This article is part of the supplement: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci

Proceedings

Density-based clustering in haplotype analysis for association mapping

Robert P Igo Jr¹ , Douglas Londono¹ , Katherine Miller² , Antonio R Parrado¹ , Shannon RE Quade¹ , Moumita Sinha³ , Sulgi Kim¹ , Sungho Won¹ , Jing Li⁴ and Katrina AB Goddard^1,5

¹Department of Epidemiology and Biostatistics, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road, Room 1300-C, Cleveland, Ohio 44106, USA

²Department of Epidemiology, Johns Hopkins School of Public Health, 615 North Wolfe Street, Baltimore, Maryland 21205, USA

³Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, Connecticut 06877, USA

⁴Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA

⁵Center for Health Research, Kaiser Permanente Northwest, 3800 N. Interstate Avenue, Portland, OR 97227, USA

author email corresponding author email

BMC Proceedings 2007, 1(Suppl 1):S27

Published:	18 December 2007

Abstract

Clustering of related haplotypes in haplotype-based association mapping has the potential to improve power by reducing the degrees of freedom without sacrificing important information about the underlying genetic structure. We have modified a generalized linear model approach for association analysis by incorporating a density-based clustering algorithm to reduce the number of coefficients in the model. Using the GAW 15 Problem 3 simulated data, we show that our novel method can substantially enhance power to detect association with the binary rheumatoid arthritis (RA) phenotype at the HLA-DRB1 locus on chromosome 6. In contrast, clustering did not appreciably improve performance at locus D, perhaps a consequence of a rare susceptibility allele and of the overwhelming effect of HLA-DRB1/locus C, 5 cM distal. Optimization of parameters governing the clustering algorithm identified a set of parameters that delivered nearly ideal performance in a variety of situations. The cluster-based score test was valid over a wide range of haplotype diversity, and was robust to severe departures from Hardy-Weinberg equilibrium encountered near HLA-DRB1 in RA case-control samples.