title: Identifying disease-associated SNP clusters via contiguous outlier detection.
abstract: BACKGROUND
  Although genome-wide association studies (GWAS) have identified many disease-susceptibility single-nucleotide polymorphisms (SNPs), these findings can only explain a small portion of genetic contributions to complex diseases, which is known as the missing heritability. A possible explanation is that genetic variants with small effects have not been detected. The chance is < 8 that a causal SNP will be directly genotyped. The effects of its neighboring SNPs may be too weak to be detected due to the effect decay caused by imperfect linkage disequilibrium. Moreover, it is still challenging to detect a causal SNP with a small effect even if it has been directly genotyped.
  
  
  RESULTS
  In order to increase the statistical power when detecting disease-associated SNPs with relatively small effects, we propose a method using neighborhood information. Since the disease-associated SNPs account for only a small fraction of the entire SNP set, we formulate this problem as Contiguous Outlier DEtection (CODE), which is a discrete optimization problem. In our formulation, we cast the disease-associated SNPs as outliers and further impose a spatial continuity constraint for outlier detection. We show that this optimization can be solved exactly using graph cuts. We also employ the stability selection strategy to control the false positive results caused by imperfect parameter tuning. We demonstrate its advantage in simulations and real experiments. In particular, the newly identified SNP clusters are replicable in two independent datasets.
  
  
  BACKGROUND
  The software is available at: http://bioinformatics.ust.hk/CODE.zip.
  
  
  BACKGROUND
  eeyu@ust.hk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
authors.forename: Can
authors.initials: C
authors.lastname: Yang
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Zhou
authors.forename: Xiang
authors.initials: X
authors.lastname: Wan
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yang
authors.forename: Hong
authors.initials: H
authors.lastname: Xue
authors.forename: Weichuan
authors.initials: W
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004194:Disease / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012984:Software