title: Identifying rare and common variants with Bayesian variable selection.
abstract: BACKGROUND
  Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Yet, the statistical aspect of testing disease association of rare variants is quite challenging as the typical assumptions fail to hold owing to low minor allele frequency (<0.5 or 1 %).
  
  
  METHODS
  I present a Bayesian variable selection approach to detect associations with both rare and common genetic variants for quantitative traits simultaneously. In my model, I frame the problem of identifying disease-associated variants as a problem of variable selection in a sparse space, that is, how best to model the relationship between phenotypes and a set of genetic variants. By constructing a risk index score for a group of rare variants, my method can effectively consider all variants in a multivariate model. I also use a within-chain permutation to generate the empirical thresholds to detect true-positive variants.
  
  
  RESULTS
  I apply our method to study the association between increases in baseline systolic and diastolic blood pressure (SBP and DBP, respectively) and genetic variants in the data from Genetic Analysis Workshop 19 unrelated samples. I identify several rare and common variants in the geneMAP4that are potentially associated with SBP and DBP.
  
  
  CONCLUSIONS
  The application shows that my method is powerful in identifying disease-associated variants even with the extreme rarity.
authors.affiliation: Biostatistics, Department of Population Health, New York University, New York, NY 10016 USA.
authors.forename: Cheongeun
authors.initials: C
authors.lastname: Oh