Genome-wide association studies for complex traits: consensus, uncertainty and challenges

Key Points Genome-wide association studies are systematic, well-powered surveys to explore the relationships between sites of common genome sequence variation and disease predisposition on a genome-wide scale. The capacity to undertake genome-wide association studies has resulted in spectacular advances in the understanding of the genetic basis of common phenotypes of biomedical importance, such as diabetes, asthma and some cancers. Application of this approach to large, well-characterized data sets has revealed over 50 disease-susceptibility loci and has provided valuable insights into the allelic architecture of multifactorial traits. The implementation of such studies requires meticulous attention to all stages of the experimental process, from the ascertainment of the samples through to analysis and interpretation of the findings. There is considerable potential for a wide variety of errors and biases to result in spurious associations if precautions are not taken. Extensive replication of positive findings remains the best guarantee against erroneous claims of association. The demand for large-scale replication is leading to extensive international collaborations between groups. Nonetheless, substantial challenges remain as researchers seek more complete descriptions of the susceptibility architecture of traits of interest, and to translate the information gathered into improvements in clinical management. Genome-wide association studies have led to an improved understanding of the genetic basis of common diseases. Following the first wave of such studies, this Review takes a critical look at progress so far and considers how future studies can be optimized. The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of