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Selecting SNPs in two-stage analysis of disease association data: a model-free approach

Published online by Cambridge University Press:  04 January 2001

J. HOH ,
A. WILLE ,
R. ZEE ,
S. CHENG ,
R. REYNOLDS ,
K. LINDPAINTNER  and
J. OTT
J. HOH
Affiliation:
Laboratory of Statistical Genetics, The Rockefeller University, 1230 York Avenue, New York, USA
A. WILLE
Affiliation:
Laboratory of Statistical Genetics, The Rockefeller University, 1230 York Avenue, New York, USA
R. ZEE
Affiliation:
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
S. CHENG
Affiliation:
Department of Human Genetics, Roche Molecular Systems, Inc., Alameda, CA, USA
R. REYNOLDS
Affiliation:
Department of Human Genetics, Roche Molecular Systems, Inc., Alameda, CA, USA
K. LINDPAINTNER
Affiliation:
Endocrine–Hypertension Division, Department of Medicine, Children's Hospital, Harvard Medical School, Boston, MA, USA
J. OTT
Affiliation:
Laboratory of Statistical Genetics, The Rockefeller University, 1230 York Avenue, New York, USA
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Abstract

For large numbers of marker loci in a genomic scan for disease loci, we propose a novel 2-stage approach for linkage or association analysis. The two stages are (1) selection of a subset of markers that are ‘important’ for the trait studied, and (2) modelling interactions among markers and between markers and trait. Here we focus on stage 1 and develop a selection method based on a 2-level nested bootstrap procedure. The method is applied to single nucleotide polymorphisms (SNPs) data in a cohort study of heart disease patients. Out of the 89 original SNPs the method selects 11 markers as being ‘important’. Conventional backward stepwise logistic regression on the 89 SNPs selects 7 markers, which are a subset of the 11 markers chosen by our method.

Type
Research Article
Information
Annals of Human Genetics , Volume 64 , Issue 5 , September 2000 , pp. 413 - 417
Copyright
© University College London 2000

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