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Record W4388231666 · doi:10.3389/fgene.2023.1235337

Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci

2023· article· en· W4388231666 on OpenAlexaff
Lisa de las Fuentes, Karen Schwander, Michael R. Brown, Amy R. Bentley, Thomas W. Winkler, Patricia B. Munroe, Clint L. Miller, Hugo Aschard, Stella Aslibekyan, Traci M. Bartz, Lawrence F. Bielak, Jin Fang Chai, Ching‐Yu Cheng, Rajkumar Dorajoo, Mary F. Feitosa, Xiuqing Guo, Fernando Pires Hartwig, A.R.V.R. Horimoto, Ivana Kolčić, Elise Lim, Ching‐Ti Liu, Alisa K. Manning, Jonathan Marten, Solomon K. Musani, Raymond Noordam, Sandosh Padmanabhan, Tuomo Rankinen, Melissa A. Richard, Paul M. Ridker, Albert V. Smith, Dina Vojinović, Alan B. Zonderman, Maris Alver, Mathilde Boissel, Kaare Christensen, Barry I. Freedman, Chuan Gao, Franco Giulianini, Sarah E. Harris, Meian He, Fang‐Chi Hsu, Brigitte Kühnel, Federica Laguzzi, Xiaoyin Li, Leo‐Pekka Lyytikäinen, Ilja M. Nolte, Alaitz Poveda, Rainer Rauramaa, Muhammad Riaz, Antonietta Robino, Tamar Sofer, Fumihiko Takeuchi, Bamidele O. Tayo, Peter J. van der Most, Niek Verweij, Erin B. Ware, Stefan Weiß, Wanqing Wen, Lisa R. Yanek, Yiqiang Zhan, Najaf Amin, Dan E. Arking, Christie M. Ballantyne, Eric Boerwinkle, Jennifer A. Brody, Ulrich Broeckel, Archie Campbell, Mickaël Canouil, Xiaoran Chai, Yii‐Der Ida Chen, Xu Chen, Kumaraswamy Naidu Chitrala, Maria Pina Concas, Ulf dé Fairé, Renée de Mutsert, H. Janaka de Silva, Paul S. de Vries, Ahn Do, Jessica D. Faul, Virginia Fisher, James S. Floyd, Terrence Forrester, Yechiel Friedlander, Giorgia Girotto, C. Charles Gu, Göran Hallmans, Sami Heikkinen, Chew‐Kiat Heng, Georg Homuth, Steven C. Hunt, M. Arfan Ikram, David R. Jacobs, Maryam Kavousi, Chiea Chuen Khor, Tuomas O. Kilpeläinen, Woon‐Puay Koh, Pirjo Komulainen, Carl D. Langefeld, Jingjing Liang, Kiang Liu, Jianjun Liu, Kurt Lohman, Reedik Mägi, Ani Manichaikul, Colin A. McKenzie, Thomas Meitinger, Yuri Milaneschi, Matthias Nauck, Christopher P. Nelson, Jeffrey R. O’Connell, Alexandre C. Pereira, Thomas T. Perls, Annette Peters, Ozren Polašek, Olli T. Raitakari, Kenneth Rice, Treva Rice, Stephen S. Rich, Charumathi Sabanayagam, Pamela J. Schreiner, Xiao‐Ou Shu, Stephen Sidney, Mario Sims, Jennifer A. Smith, John M. Starr, Konstantin Strauch, E Shyong Tai, Kent D. Taylor, Michael Y. Tsai, André G. Uitterlinden, Mélanie Waldenberger, Ya Xing Wang, Wenbin Wei, Gregory Wilson, Xuan Deng, Jie Yao, Caizheng Yu, Jian‐Min Yuan, Wei Zhao, Diane M. Becker, Amélie Bonnefond, Donald W. Bowden, Ian J. Deary, Jasmin Divers, Tõnu Esko, Paul W. Franks, Philippe Froguel, Christian Gieger, Jost B. Jonas, Norihiro Kato, Timo A. Lakka, Karin Leander, Terho Lehtimäki, Patrik K. E. Magnusson, Kari E. North, Ιωάννα Ντάλλα, Brenda W.J.H. Penninx, Nilesh J. Samani, Harold Snieder, Beatrice Spedicati, Pim van der Harst, Henry Völzke, Lynne E. Wagenknecht, David R. Weir, Mary K. Wojczynski, Tangchun Wu, Wei Zheng, Xiaofeng Zhu, Claude Bouchard, Daniel I. Chasman, Michele K. Evans, Ervin R. Fox, Vilmundur Guðnason, Caroline Hayward, Bernardo Lessa Horta, Sharon L.R. Kardia, José Eduardo Krieger, Dennis O. Mook‐Kanamori, Patricia A. Peyser, Michael Province, Bruce M. Psaty, Igor Rudan, Xueling Sim, Blair H. Smith, Rob M. van Dam, Cornelia M. van Duijn, Tien Yin Wong, Donna K. Arnett, D. C. Rao, James Gauderman, Alanna C. Morrison, Jerome I. Rotter, Myriam Fornage

Bibliographic record

VenueFrontiers in Genetics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsBristol-Myers Squibb (Canada)
FundersNational Institute on AgingNational Cancer InstituteNational Heart, Lung, and Blood InstituteMedical Research Council
KeywordsGeneticsGeneGenomeBiologyPopulationComputational biologyGenome-wide association studySingle-nucleotide polymorphismGenotypeMedicine

Abstract

fetched live from OpenAlex

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: “Some College” (yes/no, for any education beyond high school) and “Graduated College” (yes/no, for completing a 4-year college degree). Genome-wide significant ( p < 5 × 10 −8 ) and suggestive ( p < 1 × 10 −6 ) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose ( FOXP1, MBOAT4, SKP2, STIM1, STX4 ), brain ( BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A ), and liver ( BRI3, FOXP1 ) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

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)0.001
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 score0.109
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.066
GPT teacher head0.348
Teacher spread0.283 · 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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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