  Exploring the genetic basis of heritable traits remains one of the central
challenges in biomedical research. In simple cases, single polymorphic loci
explain a significant fraction of the phenotype variability. However, many
traits of interest appear to be subject to multifactorial control by groups of
genetic loci instead. Accurate detection of such multivariate associations is
nontrivial and often hindered by limited power. At the same time, confounding
influences such as population structure cause spurious association signals that
result in false positive findings if they are not accounted for in the model.
Here, we propose LMM-Lasso, a mixed model that allows for both, multi-locus
mapping and correction for confounding effects. Our approach is simple and free
of tuning parameters, effectively controls for population structure and scales
to genome-wide datasets. We show practical use in genome-wide association
studies and linkage mapping through retrospective analyses. In data from
Arabidopsis thaliana and mouse, our method is able to find a genetic cause for
significantly greater fractions of phenotype variation in 91% of the phenotypes
considered. At the same time, our model dissects this variability into
components that result from individual SNP effects and population structure. In
addition to this increase of genetic heritability, enrichment of known
candidate genes suggests that the associations retrieved by LMM-Lasso are more
likely to be genuine.
