Identifying pedigrees segregating at a major locus for a quantitative trait: an efficient strategy for linkage analysis.

Abstract

Having found evidence for segregation at a major locus for a quantitative trait, a logical next step is to identify those pedigrees in which major-locus segregation is occurring. If the quantitative trait is a risk factor for an associated disease, identifying such segregating pedigrees can be important in classifying families by etiology, in risk assessment, and in suggesting treatment modalities. Identifying segregating pedigrees can also be helpful in selecting pedigrees to include in a subsequent linkage study to map the major locus. Here, we describe a strategy to identify pedigrees segregating at a major locus for a quantitative trait. We apply this pedigree selection strategy to simulated data generated under a major-locus or mixed model with a rare dominant allele and sampled according to one of several fixed-structure or sequential sampling designs. We demonstrate that for the situations considered, the pedigree selection strategy is sensitive and specific and that a linkage study based only on the pedigrees classified as segregating extracts essentially all the linkage information in the entire sample of pedigrees. Our results suggest that for large-scale linkage studies involving many genetic markers, the savings from this strategy can be substantial and that, compared with fixed-structure sampling, sequential sampling of pedigrees can greatly improve the efficiency for linkage analysis of a quantitative trait.