In complex diseases, multiple disease loci presumably interact to produce the trait. For this reason, even with high resolution SNP marker maps, it has been difficult to map susceptibility loci by conventional locus-by-locus methods. Fine mapping strategies are needed that allow for the simultaneous detection of multiple interacting disease loci while handling large numbers of densely spaced markers. For this purpose, we propose sum statistics as a first stage analysis to combine information over multiple markers via sums over single marker statistics. Marker sets with a significant joint genetic effect are selected for the second analysis stage. The sum statistic approach circumvents multiple testing that renders locus-by-locus approaches inefficient for many markers. Furthermore, it allows one to take information into account jointly over correlated markers and to detect interaction effects among disease loci. With these properties, sum statistics provide a powerful tool to reduce the large number of markers in a study to a small set of interesting markers. The further search for disease genes can then be restricted to these preselected markers.
展开▼
