Editing DeCODE genetics (section)

== Discoveries and scientific contributions ==
Genome research in general, and deCODE's global reputation as a discovery organization, took off with the arrival of SNP genotyping chips in the mid-2000s.<ref>{{Cite journal |last=LaFramboise |first=T. |date=2009-07-01 |title=Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances |url=https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkp552 |journal=Nucleic Acids Research |language=en |volume=37 |issue=13 |pages=4181–4193 |doi=10.1093/nar/gkp552 |issn=0305-1048 |pmc=2715261 |pmid=19570852}}</ref> These tools set off a worldwide boom in genome-wide association studies ([[Genome-wide association study|GWAS]]), in which the entire genome is scanned to identify SNPs that those with a given disease tend to have one version of, while unaffected individuals tend to have another. In common diseases, as with many traits or phenotypes such as drug response, the difference is not one of causal certainty but of statistical odds representing increased or decreased risk versus the population average. The ability to conduct large studies and analyze the resulting data - from thousands of patients with a disease and many times more control subjects, ideally unaffected relatives - is therefore at a premium.<ref>{{Cite journal |last=MacArthur |first=Jacqueline |last2=Bowler |first2=Emily |last3=Cerezo |first3=Maria |last4=Gil |first4=Laurent |last5=Hall |first5=Peggy |last6=Hastings |first6=Emma |last7=Junkins |first7=Heather |last8=McMahon |first8=Aoife |last9=Milano |first9=Annalisa |last10=Morales |first10=Joannella |last11=Pendlington |first11=Zoe May |last12=Welter |first12=Danielle |last13=Burdett |first13=Tony |last14=Hindorff |first14=Lucia |last15=Flicek |first15=Paul |date=2017-01-04 |title=The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog) |url=https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw1133 |journal=Nucleic Acids Research |language=en |volume=45 |issue=D1 |pages=D896–D901 |doi=10.1093/nar/gkw1133 |issn=0305-1048 |pmc=5210590 |pmid=27899670}}</ref>

deCODE's vast collection of DNA, medical and genealogical data that could be mined together - and enriched through repeated querying and imputation - was almost perfectly suited to this type of study. Since 2003, the company has discovered and published hundreds of variants linked to susceptibility to scores of diseases and conditions, including major ongoing contributions to understanding inherited risk 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.<ref>List of [https://www.decode.com/publications/ hundreds of the company's main publications] on the publications page of its website.</ref> 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.<ref>{{Cite journal |last=Srinivasan |first=Shylaja |last2=Kaur |first2=Varinderpal |last3=Chamarthi |first3=Bindu |last4=Littleton |first4=Katherine R. |last5=Chen |first5=Ling |last6=Manning |first6=Alisa K. |last7=Merino |first7=Jordi |last8=Thomas |first8=Melissa K. |last9=Hudson |first9=Margo |last10=Goldfine |first10=Allison |last11=Florez |first11=Jose C. |date=2018-03-01 |title=TCF7L2 Genetic Variation Augments Incretin Resistance and Influences Response to a Sulfonylurea and Metformin: The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) |url=https://diabetesjournals.org/care/article/41/3/554/36607/TCF7L2-Genetic-Variation-Augments-Incretin |journal=Diabetes Care |language=en |volume=41 |issue=3 |pages=554–561 |doi=10.2337/dc17-1386 |issn=0149-5992 |pmc=5829963 |pmid=29326107}}</ref>

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.<ref>Table 2 in MC Mills and CA Rahal, "A scientometric review of genome-wide association studies," ''[https://www.nature.com/articles/s42003-018-0261-x/tables/2 Nature Communications Biology]'', vol 2, number 9 (2019)</ref> 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.<ref>Table 4, Mills and Rahal, ''[https://www.nature.com/articles/s42003-018-0261-x/tables/4 op. cit]''</ref>

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.<ref>An early overview and 20 million SNPs were put in the public domain in DF Gudbjartsson ''et al.'', "Sequence variants from whole genome sequencing a large group of Icelanders," [https://www.nature.com/articles/sdata201511 ''Nature Scientific Data''], vol 2, art 150011 (March 2015); dozens of subsequent papers using this scale of data are on the [https://www.decode.com/publications/ publications page of deCODE's website] from 2015 onward</ref>

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.<ref>See [https://www.decode.com/publications/ deCODE publications from 2014-present]</ref> 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;<ref>{{Cite journal |last=Helgadottir |first=Anna |last2=Gretarsdottir |first2=Solveig |last3=Thorleifsson |first3=Gudmar |last4=Hjartarson |first4=Eirikur |last5=Sigurdsson |first5=Asgeir |last6=Magnusdottir |first6=Audur |last7=Jonasdottir |first7=Aslaug |last8=Kristjansson |first8=Helgi |last9=Sulem |first9=Patrick |last10=Oddsson |first10=Asmundur |last11=Sveinbjornsson |first11=Gardar |last12=Steinthorsdottir |first12=Valgerdur |last13=Rafnar |first13=Thorunn |last14=Masson |first14=Gisli |last15=Jonsdottir |first15=Ingileif |date=June 2016 |title=Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease |url=https://www.nature.com/articles/ng.3561 |journal=Nature Genetics |language=en |volume=48 |issue=6 |pages=634–639 |doi=10.1038/ng.3561 |issn=1061-4036 |pmc=9136713 |pmid=27135400}}</ref> finding variants in the ASGR1 gene that protect against coronary artery disease;<ref>P Nioi ''et al.'', "Variant ''ASGR1'' Associated with a Reduced Risk of Coronary Artery Disease," ''[[Doi/10.1056/NEJMoa1508419?url|New England Journal of Medicine]]'', vol 374, pp 2131-2141 (June 2016)</ref> and defining the role of lipoprotein (a) as a major risk factor for heart attack.<ref>{{Cite journal |last=Gudbjartsson |first=Daniel F. |last2=Thorgeirsson |first2=Gudmundur |last3=Sulem |first3=Patrick |last4=Helgadottir |first4=Anna |last5=Gylfason |first5=Arnaldur |last6=Saemundsdottir |first6=Jona |last7=Bjornsson |first7=Eythor |last8=Norddahl |first8=Gudmundur L. |last9=Jonasdottir |first9=Aslaug |last10=Jonasdottir |first10=Adalbjorg |last11=Eggertsson |first11=Hannes P. |last12=Gretarsdottir |first12=Solveig |last13=Thorleifsson |first13=Gudmar |last14=Indridason |first14=Olafur S. |last15=Palsson |first15=Runolfur |date=December 2019 |title=Lipoprotein(a) Concentration and Risks of Cardiovascular Disease and Diabetes |url=https://linkinghub.elsevier.com/retrieve/pii/S0735109719380386 |journal=Journal of the American College of Cardiology |language=en |volume=74 |issue=24 |pages=2982–2994 |doi=10.1016/j.jacc.2019.10.019|doi-access=free }}</ref>

As all deCODE's data sits on its servers and can be queried simultaneously, it can also be queried with remarkable speed. In 2014, a group from the Broad Institute stopped by at deCODE on its way back from Finland, where through a major research effort they had found a variant that protected carriers against type 2 diabetes. Over coffee, the deCODE team confirmed that the Finnish variant did not exist in Iceland, but that another did.<ref>{{Cite news |last=Kolata |first=Gina |date=2014-03-02 |title=Rare Mutation Kills Off Gene Responsible for Diabetes |url=https://www.nytimes.com/2014/03/03/health/rare-gene-protects-against-type-2-diabetes-even-in-obese-people.html |access-date=2025-04-28 |work=The New York Times |language=en-US |issn=0362-4331}}</ref> The Broad group added it to the paper announcing the discovery.<ref>{{Cite journal |last=Go-T2D Consortium |last2=T2D-GENES Consortium |last3=Flannick |first3=Jason |last4=Thorleifsson |first4=Gudmar |last5=Beer |first5=Nicola L |last6=Jacobs |first6=Suzanne B R |last7=Grarup |first7=Niels |last8=Burtt |first8=Noël P |last9=Mahajan |first9=Anubha |last10=Fuchsberger |first10=Christian |last11=Atzmon |first11=Gil |last12=Benediktsson |first12=Rafn |last13=Blangero |first13=John |last14=Bowden |first14=Don W |last15=Brandslund |first15=Ivan |date=April 2014 |title=Loss-of-function mutations in SLC30A8 protect against type 2 diabetes |url=https://www.nature.com/articles/ng.2915 |journal=Nature Genetics |language=en |volume=46 |issue=4 |pages=357–363 |doi=10.1038/ng.2915 |issn=1061-4036 |pmc=4051628 |pmid=24584071}}</ref>

Because of its singular population resources and the questions its scientists can ask and answer, many of deCODE's most remarkable findings have been in basic science. One notable focus has been on elucidating how variation in the sequence of the genome is generated. Following its microsatellite-based genetic map of the genome in 2002, the company created and made available to the scientific community two more: one in 2010 built on 300,000 SNPs,<ref>{{Cite journal |last=Kong |first=Augustine |last2=Thorleifsson |first2=Gudmar |last3=Gudbjartsson |first3=Daniel F. |last4=Masson |first4=Gisli |last5=Sigurdsson |first5=Asgeir |last6=Jonasdottir |first6=Aslaug |last7=Walters |first7=G. Bragi |last8=Jonasdottir |first8=Adalbjorg |last9=Gylfason |first9=Arnaldur |last10=Kristinsson |first10=Kari Th. |last11=Gudjonsson |first11=Sigurjon A. |last12=Frigge |first12=Michael L. |last13=Helgason |first13=Agnar |last14=Thorsteinsdottir |first14=Unnur |last15=Stefansson |first15=Kari |date=October 2010 |title=Fine-scale recombination rate differences between sexes, populations and individuals |url=https://www.nature.com/articles/nature09525 |journal=Nature |language=en |volume=467 |issue=7319 |pages=1099–1103 |doi=10.1038/nature09525 |issn=0028-0836|url-access=subscription }}</ref> and another in 2019 built on WGS data.<ref>{{Cite journal |last=Halldorsson |first=Bjarni V. |last2=Palsson |first2=Gunnar |last3=Stefansson |first3=Olafur A. |last4=Jonsson |first4=Hakon |last5=Hardarson |first5=Marteinn T. |last6=Eggertsson |first6=Hannes P. |last7=Gunnarsson |first7=Bjarni |last8=Oddsson |first8=Asmundur |last9=Halldorsson |first9=Gisli H. |last10=Zink |first10=Florian |last11=Gudjonsson |first11=Sigurjon A. |last12=Frigge |first12=Michael L. |last13=Thorleifsson |first13=Gudmar |last14=Sigurdsson |first14=Asgeir |last15=Stacey |first15=Simon N. |date=2019-01-25 |title=Characterizing mutagenic effects of recombination through a sequence-level genetic map |url=https://www.science.org/doi/10.1126/science.aau1043 |journal=Science |language=en |volume=363 |issue=6425 |doi=10.1126/science.aau1043 |issn=0036-8075|url-access=subscription }}</ref> Recombination - the reshuffling of chromosomes that takes place in the making of eggs and sperm - is a primary mechanism for generating diversity and to build these maps. Over fifteen years deCODE has published a series of breakthrough papers detailing in a real human population how recombination rate varies according to sex, age and other characteristics, and how these differences impact the generation of genomic diversity and variation of many kinds. The general picture that has emerged is that the genome is generating diversity but within certain bounds, providing a dynamic but generally stable substrate for natural selection and evolution.<ref>Roger Highfield, "How humans evolve," ''[https://blog.sciencemuseum.org.uk/how-humans-evolve/ UK Science Museum blog]'', 24 January 2019</ref>

To understand the population that it is working in and to address broader questions few can in the same way, deCODE has also from its early days had its own genetic anthropology group. It has published pioneering work on mitochondrial and Y-chromosome mutation to trace the Norwegian and Celtic mix in the early population; sequenced ancient DNA from the settlement period; compared ancient and modern Icelandic genomes to see how [[genetic drift]], epidemics and natural disasters have yielded a modern-day population genetically distinct from its forebears and source populations.<ref>{{Cite journal |last=Ebenesersdóttir |first=S. Sunna |last2=Sandoval-Velasco |first2=Marcela |last3=Gunnarsdóttir |first3=Ellen D. |last4=Jagadeesan |first4=Anuradha |last5=Guðmundsdóttir |first5=Valdís B. |last6=Thordardóttir |first6=Elísabet L. |last7=Einarsdóttir |first7=Margrét S. |last8=Moore |first8=Kristjan H. S. |last9=Sigurðsson |first9=Ásgeir |last10=Magnúsdóttir |first10=Droplaug N. |last11=Jónsson |first11=Hákon |last12=Snorradóttir |first12=Steinunn |last13=Hovig |first13=Eivind |last14=Møller |first14=Pål |last15=Kockum |first15=Ingrid |date=June 2018 |title=Ancient genomes from Iceland reveal the making of a human population |url=https://www.science.org/doi/10.1126/science.aar2625 |journal=Science |language=en |volume=360 |issue=6392 |pages=1028–1032 |doi=10.1126/science.aar2625 |issn=0036-8075|hdl=11250/2592828 |hdl-access=free }}</ref> and observed variants under positive natural selection in a present-day society.<ref>{{Cite journal |last=Stefansson |first=Hreinn |last2=Helgason |first2=Agnar |last3=Thorleifsson |first3=Gudmar |last4=Steinthorsdottir |first4=Valgerdur |last5=Masson |first5=Gisli |last6=Barnard |first6=John |last7=Baker |first7=Adam |last8=Jonasdottir |first8=Aslaug |last9=Ingason |first9=Andres |last10=Gudnadottir |first10=Vala G |last11=Desnica |first11=Natasa |last12=Hicks |first12=Andrew |last13=Gylfason |first13=Arnaldur |last14=Gudbjartsson |first14=Daniel F |last15=Jonsdottir |first15=Gudrun M |date=February 2005 |title=A common inversion under selection in Europeans |url=https://www.nature.com/articles/ng1508 |journal=Nature Genetics |language=en |volume=37 |issue=2 |pages=129–137 |doi=10.1038/ng1508 |issn=1061-4036|url-access=subscription }}</ref> The company has also catalogued human knockouts - people missing certain genes - and reconstructed the genome of the first man of African descent to live in Iceland by analyzing the sequences of hundreds of his living descendants.<ref>{{Cite journal |last=Jagadeesan |first=Anuradha |last2=Gunnarsdóttir |first2=Ellen D. |last3=Ebenesersdóttir |first3=S. Sunna |last4=Guðmundsdóttir |first4=Valdis B. |last5=Thordardottir |first5=Elisabet Linda |last6=Einarsdóttir |first6=Margrét S. |last7=Jónsson |first7=Hákon |last8=Dugoujon |first8=Jean-Michel |last9=Fortes-Lima |first9=Cesar |last10=Migot-Nabias |first10=Florence |last11=Massougbodji |first11=Achille |last12=Bellis |first12=Gil |last13=Pereira |first13=Luisa |last14=Másson |first14=Gísli |last15=Kong |first15=Augustine |date=February 2018 |title=Reconstructing an African haploid genome from the 18th century |url=https://www.nature.com/articles/s41588-017-0031-6 |journal=Nature Genetics |language=en |volume=50 |issue=2 |pages=199–205 |doi=10.1038/s41588-017-0031-6 |issn=1061-4036|url-access=subscription }}</ref> These studies are avidly followed by foreign and Icelandic media alike, and constitute another type of return that deCODE renders to the society it studies and works within.{{citation needed|date=December 2023}}