title: The importance of distinguishing between the odds ratio and the incidence rate ratio in GWAS.
abstract: BACKGROUND
  In recent years, genome wide association studies have identified many genetic variants that are consistently associated with common complex diseases, but the amount of heritability explained by these risk alleles is still low. Part of the missing heritability may be due to genetic heterogeneity and small sample sizes, but non-optimal study designs in many genome wide association studies may also have contributed to the failure of identifying gene variants causing a predisposition to disease. The normally used odds ratio from a classical case-control study measures the association between genotype and being diseased. In comparison, under incidence density sampling, the incidence rate ratio measures the association between genotype and becoming diseased. We estimate the differences between the odds ratio and the incidence rate ratio under the presence of events precluding the disease of interest. Such events may arise due to pleiotropy and are known as competing events. In addition, we investigate how these differences impact the association test.
  
  
  METHODS
  We simulate life spans of individuals whose gene variants are subject to competing events. To estimate the association between genotype and disease, we applied classical case-control studies and incidence density sampling.
  
  
  RESULTS
  We find significant numerical differences between the odds ratio and the incidence rate ratio when the fact that gene variant may be associated with competing events, e.g. lifetime, is ignored. The only scenario showing little or no difference is an association with a rare disease and no other present associations. Furthermore, we find that p-values for association tests differed between the two study designs.
  
  
  CONCLUSIONS
  If the interest is on the aetiology of the disease, a design based on incidence density sampling provides the preferred interpretation of the estimate. Under a classical case-control design and in the presence of competing events, the change in p-values in the association test may lead to false positive findings and, more importantly, false negative findings. The ranking of the SNPs according to p-values may differ between the two study designs.
authors: National Center for Register-based Research, Department of Economics and Business Economics, Aarhus University, Fuglesangs allé 4 room K10, 8210, Aarhus V, Denmark. berit@econ.au.dk.|The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark. berit@econ.au.dk.|Bioinformatics Research Center, Aarhus University, C.F. Møllers Allé 8, 8000, Aarhus C, Denmark. berit@econ.au.dk.
authors: Berit Lindum
authors: BL
authors: Waltoft
authors: National Center for Register-based Research, Department of Economics and Business Economics, Aarhus University, Fuglesangs allé 4 room K10, 8210, Aarhus V, Denmark. cbp@econ.au.dk.|The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark. cbp@econ.au.dk.|The Centre for Integrated Register-based Research, Aarhus University, CIRRAU, Arhus University, Aarhus, Denmark. cbp@econ.au.dk.
authors: Carsten Bøcker
authors: CB
authors: Pedersen
authors: The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark. nyegaard@biomed.au.dk.|Department of Biomedicine, Aarhus University, Vennelyst Boulevard 4, 8000, Aarhus C, Denmark. nyegaard@biomed.au.dk.
authors: Mette
authors: M
authors: Nyegaard
authors: Bioinformatics Research Center, Aarhus University, C.F. Møllers Allé 8, 8000, Aarhus C, Denmark. asger@birc.au.dk.
authors: Asger
authors: A
authors: Hobolth
mesh_terms: D016022:Case-Control Studies; D003198:Computer Simulation; D015340:Epidemiologic Research Design; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data*; D015994:Incidence; D016015:Logistic Models; D016017:Odds Ratio