This article is part of the supplement: Genetic Analysis Workshop 16

Open Access Proceedings

Representation of genetic association via attributable familial relative risks in order to identify polymorphisms functionally relevant to rheumatoid arthritis

Justo Lorenzo Bermejo12*, Christine Fischer3, Anke Schulz4, Nadine Cremer4, Rebecca Hein4, Lars Beckmann4, Jenny Chang-Claude4 and Kari Hemminki25

Author affiliations

1 Institute of Medical Biometry and Informatics, University Hospital Heidelberg, INF 305, 69120 Heidelberg, Germany

2 Division of Molecular Genetic Epidemiology, German Cancer Research Center, INF 520, 69120 Heidelberg, Germany

3 Institute of Human Genetics, University of Heidelberg, INF 366, 69120 Heidelberg, Germany

4 Division of Cancer Epidemiology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany

5 Center for Family and Community Medicine, Karolinska Institute, 14183 Huddinge, Sweden

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Citation and License

BMC Proceedings 2009, 3(Suppl 7):S10  doi:

Published: 15 December 2009


The results from association studies are usually summarized by a measure of evidence of association (frequentist or Bayesian probability values) that does not directly reflect the impact of the detected signals on familial aggregation. This article investigates the possible advantage of a two-dimensional representation of genetic association in order to identify polymorphisms relevant to disease: a measure of evidence of association (the Bayes factor, BF) combined with the estimated contribution to familiality (the attributable sibling relative risk, λs). Simulation and data from the North American Rheumatoid Consortium (NARAC) were used to assess the possible benefit under several scenarios. Simulation indicated that the allele frequencies to reach the maximum BF and the maximum attributable λs diverged as the size of the genetic effect increased. The representation of BF versus attributable λs for selected regions of NARAC data revealed that SNPs involved in replicated associations clearly departed from the bulk of SNPs in these regions. In the 12 investigated regions, and particularly in the low-recombination major histocompatibility region, the ranking of SNPs according to BF differed from the ranking of SNPs according to attributable λs. The present results should be generalized using more extensive simulations and additional real data, but they suggest that a characterization of genetic association by both BF and attributable λs may result in an improved ranking of variants for further biological analyses.