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Recovering Independent Associations in Genetics: A Comparison

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>08/2012
<mark>Journal</mark>Journal of Computational Biology
Issue number8
Number of pages10
Pages (from-to)978–987
Publication StatusPublished
<mark>Original language</mark>English


In genetics, it is often of interest to discover single nucleotide polymorphisms (SNPs) that are directly related to a disease, rather than just being associated with it. Few methods exist, however, for addressing this so-called “true sparsity recovery” issue. In a thorough simulation study, we show that for moderate or low correlation between predictors, lasso-based methods perform well at true sparsity recovery, despite not being specifically designed for this purpose. For large correlations, however, more specialized methods are needed. Stability selection and direct effect testing perform well in all situations, including when the correlation is large.