for Alzheimer's disease, schizophrenia and other psychiatric disorders; a dozen common forms of cancer; coronary artery disease, stroke atrial fibrillation and the other most common cardiovascular diseases; as well as traits and phenotypes ranging from drug response to cognition and hair and eye color. The company publishes its discoveries in peer-reviewed journals, and many, such as the TCF7L2 variants in type 2 diabetes, are used as standard risk markers in polygenic risk modeling and in research. A review of the GWAS era published in Nature Communications in 2019 quantified deCODE's outsized contribution to the field: Icelanders accounted for 12% of all participants in all published GWAS studies globally between 2007 and 2017, with each citizen participating on average to 19 published findings in that period alone. Stefansson, deCODE's research chief Unnur Thorsteinsdottir, and statistician Gudmar Thorleifsson were respectively ranked the first-, second- and sixth-highest impact GWAS authors in the world. Adding whole-genome sequencing (WGS) on top of its genotyping data gave a new dimension and power to deCODE's discovery capabilities. By definition, the common SNPs on standard genotyping chips yielded reliable risk markers but not a determinant foothold in the biology of complex diseases. Yet by running the company's growing number of directly sequenced whole genomes through the genotyping data and genealogies as a scaffold, the company's statisticians have been able to impute very high definition WGS on the entire population. The result has been the ability to conduct GWAS studies using from 20 to 50 million variants, and to systematically search for rare variants that either cause or confer very high risk of extreme versions of common phenotypes, and thereby pointing directly to putative drug targets. The value of this approach is best known from the model of PCSK9, in which the study of families with extremely high cholesterol levels and early-onset heart disease led to an understanding of the key role of this gene and the development of a new class of cholesterol-fighting drugs. deCODE now routinely searches for such rare variants across many phenotypes and the results have provided the basis of drug discovery and development programs. For example, since 2016 its important contributions in cardiovascular disease include demonstrating that it is non-HDL cholesterol rather than merely LDL levels that most accurately reflect risk of heart disease; finding variants in the ASGR1 gene that protect against coronary artery disease; and defining the role of lipoprotein (a) as