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Record W2561040810 · doi:10.7554/elife.20320

Genetic and environmental influences on adult human height across birth cohorts from 1886 to 1994

2016· article· en· W2561040810 on OpenAlexaff
Aline Jelenkovic, Yoon-Mi Hur, Reijo Sund, Yoshie Yokoyama, Sisira Siribaddana, Matthew Hotopf, Athula Sumathipala, Frühling Rijsdijk, Qihua Tan, Dongfeng Zhang, Zengchang Pang, Sari Aaltonen, Kauko Heikkilä, Sevgi Yurt Öncel, Fazil Alıev, Esther Rebato, Ádám Domonkos Tárnoki, Dávid László Tárnoki, Kaare Christensen, Axel Skytthe, Kirsten Ohm Kyvik, Judy L. Silberg, Lindon J. Eaves, Hermine H. Maes, Tessa L. Cutler, John L. Hopper, Juan R. Ordoñana, Juan F. Sánchez-Romera, Lucía Colodro‐Conde, Wendy Cozen, Amie E. Hwang, Thomas M. Mack, Joohon Sung, Yun‐Mi Song, Sarah Yang, Kayoung Lee, Carol E. Franz, William S. Kremen, Michael J. Lyons, Andreas Busjahn, Tracy L. Nelson, Keith E. Whitfield, Christian Kandler, Kerry L. Jang, Margaret Gatz, David A. Butler, Maria A. Stazi, Corrado Fagnani, Cristina D’Ippolito, Glen E. Duncan, Dedra Buchwald, Cathérine Derom, Robert Vlietinck, Ruth J. F. Loos, Nicholas G. Martin, Sarah E. Medland, Grant W. Montgomery, Hoe-Uk Jeong, Gary E. Swan, Ruth E. Krasnow, Patrik K. E. Magnusson, Nancy L. Pedersen, A.K. Dahl-Aslan, Tom A. McAdams, Thalia C. Eley, Alice M. Gregory, Per Tynelius, Laura A. Baker, Catherine Tuvblad, Gombojav Bayasgalan, Narandalai Danshiitsoodol, Paul Lichtenstein, Timothy D. Spector, Massimo Mangino, Geneviève Lachance, Meike Bartels, Gonneke Willemsen, S. Alexandra Burt, Kelly L. Klump, Jennifer R. Harris, Ingunn Brandt, Thomas Sevenius Nilsen, Robert F. Krueger, Matt McGue, Shandell Pahlen, Robin P. Corley, Jacob Hjelmborg, Jack Goldberg, Yoshinori Iwatani, Mikio Watanabe, Chika Honda, Fujio Inui, Finn Rasmussen, Brooke M. Huibregtse, Dorret I. Boomsma, Thorkild I. A. Sørensen, Jaakko Kaprio, Karri Silventoinen

Bibliographic record

VenueeLife · 2016
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of British Columbia
FundersFP7 HealthEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Environmental Health SciencesNational Institute on Drug AbuseNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institute on Alcohol Abuse and AlcoholismDanish Agency for Science and Higher EducationMedical Research CouncilNational Institutes of HealthAcademy of FinlandNational Health and Medical Research CouncilVlaamse regeringForsknings- og InnovationsstyrelsenNational Institute for Health and Care ResearchTobacco-Related Disease Research Program
KeywordsHeritabilityDemographySecular variationPopulationGeographyEast AsiaBiologyEvolutionary biologyChina

Abstract

fetched live from OpenAlex

Human height variation is determined by genetic and environmental factors, but it remains unclear whether their influences differ across birth-year cohorts. We conducted an individual-based pooled analysis of 40 twin cohorts including 143,390 complete twin pairs born 1886-1994. Although genetic variance showed a generally increasing trend across the birth-year cohorts, heritability estimates (0.69-0.84 in men and 0.53-0.78 in women) did not present any clear pattern of secular changes. Comparing geographic-cultural regions (Europe, North America and Australia, and East Asia), total height variance was greatest in North America and Australia and lowest in East Asia, but no clear pattern in the heritability estimates across the birth-year cohorts emerged. Our findings do not support the hypothesis that heritability of height is lower in populations with low living standards than in affluent populations, nor that heritability of height will increase within a population as living standards improve.

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 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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.283
Teacher spread0.269 · 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
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

Citations61
Published2016
Admission routes1
Has abstractyes

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