Genome wide linkage analysis has been an extremely successful method for mapping rare but highly penetrant genes in monogenic disorders.¹ Its applications to common diseases have achieved limited success, however, due to the individually low heritability of each contributing gene.² Alternatively, association based genetic studies, as described by Risch and Merikangas,³ can be powerful tools for identifying causal genes in common human diseases.^1,4 To date, these methods have been used to follow up on linkage regions and to test for candidate genes and therefore have been limited by the previous linkage analyses or by the assumptions made regarding disease pathogenesis. Both of these factors can greatly diminish our ability to detect all possible causal variants contributing to complex disease traits. Over the last decade, however, technology and genetic resources have evolved dramatically. With the completion of the human genome sequence, recent development of high throughput genotyping technologies and knowledge of the patterns of genetic variation, it is now possible to perform genome wide association studies for common human diseases. Such genome wide approaches, although comprehensive, still face the analytical challenges of identifying true causal disease alleles. The prospects of identifying potentially significant associations must therefore be tempered by the perils of inaccurately drawn conclusions. Specifically, the progresses made in this new frontier will rely critically on proper execution and interpretation of genetic studies that are able to: (1) detect true positive from false positives; (2) distinguish causal variation from that which is in linkage disequilibrium (LD) (that is, cosegregation or non-random association of nearby alleles within a population); and (3) explain gene-function, gene-gene, and genotype-phenotype relationships.

The challenges presented by these three goals are nicely …