A two-step multiple-marker strategy for genome-wide association studies

Abstract

Genome-wide association studies raise study-design and analytical issues that are still being debated. Among them, stands the issue of reducing the number of markers to be genotyped without loss of efficiency in identifying trait loci, which can reduce the cost of studies and minimize the multiple testing problem. With this aim, we proposed a two-step strategy based on two analytical methods suited to examine sets of markers rather than single markers: the local score, which screens the genome to select candidate regions in Step 1, and FBAT-LC, a multiple-marker family-based association test used to obtain significance levels of regions at step 2. The performance of this strategy was evaluated on all replicates of Genetic Analysis Workshop 15 Problem 3 simulated data, using the answers to that problem. Overall, seven of the nine generated trait loci were detected in at least 87% of the replicates using a framework designed to handle either association with the disease or association with the severity of disease. This multiple-marker strategy was compared to the single-marker approach. By considering regions instead of single markers, this strategy minimizes the multiple testing problem and the number of false-positive results.

Figure 1

Schematic representation of the local scores method. A, scores X_i (e.g., Pearson chi-squared statistics for case-control genotype frequencies) at marker locations along a chromosome. B, Scores ${X^{\prime }}_i$ at marker locations along a chromosome; ${X^{\prime }}_i$ = X_i - δ, δ being the value of statistic X_i at the 5% level.