Selecting SNPs in two-stage analysis of disease association data: a model-free approach.
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.
Laboratory of Statistical Genetics, The Rockefeller University, New York, USA.
J
J
Hoh
A
A
Wille
R
R
Zee
S
S
Cheng
R
R
Reynolds
K
K
Lindpaintner
J
J
Ott
D000465:Algorithms; D002874:Chromosome Mapping; D015331:Cohort Studies; D005819:Genetic Markers; D005838:Genotype; D006331:Heart Diseases / Q000235:genetics*; D006801:Humans; D015810:Linkage Disequilibrium; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012044:Regression Analysis; D018570:Risk Assessment
D005819:Genetic Markers