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Record W4411442413 · doi:10.1038/s41398-025-03418-z

Multi-ancestry genome-wide association analyses incorporating SNP-by-psychosocial interactions identify novel loci for serum lipids

2025· review· en· W4411442413 on OpenAlexaff
Amy R. Bentley, Michael R. Brown, Solomon K. Musani, Karen Schwander, Thomas W. Winkler, Mario Sims, Tuomas O. Kilpeläinen, Hugues Aschard, Traci M. Bartz, Lawrence F. Bielak, Jin Fang Chai, Kumaraswamy Naidu Chitrala, Nora Franceschini, Mariaelisa Graff, Xiuqing Guo, Fernando Pires Hartwig, Andréa R. V. R. Horimoto, Elise Lim, Ching‐Ti Liu, Alisa K. Manning, Ilja M. Nolte, Raymond Noordam, Melissa A. Richard, Albert V. Smith, Yun Ju Sung, Dina Vojinović, Yujie Wang, Mary F. Feitosa, Sarah E. Harris, Leo‐Pekka Lyytikäinen, Giorgio Pistis, Rainer Rauramaa, Peter J. van der Most, Erin B. Ware, Stefan Weiß, Wanqing Wen, Lisa R. Yanek, Dan E. Arking, Donna K. Arnett, Christie M. Ballantyne, Eric Boerwinkle, Yii‐Der Ida Chen, Martha L. Daviglus, Lisa de las Fuentes, Paul S. de Vries, Joseph A. Delaney, Amanda M. Fretts, Lynette Ekunwe, Jessica D. Faul, Linda C. Gallo, Sami Heikkinen, Georg Homuth, M. Arfan Ikram, Carmen R. Isasi, Jost B. Jonas, Liisa Keltikangas‐Järvinen, Pirjo Komulainen, Aldi T. Kraja, José Eduardo Krieger, Lenore J. Launer, Harold Snieder, Jianjun Liu, Kurt Lohman, Annemarie I. Luik, Ani Manichaikul, Pedro Marques‐Vidal, Yuri Milaneschi, Stanford Mwasongwe, Kenneth Rice, Stephen S. Rich, Pamela J. Schreiner, Lars Schwettmann, James M. Shikany, Xiao‐Ou Shu, Jennifer A. Smith, E Shyong Tai, Kent D. Taylor, Lesley F. Tinker, Michael Y. Tsai, André G. Uitterlinden, Cornelia M. van Duijn, Mélanie Waldenberger, Hwee Lin Wee, David R. Weir, Wenbin Wei, Ko Willems van Dijk, Gregory Wilson, Jie Yao, Kristin L. Young, Xiaoyu Zhang, Wei Zhao, Xiaofeng Zhu, Alan B. Zonderman, Ian J. Deary, Christian Gieger, Hans J. Grabe, Timo A. Lakka, Terho Lehtimäki, Albertine J. Oldehinkel, Martin Preisig, Ya Xing Wang, Wei Zheng, Michele K. Evans, Michael A. Province, James Gauderman, Vilmundur Guðnason, Catharina A. Hartman, Bernardo Lessa Horta, Sharon L. R. Kardia, Charles Kooperberg, Dennis O Mook-Kanamori, Brenda W.J.H. Penninx, Alexandre C. Pereira, Patricia A. Peyser, Bruce M. Psaty, Jerome I. Rotter, Xueling Sim, Kari E. North, D. C. Rao, Laura J. Bierut, Clint L. Miller, Alanna C. Morrison, Charles N. Rotimi, Myriam Fornage, Ervin R. Fox

Bibliographic record

VenueTranslational Psychiatry · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of Manitoba
FundersCenter for Information TechnologyNational Institute of Environmental Health SciencesNational Human Genome Research InstituteNational Cancer InstituteU.S. Department of Health and Human ServicesNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesNIH Office of the DirectorNational Heart, Lung, and Blood InstituteNovo NordiskNovo Nordisk Fonden
KeywordsGenome-wide association studySNPPsychosocialGenetic associationAnxietyBiologyGeneticsGenetic modelClinical psychologyMedicineSingle-nucleotide polymorphismGeneGenotypePsychiatry

Abstract

fetched live from OpenAlex

Abstract Serum lipid levels, which are influenced by both genetic and environmental factors, are key determinants of cardiometabolic health and are influenced by both genetic and environmental factors. Improving our understanding of their underlying biological mechanisms can have important public health and therapeutic implications. Although psychosocial factors, including depression, anxiety, and perceived social support, are associated with serum lipid levels, it is unknown if they modify the effect of genetic loci that influence lipids. We conducted a genome-wide gene-by-psychosocial factor interaction (G×Psy) study in up to 133,157 individuals to evaluate if G×Psy influences serum lipid levels. We conducted a two-stage meta-analysis of G×Psy using both a one-degree of freedom (1df) interaction test and a joint 2df test of the main and interaction effects. In Stage 1, we performed G×Psy analyses on up to 77,413 individuals and promising associations ( P < 10 −5 ) were evaluated in up to 55,744 independent samples in Stage 2. Significant findings ( P < 5 × 10 −8 ) were identified based on meta-analyses of the two stages. There were 10,230 variants from 120 loci significantly associated with serum lipids. We identified novel associations for variants in four loci using the 1df test of interaction, and five additional loci using the 2df joint test that were independent of known lipid loci. Of these 9 loci, 7 could not have been detected without modeling the interaction as there was no evidence of association in a standard GWAS model. The genetic diversity of included samples was key in identifying these novel loci: four of the lead variants displayed very low frequency in European ancestry populations. Functional annotation highlighted promising loci for further experimental follow-up, particularly rs73597733 ( MACROD2 ), rs59808825 ( GRAMD1B ), and rs11702544 ( RRP1B ). Notably, one of the genes in identified loci ( RRP1B ) was found to be a target of the approved drug Atenolol suggesting potential for drug repurposing. Overall, our findings suggest that taking interaction between genetic variants and psychosocial factors into account and including genetically diverse populations can lead to novel discoveries for serum lipids.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.093
GPT teacher head0.429
Teacher spread0.337 · 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.

Study designNot applicable
Domainnot available
GenreReview

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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Citations0
Published2025
Admission routes1
Has abstractyes

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