Mixed-model association for biobank-scale datasets
Open accessPublished version of a preprint
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?
To assess for confounding, the attenuation ratio of each trait was determined via LD score regression and was within the expected range for polygenic traits.
“To assess for confounding we determined the attenuation ratio of each trait via LD score regression, which was within the expected range for polygenic traits (Table S3).104”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [104]
Using BOLT-LMM, which adjusts for population structure and relatedness, allowed inclusion of 13,320 additional individuals compared with analysing a reduced set of unrelated white British individuals using linear regression.
“To improve our power to detect associations, we used BOLT-LMM15 to perform linear mixed models analysis. As this adjusts for population structure and relatedness, we could include many additional individuals (n = 13,320) than if following the common practice of analysing a reduced set of unrelated white British individuals using linear regression60.”
From GWAS identifies 14 loci for device-measured physical activity and sleep duration (2018) · cites this paper as [60]
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
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Self-citationYu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M., Doebley, J., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2005) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,155 · see citing works
Efficient Bayesian mixed-model analysis increases association power in large cohorts
Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507 · see citing works
Self-citationZhou, W., Nielsen, J. B., Fritsche, L. G., Dey, R., Gabrielsen, M. E., Wolford, B. N., LeFaive, J., VandeHaar, P., Taliun, S. A. G., Gifford, A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0184-y · Cited by 1,406 · see citing works
Advantages and pitfalls in the application of mixed-model association methods
Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L. (2014) · Nature Genetics · doi:10.1038/ng.2876 · Cited by 981 · see citing works
Self-citationAn atlas of genetic associations in UK Biobank
Canela‐Xandri, O., Rawlik, K., & Tenesa, A. (2018) · Nature Genetics · doi:10.1038/s41588-018-0248-z · Cited by 657 · see citing works
Genome-wide genetic data on ~500,000 UK Biobank participantsPreprint
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166298 · Cited by 606 · see citing works
Gazal, S., Finucane, H. K., Furlotte, N. A., Loh, P.-R., Palamara, P. F., Liu, X., Schoech, A., Bulik-Sullivan, B., Neale, B. M., Gusev, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3954 · Cited by 525 · see citing works
Self-citationImproved linear mixed models for genome-wide association studies
Listgarten, J., Lippert, C., Kadie, C. M., Davidson, R. I., Eskin, E., & Heckerman, D. E. (2012) · Nature Methods · doi:10.1038/nmeth.2037 · Cited by 403 · see citing works
Hormonal Contraception and the Risk of HIV Acquisition: An Individual Participant Data Meta-analysis
MORRISON, C. S., Chen, P., Kwok, C., Baeten, J. M., Brown, J., Crook, A. M., Damme, L. V., Delany‐Moretlwe, S., Francis, S. C., Friedland, B., et al. (2015) · PLoS Medicine · doi:10.1371/journal.pmed.1001778 · Cited by 349 · see citing works
Zhou, W., Nielsen, J. B., Fritsche, L. G., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/212357 · Cited by 129 · see citing works
An atlas of genetic associations in UK BiobankPreprint
Canela‐Xandri, O., Rawlik, K., & Tenesa, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/176834 · Cited by 35 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Genome-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
Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00857-4 · Cited by 1,517 · see citing works
Computationally efficient whole-genome regression for quantitative and binary traits
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00870-7 · Cited by 1,502 · see citing works
Leveraging Polygenic Functional Enrichment to Improve GWAS Power
Kichaev, G., Bhatia, G., Loh, P.-R., Gazal, S., Burch, K., Freund, M. K., Schoech, A., Pasaniuc, B., & Price, A. L. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.11.008 · Cited by 926 · see citing works
Self-citationRichardson, T. G., Sanderson, E., Palmer, T. M., Ala-Korpela, M., Ference, B. A., Davey Smith, G., & Holmes, M. V. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003062 · Cited by 843 · see citing works
The Polygenic and Monogenic Basis of Blood Traits and Diseases
Vuckovic, D., Bao, E. L., Akbari, P., Lareau, C. A., Mousas, A., Jiang, T., Chen, M.-H., Raffield, L. M., Tardaguila, M., Huffman, J. E., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.008 · Cited by 671 · see citing works
Chen, M.-H., Raffield, L. M., Mousas, A., Sakaue, S., Huffman, J. E., Moscati, A., Trivedi, B., Jiang, T., Akbari, P., Vuckovic, D., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.045 · Cited by 661 · see citing works
Klimentidis, Y. C., Raichlen, D. A., Bea, J., Garcia, D. O., Wineinger, N. E., Mandarino, L. J., Alexander, G. E., Chen, Z., & Going, S. B. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0120-3 · Cited by 403 · see citing works
Distinguishing genetic correlation from causation across 52 diseases and complex traits
O’Connor, L. J., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0255-0 · Cited by 394 · see citing works
Self-citationGWAS identifies 14 loci for device-measured physical activity and sleep duration
Doherty, A., Smith-Byrne, K., Ferreira, T., Holmes, M. V., Holmes, C., Pulit, S. L., & Lindgren, C. M. (2018) · Nature Communications · doi:10.1038/s41467-018-07743-4 · Cited by 408 · see citing works
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., et al. (2019) · eLife · doi:10.7554/elife.39702 · Cited by 359 · see citing works
Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour
Jones, S. E., van Hees, V. T., Mazzotti, D. R., Marques-Vidal, P., Sabia, S., van der Spek, A., Dashti, H. S., Engmann, J., Kocevska, D., Tyrrell, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09576-1 · Cited by 300 · see citing works
Akbari, P., Gilani, A., Sosina, O., Kosmicki, J. A., Khrimian, L., Fang, Y.-Y., Persaud, T., Garcia, V., Sun, D., Li, A., et al. (2021) · Science · doi:10.1126/science.abf8683 · Cited by 276 · see citing works
Extreme Polygenicity of Complex Traits Is Explained by Negative Selection
O'Connor, L. J., Schoech, A. P., Hormozdiari, F., Gazal, S., Patterson, N., & Price, A. L. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.07.003 · Cited by 268 · see citing works
Self-citationPirruccello, J. P., Bick, A., Wang, M., Chaffin, M., Friedman, S., Yao, J., Guo, X., Venkatesh, B. A., Taylor, K. D., Post, W. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15823-7 · Cited by 267 · see citing works
Lotta, L. A., Wittemans, L. B. L., Zuber, V., Stewart, I. D., Sharp, S. J., Luan, J., Day, F. R., Li, C., Bowker, N., Cai, L., et al. (2018) · JAMA · doi:10.1001/jama.2018.19329 · Cited by 229 · see citing works
Deep learning enables genetic analysis of the human thoracic aorta
Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00962-4 · Cited by 210 · see citing works
Coleman, J. R. I., Gaspar, H. A., Bryois, J., Byrne, E. M., Forstner, A. J., Holmans, P. A., de Leeuw, C. A., Mattheisen, M., McQuillin, A., Whitehead Pavlides, J. M., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.10.015 · Cited by 199 · see citing works
A genetic map of the mouse dorsal vagal complex and its role in obesity
Ludwig, M. Q., Cheng, W., Gordian, D., Lee, J., Paulsen, S. J., Hansen, S. N., Egerod, K. L., Barkholt, P., Rhodes, C. J., Secher, A., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00363-1 · Cited by 186 · see citing works
Jamialahmadi, O., Mancina, R. M., Ciociola, E., Tavaglione, F., Luukkonen, P. K., Baselli, G., Malvestiti, F., Thuillier, D., Raverdy, V., Männistö, V., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.12.023 · Cited by 148 · 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
Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture
Hou, K., Burch, K. S., Majumdar, A., Shi, H., Mancuso, N., Wu, Y., Sankararaman, S., & Pasaniuc, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0465-0 · Cited by 91 · see citing works
The effect of X-linked dosage compensation on complex trait variation
Sidorenko, J., Kassam, I., Kemper, K. E., Zeng, J., Lloyd-Jones, L. R., Montgomery, G. W., Gibson, G., Metspalu, A., Esko, T., Yang, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10598-y · Cited by 82 · see citing works
Ward, J., Lyall, L. M., Bethlehem, R. A. I., Ferguson, A., Strawbridge, R. J., Lyall, D. M., Cullen, B., Graham, N., Johnston, K. J. A., Bailey, M. E. S., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0635-y · Cited by 83 · see citing works
Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk
Reshef, Y. A., Finucane, H. K., Kelley, D. R., Gusev, A., Kotliar, D., Ulirsch, J. C., Hormozdiari, F., Nasser, J., O’Connor, L., van de Geijn, B., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0196-7 · Cited by 68 · see citing works
Self-citationKim, S. S., Truong, B., Jagadeesh, K., Dey, K. K., Shen, A. Z., Raychaudhuri, S., Kellis, M., & Price, A. L. (2024) · Nature Communications · doi:10.1038/s41467-024-44742-0 · Cited by 56 · 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
Russell, A. E., Ford, T., Gunnell, D., Heron, J., Joinson, C., Moran, P., Relton, C., Suderman, M., Hemani, G., & Mars, B. (2020) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2020.05.065 · Cited by 39 · see citing works
Kleeman, S. O., Thakir, T. M., Demestichas, B., Mourikis, N., Loiero, D., Ferrer, M., Bankier, S., Riazat-Kesh, Y. J. R. A., Lee, H., Chantzichristos, D., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100347 · Cited by 37 · see citing works
- Authors
- Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P., & Price, A. L.
- Affiliations
- Broad Institute (US)
- University of California, Los Angeles (US)
- Corresponding author
- Loh, Po-Ru
- Price, Alkes L.
- Venue
- Nature Genetics
- Published
- 2018-06-11
- Volume
- 50
- Issue
- 7
- Pages
- 906-908
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6309610
- OpenAlex
- W2950598813
- Cited by
- 758
- References
- 13
- Citations by year
- 2026: 69 · 2025: 128 · 2024: 140 · 2023: 78 · 2022: 92 · 2021: 99 · 2020: 68 · 2019: 54 · 2018: 28 · 2017: 2
- FWCI
- 38.04
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Boehringer Ingelheim Fonds
- National Institutes of Health (1s10rr028832-01, MH107649)
- National Institute of Mental Health (R01MH107649)
- National Human Genome Research Institute (R01HG006399, U01HG009379)
- National Institute of General Medical Sciences (R01GM105857)
- National Center for Research Resources (S10 RR028832, 1S10RR028832, 1S10RR028832-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- biobank-scale data, genome-wide association study, genetic association, primary sclerosing cholangitis, genetic susceptibility loci
- MeSH terms
- United Kingdom, Humans, Genome, Human, Linear Models, Biological Specimen Banks, Genome-Wide Association Study, Datasets as Topic
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
