Abstract

We applied a penalized regression approach to single-nucleotide polymorphisms in regions on chromosomes 1, 6, and 9 of the North American Rheumatoid Arthritis Consortium data. Results were compared with a standard single-locus association test. Overall, the penalized regression approach did not appear to offer any advantage with respect to either detection or localization of disease-associated polymorphisms, compared with the single-locus approach.

Highlights

  • Penalized regression approaches are an attractive option for the analysis of large numbers of predictor variables that may influence a response variable

  • Regression methods allow the simultaneous inclusion of several different variables in the regression equation, e.g., variables coding for genotype rather than allele effects, or variables that encode effects at several different loci

  • Data We analyzed the North American Rheumatoid Arthritis Consortium (NARAC) data, consisting of 868 rheumatoid arthritis (RA) cases and 1194 controls genotyped at 545,080 single-nucleotide polymorphisms (SNPs) across 22 autosomal chromosomes

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Summary

Introduction

Penalized regression approaches are an attractive option for the analysis of large numbers of predictor variables (such as genotypes at many genetic loci) that may influence a response variable (such as disease status). Regression methods allow the simultaneous inclusion of several different variables, e.g., variables coding for genotype rather than allele effects ( modeling “dominance"), or variables that encode effects at several different loci. Standard linear regression can be formulated as finding the vector b of parameter estimates (regression coefficients) bj (j = 1,...,p) at p predictors that minimizes the ∑ ∑ n ⎛ p. Where, for person i, yi is a quantitative outcome variable and xij is a predictor variable (such as a genotype variable taking values 0, 1, or 2 according to the number of risk alleles at locus j). One minimizes this function subject to a constraint on the coefficients p

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