The SgenoLasso and its cousins for selective genotyping and extreme sampling: application to association studies and genomic selection. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Rabier, Charles-Elie; Delmas, Céline
• Is Part Of: Statistics. Volume 55:Issue 1(2021)
• Page Start: 18
• Page End: 44
• Publication Date: 2021-01-02
• Subjects: Gaussian process -- selective genotyping -- genomic selection -- high-dimensional linear model -- variable selection -- sparsity; Primary 60G15 -- 62F03 -- 62F05; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2021.1881785
• Languages: English
• ISSNs: 0233-1888

Abstract

ABSTRACT: We introduce a new variable selection method, called SgenoLasso, that handles extreme data. Our method relies on the construction of a specific statistical test, a transformation of the data and by the knowledge of the correlation between regressors. It is appropriate in genomics since once the genetic map has been built, the correlation is perfectly known. This new technique is inspired by stochastic processes arising from statistical genetics. We prove that the signal to noise ratio is largely increased by considering the extremes. Our approach and existing methods are compared on simulated and real data, and the results point to the validity of our approach.