← Back to cohort
A second generation human haplotype map of over 3.1 million SNPs
Sarah S. Murray, David A. Hinds, Laura L. Stuvé, Dennis G. Ballinger, David R. Cox, George M. Weinstock, Donna M. Muzny, Imtaz Yakub, John W. Belmont, Erica Sodergren, Lynne Nazareth, Suzanne M. Leal, T. D. Willis, Andrew Boudreau, Paul Hardenbol, Albert V. Smith, Lalitha Krishnan, Shiran Pasternak, Marcela K. Tello-Ruiz, Richa Saxena, Paul I. W. de Bakker, Mary Goyette, Yves Chrétien, Jessica Roy, Maura Faggart, Steve McCarroll, Brendan Blumenstiel, Huy Nguyen, Daniel J. Richter, Supriya Gupta, Melissa Parkin, Pardis C. Sabeti, Patrick Varilly, Alkes L. Price, Erich Stahl, Ellen Winchester, Rachel Barry, Chris Spencer, Fuli Yu, S. F. Schaffner, Bruce W. Birren, Mark J. Daly, Liuda Ziaugra, Jeff Barrett, Jamie Moore, Amy L. Camargo, Matthew DeFelice, Robert C. Onofrio, Hongbin Zhao, Hui Zhao, Xiaoli Tang, Weitao Hu, Wei Wang, Bo Zhang, Wei Lin, Qingrun Zhang, Siqi Liu, Changqing Zeng, Haoran Hu, Jun Yu, Jun Zhou, Hao Pan, Jian Wang, Yang Gao, Chaohua Li, Shaun Purcell, Zhijian Yao, Yayun Shen, Ying Wang, Weiwei Sun, Yungang He, Xun Chu, Yangfan Liu, Xiaoyan Xiong, Liang Xu, Haifeng Wang, Yi Wang, Jin Li, Stephen Kwok‐Wing Tsui, J. Tze‐Fei Wong, Arthur L. Holden, Stéphanie Roumy, Kevin L. Gunderson, Arnold Oliphant, Mark S. Chee, Andrei Verner, Michael Phillips, Fanny Chagnon, Vincent Ferretti, Martin Leboeuf, Bartha Maria Knoppers, Clémentine Sallée, Ming Xiao, Dongmei Cai, Ludmila Pawlikowska, Raymond D. Miller, Patricia Taillon‐Miller, Daniel C. Koboldt, William Mak, Paul Kwong Hang Tam, Pak C. Sham, Akihiro Sekine, Todd A. Johnson, Yozo Ohnishi, Takahisa Kawaguchi, Takuya Kitamoto, Takashi Morizono, Toshihiro Tanaka, Atsushi Nagashima, Tatsuhiko Tsunoda, Don Powell, Kirsten McLay, Christopher M. Clee, Sarah K. Sims, R. W. Plumb, Mark Griffiths, Nigel P. Carter, Sarah Hunt, Barbara E. Stranger, Mark T. Ross, Matthew Jones, David L. Willey, Emmanouil T. Dermitzakis, Christine Bird, Rhian Gwilliam, John H. Burton, Marcos Delgado, Pamela Whittaker, Jonathan J. Morrison, Guðmundur Á. Þórisson, David J. Cutler, Peter E. Chen, Carl Kashuk, Shin Lin, Weihua Guan, Yun Li, Zhaohui Qin, Heather M. Munro, Daryl J. Thomas, Jonathan Marchini, Leonardo Bottolo, Simon Myers, Niall J. Cardin, Colin Freeman, S. Eyheramendy, Adam Auton, Matthew Stephens, David M. Evans, Geraldine M Clarke, Andrew P. Morris, Bruce S. Weir, James C. Mullikin, Stephen T. Sherry, Michael Feolo, Andrew D. Skol, Houcan Zhang, Yoshimitsu Fukushima, Darryl Macer, Eiko Suda, Charmaine Royal, Clement Adebamowo, Isaac F. Adewole, IkeOluwapo O. Ajayi, Chibuzor Nkwodimmah, Toyin Aniagwu, Patricia A. Marshall, Missy Dixon, Andy Peiffer, Renzong Qiu, Alastair Kent, Norio Niikawa, Morris W. Foster, Ellen Wright Clayton, Ruth Jamieson, Jessica Watkin, John Stewart, Richard K. Wilson, Lucinda Fulton, Zhu Chen, Hua Han, Le Kang, Karen Kennedy, Martin Godbout, John C. Wallenburg, Guy Bellemare, K. Saeki, Hongguang Wang, Qing Li, Hongbo Fu, Daochang An, Zhen Wang, Renwu Wang, Jean E. McEwen, Lynn F. Zacharia, Mark S. Guyer
Bibliographic record
VenueNature · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsGenome CanadaMcGill University and Génome Québec Innovation CentreOntario Institute for Cancer ResearchUniversité de MontréalMcGill University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Dental and Craniofacial ResearchNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Institute of Mental HealthNational Cancer InstituteNational Eye InstituteNational Institute on Alcohol Abuse and AlcoholismW. M. Keck FoundationNational Institute on Deafness and Other Communication DisordersNational Institute on Drug AbuseEngineering and Physical Sciences Research CouncilNational Institutes of HealthMinistry of Education, Culture, Sports, Science and TechnologyMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of ChinaBeijing Normal UniversityChinese Academy of SciencesUniversity Grants CommitteeNational Institute on AgingInnovation and Technology CommissionNational Human Genome Research InstituteWellcome TrustGenome Canada
KeywordsInternational HapMap ProjectLinkage disequilibriumSingle-nucleotide polymorphismImputation (statistics)BiologyGeneticsHaplotypeGenetic associationGenotypingPopulationTag SNPEvolutionary biologySNP genotypingAlleleGeneGenotypeMissing dataStatisticsMathematicsDemography
Abstract
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 imitationNot 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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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 head0.010
GPT teacher head0.289
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
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
Citations4,585
Published2007
Admission routes2
Has abstractno