Fast algorithms for fitting a Cox mixed-effects model for e.g., genome-wide association studies.
coxmeg is an R package for efficiently conducting genome-wide association analysis (GWAS) of age-at-onset traits using a Cox mixed-effects model. Compared to
coxmeg substantially improves the computational efficiency for estimating or testing genetic effects by using a variance component estimated once from a null model, and introducing fast algorithms, including inexact newton methods, preconditioned conjugate gradient methods and stochastic Lanczos quadrature. Moreover, compared to the
coxme package, which is optimized only for block-diagonal matrices,
coxmeg is efficient for both general sparse and dense relatedness matrices.
coxmeg can also handle positive semidefinite relatedness matrices, which are common in GWAS, but not supported in
- He L. and Kulminski A.M. (2019) Genome-wide association analysis of age-at-onset traits using Cox mixed-effects models. [ doi:10.1101/729285 ]