title: Haplotype association analyses in resources of mixed structure using Monte Carlo testing.
abstract: BACKGROUND
  Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.
  
  
  RESULTS
  Our new phasing algorithm substantially outperformed the standard expectation-maximization algorithm that is ignorant of pedigree structure, and hence is preferable for resources that include pedigree structure. Through simulation we show that our Monte Carlo procedure maintains the correct type 1 error rates for all resource types. Power comparisons suggest that transmission-disequilibrium statistics are superior for performing association in resources of only nuclear families. For mixed structure resources, however, the newly implemented pseudocontrol approach appears to be the best choice. Results also indicated the value of large high-risk pedigrees for association analysis, which, in the simulations considered, were comparable in power to case-control resources of the same sample size.
  
  
  CONCLUSIONS
  We propose hapMC as a valuable new tool to perform haplotype association analyses, particularly for resources of mixed structure. The availability of meta-association and haplotype-mining modules in our suite of Monte Carlo haplotype procedures adds further value to the approach.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, USA. ryan.abo@hsc.utah.edu
authors.forename: Ryan
authors.initials: R
authors.lastname: Abo
authors.forename: Jathine
authors.initials: J
authors.lastname: Wong
authors.forename: Alun
authors.initials: A
authors.lastname: Thomas
authors.forename: Nicola J
authors.initials: NJ
authors.lastname: Camp
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D056726:Genetic Association Studies; D006239:Haplotypes; D006801:Humans; D009010:Monte Carlo Method; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D012984:Software