Accurate and consistent phenotyping of cases and disease-free control participants is important to maximise study power and reduce the risk of misclassification bias in genetic association studies. In this analysis of UK Biobank data, the definition of self-report of gout or urate lowering therapy use detected the highest number of gout cases and had greatest precision for genetic association analysis. This study supports the use of the self-report of gout or urate lowering therapy use definition for use in epidemiological studies when more detailed gout-specific clinical data are not available. The main results are the output from a logistic regression using plink2 with the added column of GoutDef at the start which defined the gout definition used (see below) and the plink2 specification for the other columns can also be found here https://www.cog-genomics.org/plink2/formats under ".assoc.linear, .assoc.logistic (multi-covariate association analysis report)" BP is base position and NMISS is number of non-missing individuals included in analysis. A2, A1/A2, and INFO weren't generated. A1 is the minor allele. 15 genetic PCAs (f.22009) that were provided with the interim data release GoutDef is the definition of gout used and is described best in the paper (Cadzow_et_al_2017.pdf) but in summary is 6 different definitions (self reported, self reported or on self reported urate lowering therapy (ULT), on self reported ULT, the "Winnard" definition, Hospital diagnosis (HES I think), or any of the previous (all). ULT was based on a medical doctor going through the list of self reported drugs and indicating what drugs were relevant to gout (UKBio_drugs_ND_formatted.csv).
A genome-wide association study in gout: the NZ/Eurogout/US Consortium
Gout is a form of arthritis with the primary cause elevated levels of uric acid. In some but not all people the uric acid crystallises in the joints with gout resulting from a painful immune system response. Genetic causes of elevated uric acid are relatively well understood, however knowledge of the genetic factors controlling crystallisation of uric acid and subsequent immune response are extremely poorly understood. Therefore the aim of the proposed research is to identify genetic variants that influence the risk of gout, particularly those controlling crystallisation and immune response. UK Biobank has the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses. Gout is a serious illness. It affects up to 3% of adults in Westernised countries and can be a disabling form of arthritis in some people. It is also associated with other serious metabolic conditions such as diabetes and heart and kidney disease. With our aim to find out why some people get gout and others don't, the proposed purpose directly meets the UK Biobank's stated purpose. We will create a group of people with gout and a group without gout, matched by age and sex. Gout will be defined as those with doctor diagnosed gout, as determined from the medical records. We will then take the genome-wide genotype data and scan the genome for genetic variants that have a statistically significant difference between people with and without gout. These genetic variants will pinpoint genes involved in gout. To determine which genes are involved in crystallisation and immune response we will also compare to people with elevated uric acid but without gout. All people of European Caucasian ancestry with doctor-diagnosed gout from medical records (10-15,000). Two age- and sex-matched controls for every case (20-30,000). A separate group of asymptomatic hyperuricaemic controls (20-30,000). We would select the participants, therefore we request access to the entire dataset.
|Lead investigator:||Professor Tony Merriman|
|Lead institution:||University of Otago|
|1826||Performance of gout definitions for genetic epidemiological studies: analysis of UK Biobank||Cadzow et al||2017||Arthritis Research & Therapy 2017|