The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
A reference panel of 64,976 haplotypes for genotype imputation
Open accessPublished version of a preprint
Abstract
We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Furthermore, imputation reference panels share the same study biases as in GWAS41, creating challenges for imputing sites that are rare in European populations but common elsewhere when the catalog of non-European haplotypes is substantially smaller.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [41]
Experimental — accuracy not yet measuredIn the citing paper
“Recently, increased access to exome and whole-genome sequence data has, through both direct association analysis45,46 and imputation3,4, extended discovery to low-frequency and rare alleles previously inaccessible to GWAS.”
From A brief history of human disease genetics (2020) · cites this paper as [4]
In the citing paper
“For the common variants that underlie the bulk of complex-disease risk, the resolution of association mapping is often limited by the haplotype structure of the human genome2–4.”
From A brief history of human disease genetics (2020) · cites this paper as [4]
In the citing paper
“However, the principal benefit to date of whole-genome sequence data to genetic discovery has been to bolster array-based access to lower-frequency alleles, either directly, through their inclusion on genotyping platforms, or indirectly, through imputation from sequence-based reference samples3,4.”
From A brief history of human disease genetics (2020) · cites this paper as [4]
In the citing paper
“In the 406 GWAS loci, 4% of sentinel variants were nonsynonymous, representing a significant enrichment (p=2.5×10−10; 0.4% of variants with MAF>0.1% in the imputation panel41 were nonsynonymous).”
From Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use (2019) · cites this paper as [41]
In the citing paper
“We also excluded all variants with minor allele frequency less than 0.001, the lower bound of moderate imputation accuracy with the currently best available imputation reference panel41.”
From Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use (2019) · cites this paper as [41]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609
Self-citationGenetic Structure of Human Populations
Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A., & Feldman, M. W. (2002) · Science · doi:10.1126/science.1078311 · Cited by 2,222
MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes
Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20533 · Cited by 1,843
Fast and accurate genotype imputation in genome-wide association studies through pre-phasing
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., & Abecasis, G. R. (2012) · Nature Genetics · doi:10.1038/ng.2354 · Cited by 1,737
Genotype imputation for genome-wide association studies
Marchini, J., & Howie, B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2796 · Cited by 1,686
Improved whole-chromosome phasing for disease and population genetic studies
Delaneau, O., Zagury, J., & Marchini, J. (2012) · Nature Methods · doi:10.1038/nmeth.2307 · Cited by 1,351
Cohort Profile: The Study of Health in Pomerania
Völzke, H., Alte, D., Schmidt, C. O., Radke, D., Lorbeer, R., Friedrich, N., Aumann, N., Lau, K., Piontek, K., Born, G., et al. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyp394 · Cited by 1,006
Flagged first, then by citations
Cited by
Sorted by citations
Items 901–1,000 of 1,016
The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology
Ge, T., Chen, C., Doyle, A. E., Vettermann, R., Tuominen, L. J., Holt, D. J., Sabuncu, M. R., & Smoller, J. W. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy216 · Cited by 30
Han, X., Qassim, A., An, J., Marshall, H. N., Zhou, T., Ong, J., Hassall, M., Hysi, P. G., Foster, P. J., Khaw, P. T., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz193 · Cited by 30
Qi, X., Jia, Y., Pan, C., Li, C., Wen, Y. F., Hao, J., Liu, L., Cheng, B., Cheng, S., Yao, Y., et al. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104806 · Cited by 30
Veatch, O. J., Bauer, C. R., Keenan, B. T., Josyula, N. S., Mazzotti, D. R., Bagai, K., Malow, B. A., Robishaw, J. D., Pack, A. I., & Pendergrass, S. A. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-00755-4 · Cited by 29
Inkster, B., Simmons, A., Cole, J. H., Schoof, E. M., Linding, R., Nichols, T., Muglia, P., Holsboer, F., Sämann, P. G., McGuffin, P., et al. (2018) · Psychiatric Genetics · doi:10.1097/ypg.0000000000000203 · Cited by 29
The phenotype-genotype reference map: Improving biobank data science through replication
Bastarache, L. A., Delozier, S., Pandit, A., He, J., Lewis, A. J., Annis, A. C., LeFaive, J., Denny, J. C., Carroll, R. J., Altman, R. B., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.07.012 · Cited by 30
Myocardial Recovery in Recent Onset Dilated Cardiomyopathy: Role of CDCP1 and Cardiac Fibrosis
Liu, D., Wang, M., Murthy, V., McNamara, D. M., Nguyen, T. T. L., Philips, T. J., Vyas, H., Gao, H., Sahni, J., Starling, R. C., et al. (2023) · Circulation Research · doi:10.1161/circresaha.123.323200 · Cited by 29
Yap, C. X., Vo, D., Heffel, M. G., Bhattacharya, A., Wen, C. H., Yang, Y., Kemper, K. E., Zeng, J., Zheng, Z., Zhu, Z., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn7655 · Cited by 29
Fine-mapping genomic loci refines bipolar disorder risk genes
Koromina, M., Ravi, A., Panagiotaropoulou, G., Schilder, B. M., Humphrey, J., Braun, A., Bidgeli, T., Chatzinakos, C., Coombes, B. J., Kim, J., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01998-z · Cited by 29
Genetic determinants of childhood and adult height associated with osteosarcoma risk
Zhang, C., Morimoto, L. M., de Smith, A. J., Hansen, H. M., Gonzalez‐Maya, J., Endicott, A. A., Smirnov, I. V., Metayer, C., Wei, Q., Eward, W. C., et al. (2018) · Cancer · doi:10.1002/cncr.31645 · Cited by 29
Phenome-wide screening for traits causally associated with the risk of coronary artery disease
Nikpay, M., & Mohammadzadeh, S. (2020) · Journal of Human Genetics · doi:10.1038/s10038-019-0716-z · Cited by 29
van Reij, R. R. I., Hoofwijk, D. M. N., Rutten, B. P. F., Weinhold, L., Leber, M., Joosten, E. A. J., Ramı́rez, A., van den Hoogen, N. J., & the Italian Pain Group (2020) · Anaesthesia · doi:10.1111/anae.14832 · Cited by 29
Schnurr, T. M., Morgen, C. S., Borisevich, D., Beaumont, R. N., Engelbrechtsen, L., Ängquist, L. H., Have, C. T., Freathy, R. M., Smith, G. D., Nohr, E. A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61719-3 · Cited by 29
Association between herpes simplex virus 1 exposure and the risk of depression in UK Biobank
Ye, J., Wen, Y. F., Chu, X., Li, P., Cheng, B., Cheng, S., Liu, L., Zhang, L., Ma, M., Qi, X., et al. (2020) · Clinical and Translational Medicine · doi:10.1002/ctm2.108 · Cited by 29
Clay‐Gilmour, A., Hildebrandt, M. A. T., Brown, E. E., Hofmann, J. N., Spinelli, J. J., Giles, G. G., Cozen, W., Bhatti, P., Wu, X., Waller, R. G., et al. (2020) · Blood Advances · doi:10.1182/bloodadvances.2020001435 · Cited by 29
Dashti, H. S., Hivert, M., Levy, D. E., McCurley, J. L., Saxena, R., & Thorndike, A. N. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003219 · Cited by 29
A genome-wide association study of asthma hospitalizations in adults
Yan, Q., Forno, E., Luis, E. H., Pino-Yanes, M. D. M., Yang, G., Oh, S. S., Acosta‐Pérez, E., Hu, D., Eng, C. S., Huntsman, S., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.08.020 · Cited by 29
Analysis of common genetic variation and rare CNVs in the Australian Autism Biobank
Yap, C. X., Alvares, G. A., Henders, A. K., Lin, T., Wallace, L. M., Farrelly, A., McLaren, T., Berry, J., Vinkhuyzen, A. A. E., Trzaskowski, M., et al. (2021) · Molecular Autism · doi:10.1186/s13229-020-00407-5 · Cited by 29
The Intersection of the Genetic Architectures of Orofacial Clefts and Normal Facial Variation
Indencleef, K., Hoskens, H., Lee, M. K., White, J. D., Liu, C., Eller, R. J., Naqvi, S., Wehby, G. L., Uribe, L. M. M., Hecht, J. T., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.626403 · Cited by 29
Cerebrospinal fluid proteomics targeted for central nervous system processes in bipolar disorder
Göteson, A., Isgren, A., Jönsson, L., Sparding, T., Smedler, E., Pelanis, A., Zetterberg, H., Jakobsson, J., Pålsson, E., Holmén‐Larsson, J., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01236-5 · Cited by 29
Zhang, Z., Yang, X., Jia, Y., Wen, Y. F., Cheng, S., Meng, P., Li, C., Zhang, H., Pan, C., Zhang, J., et al. (2021) · Nutrients · doi:10.3390/nu13103343 · Cited by 29
Dey, K. K., Gazal, S., van de Geijn, B., Kim, S. S., Nasser, J., Engreitz, J., & Price, A. L. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100145 · Cited by 30
Gilchrist, J. J., Makino, S., Naranbhai, V., Sharma, P. K., Koturan, S., Tong, O., Taylor, C. A., Watson, R. A., Aires, A. V. D. L., Cooper, R. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31626-4 · Cited by 29
Rare genetic variants explain missing heritability in smoking
Jang, S.-K., Evans, L. M., Fialkowski, A., Arnett, D. K., Ashley‐Koch, A. E., Barnes, K. C., Becker, D. M., Bis, J. C., Blangero, J., Bleecker, E. R., et al. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01408-5 · Cited by 29
Discerning asthma endotypes through comorbidity mapping
Jia, G., Zhong, X. Y., Im, H. K., Schoettler, N., Pividori, M., Hogarth, D. K., Sperling, A. I., White, S. R., Naureckas, E. T., Lyttle, C. S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33628-8 · Cited by 29
Granell, R., Curtin, J. A., Haider, S., Kitaba, N. T., Mathie, S. A., Gregory, L. G., Yates, L. L., Tutino, M., Hankinson, J., Perretti, M., et al. (2023) · eLife · doi:10.7554/elife.84315 · Cited by 29
Gomez, D. F., Bekkering, S., ter Horst, R., Cossins, B., van den Munckhof, I., Rutten, J. H. W., Joosten, L. A. B., Netea, M. G., & Riksen, N. P. (2023) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiad118 · Cited by 29
Zhong, H., Liu, S., Zhu, J., Xu, T. H., Yu, H., & Wu, L. (2023) · International Journal of Cancer · doi:10.1002/ijc.34771 · Cited by 29
Genetic determinants of IgG antibody response to COVID-19 vaccination
Bian, S., Guo, X., Yang, X., Wei, Y., Yang, Z., Cheng, S., Yan, J., Chen, Y., Chen, G., Du, X., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.12.005 · Cited by 29
Ghatan, S., van Rooij, J., van Hoek, M., Boer, C. G., Felix, J. F., Kavousi, M., Jaddoe, V. W. V., Sijbrands, E. J. G., Medina‐Gómez, C., Rivadeneira, F., et al. (2024) · Genome Medicine · doi:10.1186/s13073-023-01255-7 · Cited by 29
A phenome-wide association study to discover pleiotropic effects of PCSK9, APOB, and LDLR
Safarova, M. S., Satterfield, B. A., Fan, X., Austin, E. E., Ye, Z., Bastarache, L. A., Zheng, N. S., Ritchie, M. D., Borthwick, K. M., Williams, M. S., et al. (2019) · npj Genomic Medicine · doi:10.1038/s41525-019-0078-7 · Cited by 29
Narrow-sense heritability estimation of complex traits using identity-by-descent information
Evans, L. M., Tahmasbi, R., Jones, M., Vrieze, S. I., Abecasis, G. R., Das, S., Bjelland, D. W., de Candia, T. R., Abecasis, G., Altshuler, D., et al. (2018) · Heredity · doi:10.1038/s41437-018-0067-0 · Cited by 29
Grigoroiu‐Serbânescu, M., Giaroli, G., Thygesen, J. H., Shenyan, O., Bigdeli, T. B., Bass, N. J., Diaconu, C. C., Neagu, A. I., Forstner, A. J., Degenhardt, F., et al. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.11.109 · Cited by 29
Genome‐Wide Association Study of Liver Fat: The Multiethnic Cohort Adiposity Phenotype Study
Park, S., Li, Y., Sheng, X., Hom, V., Xia, L. Y., Zhao, K., Pooler, L., Setiawan, V. W., Lim, U., Monroe, K. R., et al. (2020) · Hepatology Communications · doi:10.1002/hep4.1533 · Cited by 30
Luis, E. H., Espuela‐Ortiz, A., Lorenzo-Díaz, F., Keys, K. L., Mak, A. C. Y., Eng, C. S., Huntsman, S., Villar, J., Rodríguez‐Santana, J., Burchard, E. G., et al. (2020) · Pediatric Allergy and Immunology · doi:10.1111/pai.13337 · Cited by 29
Mapping the 17q12–21.1 Locus for Variants Associated with Early-Onset Asthma in African Americans
Gui, H., Levin, A. M., Hu, D., Abou‐Sleiman, P. M., Xiao, S., Mak, A. C. Y., Yang, M., Barczak, A. J., Huntsman, S., Eng, C. S., et al. (2020) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202006-2623oc · Cited by 29
Ortiz‐Fernández, L., Coit, P., Yılmaz, V., Yentur, S. P., Alıbaz-Öner, F., Aksu, K., Erken, E., Düzgün, N., Keser, G., Çefle, A., et al. (2021) · Arthritis & Rheumatology · doi:10.1002/art.41637 · Cited by 29
Piciocchi, A., Germinario, E. A. P., García‐Etxebarria, K., Rossi, S., Sanchez-Mete, L., Porowska, B., Stigliano, V., Trentino, P., Oddi, A., Accarpio, F., et al. (2021) · Toxins · doi:10.3390/toxins13080569 · Cited by 29
Lee, S. H., Garske, K. M., Arasu, U. T., Kar, A., Miao, Z., Alvarez, M., Koka, A., Darci-Maher, N., Benhammou, J. N., Pan, D. Z., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105232 · Cited by 31
Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses
Pujol‐Gualdo, N., Džigurski, J., Rukins, V., Pajuste, F., Wolford, B. N., Võsa, M., GOLOB, M., Haug, L., Alver, M., Läll, K., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03543-8 · Cited by 28
Jiang, Y., Chen, S., McGuire, D., Chen, F., Liu, M., Iacono, W. G., Hewitt, J. K., Hokanson, J. E., Krauter, K. S., Laakso, M., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007452 · Cited by 28
Genetic Heritability of Pigmentary Glaucoma and Associations With Other Eye Phenotypes
Simcoe, M. J., Weisschuh, N., Wissinger, B., Hysi, P. G., & Hammond, C. J. (2020) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2019.5961 · Cited by 28
European genetic ancestry associated with risk of childhood ependymoma
Zhang, C., Ostrom, Q. T., Hansen, H. M., Gonzalez‐Maya, J., Hu, D., Ziv, E., Morimoto, L. M., de Smith, A. J., Muskens, I. S., Kline, C., et al. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa130 · Cited by 28
Internalizing symptoms associate with the pace of epigenetic aging in childhood
Tollenaar, M. S., Beijers, R., Garg, E., Nguyen, T. T. T., Lin, D. T. S., MacIsaac, J. L., Shalev, I., Kobor, M. S., Meaney, M. J., O’Donnell, K. J., et al. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108021 · Cited by 28
A Polygenic Risk Score to Predict Future Adult Short Stature Among Children
Lu, T., Forgetta, V., Wu, H., Perry, J. R. B., Ong, K. K., Greenwood, C. M. T., Timpson, N. J., Manousaki, D., & Richards, J. B. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab215 · Cited by 28
Quattrone, D., Reininghaus, U., Richards, A. L., Tripoli, G., Ferraro, L., Quattrone, A., Marino, P., Rodríguez, V., Spinazzola, E., Gayer-Anderson, C. E., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01526-0 · Cited by 28
Wang, T., Liu, Y., Yin, Q., Geng, J., Chen, J., Yin, X., Wang, Y., Shang, X., Tian, C., Wang, Y., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab370 · Cited by 28
Dickson, A. L., Daniel, L. L., Zanussi, J., Plummer, W. D., Wei, W., Liu, G., Reese, T. S., Anandi, P., Birdwell, K. A., Kawai, V. K., et al. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2428 · Cited by 28
Somatic mutational profiles and germline polygenic risk scores in human cancer
Liu, Y., Gusev, A., Heng, Y. J., Alexandrov, L. B., & Kraft, P. L. (2022) · Genome Medicine · doi:10.1186/s13073-022-01016-y · Cited by 28
Voloudakis, G., Vicari, J. M., Venkatesh, S., Hoffman, G. E., Dobrindt, K., Zhang, W., Beckmann, N. D., Higgins, C. A., Argyriou, S., Jiang, S., et al. (2022) · npj Genomic Medicine · doi:10.1038/s41525-022-00324-x · Cited by 28
Brown, A., Cohen, C., Mielczarek, O., Migliorini, G., Costantino, F., Allcock, A., Davidson, C., Elliott, K. S., Fang, H., Lara, A. L., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100306 · Cited by 28
Tan, H., Wang, S., Huang, F., & Tong, Z. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1138149 · Cited by 28
The collaborative study on the genetics of alcoholism: Genetics
Johnson, E. C., Salvatore, J. E., Lai, D., Merikangas, A. K., Nürnberger, J. I., Tischfield, J. A., Xuei, X., Kamarajan, C., Wetherill, L. F., Collaborators, C., et al. (2023) · Genes Brain & Behavior · doi:10.1111/gbb.12856 · Cited by 28
Olivera, P. A., Martínez-Lozano, H., Leibovitzh, H., Xue, M., Neustaeter, A., Espin‐Garcia, O., Xu, W., Madsen, K. L., Guttman, D. S., Bernstein, C. N., et al. (2024) · Gastroenterology · doi:10.1053/j.gastro.2024.08.031 · Cited by 28
Low‐Kam, C., Rhainds, D., Lo, K. S., Barhdadi, A., Boulé, M., Alem, S., Pedneault-Gagnon, V., Rhéaume, É., Dubé, M., Busseuil, D., et al. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.009545 · Cited by 28
Turpin, W., Espin‐Garcia, O., Bedrani, L., Madsen, K. L., Meddings, J. B., Garay, J. A. R., Silverberg, M. S., Smith, M. I., Griffiths, A. M., Moayyedi, P., et al. (2019) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izz116 · Cited by 28
Nasykhova, Y. A., Barbitoff, Y. A., Serebryakova, E. A., Katserov, D., & Glotov, A. S. (2019) · World Journal of Diabetes · doi:10.4239/wjd.v10.i7.376 · Cited by 28
Legacy Data Confound Genomics Studies
Anderson-Trocmé, L., Farouni, R., Bourgey, M., Kamatani, Y., Higasa, K., Seo, J., Kim, C., Matsuda, F., & Gravel, S. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz201 · Cited by 28
Córdova‐Palomera, A., Tcheandjieu, C., Fries, J., Varma, P., Chen, V. S., Fiterau, M., Xiao, K., Tejeda, H., Keavney, B. D., Cordell, H. J., et al. (2020) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.120.003014 · Cited by 28
Genome-wide association study identifies new locus associated with OCDPreprint
Strom, N. I., Yu, D., Gerring, Z. F., Halvorsen, M., Abdellaoui, A., Rodriguez‐Fontenla, C., Sealock, J., Bigdeli, T. B., Coleman, J. R. I., Mahjani, B., et al. (2021) · medRxiv · doi:10.1101/2021.10.13.21261078 · Cited by 28
Chiou, J., Zeng, C., Cheng, Z., Han, J. Y., Schlichting, M., Huang, S., Wang, J., Sui, Y., Deogaygay, A., Okino, M.-L., et al. (2019) · doi:10.1101/693671 · Cited by 189
Crouse, J. J., Park, S. H., Byrne, E. M., Mitchell, B. L., Scott, J. L., Medland, S. E., Lin, T., Wray, N. R., Martin, N. G., & Hickie, I. B. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02492-x · Cited by 27
Limited overlap between genetic effects on disease susceptibility and disease survival
Yang, Z., Pajuste, F., Zguro, K., Cheng, Y., Kurant, D. E., Eoli, A., Wanner, J., Jermy, B. S., Rämö, J. T., FinnGen, et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02342-8 · Cited by 27
Jaeger, M., Matzaraki, V., Aguirre‐Gamboa, R., Gresnigt, M. S., Chu, X., Johnson, M. D., Oosting, M., Smeekens, S. P., Withoff, S., Jonkers, I. H., et al. (2019) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiz206 · Cited by 27
de Lima, R. M. S., Barth, B., Arcego, D. M., Filho, E. J. D. M., Clappison, A. S., Patel, S. H., Wang, Z., Pokhvisneva, I., Sassi, R. B., Hall, G. B., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00198 · Cited by 27
Akçimen, F., Martins, S., Liao, C., Bourassa, C. V., Catoire, H., Nicholson, G. A., Rieß, O., Raposo, M., França, M. C., Vasconcelos, J., et al. (2020) · Aging · doi:10.18632/aging.102825 · Cited by 27
Leu, C., Richardson, T. G., Kaufmann, T., van der Meer, D., Andreassen, O. A., Westlye, L. T., Busch, R. M., Smith, G. D., & Lal, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232292 · Cited by 27
Jami, E. S., Eilertsen, E. M., Hammerschlag, A. R., Qiao, Z., Evans, D. M., Ystrøm, E., Bartels, M., & Middeldorp, C. M. (2020) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32784 · Cited by 27
Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis
Placek, K., Benatar, M. G., Wuu, J., Rampersaud, E., Hennessy, L., Deerlin, V. M. V., Grossman, M., Irwin, D. J., Elman, L. B., McCluskey, L. F., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012595 · Cited by 27
Engen, M. J., Lyngstad, S. H., Ueland, T., Simonsen, C., Vaskinn, A., Smeland, O. B., Bettella, F., Lagerberg, T. V., Djurovic, S., Andreassen, O. A., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01094-9 · Cited by 27
Sun, J., Wang, Y., Folkersen, L., Borné, Y., Amlien, I. K., Buil, A., Orho‐Melander, M., Børglum, A. D., HOUGAARD, D. M., Lotta, L. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25014-7 · Cited by 27
Li‐Gao, R., Hughes, D. A., van Klinken, J. B., de Mutsert, R., Rosendaal, F. R., Mook-Kanamori, D. O., Timpson, N. J., & van Dijk, K. W. (2021) · Diabetes · doi:10.2337/db21-0397 · Cited by 27
A large Canadian cohort provides insights into the genetic architecture of human hair colour
Lona‐Durazo, F., de Aquino, M. M., Thakur, R., Funderburk, K. M., Zhang, T., Kovacs, M. A., Choi, J., Brown, K. M., & Parra, E. J. (2021) · Communications Biology · doi:10.1038/s42003-021-02764-0 · Cited by 27
Zhang, Z., Ma, P., Zhang, Z., Wang, Z., Wang, Q., & Pan, Y. (2021) · Genomics · doi:10.1016/j.ygeno.2021.12.016 · Cited by 27
Yang, X., Zhu, Q., Zhang, L., Pei, Y., Xu, X., Liu, X., Lu, G., Pan, J., & Wang, Y. (2022) · European Journal of Clinical Nutrition · doi:10.1038/s41430-021-01065-3 · Cited by 27
Werner, M. C. F., Wirgenes, K. V., Shadrin, A. A., Lunding, S. H., Rødevand, L. N., Hjell, G., Ormerod, M. B. E. G., Haram, M., Agartz, I., Djurovic, S., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01811-6 · Cited by 27
Protein prediction for trait mapping in diverse populations
Schubert, R., Geoffroy, E., Gregga, I., Mulford, A. J., Aguet, F., Ardlie, K., Gerszten, R. E., Clish, C. B., Berg, D. V. D., Taylor, K. D., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264341 · Cited by 28
Campagna, M. P., Xavier, A., Lea, R. A., Stankovich, J. M., Maltby, V. E., Butzkueven, H., Lechner‐Scott, J., Scott, R. J., & Jokubaitis, V. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01397-2 · Cited by 27
Oatman, S. R., Reddy, J. S., Quicksall, Z., Carrasquillo, M. M., Wang, X., Liu, C., Yamazaki, Y., Nguyen, T., Malphrus, K. G., Heckman, M. G., et al. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00592-2 · Cited by 27
Hamilton, O. S., Steptoe, A., & Ajnakina, O. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02622-z · Cited by 28
Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology
Naito, T., & Okada, Y. (2024) · Journal of Human Genetics · doi:10.1038/s10038-023-01213-6 · Cited by 28
Maj, C., Azevedo, T., Giansanti, V., Borisov, O., Dimitri, G. M., Spasov, S., Initiative, A. D. N., Lió, P., & Merelli, I. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00726 · Cited by 27
Olsen, C. M., Pandeya, N., Law, M. H., MacGregor, S., Iles, M. M., Thompson, B. S., Green, A. C., Neale, R. Ε., Whiteman, D. C., & Study, F. T. Q. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18703 · Cited by 27
Association of blood-based transcriptional risk scores with biomarkers for Alzheimer disease
Park, Y. H., Hodges, A., Simmons, A., Lovestone, S., Weiner, M. W., Kim, S., Saykin, A. J., & Nho, K. T. (2020) · Neurology Genetics · doi:10.1212/nxg.0000000000000517 · Cited by 27
Zhou, W., Fritsche, L. G., Das, S., Zhang, H., Nielsen, J. B., Holmen, O. L., Chen, J., Lin, M., Elvestad, M. B., Hveem, K., et al. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22067 · Cited by 27
HbA1c for type 2 diabetes diagnosis in Africans and African Americans: personalized medicine now
Paterson, A. D. (2017) · PLOS Medicine · doi:10.1371/journal.pmed.1002384 · Cited by 26
Rivier, C. A., Renedo, D. B., de Havenon, A., Sunmonu, N. A., Gill, T. M., Payabvash, S., Sheth, K. N., & Falcone, G. J. (2023) · Neurology · doi:10.1212/wnl.0000000000208010 · Cited by 26
Analysis of 1386 epileptogenic brain lesions reveals association with DYRK1A and EGFR
Boßelmann, C. M., Leu, C., Brünger, T., Hoffmann, L., Baldassari, S., Chipaux, M., Coras, R., Kobow, K., Hamer, H. M., Delev, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54911-w · Cited by 27
Lu, Z., Wang, X., Carr, M., Kim, A., Gazal, S., Mohammadi, P., Wu, L., Pirruccello, J. P., Kachuri, L., Gusev, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02262-7 · Cited by 26
Prummel, K. D., Woods, K., Kholmatov, M. M., Schmitt, E. C., Vlachou, E. P., Labyadh, M., Wehner, R., Poschmann, G., Stühler, K., Winter, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65803-y · Cited by 30
González‐Serna, D., Ochoa, E., López‐Isac, E., Julià, A., Degenhardt, F. C., Ortego‐Centeno, N., Radstake, T. R. D. J., Franke, A., Marsal, S., Mayes, M. D., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58741-w · Cited by 26
Nethander, M., Pettersson, U., Vandenput, L., Lorentzon, M., Karlsson, M. K., Mellström, D., & Ohlsson, C. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa082 · Cited by 26
Fani, L., Ahmad, S., Ikram, M. K., Ghanbari, M., & Ikram, M. A. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12212 · Cited by 26
Imputed gene expression risk scores: a functionally informed component of polygenic risk
Pain, O., Glanville, K. P., Hagenaars, S. P., Selzam, S., Fürtjes, A. E., Coleman, J. R. I., Rimfeld, K., Breen, G. D., Folkersen, L., & Lewis, C. M. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab053 · Cited by 26
Quistrebert, J., Orlova, M., Kerner, G., Ton, L. T., Luong, N. T., Danh, N. T., Vincent, Q. B., Jabot‐Hanin, F., Seeleuthner, Y., Bustamante, J. C., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009392 · Cited by 26
Mehrotra, R., Stanaway, I. B., Jarvik, G. P., Lambie, M., Morelle, J., Perl, J. M., Himmelfarb, J., Heimbürger, O., Johnson, D. W., Imam, T. H., et al. (2021) · Kidney International · doi:10.1016/j.kint.2021.05.037 · Cited by 26
Genetics Contributes to Concomitant Pathology and Clinical Presentation in Dementia with Lewy Bodies
van der Lee, S. J., van Steenoven, I., Beek, M. H. C. T. V., Tesi, N., Jansen, I. E., van Schoor, N. M., Reinders, M. J. T., Huisman, M. A., Scheltens, P., Teunissen, C. E., et al. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210365 · Cited by 26
Pyun, J., Park, Y. H., Lee, K., Kim, S., Saykin, A. J., Nho, K. T., & Initiative, A. D. N. (2021) · Translational Neurodegeneration · doi:10.1186/s40035-021-00259-w · Cited by 26
Heath, L. M., Earls, J. C., Magis, A. T., Kornilov, S. A., Lovejoy, J. C., Funk, C. C., Rappaport, N., Logsdon, B. A., Mangravite, L. M., Kunkle, B. W., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-09825-2 · Cited by 26
Fadista, J., Skotte, L., Karjalainen, J. M., Abner, E., Sørensen, E., Ullum, H., Werge, T., iPSYCH Group, Werge, T., HOUGAARD, D. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30921-4 · Cited by 26
Metadata
- Authors
- the Haplotype Reference Consortium
- Venue
- Nature Genetics
- Published
- 2016-08-22
- Volume
- 48
- Issue
- 10
- Pages
- 1279-1283
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC5388176
- OpenAlex
- W2511515754
- Cited by
- 2,836
- References
- 24
- Citations by year
- 2026: 143 · 2025: 219 · 2024: 327 · 2023: 343 · 2022: 389 · 2021: 422 · 2020: 321 · 2019: 288 · 2018: 248 · 2017: 118 · 2016: 16
- FWCI
- 180.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- VU Research Portal (publishedVersion)
- PubMed Central (acceptedVersion)
- Explore Bristol Research (acceptedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Padua Research Archive (University of Padova) (submittedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion)
- IRIS (submittedVersion)
- UnissResearch (Università degli Studi di Sassari) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by-nc-nd)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- Wellcome Trust (UK10K, WT 098051, 202802/Z/16/Z, WT097307, WT091310, WT090851)
- National Institute for Health and Care Research (NF-SI-0611-10099, NF-SI-0611-10219)
- British Heart Foundation (SP/04/002)
- Versus Arthritis
- National Institutes of Health (n01ey50007-15-0-1, HHSN268201100004C, n01mh80001-12-0-0, HHSN268201100001C, HHSN268201100002C, 5rc2dk088389-02, HHSN268201100003C)
- Canadian Institutes of Health Research (172013_1)
- Medical Research Council (MR/J00314X/1, MC_PC_15018)
- National Institute on Alcohol Abuse and Alcoholism (R01AA009367)
- National Institute on Aging (HHSN-271-2011-00004C,, U19AG023122)
- National Heart, Lung, and Blood Institute (HHSN268201300050C, R01HL127564, HHSN268201300046C, U01HL117626, HHSN268201300048C, HHSN268201300049C, HHSN268201100046C, R01HL102830, HHSN268201300047C, R01HL117626, R01HL109946)
- National Institute of Mental Health (U01MH105641, R01MH090937, R01MH101820, U01MH105653, R01MH085548, R01MH104964, R01MH094145, N01 MH080001)
- National Institute on Drug Abuse (R01DA037904, R01DA024417, R01DA005147, R01DA036216)
- National Human Genome Research Institute (U01HG005773, R01HG000376, R01HG007022)
- National Eye Institute (N01-EY-5-0007, ZIAEY000546, P30EY001583)
- National Cancer Institute (P50CA127003, R01CA151993, R01CA137178, R01CA059045, U01CA164930, U01CA137088, P01CA055075, UM1CA167552)
- National Institute of General Medical Sciences (T32GM007753)
- National Institute of Diabetes and Digestive and Kidney Diseases (U01DK085526, U01DK085501, R01DK072193, U01DK062370, RC2-DK088389, U01DK085545, R01DK073541, K24DK080140, K99DK092251, R01DK098032, U54DK083912, R01DK093757, P30DK020595)
- FP7 Ideas: European Research Council (617306)
- 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 › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- genotype imputation, haplotype reference panel, whole genome sequencing, European ancestry, phasing, association studies
- MeSH terms
- Alleles, Genetic Techniques, Genotype, Haplotypes, Humans, Reference Values, Internet, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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
