Selecting SNPs in two-stage analysis of disease association data: a model-free approach
Related Experiment Video
Updated: Oct 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
1Laboratory of Statistical Genetics, The Rockefeller University, New York, USA.
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.
Related Concept Videos
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Single Nucleotide Polymorphisms-SNPs
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Analysis of Population Pharmacokinetic Data
Pharmacogenomics: Identification of New Drug Targets