Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies
Open accessPublished version of a preprint
Abstract
Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Polygenic adaptation tests are sensitive to the choice of GWAS data that provide the effect sizes; effect sizes estimated from one study can generate erroneously exaggerated signatures of polygenic adaptation when assessing polygenic adaptation in a second dataset, even if two sets of effect sizes produce correlated polygenic scores.
“Recent studies of height have suggested that polygenic adaptation tests are sensitive to the choice of GWAS data that provide the effect sizes: even if two sets of effect sizes produce correlated polygenic scores, effect sizes estimated from one study can generate erroneously exaggerated signatures of polygenic adaptation when assessing polygenic adaptation in a second dataset [34, 35].”
From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [35]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredGiven previous concerns, we performed several sensitivity analyses for the height analysis.
“Given previous concerns26,27, we performed several sensitivity analyses for the height analysis detailed in the Supplementary Methods.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [27]
Population stratification in GWAS data likely confounded previous estimates of polygenic adaptation on height.
“However, this approach is sensitive to population stratification as illustrated by recent work using UK Biobank data which showed that population stratification in GWAS data likely confounded previous estimates of polygenic adaptation on height26,27.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [27]
Within-sibship GWAS data are robust against population stratification.
“Within-sibship GWAS data are particularly useful in this context as they are robust against population stratification26,27,29.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [27]
A previous study reporting polygenic adaptation on height in the UK population was found to be biased by population stratification in the GIANT consortium.
“A previous study reporting polygenic adaptation on height in the UK population was found to be biased by population stratification in the Genetic Investigation of ANthropometric Traits (GIANT) consortium26–28.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [27]
Even among populations with European ancestry, subtle ancestry differences between samples can lead to polygenic scores that overstate between-population differences: small biases in locus-wise effect estimates that arise from the ancestry differences can potentially accumulate across loci.
“Even among populations with European ancestry, subtle ancestry differences between samples can lead to polygenic scores that overstate between-population differences: small biases in locus-wise effect estimates that arise from the ancestry differences can potentially accumulate across loci [34, 35].”
From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [35]
The erroneously exaggerated signatures of polygenic adaptation from using effect sizes from one study to assess a second dataset arise from subtle population differences between study samples.
“This result, which arises from subtle population differences between study samples, calls into question claims about polygenic adaptation even of traits for which it has been most extensively investigated.”
From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [35]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citationSecond-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248 · see citing works
Self-citationAn integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711 · see citing works
Population Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280 · see citing works
Self-citationCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446 · see citing works
Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0183-z · Cited by 2,953 · see citing works
An Expanded View of Complex Traits: From Polygenic to Omnigenic
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017) · Cell · doi:10.1016/j.cell.2017.05.038 · Cited by 2,851 · see citing works
23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470 · cited 2× · see citing works
“, 2018), and use for sociogenomics applications (Lee et al., 2018; Savage et al., 2018; Nagel et al., 2018).”
BackgroundInfluentialHundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945 · see citing works
Self-citationMassive migration from the steppe was a source for Indo-European languages in Europe
Haak, W., Lazaridis, I., Patterson, N., Rohland, N., Mallick, S., Llamas, B., Brandt, G., Nordenfelt, S., Harney, E., Stewardson, K., et al. (2015) · Nature · doi:10.1038/nature14317 · Cited by 1,940 · see citing works
Self-citationThe Electronic Medical Records and Genomics (eMERGE) Consortium, Wood, A. R., The MIGen Consortium, The PAGE Consortium, Study, T. L. C., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., et al. (2014) · Nature Genetics · doi:10.1038/ng.3097 · Cited by 1,853 · see citing works
Self-citationGenome-wide patterns of selection in 230 ancient Eurasians
Mathieson, I., Lazaridis, I., Rohland, N., Mallick, S., Patterson, N., Roodenberg, S. A., Harney, E., Stewardson, K., Fernandes, D., Novak, M., et al. (2015) · Nature · doi:10.1038/nature16152 · Cited by 1,515 · see citing works
Self-citationSavage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0152-6 · Cited by 1,443 · see citing works
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.004 · Cited by 1,391 · see citing works
Self-citationModeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., Genovese, G., Loh, P.-R., Bhatia, G., Do, R., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.09.001 · Cited by 1,303 · see citing works
Self-citationNagel, M., Jansen, P. R., Stringer, S., Watanabe, K., de Leeuw, C., Bryois, J., Savage, J. E., Hammerschlag, A. R., Skene, N., Muñoz‐Manchado, A. B., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0151-7 · Cited by 806 · see citing works
Mixed-model association for biobank-scale datasets
Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0144-6 · Cited by 758 · see citing works
The genomic history of southeastern Europe
Mathieson, I., Alpaslan-Roodenberg, S., Posth, C., Szécsényi‐Nagy, A., Rohland, N., Mallick, S., Olalde, Í., Broomandkhoshbacht, N., Candilio, F., Cheronet, O., et al. (2018) · Nature · doi:10.1038/nature25778 · Cited by 720 · see citing works
Self-citationPrediction of individual genetic risk to disease from genome-wide association studiesPreprint
Wray, N. R., Goddard, M. E., & Visscher, P. M. (2007) · Genome Research · doi:10.1101/gr.6665407 · Cited by 714 · see citing works
Approximately independent linkage disequilibrium blocks in human populations
Berisa, T., & Pickrell, J. K. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv546 · Cited by 534 · see citing works
A population genetic signal of polygenic adaptation
Berg, J. J., & Coop, G. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004412 · Cited by 487 · see citing works
Signatures of negative selection in the genetic architecture of human complex traits
Zeng, J., de Vlaming, R., Wu, Y., Robinson, M. R., Lloyd-Jones, L. R., Yengo, L., Yap, C. X., Xue, A., Sidorenko, J., McRae, A. F., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0101-4 · Cited by 391 · see citing works
Detection of human adaptation during the past 2000 years
Field, Y., Boyle, E. A., Telis, N., Gao, Z., Gaulton, K. J., Golan, D., Yengo, L., Rocheleau, G., Froguel, P., McCarthy, M. I., et al. (2016) · Science · doi:10.1126/science.aag0776 · Cited by 383 · see citing works
Reduced signal for polygenic adaptation of height in UK Biobank
Berg, J. J., Harpak, A., Sinnott-Armstrong, N., Joergensen, A. M., Mostafavi, H., Field, Y., Boyle, E. A., Zhang, X., Racimo, F., Pritchard, J. K., et al. (2019) · eLife · doi:10.7554/elife.39725 · Cited by 339 · see citing works
Evidence of widespread selection on standing variation in Europe at height-associated SNPs
Turchin, M. C., Chiang, C. W., Palmer, C. D., Sankararaman, S., Reich, D., Genetic Investigation of ANthropometric Traits (GIANT) Consortium, & Hirschhorn, J. N. (2012) · Nature Genetics · doi:10.1038/ng.2368 · Cited by 329 · see citing works
Self-citationApparent latent structure within the UK Biobank sample has implications for epidemiological analysis
Haworth, S., Mitchell, R., Corbin, L., Wade, K. H., Dudding, T., Budu-Aggrey, A., Carslake, D., Hemani, G., Paternoster, L., Smith, G. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08219-1 · Cited by 314 · see citing works
Nelson, M. R., Bryc, K., King, K. S., Indap, A., Boyko, A. R., Novembre, J., Briley, L. P., Maruyama, Y., Waterworth, D. M., Waeber, G., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.08.005 · Cited by 293 · see citing works
Yengo, L., Sidorenko, J., Kemper, K. E., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/274654 · Cited by 260 · see citing works
Population genetic differentiation of height and body mass index across Europe
Robinson, M. R., Hemani, G., Medina‐Gómez, C., Mezzavilla, M., Esko, T., Shakhbazov, K., Powell, J. E., Vinkhuyzen, A. A. E., Berndt, S. I., Gustafsson, S., et al. (2015) · Nature Genetics · doi:10.1038/ng.3401 · Cited by 225 · see citing works
Self-citationMultilocus Genetic Risk Scores for Coronary Heart Disease Prediction
Ganna, A., Magnusson, P. K. E., Pedersen, N. L., de Faire, U., Reilly, M., Ärnlöv, J., Sundström, J., Hamsten, A., & Ingelsson, E. (2013) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.113.301218 · Cited by 156 · see citing works
Self-citationDetecting Polygenic Adaptation in Admixture Graphs
Racimo, F., Berg, J. J., & Pickrell, J. K. (2018) · Genetics · doi:10.1534/genetics.117.300489 · Cited by 115 · see citing works
Negative selection in humans and fruit flies involves synergistic epistasis
Sohail, M., Vakhrusheva, O. A., Sul, J. H., Pulit, S. L., Francioli, L. C., Initiative, A. D. N., van den Berg, L. H., Veldink, J. H., de Bakker, P. I. W., Bazykin, G. A., et al. (2017) · Science · doi:10.1126/science.aah5238 · Cited by 115 · see citing works
Self-citationTread Lightly Interpreting Polygenic Tests of Selection
Novembre, J., & Barton, N. H. (2018) · Genetics · doi:10.1534/genetics.118.300786 · Cited by 97 · see citing works
Global genetic differentiation of complex traits shaped by natural selection in humans
Guo, J., Wu, Y., Zhu, Z., Zheng, Z., Trzaskowski, M., Zeng, J., Robinson, M. R., Visscher, P. M., & Yang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04191-y · Cited by 87 · see citing works
Galinsky, K. J., Loh, P., Mallick, S., Patterson, N. J., & Price, A. L. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.09.014 · Cited by 51 · see citing works
Self-citationPolygenic Adaptation has Impacted Multiple Anthropometric TraitsPreprint
Berg, J. J., Zhang, X., & Coop, G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/167551 · Cited by 45 · see citing works
Polygenic selection underlies evolution of human brain structure and behavioral traitsPreprint
Beiter, E. R., Khramtsova, E. A., van der Merwe, C., Chimusa, E. R., Simonti, C., Stein, J., Thompson, P. M., Fisher, S. E., Stein, D. J., Capra, J. A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/164707 · Cited by 7 · see citing works
Non-parametric polygenic risk prediction using partitioned GWAS summary statistics
Chun, S., Imakaev, M., Hui, D., Patsopoulos, N. A., Neale, B. M., Kathiresan, S., Stitziel, N. O., & Sunyaev, S. R. (2018) · doi:10.1101/370064 · Cited by 4
Self-citationData from: Detection of human adaptation during the past 2000 years
Field, Y., Boyle, E. A., Telis, N., Gao, Z., Gaulton, K. J., Golan, D., Yengo, L., Rocheleau, G., Froguel, P., McCarthy, M. I., et al. (2016) · Data Archiving and Networked Services (DANS) · doi:10.5061/dryad.kd58f · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,831 · see citing works
Self-citationGenome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849 · see citing works
Self-citationAnalysis of polygenic risk score usage and performance in diverse human populations
Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R., & Domingue, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11112-0 · Cited by 1,038 · see citing works
Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects
Howe, L. J., Nivard, M. G., Morris, T. T., Hansen, A. F., Rasheed, H., Cho, Y., Chittoor, G., Ahlskog, R., Lind, P. A., Palviainen, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01062-7 · Cited by 377 · see citing works
“However, this approach is sensitive to population stratification as illustrated by recent work using UK Biobank data which showed that population stratification in GWAS data likely confounded previous estimates of polygenic adaptation on height 26,27 .”
Genetic correlates of social stratification in Great Britain
Abdellaoui, A., Hugh-Jones, D., Yengo, L., Kemper, K. E., Nivard, M. G., Veul, L., Holtz, Y., Zietsch, B. P., Frayling, T. M., Wray, N. R., et al. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0757-5 · Cited by 261 · see citing works
Using genetics for social science
Harden, K. P., & Koellinger, P. D. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0862-5 · Cited by 180 · see citing works
Validity of polygenic risk scores: are we measuring what we think we are?
Janssens, A. C. J. W. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz205 · Cited by 129 · see citing works
“We need more insight in the variation of effect sizes within populations of the same ethnicity if we want to understand the generalizability of PRSs (53,54).”
BackgroundSanderson, E., Richardson, T. G., Hemani, G., & Davey Smith, G. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyaa288 · Cited by 115 · see citing works
Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences
Rosenberg, N. A., Edge, M. D., Pritchard, J. K., & Feldman, M. W. (2019) · Evolution, Medicine, and Public Health · doi:10.1093/emph/eoy036 · Cited by 105 · see citing works
The distribution of common-variant effect sizes
O’Connor, L. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00901-3 · Cited by 81 · see citing works
The evolution of group differences in changing environments
Harpak, A., & Przeworski, M. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001072 · Cited by 50 · see citing works
Ratanatharathorn, A., Koenen, K. C., Chibnik, L. B., Weisskopf, M. G., Rich-Edwards, J. W., & Roberts, A. L. (2021) · Molecular Psychiatry · doi:10.1038/s41380-020-00996-w · Cited by 45 · see citing works
Family-GWAS reveals effects of environment and mating on genetic associations
Tan, T., Jayashankar, H., Guan, J., Nehzati, S. M., Mir, M., Bennett, M., Agerbo, E., Ahlskog, R., de Andrade Anapaz, V. P., Benonisdottir, S., et al. (2024) · doi:10.1101/2024.10.01.24314703 · Cited by 47 · see citing works
Self-citationA selection pressure landscape for 870 human polygenic traits
Song, W., Shi, Y., Wang, W., Pan, W., Qian, W., Yu, S., Zhao, M., & Lin, G. N. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01231-4 · Cited by 25 · see citing works
Young, A. I., Nehzati, S. M., Lee, C., Benonisdottir, S., Cesarini, D., Benjamin, D. J., Turley, P., & Kong, A. (2020) · doi:10.1101/2020.07.02.185199 · Cited by 143 · see citing works
Bird, K. A. (2021) · American Journal of Physical Anthropology · doi:10.1002/ajpa.24216 · Cited by 19 · see citing works
- Authors
- Sohail, M., Maier, R. M., Ganna, A., Bloemendal, A., Martin, A. R., Turchin, M. C., Chiang, C. W., Hirschhorn, J., Daly, M. J., Patterson, N., Neale, B., Mathieson, I., Reich, D., & Sunyaev, S. R.
- Affiliations
- Broad Institute (US)
- Brown University (US)
- University of Southern California (US)
- University of Pennsylvania (US)
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-03-21
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC6428571
- OpenAlex
- W2924359406
- Cited by
- 359
- References
- 41
- Citations by year
- 2026: 28 · 2025: 30 · 2024: 35 · 2023: 36 · 2022: 59 · 2021: 63 · 2020: 58 · 2019: 42 · 2018: 7
- FWCI
- 33.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- National Institutes of Health (HG009088, MH101244, GM100233, HG006399, GM127131)
- Alfred P. Sloan Foundation (Sloan Research Fellowship)
- Charles E Kaufman Foundation (New Investigator Research Grant)
- Paul Allen Foundation (Allen Discovery Center)
- Howard Hughes Medical Institute (Investigator)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- polygenic adaptation, height, population stratification, polygenic risk score, UK Biobank, genetic effect size
- MeSH terms
- Adaptation, Biological, Body Height, United Kingdom, Humans, Phylogeny, Databases, Factual, Computational Biology, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Biostatistics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
