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Selecting SNPs in two‐stage analysis of disease association data: a model‐free approach
Author(s) -
HOH J.,
WILLE A.,
ZEE R.,
CHENG S.,
REYNOLDS R.,
LINDPAINTNER K.,
OTT J.
Publication year - 2000
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1046/j.1469-1809.2000.6450413.x
Subject(s) - single nucleotide polymorphism , logistic regression , quantitative trait locus , selection (genetic algorithm) , biology , genetic association , genetics , linkage (software) , trait , genetic marker , computational biology , genotype , statistics , computer science , gene , artificial intelligence , mathematics , programming language
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.