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

doi:10.1046/j.1469-1809.2000.6450413.x

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Wiley InterScience

Annals of Human Genetics

Volume 64 Issue 5, Pages 413 - 417

Published Online: 18 Feb 2003

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

J. HOH ¹ , A. WILLE ¹ , R. ZEE ² , S. CHENG ³ , R. REYNOLDS ³ , K. LINDPAINTNER ⁴ J. OTT ¹

¹ Laboratory of Statistical Genetics, The Rockefeller University, 1230 York Avenue, New York, USA ² Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA ³ Department of Human Genetics, Roche Molecular Systems, Inc., Alameda, CA, USA ⁴ Endocrine–Hypertension Division, Department of Medicine, Children's Hospital, Harvard Medical School, Boston, MA, USA

Correspondence to J. Ott. E-mail: ott@linkage.rockefeller.edu

These authors contributed equally to this work.

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.

Received: 27 April 00; Accepted: 07 July 00;

DIGITAL OBJECT IDENTIFIER (DOI)

10.1046/j.1469-1809.2000.6450413.x About DOI