  We consider resequencing studies of associated loci and the problem of
prioritizing sequence variants for functional follow-up. Working within the
multivariate linear regression framework helps us to account for correlation
across variants, and adopting a Bayesian approach naturally leads to posterior
probabilities that incorporate all information about the variants' function. We
describe two novel prior distributions that facilitate learning the role of
each variant by borrowing evidence across phenotypes and across mutations in
the same gene. We illustrate their potential advantages with simulations and
re-analyzing a dataset of sequencing variants.
