Email updates

Keep up to date with the latest news and content from BMC Genetics and BioMed Central.

This article is part of the supplement: Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors

Open Access Proceedings

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis

Alison P Klein1, Ilija Kovac1, Alexa JM Sorant1, Agnes Baffoe-Bonnie12, Betty Q Doan136, Grace Ibay1, Erica Lockwood1, Diptasri Mandal4, Lekshmi Santhosh1, Karen Weissbecker5, Jessica Woo1, April Zambelli-Weiner6, Jie Zhang3, Daniel Q Naiman7, James Malley8 and Joan E Bailey-Wilson1*

Author Affiliations

1 Inherited Disease Research Branch, NHGRI, NIH, Baltimore, Maryland, USA

2 Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA

3 CIDR, Johns Hopkins Medical School, Baltimore, Maryland, USA

4 Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA

5 Department of Psychiatry and Neurology and the Hayward Genetics Program, Tulane University, New Orleans, Louisiana, USA

6 Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA

7 Department of Mathematical Sciences, Johns Hopkins University, Baltimore, Maryland, USA

8 Center for Information Technology, NIH, Bethesda, Maryland, USA

For all author emails, please log on.

BMC Genetics 2003, 4(Suppl 1):S73  doi:10.1186/1471-2156-4-S1-S73

Published: 31 December 2003

Abstract

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.