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.

journal_name

BMC Med Genet

journal_title

BMC medical genetics

authors

Waltoft BL,Pedersen CB,Nyegaard M,Hobolth A

doi

10.1186/s12881-015-0210-1

subject

Has Abstract

pub_date

2015-08-30 00:00:00

pages

71

issn

1471-2350

pii

10.1186/s12881-015-0210-1

journal_volume

16

pub_type

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