title: A Lasso multi-marker mixed model for association mapping with population structure correction.
abstract: BACKGROUND
  Exploring the genetic basis of heritable traits remains one of the central challenges in biomedical research. In traits with simple Mendelian architectures, single polymorphic loci explain a significant fraction of the phenotypic variability. However, many traits of interest seem to be subject to multifactorial control by groups of genetic loci. Accurate detection of such multivariate associations is non-trivial and often compromised by limited statistical power. At the same time, confounding influences, such as population structure, cause spurious association signals that result in false-positive findings.
  
  
  RESULTS
  We propose linear mixed models 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; it effectively controls for population structure and scales to genome-wide datasets. LMM-Lasso simultaneously discovers likely causal variants and allows for multi-marker-based phenotype prediction from genotype. We demonstrate the practical use of LMM-Lasso in genome-wide association studies in Arabidopsis thaliana and linkage mapping in mouse, where our method achieves significantly more accurate phenotype prediction for 91% of the considered phenotypes. At the same time, our model dissects the phenotypic variability into components that result from individual single nucleotide polymorphism effects and population structure. Enrichment of known candidate genes suggests that the individual associations retrieved by LMM-Lasso are likely to be genuine.
  
  
  BACKGROUND
  Code available under http://webdav.tuebingen. mpg.de/u/karsten/Forschung/research.html.
  
  
  BACKGROUND
  rakitsch@tuebingen.mpg.de, ippert@microsoft.com or stegle@ebi.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany. rakitsch@tuebingen.mpg.de
authors.forename: Barbara
authors.initials: B
authors.lastname: Rakitsch
authors.forename: Christoph
authors.initials: C
authors.lastname: Lippert
authors.forename: Oliver
authors.initials: O
authors.lastname: Stegle
authors.forename: Karsten
authors.initials: K
authors.lastname: Borgwardt
mesh_terms: D000818:Animals; D002874:Chromosome Mapping; D056426:Genetic Loci; D016678:Genome; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D016014:Linear Models; D051379:Mice; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D044382:Population Groups / Q000235:genetics