  Analysis of high-dimensional data is currently a popular field of research,
thanks to many applications e.g. in genetics (DNA data in genomewide
association studies), spectrometry or web analysis. At the same time, the type
of problems that tend to arise in genetics can often be modelled using linear
mixed models in conjunction with high-dimensional data because linear mixed
models allow us to specify the covariance structure of the models. This enables
us to capture relationships in data such as the population structure, family
relatedness, etc. In this paper we introduce two new convex methods for
variable selection in high-dimensional linear mixed models which, thanks to
convexity, can handle many more variables than existing non-convex methods.
Both methods are compared with existing methods and in the end we suggest an
approach for a wider class of linear mixed models.
