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 801–900 of 1,016
Zhang, C., Hansen, H. M., Semmes, E. C., Gonzalez‐Maya, J., Morimoto, L. M., Wei, Q., Eward, W. C., DeWitt, S. B., Hurst, J. H., Metayer, C., et al. (2019) · Bone · doi:10.1016/j.bone.2019.115070 · Cited by 35
A Genetic Approach to the Association Between PCSK9 and Sepsis
Feng, Q., Wei, W., Chaugai, S., Leon, B. G. C., Kawai, V. K., Leon, D. A. C., Jiang, L., Zhong, X. Y., Liu, G., Ihegword, A., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.11130 · Cited by 35
Dunbar, E. K., Greer, P. J., Melhem, N., Alkaade, S., Amann, S. T., Brand, R. E., Coté, G. A., Forsmark, C. E., Gardner, T. B., Gelrud, A., et al. (2020) · Journal of Gastroenterology · doi:10.1007/s00535-020-01703-w · Cited by 35
Accurate assembly of the olive baboon ( Papio anubis ) genome using long-read and Hi-C data
Batra, S. S., Levy‐Sakin, M., Robinson, J. A., Guillory, J., Durinck, S., Vilgalys, T. P., Kwok, P., Cox, L. A., Seshagiri, S., Song, Y. S., et al. (2020) · GigaScience · doi:10.1093/gigascience/giaa134 · Cited by 35
Privacy-preserving genotype imputation in a trusted execution environment
Dokmai, N., Koçkan, C., Zhu, K., Wang, X., Şahinalp, S. C., & Cho, H. (2021) · Cell Systems · doi:10.1016/j.cels.2021.08.001 · Cited by 35
Identification of Rare Loss-of-Function Genetic Variation Regulating Body Fat Distribution
Koprulu, M., Zhao, Y., Wheeler, E., Dong, L., Rocha, N., Li, C., Griffin, J. D., Patel, S., de Streek, M. V., Glastonbury, C. A., et al. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab877 · Cited by 35
Wang, Y., Grydeland, H., Roe, J. M., Pan, M., Magnussen, F., Amlien, I. K., Watne, L. O., Idland, A., Bertram, L., Gundersen, T. E., et al. (2021) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2021.12.008 · Cited by 35
Jin, H., Yoo, H. J., Kim, Y. A., Lee, J., Lee, Y., Kwon, S., Seo, Y. J., Lee, S. H., Koh, J., Ji, Y., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-07567-9 · Cited by 35
Sex-specific analysis in Behçet's disease reveals higher genetic risk in male patients
Jo, Y. G., Ortiz‐Fernández, L., Coit, P., Yılmaz, V., Yentur, S. P., Alıbaz-Öner, F., Aksu, K., Erken, E., Düzgün, N., Keser, G., et al. (2022) · Journal of Autoimmunity · doi:10.1016/j.jaut.2022.102882 · Cited by 35
COX7A2L genetic variants determine cardiorespiratory fitness in mice and human
Benegiamo, G., Sleiman, M. S. B., Wohlwend, M., Rodríguez-López, S., Goeminne, L. J. E., Laurila, P., Klevjer, M., Salonen, M. K., Lahti, J. M. T., Jha, P., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00655-0 · Cited by 35
The UCLA ATLAS Community Health Initiative: Promoting precision health research in a diverse biobank
Johnson, R., Ding, Y., Bhattacharya, A., Knyazev, S., Chiu, A. M., Lajonchere, C., Geschwind, D. H., & Paşaniuc, B. (2023) · Cell Genomics · doi:10.1016/j.xgen.2022.100243 · Cited by 35
Albiñana, C., Zhu, Z., Borbye‐Lorenzen, N., Boelt, S. G., Cohen, A. S., Skogstrand, K., Wray, N. R., Revez, J. A., Privé, F., Petersen, L. V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36392-5 · Cited by 35
Toll-Like Receptor 3 Mediates Aortic Stenosis Through a Conserved Mechanism of Calcification
Gollmann‐Tepeköylü, C., Graber, M., Hirsch, J., Mair, S., Naschberger, A., Pölzl, L., Nägele, F., Kirchmair, E., Degenhart, G., Demetz, E., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.063481 · Cited by 35
A Maize Practical Haplotype Graph Leverages Diverse NAM AssembliesPreprint
Franco, J. A. V., Gage, J. L., Bradbury, P. J., Johnson, L., Miller, Z. R., Buckler, E. S., & Romay, M. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.31.268425 · Cited by 35
A novel age-informed approach for genetic association analysis in Alzheimer’s disease
Initiative, F. T. A. D. N., Le Guen, Y., Belloy, M. E., Napolioni, V., Eger, S. J., Kennedy, G., Tao, R., He, Z., & Greicius, M. D. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00808-5 · Cited by 35
Choi, J., Kim, S., Kim, J., Son, H., Yoo, S., Kim, C.-U., Park, Y. J., Moon, S., Cha, B., Jeon, M. C., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg6319 · Cited by 34
Brotman, S. M., Moustafa, J. S. E.-S., Guan, L., Broadaway, K. A., Wang, D., Jackson, A., Welch, R., Currin, K. W., Tomlinson, M., Vadlamudi, S., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-01982-6 · Cited by 34
Odintsova, V. V., Willemsen, G. A. H. M., Dolan, C. V., Hottenga, J., Martin, N. G., Slagboom, P. E., Ordoñana, J. R., & Boomsma, D. I. (2018) · Twin Research and Human Genetics · doi:10.1017/thg.2018.23 · Cited by 34
Sequence variation at ANAPC1 accounts for 24% of the variability in corneal endothelial cell density
Ivarsdottir, E. V., Benónísdóttir, S., Þorleifsson, G., Sulem, P., Oddsson, Á., Styrkársdóttir, U., Kristmundsdóttir, S., Arnadottir, G. A., Þorgeirsson, G., Jónsdóttir, I., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09304-9 · Cited by 34
Walker, C. J., Mrózek, K., Özer, H. G., Nicolet, D., Kohlschmidt, J., Papaioannou, D., Genutis, L. K., Bill, M., Powell, B. L., Uy, G. L., et al. (2021) · Blood Advances · doi:10.1182/bloodadvances.2020003727 · Cited by 34
Cis -epistasis at the LPA locus and risk of cardiovascular diseases
Zeng, L., Moser, S., Mirza‐Schreiber, N., Lamina, C., Coassin, S., Nelson, C. P., Annilo, T., Franzén, O., Kleber, M. E., Mack, S. R., et al. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab136 · Cited by 34
Polygenic Risk Score for Alzheimer's Disease in Caribbean Hispanics
Sariya, S., Felsky, D., Reyes‐Dumeyer, D., Lali, R., Lantigua, R., Vardarajan, B. N., Jiménez‐Velázquez, I. Z., Haines, J. L., Shellenberg, G. D., Pericak‐Vance, M. A., et al. (2021) · Annals of Neurology · doi:10.1002/ana.26131 · Cited by 34
Shaw, D. M., Polikowsky, H. P., Pruett, D. G., Chen, H., Petty, L. E., Viljoen, K. Z., Beilby, J. M., Jones, R. M., Kraft, S. J., & Below, J. E. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.004 · Cited by 34
Simcoe, M. J., Shah, A., Fan, B., Choquet, H., Weisschuh, N., Waseem, N. H., Chen, J., Melles, R. B., Ritch, R. H., Mahroo, O. A., et al. (2022) · Ophthalmology · doi:10.1016/j.ophtha.2022.01.005 · Cited by 34
Grishin, D., & Gusev, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01075-2 · Cited by 35
Hanks, S. C., Forer, L., Schönherr, S., LeFaive, J., Martins, T., Welch, R., Taliun, S. A. G., BRAFF, D. L., Johnsen, J. M., Kenny, E. E., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.07.012 · Cited by 34
Genome-Wide Association Study Points to Novel Locus for Gilles de la Tourette Syndrome
Tsetsos, F., Topaloudi, A., Jain, P., Yang, Z., Yu, D., Kolovos, P., Tümer, Z., Rizzo, R., Hartmann, A., Depienne, C., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.01.023 · Cited by 34
Brackman, D. J., Yee, S. W., Enogieru, O. J., Shaffer, C. M., Ranatunga, D. K., Denny, J. C., Wei, W., Kamatani, Y., Kubo, M., Roden, D. M., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1439 · Cited by 34
Zhao, J., Feng, Q., Wu, P., Warner, J. L., Denny, J. C., & Wei, W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212112 · Cited by 34
Hwang, M. Y., Choi, N.-H., Won, H., Kim, B.-J., & Kim, Y. J. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1008646 · Cited by 33
Ying, P., Chen, C., Lu, Z., Chen, S., Zhang, M., Cai, Y., Zhang, F., Huang, J., Fan, L., Ning, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41690-z · Cited by 37
Ruth, K. S., Soares, A. G., Borges, M. C., Eliassen, A. H., Hankinson, S. E., Jones, M. E., Kraft, P. L., Nichols, H. B., Sandler, D. P., Schoemaker, M. J., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz015 · Cited by 33
Fritsche, L. G., Beesley, L. J., VandeHaar, P., Peng, R. B., Salvatore, M., Zawistowski, M., Taliun, S. A. G., Das, S., LeFaive, J., Kaleba, E. O., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008202 · Cited by 33
Cannon, M. E., Currin, K. W., Young, K. L., Perrin, H. J., Vadlamudi, S., Safi, A., Song, L., Wu, Y., Wabitsch, M., Laakso, M., et al. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.400294 · Cited by 33
Gao, Y., Wei, Y., Zhou, X., Huang, S., Zhao, H., & Zeng, P. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.583106 · Cited by 33
Association of structural variation with cardiometabolic traits in Finns
Chen, L., Abel, H., Das, I., Larson, D. E., Ganel, L., Kanchi, K., Regier, A., Young, E. P., Kang, C. J., Scott, A. J., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.008 · Cited by 33
Madrid, L., Initiative, A. D. N., Moreno–Grau, S., Ahmad, S., González-Pérez, A., de Rojas, I., Xia, R., Adami, P. V. M., García‐González, P., Kleineidam, L., et al. (2021) · Aging · doi:10.18632/aging.202950 · Cited by 33
Liu, D., Zhu, J., Zhou, D., Nikas, E. G., Mitanis, N. T., Sun, Y., Wu, C., Mancuso, N., Cox, N. J., Wang, L., et al. (2021) · International Journal of Cancer · doi:10.1002/ijc.33808 · Cited by 33
Constructing germline research cohorts from the discarded reads of clinical tumor sequences
Gusev, A., Groha, S., Taraszka, K., Semenov, Y. R., & Zaitlen, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00999-4 · Cited by 33
Mignogna, G., Carey, C. E., Wedow, R., Baya, N. A., Cordioli, M., Pirastu, N., Bellocco, R., Malerbi, K. F., Nivard, M. G., Neale, B. M., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01632-7 · Cited by 34
Concordance of a High Lipoprotein(a) Concentration Among Relatives
Reeskamp, L. F., Tromp, T. R., Patel, A. P., Ibrahim, S., Trinder, M., Haidermota, S., Hovingh, G. K., Stroes, E. S. G., Natarajan, P., & Khera, A. V. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.3548 · Cited by 33
Couvy‐Duchesne, B., O’Callaghan, V. S., Parker, R., Mills, N. T., Kirk, K. M., Scott, J. L., Vinkhuyzen, A. A. E., Hermens, D. F., Lind, P. A., Davenport, T. A., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-018959 · Cited by 33
Carnero‐Montoro, E., Barturen, G., Povedano, E., Kerick, M., Martínez‐Bueno, M., Ballestar, E., Martin, J. J., Teruel, M., & Alarcón-Riquelme, M. E. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01880 · Cited by 33
Gueuning, M., Thun, G. A., Wittig, M., Galati, A.-L., Meyer, S., Trost, N., Gourri, E., Fuß, J., Sigurdardottir, S., Merki, Y., et al. (2022) · Blood Advances · doi:10.1182/bloodadvances.2022007133 · Cited by 32
Unravelling the complex causal effects of substance use behaviours on common diseases
Xue, A., Zhu, Z., Wang, H., Jiang, L., Visscher, P. M., Zeng, J., & Yang, J. (2024) · Communications Medicine · doi:10.1038/s43856-024-00473-3 · Cited by 32
de Vries, D. H., Matzaraki, V., Bakker, O. B., Brugge, H., Westra, H.-J., Netea, M. G., Franke, L., Kumar, V., & van der Wijst, M. G. P. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008408 · Cited by 32
Study, T. N. B. D. P., Justice, C. M., Cuellar, A., Bala, K., Sabourin, J., Cunningham, M. L., Crawford, K. L., Phipps, J. M., Zhou, Y., Cilliers, D. D., et al. (2020) · Human Genetics · doi:10.1007/s00439-020-02157-z · Cited by 32
Associations between Genetically Predicted Blood Protein Biomarkers and Pancreatic Cancer Risk
Zhu, J., Shu, X., Guo, X., Liu, D., Bao, J., Milne, R. L., Giles, G. G., Wu, C., Du, M., White, E., et al. (2020) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-20-0091 · Cited by 32
Fine-scale population structure in the UK Biobank: implications for genome-wide association studies
Cook, J. P., Mahajan, A., & Morris, A. P. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa157 · Cited by 32
Jäger, S., Cuadrat, R. R. C., Hoffmann, P., Wittenbecher, C., & Schulze, M. B. (2020) · Nutrients · doi:10.3390/nu12082261 · Cited by 32
Glastonbury, C. A., Pulit, S. L., Honecker, J., Censin, J. C., Laber, S., Yaghootkar, H., Rahmioğlu, N., Pastel, E., Kos, K., Pitt, A., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008044 · Cited by 31
Aylward, A., Okino, M.-L., Benaglio, P., Chiou, J., Beebe, E., Padilla, J. A., Diep, S., & Gaulton, K. J. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009531 · Cited by 32
3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies
Hoskens, H., Liu, D., Naqvi, S., Lee, M. K., Eller, R. J., Indencleef, K., White, J. D., Li, J., Larmuseau, M., Hens, G., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009528 · Cited by 32
Blood copper and risk of cardiometabolic diseases: a Mendelian randomization study
Jäger, S., Cabral, M., Kopp, J. F., Hoffmann, P., Ng, E., Whitfield, J. B., Morris, A. P., Lind, L., Schwerdtle, T., & Schulze, M. B. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab275 · Cited by 32
Park, Y. H., Pyun, J., Hodges, A., Jang, J., Bice, P. J., Kim, S., Saykin, A. J., & Nho, K. T. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00919-z · Cited by 32
Deakin, C. T., Bowes, J., Rider, L. G., Miller, F. W., Pachman, L. M., Sanner, H., Rouster‐Stevens, K. A., Mamyrova, G., Curiel, R., Feldman, B. M., et al. (2022) · Human Molecular Genetics · doi:10.1093/hmg/ddac019 · Cited by 32
Genome-wide gene-air pollution interaction analysis of lung function in 300,000 individuals
Melbourne, C., Erzurumluoglu, A. M., Shrine, N. R. G., Chen, J., Tobin, M. D., Hansell, A., & Wain, L. V. (2021) · Environment International · doi:10.1016/j.envint.2021.107041 · Cited by 32
Strain, T., Dempsey, P. C., Wijndaele, K., Sharp, S. J., Kerrison, N. D., Gonzales, T. I., Li, C., Wheeler, E., Langenberg, C., Brage, S., et al. (2023) · Diabetes Care · doi:10.2337/dc22-1467 · Cited by 32
Transcriptomics of atopy and atopic asthma in white blood cells from children and adolescents
Jiang, Y., Gruzieva, O., Wang, T., Forno, E., Boutaoui, N., Sun, T., Merid, S. K., Acosta‐Pérez, E., Kull, I., CANINO, G. J., et al. (2019) · European Respiratory Journal · doi:10.1183/13993003.00102-2019 · Cited by 32
Deng, Q., Hu, H., Yu, X., Liu, S., Wang, L., Chen, W., Zhang, C., Zeng, Z., Cao, Y., Xu-Monette, Z. Y., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13002-x · Cited by 32
Deep genotype imputation captures virtually all heritability of autoimmune vitiligo
Roberts, G. H. L., Santorico, S. A., & Spritz, R. A. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa005 · Cited by 32
Cai, N., Bigdeli, T. B., Kretzschmar, W. W., Li, Y., Liang, J., Hu, J., Peterson, R. E., Bacanu, S., Webb, B. T., Riley, B. P., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.11 · Cited by 32
RetractedMoksnes, M. R., Hansen, A. F., Wolford, B. N., Thomas, L. F., Rasheed, H., Simić, A., Bhatta, L., Brantsæter, A. L., Surakka, I. L., Zhou, W., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06101-z · Cited by 31
Sex differences in the genetic regulation of the human plasma proteome
Koprulu, M., Wheeler, E., Kerrison, N. D., Denaxas, S., Carrasco-Zanini, J., Orkin, C. M., Hemingway, H., Wareham, N. J., Pietzner, M., & Langenberg, C. (2025) · Nature Communications · doi:10.1038/s41467-025-59034-4 · Cited by 32
Tanskanen, T. J., van den Berg, L., Välimäki, N., Aavikko, M., Ness‐Jensen, E., Hveem, K., Wettergren, Y., Lindskog, E. B., Tõnisson, N., Metspalu, A., et al. (2017) · International Journal of Cancer · doi:10.1002/ijc.31076 · Cited by 31
López‐Mejías, R., Carmona, F. D., Genre, F., Remuzgo‐Martínez, S., González‐Juanatey, C., Corrales, A., Vicente, E. F., Pulito‐Cueto, V., Miranda-Filloy, J. A., Huaranga, M. A. R., et al. (2018) · Arthritis & Rheumatology · doi:10.1002/art.40734 · Cited by 31
CDH6 and HAGH protein levels in plasma associate with Alzheimer’s disease in APOE ε4 carriers
Ahmad, S., Milan, M. D. C., Hansson, O., Demirkan, A., Agustin, R., Sáez, M. E., Giagtzoglou, N., Cabrera-Socorro, A., Bakker, M. H. M., Ramirez, A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-65038-5 · Cited by 31
Mitochondrial GWAS and association of nuclear – mitochondrial epistasis with BMI in T1DM patients
Ludwig‐Słomczyńska, A. H., Seweryn, M. T., Kapusta, P., Pitera, E., Handelman, S. K., Mantaj, U., Cyganek, K., Gutaj, P., Dobrucka, Ł., Wender-Ożegowska, E., et al. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-00752-7 · Cited by 31
Lundtoft, C., Pucholt, P., Imgenberg‐Kreuz, J., Almlöf, J. C., Eloranta, M., Syvänen, A., Nordmark, G., Sandling, J. K., Kockum, I. S., Olsson, T. P., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009199 · Cited by 31
Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation
Gao, Y., Yang, Z., Yang, W., Yang, Y., Gong, J., Yang, Q., & Niu, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa953 · Cited by 31
Genome-wide association study of asthma exacerbations despite inhaled corticosteroid use
Hernandez‐Pacheco, N., Vijverberg, S. J. H., Luis, E. H., Li, J., Sio, Y. Y., Granell, R., Corrales, A., Maroteau, C., Lethem, R., Pérez-García, J., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.03388-2020 · Cited by 31
Suri, P., Stanaway, I. B., Zhang, Y., Freidin, M. B., Tsepilov, Y. A., Carrell, D., Williams, F. M. K., Aulchenko, Y. S., Hakonarson, H. H., Namjou, B., et al. (2021) · Pain · doi:10.1097/j.pain.0000000000002221 · Cited by 31
Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin
Joseph, C. B., Mariniello, M., Yoshifuji, A., Schiano, G., Lake, J. L., Marten, J., Richmond, A., Huffman, J. E., Campbell, A., Harris, S. E., et al. (2022) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021040491 · Cited by 31
Genetic regulation of RNA splicing in human pancreatic islets
Atla, G., Bonàs‐Guarch, S., Cuenca-Ardura, M., Beucher, A., Crouch, D. J. M., García-Hurtado, J., Morán, I., Cnop, M., Eliasson, L., Esguerra, J. L. S., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02757-0 · Cited by 31
Rodríguez‐Fernández, B., Vilor‐Tejedor, N., Arenaza‐Urquijo, E. M., Sánchez‐Benavides, G., Suárez‐Calvet, M., Operto, G., Minguillón, C., Fauria, K., Kollmorgen, G., Suridjan, I., et al. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-022-01101-9 · Cited by 31
HLA Variation and SARS-CoV-2 Specific Antibody Response
Wolday, D., Fung, C. Y. J., Morgan, G., Casalino, S., Frangione, E., Taher, J., & Lerner‐Ellis, J. P. (2023) · Viruses · doi:10.3390/v15040906 · Cited by 31
Worthey, E. A. (2017) · Current Protocols in Human Genetics · doi:10.1002/cphg.49 · Cited by 31
Chandler, H. L., Wise, R. G., Murphy, K., Tansey, K. E., Linden, D. E. J., & Lancaster, T. M. (2019) · Scientific Reports · doi:10.1038/s41598-018-36820-3 · Cited by 31
Lin, M. H., Caberto, C., Wan, P. C., Li, Y., Lum‐Jones, A., Tiirikainen, M., Pooler, L., Nakamura, B. N., Sheng, X., Porcel, J., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa083 · Cited by 32
A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels
de Vries, P. S., Reventún, P., Brown, M. R., Heath, A. S., Huffman, J. E., Le, N., Bebo, A., Brody, J. A., Temprano‐Sagrera, G., Raffield, L. M., et al. (2024) · Blood · doi:10.1182/blood.2023021452 · Cited by 30
de Boer, A., van den Bosch, A. M. R., Mekkes, N., Fransen, N. L., Dagkesamanskaia, E., Hoekstra, E., Hamann, J., Smolders, J., Huitinga, I., & Holtman, I. R. (2024) · Acta Neuropathologica · doi:10.1007/s00401-024-02742-w · Cited by 30
Strategies for phasing and imputation in a population isolate
Herzig, A. F., Nutile, T., Babron, M., Ciullo, M., Bellenguez, C., & Leutenegger, A. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22109 · Cited by 30
Masip, G., Silventoinen, K., Keski‐Rahkonen, A., Palviainen, T., Sipilä, P. N., Kaprio, J. A., & Bogl, L. H. (2020) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqaa181 · Cited by 30
Bruno, M., Dewi, I. M. W., Matzaraki, V., ter Horst, R., Pekmezovic, M. G., Rösler, B., Groh, L. A., Röring, R. J., Kumar, V., Li, Y., et al. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.036 · Cited by 30
Kertai, M. D., Mosley, J. D., He, J., Ramakrishnan, A., Abdelmalak, M., Hong, Y., Shoemaker, M. B., Roden, D. M., & Bastarache, L. A. (2021) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.120.003269 · Cited by 30
Model-based assessment of replicability for genome-wide association meta-analysis
McGuire, D., Jiang, Y., Liu, M., Weissenkampen, J. D., Eckert, S., Yang, L., Chen, F., Liu, M., Jiang, Y., Wedow, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21226-z · Cited by 30
Association of Monogenic and Polygenic Risk With the Prevalence of Open-Angle Glaucoma
Siggs, O. M., Han, X., Qassim, A., Souzeau, E., Kuruvilla, S. E., Marshall, H. N., Mullany, S., Mackey, D. A., Hewitt, A. W., Gharahkhani, P., et al. (2021) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2021.2440 · Cited by 30
Fitzsimons, E., Moulton, V., Hughes, D. A., Neaves, S., Ho, K. M., Hemani, G., Timpson, N. J., Calderwood, L., Gilbert, E., & Ring, S. M. (2021) · Longitudinal and Life Course Studies · doi:10.1332/175795921x16223668101602 · Cited by 30
Yan, G., Liu, X., Xiao, S., Xin, W., Xu, W., Li, Y., Huang, T., Qin, J., Xie, L., Ma, J., et al. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1960-9 · Cited by 30
Best practices for analyzing imputed genotypes from low-pass sequencing in dogs
Buckley, R. M., Harris, A. C., Wang, G., Whitaker, D. T., Zhang, Y., & Ostrander, E. A. (2021) · Mammalian Genome · doi:10.1007/s00335-021-09914-z · Cited by 30
Placental multi-omics integration identifies candidate functional genes for birthweight
Ayele, F. T., Zeng, X., Chatterjee, S., Ouidir, M., Lesseur, C., Hao, K., Chen, J., Tesfaye, M., Marsit, C. J., Workalemahu, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30007-1 · Cited by 30
Construction and Application of Polygenic Risk Scores in Autoimmune Diseases
Khunsriraksakul, C., Markus, H., Olsen, N. J., Carrel, L., Jiang, B., & Liu, D. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.889296 · Cited by 31
3D genome organization links non-coding disease-associated variants to genes
Orozco, G., Schoenfelder, S., Walker, N. J., Eyre, S., & Fraser, P. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.995388 · Cited by 30
Ken, S., Hatzikotoulas, K., Southam, L., Taylor, H. J., Yin, X., Lorenz, K., Mandla, R., Huerta‐Chagoya, A., Rayner, N. W., Bocher, O., et al. (2023) · Yearbook of pediatric endocrinology · doi:10.1530/ey.20.13.6 · Cited by 32
Wang, Z., Zhao, G., Zhu, Z., Wang, Y., Xiang, X., Zhang, S., Luo, T., Zhou, Q., Qiu, J. H., Tang, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1061 · Cited by 30
Genome-wide association study of preserved ratio impaired spirometry (PRISm)
Higbee, D., Lirio, A., Hamilton, F., Granell, R., Wyss, A. B., London, S. J., Bartz, T. M., Gharib, S. A., Cho, M. H., Wan, E. S., et al. (2023) · European Respiratory Journal · doi:10.1183/13993003.00337-2023 · Cited by 30
Fadista, J., Lund, M., Skotte, L., Geller, F., Nandakumar, P., Chatterjee, S., Matsson, H., Granström, A. L., Wester, T., Salo, P. P., et al. (2018) · European Journal of Human Genetics · doi:10.1038/s41431-017-0053-7 · Cited by 30
Genetics of HbA1c: a case study in clinical translation
Leong, A., & Wheeler, E. (2018) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2018.02.008 · Cited by 31
Haplosaurus computes protein haplotypes for use in precision drug design
Spooner, W., McLaren, W. M., Slidel, T. W. F., Finch, D. K., Butler, R. J., Campbell, J., Eghobamien, L., Rider, D. A., Kiefer, C. M., Robinson, M. J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06542-1 · Cited by 30
A Phenome-Wide Mendelian Randomization Study of Pancreatic Cancer Using Summary Genetic Data
Langdon, R., Richmond, R. C., Hemani, G., Zheng, J., Wade, K. H., Carreras‐Torres, R., Johansson, M., Brennan, P., Wootton, R. E., Munafo, M. R., et al. (2019) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-0036 · Cited by 30
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