Background: Currently, a commonly used strategy for mapping complex quantitative traits is to use a genome-wide linkage analysis to narrow suspected genes to regions on a scale of centiMorgans (cM), followed by an association analysis to fine map the genetic variation in regions showing linkage. Two important questions arise in the design and the resulting inference at the association stage of this sequential procedure: (1) how should we design an efficient association study given the information provided by the previous linkage study? and (2) can an association in a linkage region explain, in part, the detected linkage signal?
Results: We derive a quantitative linkage score (QLS) based on Haseman-Elston regression (Haseman and Elston 1972) and make use of this score to address both questions. In designing an association study, the selection of a subsample from the linkage study sample can be guided by the linkage information summarized in the QLS. When heterogeneity exists, we show that selection based on the QLS can increase the proportion of sample individuals from the subpopulation affected by a disease allele and therefore greatly improves the power of the association study. For the resulting inference, we frame as a hypothesis test the question of whether a linkage signal in a region can be in part explained by a marker allele. A simple one sided paired t-statistic is defined by comparing the two sets of QLSs obtained with/without modeling a marker association: a significant difference indicates that the marker can at least partly account for the detected linkage. We also show that this statistic can be used to detect a spurious association.
Conclusion: All our results suggest that a careful examination of QLSs should be helpful for understanding the results of both association and linkage studies.