Penalized regression methods offer an attractive alternative to single marker testing in
genetic association analysis. Penalized regression methods shrink down to zero the …

A central goal of medical genetics is to accurately predict complex disease from genotypes.
Here, we present a comprehensive analysis of simulated and real data using lasso and …

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a
disease or phenotype. They can be used to group participants into different risk categories …

Polygenic risk scores (PRS) combine many single-nucleotide polymorphisms into a score
reflecting the genetic risk of developing a disease. Privé, Aschard, and Blum present an …

Motivation: In ordinary regression, imposition of a lasso penalty makes continuous model
selection straightforward. Lasso penalized regression is particularly advantageous when the …

Motivation: This article extends our recent research on penalized estimation methods in
genome-wide association studies to the realm of rare variants. Results: The new strategy is …

ABSTRACT Sorted L-One Penalized Estimation (SLOPE; Bogdan et al.,) is a relatively new
convex optimization procedure, which allows for adaptive selection of regressors under …

Abstract We consider applying Bayesian Variable Selection Regression, or BVSR, to
genome-wide association studies and similar large-scale regression problems. Currently …

Penalized Multiple Regression (PMR) can be used to discover novel disease associations in
GWAS datasets. In practice, proposed PMR methods have not been able to identify well …

Recently, large scale genome‐wide association study (GWAS) meta‐analyses have boosted
the number of known signals for some traits into the tens and hundreds. Typically, however …