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Record W3005931037 · doi:10.1101/2020.02.02.20020065

The Polygenic and Monogenic Basis of Blood Traits and Diseases

2020· preprint· en· W3005931037 on OpenAlex
Dragana Vuckovic, Erik L. Bao, Parsa Akbari, Caleb A. Lareau, Abdou Mousas, Tao Jiang, Ming‐Huei Chen, Laura M. Raffield, Manuel Tardáguila, Jennifer E. Huffman, Scott C. Ritchie, Karyn Mégy, Hannes Ponstingl, Christopher J. Penkett, Patrick K. Albers, Emilie M. Wigdor, Saori Sakaue, Arden Moscati, Regina Manansala, Ken Sin Lo, Huijun Qian, Masato Akiyama, Traci M. Bartz, Yoav Ben‐Shlomo, Andrew D Beswick, Jette Bork‐Jensen, Erwin P. Böttinger, Jennifer A. Brody, Frank J.A. van Rooij, Kumaraswamy Naidu Chitrala, Kelly Cho, Hélène Choquet, Adolfo Correa, John Danesh, Emanuele Di Angelantonio, Niki Dimou, Jingzhong Ding, Paul Elliott, Tõnu Esko, Michele K. Evans, Stephan B. Felix, James S. Floyd, Linda Broer, Niels Grarup, Michael H. Guo, Andreas Greinacher, Jeff Haessler, Torben Hansen, Joanna M. M. Howson, Wei Huang, Eric Jorgenson, Tim Kacprowski, Mika Kähönen, Yoichiro Kamatani, Masahiro Kanai, Savita Karthikeyan, Leslie A. Lange, Terho Lehtimäki, Allan Linneberg, Yongmei Liu, Leo‐Pekka Lyytikäinen, Ani Manichaikul, Koichi Matsuda, Karen L. Mohlke, Nina Mononen, Yoshinori Murakami, Girish N. Nadkarni, Kjell Nikus, Nathan Pankratz, Oluf Pedersen, Michael Preuß, Bruce M. Psaty, Olli T. Raitakari, Stephen S. Rich, Benjamin A.T. Rodriguez, Jonathan D. Rosen, Jerome I. Rotter, Petra Schubert, Cassandra N. Spracklen, Praveen Surendran, Hua Tang, Jean‐Claude Tardif, Mohsen Ghanbari, Uwe Völker, Henry Völzke, Nicholas A. Watkins, Stefan Weiß, Na Cai, Kousik Kundu, Stephen B. Watt, Klaudia Walter, Alan B. Zonderman, Peter W.F. Wilson, Yun Li, Ruth J. F. Loos, Julian C. Knight, Michel Georges, Oliver Stegle, Εvangelos Εvangelou, Yukinori Okada, David J. Roberts, Michael Inouye, Andrew D. Johnson, Paul L. Auer, William J. Astle, Alex P. Reiner, Adam S. Butterworth, Willem H. Ouwehand, Guillaume Lettre, Vijay G. Sankaran, Nicole Soranzo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersWellcome Trust
KeywordsBiobankGenome-wide association studyBiologyGenetic architectureMendelian inheritanceQuantitative trait locusHuman genetic variationAllelePhenotypeGenetic associationGeneticsHuman geneticsPolygeneGenetic variationBlood cellComputational biologySingle-nucleotide polymorphismGeneGenotypeGenomeHuman genome

Abstract

fetched live from OpenAlex

Summary Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including 563,946 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering the full allele frequency spectrum of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood cell traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell GWAS to interrogate clinically meaningful variants across the full allelic spectrum of human variation.

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.

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 score0.126
Threshold uncertainty score0.465

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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