title: 'Location, Location, Location': a spatial approach for rare variant analysis and an application to a study on non-syndromic cleft lip with or without cleft palate.
abstract: BACKGROUND
  For the analysis of rare variants in sequence data, numerous approaches have been suggested. Fixed and flexible threshold approaches collapse the rare variant information of a genomic region into a test statistic with reduced dimensionality. Alternatively, the rare variant information can be combined in statistical frameworks that are based on suitable regression models, machine learning, etc. Although the existing approaches provide powerful tests that can incorporate information on allele frequencies and prior biological knowledge, differences in the spatial clustering of rare variants between cases and controls cannot be incorporated. Based on the assumption that deleterious variants and protective variants cluster or occur in different parts of the genomic region of interest, we propose a testing strategy for rare variants that builds on spatial cluster methodology and that guides the identification of the biological relevant segments of the region. Our approach does not require any assumption about the directions of the genetic effects.
  
  
  RESULTS
  In simulation studies, we assess the power of the clustering approach and compare it with existing methodology. Our simulation results suggest that the clustering approach for rare variants is well powered, even in situations that are ideal for standard methods. The efficiency of our spatial clustering approach is not affected by the presence of rare variants that have opposite effect size directions. An application to a sequencing study for non-syndromic cleft lip with or without cleft palate (NSCL/P) demonstrates its practical relevance. The proposed testing strategy is applied to a genomic region on chromosome 15q13.3 that was implicated in NSCL/P etiology in a previous genome-wide association study, and its results are compared with standard approaches.
  
  
  BACKGROUND
  Source code and documentation for the implementation in R will be provided online. Currently, the R-implementation only supports genotype data. We currently are working on an extension for VCF files.
  
  
  BACKGROUND
  heide.fier@googlemail.com.
authors.affiliation: Department of Genomic Mathematics, University of Bonn, 53127, Germany. heide.fier@googlemail.com
authors.forename: Heide
authors.initials: H
authors.lastname: Fier
authors.forename: Sungho
authors.initials: S
authors.lastname: Won
authors.forename: Dmitry
authors.initials: D
authors.lastname: Prokopenko
authors.forename: Taofik
authors.initials: T
authors.lastname: AlChawa
authors.forename: Kerstin U
authors.initials: KU
authors.lastname: Ludwig
authors.forename: Rolf
authors.initials: R
authors.lastname: Fimmers
authors.forename: Edwin K
authors.initials: EK
authors.lastname: Silverman
authors.forename: Marcello
authors.initials: M
authors.lastname: Pagano
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Mangold
authors.forename: Christoph
authors.initials: C
authors.lastname: Lange
mesh_terms: D000483:Alleles; D001921:Brain / Q000002:abnormalities; D002884:Chromosomes, Human, Pair 15 / Q000235:genetics; D002971:Cleft Lip / Q000235:genetics*; D002972:Cleft Palate / Q000235:genetics*; D016000:Cluster Analysis; D003198:Computer Simulation; D005787:Gene Frequency; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans