\magnification=1200
\baselineskip=20pt
\nopagenumbers
\font\big=cmr12 scaled \magstep2

\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
\bigskip

\baselineskip=12pt
\centerline{4:15 p.m., Tuesday, October 19, 2004}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

\bigskip
\baselineskip=15pt
\centerline{\sl David O. Siegmund}
\centerline{\sl Stanford University}
\bigskip
\centerline{\bf Genome Scans With Gene-Covariate Interaction}
\bigskip

Abstract: Genetic models for gene-covariate interaction are
described. Methods of linkage analysis are developed that utilize
special features of these models and the corresponding score
statistics are derived. Their power is compared with the score
statistics of simple genome scans that ignore these special features
(naive statistics).  Substantial gains in power are observed when the
gene-covariate interaction is strong. Both QTL mapping and affected
sibpair mapping are discussed. For the latter case, a simpler
statistic is proposed which has similar performance to the score
statistic, but does not require the estimation of nuisance
parameters. Since the nuisance parameters are not estimable solely by
affected sibpair data, this statistic is much easier to apply in
practice. Similarities with linkage analysis of models for
multivariate phenotypes and for longitudinal data are also briefly
discussed. Approximations for the genome wide p-value and power are
derived under the framework of local alternatives.

This is joint research with Jie Peng and Hsiu-Khuern Tang.
\bye