashi

Contact details:

Dr. Zahra Sohani
Clinical Epidemiology & Biostatistics

McMaster University

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Research Links

  • Health Research Services (HRS): The home page for McMaster's Health Research Services office, who is able to help with grants and resource management, and administers ethics review for research here.
  • Research Funding Sources: A page of links gathered by the HRS for funding sources available to McMaster health researchers.
  • SIGMA Lab: The home page for Dr Joseph Beyene's Statistics for Integrative Genomics and Methods Advancement Laboratory.
  • David Meyre Lab: The home page for Dr David Meyre's laboratory for studying genetics and obesity.
  • MPower calculator: Dr Mateen Shaikh has created a tool to estimate power in a regression with some combination of binary and continuous variables.
  • Stearns lab: Dr Jennifer Stearns' microbiome lab for understanding healthy microbial colonization in infants.