Figure 1

Association study approaches: Efficiency versus comprehensiveness. Studies vary in their efficiency (the a priori likelihood of a tested single nucleotide polymorphism [SNP] being associated with a disease), which has an impact on genotyping and multiple testing costs. Highly efficient designs (as defined by multiple testing costs) are shown on the right, with less efficient designs on the left. Studies also vary in comprehensiveness, both in terms of the allele frequency spectrum assessed (A) and the extent the region under study is assessed (B). Highly comprehensive studies extend from top to bottom. The efficiency (or comprehensiveness) for a specific study type relative to another in this figure is certainly not meant to be quantitative but merely indicative of the direction (bigger or smaller). This figure is applicable to large-scale studies of candidate genes, regions or the whole genome. Different functional SNP approaches are represented in blue, while non-functional approaches are represented in green. Re-sequencing is currently only feasible for examining one or a few candidate genes and is therefore not depicted. (A) Using linkage disequilibrium (LD) approaches, rare alleles are less likely to be tagged and hence the rare allele region is not covered. Since non-synonymous SNPs (nsSNPs) are assessed directly, association with rare alleles can be readily detected; however, this is limited by the availability of these SNPs. The light colour in the rare allele region is to indicate that coverage is dependent on SNP discovery. In this figure, we consider the most obvious functional SNPs, the nsSNPs. We presume the efficiency of the other functional categories may be significantly lower. (B) Typically, there is a trade-off between efficiency and comprehensiveness. One may limit the study to nsSNPs in order to have high efficiency at the cost of comprehensiveness. Further increase in the efficiency (and decrease in comprehensiveness) can be achieved by focusing only on nsSNPs predicted or known to have a functional consequence. Similarly, it has been proposed that a study utilising SNPs that tag the highest number of other SNPs (ie SNPs in high LD regions) would be more efficient (but less comprehensive) than a study aiming at LD coverage of the full genome [75].