From association to causality: the new frontier for complex traits

The sequencing of several genomes and the HapMap project, coupled to high-density genotyping technologies and new statistical tools, have made possible the systematic interrogation of the genetic basis of complex traits in humans [1,2]. Largely consistent with the community's expectations, the past two years have seen a logarithmically expanding number of genomic regions associated with a broad range of phenotypes (see [3] for an updated list). We now know of at least 16 potential susceptibility loci for type 2 diabetes in the human genome [4], and there are similar or better numbers for other diseases, including type 1 diabetes (24+ loci) and Crohn's disease (30+ loci; see [5,6] for examples). Similar progress has been made in the identification of genomic regions that influence quantitative traits, such as height [7], metabolite abundance [8] and drug response [9].These discoveries infuse us with optimism about the potential for understanding the key homeostatic pathways that are causally related to these disorders, which could lead to new drug development and better patient management. At the same time, these studies have presented us with some surprising findings. Most importantly, lost in the collective euphoria of apparently rapid progress is the fact that, some notable exceptions notwithstanding, we are now faced with three major challenges. Firstly, the total sum of disease risk attributed to common variants detected by genome-wide association studies (GWASs) remains modest, raising a problem analogous to the astrophysicists' search for 'dark matter'. Secondly, the overwhelming majority of single nucleotide polymorphisms (SNPs) associated with disease lie in intra- or intergenic regions; there has been a striking dearth of coding SNPs. Finally, almost none of the association studies have led to the identification of the causal allele(s), and thus neither the 'disease-associated' gene, nor indeed the type of lesion (gain-of-function, loss-of-function, and so