title: Analysis of case-control association studies with known risk variants.
abstract: BACKGROUND
  The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature.
  
  
  RESULTS
  We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants.
  
  
  BACKGROUND
  LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/.
authors.affiliation: Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. nzaitlen@hsph.harvard.edu
authors.forename: Noah
authors.initials: N
authors.lastname: Zaitlen
authors.forename: Bogdan
authors.initials: B
authors.lastname: Pasaniuc
authors.forename: Nick
authors.initials: N
authors.lastname: Patterson
authors.forename: Samuela
authors.initials: S
authors.lastname: Pollack
authors.forename: Benjamin
authors.initials: B
authors.lastname: Voight
authors.forename: Leif
authors.initials: L
authors.lastname: Groop
authors.forename: David
authors.initials: D
authors.lastname: Altshuler
authors.forename: Brian E
authors.initials: BE
authors.lastname: Henderson
authors.forename: Laurence N
authors.initials: LN
authors.lastname: Kolonel
authors.forename: Loic
authors.initials: L
authors.lastname: Le Marchand
authors.forename: Kevin
authors.initials: K
authors.lastname: Waters
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Haiman
authors.forename: Barbara E
authors.initials: BE
authors.lastname: Stranger
authors.forename: Emmanouil T
authors.initials: ET
authors.lastname: Dermitzakis
authors.forename: Peter
authors.initials: P
authors.lastname: Kraft
authors.forename: Alkes L
authors.initials: AL
authors.lastname: Price
mesh_terms: D016022:Case-Control Studies; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D015995:Prevalence; D012306:Risk; D012984:Software