title: From single-SNP to wide-locus: genome-wide association studies identifying functionally related genes and intragenic regions in small sample studies.
abstract: BACKGROUND
  Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts.
  
  
  METHODS
  Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (µStat) are not confounded by unrealistic assumptions (e.g., linearity, independence).
  
  
  RESULTS
  By incorporating knowledge about relationships between SNPs, µGWAS (GWAS based on µStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes.
  
  
  CONCLUSIONS
  With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development.
authors: Center for Clinical & Translational Science, The Rockefeller University, 1230 York Ave Box 322, New York, NY 10021, USA. kmw@rockefeller.edu
authors: Knut M
authors: KM
authors: Wittkowski
authors: Vikas
authors: V
authors: Sonakya
authors: Tingting
authors: T
authors: Song
authors: Martin P
authors: MP
authors: Seybold
authors: Mehdi
authors: M
authors: Keddache
authors: Martina
authors: M
authors: Durner
mesh_terms: D017326:Clinical Trials, Phase III as Topic; D004827:Epilepsy / Q000235:genetics*; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D018709:Statistics, Nonparametric; D018631:ras Proteins / Q000235:genetics
chemical_list: D018631:ras Proteins