Multilocus linkage analysis of affected sib pairs.
OBJECTIVE
The conventional affected sib pair methods evaluate the linkage information at a locus by considering only marginal information. We describe a multilocus linkage method that uses both the marginal information and information derived from the possible interactions among several disease loci, thereby increasing the significance of loci with modest effects.


METHODS
Our method is based on a statistic that quantifies the linkage information contained in a set of markers. By a marker selection-reduction process, we screen a set of polymorphisms and select a few that seem linked to disease.


RESULTS
We test our approach on genome scan data for inflammatory bowel disease (InfBD) and on simulated data. On real data we detect 6 of the 8 known InfBD loci; on simulated data we obtain improvements in power of up to 40% compared to a conventional single-locus method.


CONCLUSIONS
Our extensive simulations and the results on real data show that our method is in general more powerful than single-locus methods in detecting disease loci responsible for complex traits. A further advantage of our approach is that it can be extended to make use of both the linkage and the linkage disequilibrium between disease loci and nearby markers.
Ionita
Iuliana
I
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA. ionita@cs.nyu.edu
Lo
Shaw-Hwa
SH
D008040:Genetic Linkage / Q000235:genetics*; D005819:Genetic Markers; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D015212:Inflammatory Bowel Diseases / Q000235:genetics*; D008957:Models, Genetic; D040641:Quantitative Trait Loci / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods
D005819:Genetic Markers