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 appea
r 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.
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a direct causal link or only due to a connecting causal path that contains an unobserved variable of low complexity, e.g., a binary var
iable. This problem is motivated by statistical genetics. Given a genetic marker that is correlated with a phenotype of interest, we want to detect whether this marker is causal or it only correlates with a causal one. Our method is based on the analysis of the location of the conditional distributions P(Y|x) in the simplex of all distributions of Y. We report encouraging results on semi-empirical data.
