title: Genome-wide genetic heterogeneity discovery with categorical covariates.
abstract: BACKGROUND
  Genetic heterogeneity is the phenomenon that distinct genetic variants may give rise to the same phenotype. The recently introduced algorithm Fast Automatic Interval Search ( FAIS ) enables the genome-wide search of candidate regions for genetic heterogeneity in the form of any contiguous sequence of variants, and achieves high computational efficiency and statistical power. Although FAIS can test all possible genomic regions for association with a phenotype, a key limitation is its inability to correct for confounders such as gender or population structure, which may lead to numerous false-positive associations.
  
  
  RESULTS
  We propose FastCMH , a method that overcomes this problem by properly accounting for categorical confounders, while still retaining statistical power and computational efficiency. Experiments comparing FastCMH with FAIS and multiple kinds of burden tests on simulated data, as well as on human and Arabidopsis samples, demonstrate that FastCMH can drastically reduce genomic inflation and discover associations that are missed by standard burden tests.
  
  
  METHODS
  An R package fastcmh is available on CRAN and the source code can be found at: https://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/fastcmh.html.
  
  
  BACKGROUND
  felipe.llinares@bsse.ethz.ch.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Felipe
authors.initials: F
authors.lastname: Llinares-López
authors.affiliation: Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Laetitia
authors.initials: L
authors.lastname: Papaxanthos
authors.affiliation: Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Dean
authors.initials: D
authors.lastname: Bodenham
authors.affiliation: Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Damian
authors.initials: D
authors.lastname: Roqueiro
authors.affiliation: Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Karsten
authors.initials: K
authors.lastname: Borgwardt
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D005260:Female; D018740:Genetic Heterogeneity; D005828:Genetics, Population / Q000379:methods; D023281:Genomics / Q000379:methods*; D006801:Humans; D008297:Male; D012984:Software