Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.Many common human diseases and traits are known to cluster in families and are believed to be influenced by several genetic and environmental factors, but until recently the identification of genetic variants contributing to these 'complex diseases' has been slow and arduous 1 . Genome-wide association studies (GWAS), in which several hundred thousand to more than a million single nucleotide polymorphisms (SNPs) are assayed in thousands of individuals, represent a powerful new tool for investigating the genetic architecture of complex diseases 1, 2. In the past few years, these studies have identified hundreds of genetic variants associated with such conditions and have provided valuable insights into the complexities of their genetic architecture3 , 4.The genome-wide association (GWA) method represents an important advance compared to 'candidate gene' studies, in which sample sizes are generally smaller and the variants assayed are limited to a selected few, often on the basis of imperfect understanding of biological pathways and often yielding associations that are difficult to replicate 5,6. GWAS are also an important step beyond family-based linkage studies, in which inheritance patterns are related to several hundreds to thousands of genomic markers. Despite many clear successes in singlegene 'Mendelian' disorders7 , 8, the limited success of linkage studies in complex diseases has been attributed to their low power and resolution for variants of modest effect 9-11 .The underlying rationale for GWAS is the 'common disease, common variant' hypothesis, positing that common diseases are attributable in part to allelic variants present in more than 1-5% of the population12 -14. They have been facilitated by the development of commercial 'SNP chips' or arrays that capture most, although not all, common variation in the genome. Although the allelic architecture of some conditions, notably age-related macular degeneration, for the most part reflects the contributions of several variants of large effect (defined loosely here as those increasing disease risk by twofold or more), most common variants individually or in combination confer relatively small increments in risk (1.1-1.5-fold) and explain only a small proportion of heritability-the portion of phenotypic variance in a population attributable to additive ...

The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.

catalog ͉ evolution ͉ GWAS ͉ polymorphism ͉ disorders

The National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (GWAS) Catalog provides a publicly available manually curated collection of published GWAS assaying at least 100 000 single-nucleotide polymorphisms (SNPs) and all SNP-trait associations with P <1 × 10−5. The Catalog includes 1751 curated publications of 11 912 SNPs. In addition to the SNP-trait association data, the Catalog also publishes a quarterly diagram of all SNP-trait associations mapped to the SNPs’ chromosomal locations. The Catalog can be accessed via a tabular web interface, via a dynamic visualization on the human karyotype, as a downloadable tab-delimited file and as an OWL knowledge base. This article presents a number of recent improvements to the Catalog, including novel ways for users to interact with the Catalog and changes to the curation infrastructure.