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Record W1981946201 · doi:10.1016/j.ajhg.2013.07.010

Genome-wide Association Analysis of Blood-Pressure Traits in African-Ancestry Individuals Reveals Common Associated Genes in African and Non-African Populations

2013· review· en· W1981946201 on OpenAlexaff
Nora Franceschini, Ervin R. Fox, Zhaogong Zhang, Todd L. Edwards, Michael A. Nalls, Bamidele O. Tayo, Yan V. Sun, Omri Gottesman, Andrew D. Johnson, J. Hunter Young, Kenneth Rice, Qing Duan, Fang Chen, Yun Li, Hua Tang, Myriam Fornage, Keith L. Keene, Jeanette S. Andrews, Jennifer A. Smith, Jessica D. Faul, Guangfa Zhang, Wei Guo, Yu Liu, Sarah S. Murray, Solomon K. Musani, Sathanur R. Srinivasan, Digna R. Velez Edwards, Heming Wang, Lewis C. Becker, Pascal Bovet, Murielle Bochud, Ulrich Broeckel, Michel Burnier, Cara L. Carty, Daniel I. Chasman, Georg Ehret, Wei‐Min Chen, Guanjie Chen, Wei Chen, Jingzhong Ding, Albert W. Dreisbach, Michele K. Evans, Xiuqing Guo, Melissa E. Garcia, Rich Jensen, Margaux F. Keller, Guillaume Lettre, Vaneet Lotay, Lisa W. Martin, Jason H. Moore, Alanna C. Morrison, Thomas H. Mosley, Adesola Ogunniyi, Walter Palmas, George Papanicolaou, Alan D. Penman, Joseph F. Polak, Paul M. Ridker, Babatunde Salako, Andrew B. Singleton, Daniel Shriner, Kent D. Taylor, Ramachandran S. Vasan, Kerri L. Wiggins, Scott M. Williams, Lisa R. Yanek, Wei Zhao, Alan B. Zonderman, Diane M. Becker, Gerald S. Berenson, Eric Boerwinkle, Erwin P. Böttinger, Mary Cushman, Charles B. Eaton, Fredrik Nyberg, Gerardo Heiss, Joel N. Hirschhron, Virginia J. Howard, Konrad J. Karczewsk, Matthew B. Lanktree, Kiang Liu, Ching‐Ti Liu, Ruth J. F. Loos, Karen L. Margolis, M Snyder, Min Jin Go, Young Jin Kim, Jong‐Young Lee, Jae‐Pil Jeon, Sung Soo Kim, Bok‐Ghee Han, Yoon Shin Cho, Xueling Sim, Wan Ting Tay, Rick Twee‐Hee Ong, Mark Seielstad, Jianjun Liu, Tin Aung, Tien Yin Wong, Yik Ying Teo, E Shyong Tai, Chien-Hsiun Chen, Li‐Ching Chang, Yuan-Tsong Chen, Jer‐Yuarn Wu, Tanika N. Kelly, Dongfeng Gu, James E. Hixson, Jiang He, Yasuharu Tabara, Yoshihiro Kokubo, Tetsuro Miki, Naoharu Iwai, Norihiro Kato, Fumihiko Takeuchi, Tomohiro Katsuya, Toru Nabika, Takao Sugiyama, Yi Zhang, Wei Huang, Xuegong Zhang, Xueya Zhou, Jin Li, Dingliang Zhu, Bruce M. Psaty, Nicholas J. Schork, David R. Weir, Charles N. Rotimi, Michèle M. Sale, Tamara Harris, Sharon L. R. Kardia, Steven C. Hunt, Donna K. Arnett, Susan Redline, Neil Risch, D. C. Rao, Jerome I. Rotter, Aravinda Chakravarti, Alex P. Reiner, Daniel Levy, Brendan J. Keating, Xiaofeng Zhu

Bibliographic record

VenueThe American Journal of Human Genetics · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsWestern UniversityUniversité de MontréalMontreal Heart Institute
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsGenome-wide association studyGenetic associationGeneticsBiologyQuantitative trait locusLocus (genetics)Ancestry-informative markerGenetic genealogy1000 Genomes ProjectTraitSingle-nucleotide polymorphismMeta-analysisGeneAllele frequencyAllelePopulationGenotypeDemographyMedicineInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.336
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Quick stats

Citations233
Published2013
Admission routes1
Has abstractno

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