Often, at the start of a new year, a journal editor tends to prelude on what this year might bring. In this case, however, I would like to reflect on the past year. Indeed, a major development in the field of Human Genetics has been is the arrival of the Genome-Wide Association Studies or GWAS. Many previous years have seen reports of GWAS, and a frequently heard complaint has been, with some exceptions, that the findings were met with irregular or downright controversial replication results. If anything, the year 2007 has been the year of the publication of a large amount of rapidly and widely replicated GWAS. Notably, this has taken place in the field of diabetes, with now over 20 loci identified in total. The largest harvest for 1 year reaped by type 2 diabetes with now in total 11 loci confidently identified, six of which last year by GWAS;1, 2, 3, 4, 5, 6, 7 type 1 coming in second,8, 9 with five loci last year on a total of 11 as well, and additional loci for prostate cancer10 and rheumatoid arthritis,11 the latter replicated in parallel by candidate gene analysis.12 And with bonuses coming along: dissection for body mass index has yielded the first obesity gene, FTO,2 the prostate cancer risk allele is protective for type 2 diabetes, and some type 2 diabetes risk genes when highly expressed, looks like predisposing for colon cancer when poorly expressed.6 These findings – and I count on correspondence telling us that we overlooked other examples – supports the concept of connectivity: our biology does not respect classical clinical discipline boundaries, and the key switches are rather involved in maintaining balance, with quite different pathology following dearth or excess. The main accelerator of these breakthroughs has been the development of the HapMap resource and the Perlegen SNP set, heirs of the early days SNP consortium. All in the wake of the Human Genome Project, resulting, within half a decade, in robust, very high-density SNP detection platforms of Affymetrix and Illumina. And as each of these is derived from the same HapMap set, tools have already been developed to jointly analyse large study samples typed by either platform by imputation, computing missing values by interpolation (eg, Servin and Stephens13).

Similar to positional cloning, GWAS do not need any a priori hypothesis of the underlying pathology. However, we are also at the entry of a new era: for the first time in the history of biomedicine, GWAS provide us with a powerful and accurate tool to tackle the complete genome for disease gene search entirely without segregation information.14 Another crowning event here is undoubtedly the development of the giga-sequencing machines of Roche/454, Solexa-Illumina and recently ABI. These tools allow one to ask and answer genome-wide, yet very refined, molecular questions, as shown recently for the probing of chromatin structure at a genome-wide scale.15, 16 For those of us who have had – or still have – difficult times with grant applications of a non-hypothesis-driven, prospective nature, as they were seen to be out on fishing expeditions, the advent of these exploratory high-throughput approaches is especially welcomed. Indeed, one might maliciously wonder if we are not (temporarily, in this field and pending subsequent functional studies) close to the ultimate consumption date of the Popperian approach of hypothesis-driven research. For was not a main goal of this to unravel the truth in the most efficient, that is, plausible way, faced with a daunting scarcity of collectible data? Well, if it becomes cheaper to just collect all data required than to run after a hundred consecutive, plausible, but wrong hypotheses, starting with a hypothesis becomes an economic futility. The hypothesis as a guiding principle is then replaced by a truism: if one does not throw away anything before thoroughly assessing its irrelevance, one will always find what one is looking for▪