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Transethnic Genome-Wide Association Study Provides Insights in the Genetic Architecture and Heritability of Long QT Syndrome

2020· article· en· W3027724762 on OpenAlexaff
Najim Lahrouchi, Rafik Tadros, Lia Crotti, Yuka Mizusawa, Pieter G. Postema, Leander Beekman, Roddy Walsh, Kanae Hasegawa, Julien Barc, Marko Ernsting, Kari L. Turkowski, Andrea Mazzanti, Britt M. Beckmann, Keiko Shimamoto, Ulla‐Britt Diamant, Yanushi D. Wijeyeratne, Yu Kucho, Tomas Robyns, Taisuke Ishikawa, Elena Arbelo, Michael Christiansen, Annika Winbo, Reza Jabbari, Steven A. Lubitz, Johannes Steinfurt, Boris Rudic, Bart Loeys, Moore B. Shoemaker, Peter Weeke, Ryan Pfeiffer, Brianna Davies, Antoine Andorin, Nynke Hofman, Federica Dagradi, Matteo Pedrazzini, David J. Tester, J. Martijn Bos, Georgia Sarquella‐Brugada, Óscar Campuzano, Pyotr G. Platonov, Birgit Stallmeyer, Sven Zumhagen, Eline A. Nannenberg, Jan H. Veldink, Leonard H. van den Berg, Ammar Al‐Chalabi, Christopher E. Shaw, Pamela J. Shaw, Karen Morrison, Peter M. Andersen, Martina Müller‐Nurasyid, Daniele Cusi, Cristina Barlassina, Pilar Galán, Mark Lathrop, Markus Munter, Thomas Werge, Marta Ribasés, Tin Aung, Chiea Chuen Khor, Mineo Ozaki, Peter Lichtner, Thomas Meitinger, J. Peter van Tintelen, Yvonne M. Hoedemaekers, Isabelle Denjoy, Antoine Leenhardt, Carlo Napolitano, Wataru Shimizu, Jean‐Jacques Schott, Jean‐Baptiste Gourraud, Takeru Makiyama, Seiko Ohno, Hideki Itoh, Andrew D. Krahn, Charles Antzelevitch, Dan M. Roden, Johan Saenen, Martin Borggrefe, Katja E. Odening, Patrick T. Ellinor, Jacob Tfelt‐Hansen, Jonathan R. Skinner, Maarten P. van den Berg, Morten S. Olesen, Josép Brugada, Ramón Brugada, Naomasa Makita, Jeroen Breckpot, Masao Yoshinaga, Elijah R. Behr, Annika Rydberg, Takeshi Aiba, Stefan Kääb, Silvia G. Priori, Pascale Guicheney, Hanno L. Tan, Christopher Newton‐Cheh, Michael Ackerman, Peter J. Schwartz, Eric Schulze‐Bahr, Vincent Probst, Minoru Horie, Arthur A.M. Wilde, Michael W.T. Tanck, Connie R. Bezzina

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsMcGill University and Génome Québec Innovation CentreMontreal Heart Institute
FundersEuropean Social FundNational Heart, Lung, and Blood InstituteMünchner Zentrum für GesundheitswissenschaftenHelmholtz Zentrum MünchenEuropean Society of CardiologyInstituto de Salud Carlos IIINational Institute of General Medical SciencesH. Lundbeck A/SNovo NordiskHjärt-LungfondenLundbeckfondenFondation pour la Recherche MédicaleAgence Nationale de la RechercheFondation LeducqBritish Heart FoundationWellcome TrustEuropean Regional Development FundBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchHeart Rhythm SocietyBroad InstituteEuropean CommissionFondation Maladies RaresPfizerJapan Agency for Medical Research and DevelopmentNational Center for Advancing Translational SciencesMedical Research CouncilBayerBristol-Myers Squibb
KeywordsHeritabilityMedicineGenetic architectureGenome-wide association studyLong QT syndromeGenetic associationGeneticsAssociation (psychology)Single-nucleotide polymorphismQT intervalInternal medicineGenotypeGeneQuantitative trait locusBiologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Background: Long QT syndrome (LQTS) is a rare genetic disorder and a major preventable cause of sudden cardiac death in the young. A causal rare genetic variant with large effect size is identified in up to 80% of probands (genotype positive) and cascade family screening shows incomplete penetrance of genetic variants. Furthermore, a proportion of cases meeting diagnostic criteria for LQTS remain genetically elusive despite genetic testing of established genes (genotype negative). These observations raise the possibility that common genetic variants with small effect size contribute to the clinical picture of LQTS. This study aimed to characterize and quantify the contribution of common genetic variation to LQTS disease susceptibility. Methods: We conducted genome-wide association studies followed by transethnic meta-analysis in 1656 unrelated patients with LQTS of European or Japanese ancestry and 9890 controls to identify susceptibility single nucleotide polymorphisms. We estimated the common variant heritability of LQTS and tested the genetic correlation between LQTS susceptibility and other cardiac traits. Furthermore, we tested the aggregate effect of the 68 single nucleotide polymorphisms previously associated with the QT-interval in the general population using a polygenic risk score. Results: Genome-wide association analysis identified 3 loci associated with LQTS at genome-wide statistical significance ( P <5×10 −8 ) near NOS1AP , KCNQ1 , and KLF12 , and 1 missense variant in KCNE1 (p.Asp85Asn) at the suggestive threshold ( P <10 −6 ). Heritability analyses showed that ≈15% of variance in overall LQTS susceptibility was attributable to common genetic variation ( h2SNP 0.148; standard error 0.019). LQTS susceptibility showed a strong genome-wide genetic correlation with the QT-interval in the general population (r g =0.40; P =3.2×10 −3 ). The polygenic risk score comprising common variants previously associated with the QT-interval in the general population was greater in LQTS cases compared with controls ( P <10−13), and it is notable that, among patients with LQTS, this polygenic risk score was greater in patients who were genotype negative compared with those who were genotype positive ( P <0.005). Conclusions: This work establishes an important role for common genetic variation in susceptibility to LQTS. We demonstrate overlap between genetic control of the QT-interval in the general population and genetic factors contributing to LQTS susceptibility. Using polygenic risk score analyses aggregating common genetic variants that modulate the QT-interval in the general population, we provide evidence for a polygenic architecture in genotype negative LQTS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.235
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations141
Published2020
Admission routes1
Has abstractyes

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