Abstract
We compared family-based single-marker association analysis using Merlin and multi-marker analysis using LASSO (least absolute shrinkage and selection operator) for the low-density lipoprotein phenotype at the first visit for all 200 replicates of the Genetic Analysis Workshop 16 Framingham simulated data sets. Using "answers," we selected single-nucleotide polymorphisms (SNPs) on chromosome 22 for comparison of results between single-marker and multi-marker analyses. For the major causal SNP rs2294207 on chromosome 22, both single-marker and multi-marker analyses provided similar results, indicating the importance of this SNP. For the 12 polygenic SNPs on the same chromosome, both single-marker and multi-marker analyses failed to provide statistically significant associations, indicating that their effects were too weak to be detected by either method. The main difference between the two methods was that for the 14 SNPs near the causal SNPs, p-values from Merlin were the next smallest, whereas LASSO often excluded these non-causal neighboring SNPs entirely from the first 10,000 models.
Highlights
Association analysis is often performed using single markers or haplotype analysis of multiple single-nucleotide polymorphisms (SNPs) within adjoining short regions or candidate genes
BMC Proceedings 2009, 3(Suppl 7):S27 http://www.biomedcentral.com/1753-6561/3/S7/S27 this penalty induces shrinkage, prediction using least absolute shrinkage and selection operator (LASSO) is more reproducible than the regular multiple linear regression, in the case when there are more predictors than individuals
We evaluated the first 10,000 models in the LASSO solution path, using R package lars [7]
Summary
Association analysis is often performed using single markers or haplotype analysis of multiple single-nucleotide polymorphisms (SNPs) within adjoining short regions or candidate genes. Analysis that simultaneously uses multiple markers may be more powerful for detecting several causal genes and, may be more appropriate for complex diseases [1]. The least absolute shrinkage and selection operator (LASSO) is a penalized least squares method imposing the L1-penalty on the regression coefficients [2]. BMC Proceedings 2009, 3(Suppl 7):S27 http://www.biomedcentral.com/1753-6561/3/S7/S27 this penalty induces shrinkage, prediction using LASSO is more reproducible than the regular multiple linear regression, in the case when there are more predictors than individuals (small n large p). Compared with a regular multiple linear regression (ordinary least squares), LASSO can handle the multicollinearity resulting from the highly correlated markers. LASSO does both shrinkage and automatic variable selection simultaneously, a form of parsimonious model selection
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