The DOI matches its Crossref record; 35 of the 37 references it cites were found in a registry or an index.
Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits
Open accessAbstract
We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.
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?
In the citing paper
“A conditional analysis 59 was used to identify independently-segregating variants in each of the merged loci that was genome-wide significant, using the linkage disequilibrium structure of UK Biobank as a reference panel.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [59]
In the citing paper
“GCTA COJO27 conditional analysis of the genome-wide loci indicates that TREM2 and three other loci (BIN1, ABCA7, and PTK2B/CLU) have multiple independent LOAD association signals”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [27]
In the citing paper
“We performed approximate conditional analysis in each locus reaching genome-wide significance using Genome-wide Complex Trait Analysis27 (GCTA) but did not identify secondary signal in any locus.”
From GWAS identifies 14 loci for device-measured physical activity and sleep duration (2018) · cites this paper as [27]
In the citing paper
“Within these 136 loci, multi-SNP-based conditional analysis (Yang et al., 2012) identified 10 additional SNPs with independent associations, resulting in a total of 146 independent lead SNPs”
From Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders (2019) · cites this paper as Yang et al., 2012
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
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Self-citationA map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities
Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,633
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
Self-citationA 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
Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013
Genomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162 · cited 1×
“(20) As in a joint analysis, if the individual-level genotype data of the discovery sample are unavailable, we can estimate the LD correlations from the reference sample and approximate a conditional analysis as”
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
MAGIC, Speliotes, E. K., on behalf of Procardis Consortium, Willer, C. J., Berndt, S. I., Monda, K. L., Thorleifsson, G., Jackson, A. U., Allen, H. L., Lindgren, C. M., et al. (2010) · Nature Genetics · doi:10.1038/ng.686 · Cited by 2,912
Self-citationHundreds 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
Self-citation
Flagged first, then by citations
2 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534
Self-citation10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719
Self-citationIntegration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352
Self-citationeQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0090-3 · Cited by 3,005
Self-citationAlzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946
Mapping genomic loci implicates genes and synaptic biology in schizophrenia
Trubetskoy, V., Pardiñas, A. F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T. B., Bryois, J., Chen, C.-Y., Dennison, C. A., Hall, L. S., et al. (2022) · Nature · doi:10.1038/s41586-022-04434-5 · Cited by 2,964
Howard, D. M., Adams, M. J., Clarke, T.-K., Hafferty, J. D., Gibson, J., Shirali, M., Coleman, J. R. I., Hagenaars, S. P., Ward, J., Wigmore, E. M., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0326-7 · Cited by 2,565
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479
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
Self-citationNalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30320-5 · Cited by 2,454
Self-citationA comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277
Genomic atlas of the human plasma proteome
Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., Burgess, S., Jiang, T., Paige, E., Surendran, P., et al. (2018) · Nature · doi:10.1038/s41586-018-0175-2 · Cited by 2,268
Benefits and limitations of genome-wide association studies
Tam, V., Patel, N. K., Turcotte, M., Bossé, Y., Paré, G., & Meyre, D. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0127-1 · Cited by 2,208
Polygenic prediction via Bayesian regression and continuous shrinkage priors
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A., & Smoller, J. W. (2019) · Nature Communications · doi:10.1038/s41467-019-09718-5 · Cited by 1,922
the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segrè, A. V., Steinthorsdottir, V., Strawbridge, R. J., Khan, H., Grallert, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2383 · Cited by 1,912
Self-citation23andMe Research Team, Liu, M., Psychiatry, H. A.-I., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., Datta, G., Davila-Velderrain, J., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0307-5 · Cited by 1,896
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
A review of instrumental variable estimators for Mendelian randomization
Burgess, S., Small, D. S., & Thompson, S. G. (2015) · Statistical Methods in Medical Research · doi:10.1177/0962280215597579 · Cited by 1,769
Mahajan, A., Taliun, D., Thurner, M., Robertson, N. R., Torres, J. M., Rayner, N. W., Payne, A. J., Steinthorsdottir, V., Scott, R. A., Grarup, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0241-6 · Cited by 1,744
Self-citationPlasma proteomic associations with genetics and health in the UK Biobank
Sun, B. B., Chiou, J., Traylor, M., Benner, C., Hsu, Y.-H., Richardson, T. G., Surendran, P., Mahajan, A., Robins, C., Vasquez-Grinnell, S. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06592-6 · Cited by 1,880
Self-citationLarge-scale association analysis identifies new risk loci for coronary artery disease
The CARDIoGRAMplusC4D Consortium, Deloukas, P., DIAGRAM Consortium, CARDIOGENICS Consortium, MuTHER Consortium, Wellcome Trust Case Control Consortium, Kanoni, S., Willenborg, C., Farrall, M., Assimes, T. L., et al. (2013) · Nature Genetics · doi:10.1038/ng.2480 · Cited by 1,614
Self-citationMalik, R., Chauhan, G., Traylor, M., Sargurupremraj, M., Okada, Y., Mishra, A., Rutten‐Jacobs, L. C. A., Giese, A., van der Laan, S. W., Grétarsdóttir, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0058-3 · Cited by 1,583
New genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580
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
Self-citationEvaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496
Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders
Lee, P. H., Anttila, V., Won, H., Feng, Y.-C. A., Rosenthal, J., Zhu, Z., Tucker-Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.020 · Cited by 1,381
Self-citationBurgess, S., Dudbridge, F., & Thompson, S. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6835 · Cited by 1,153
Initiative, A. N. G., Watson, H. J., Eating Disorders Working Group of the Psychiatric Genomics Consortium, Yilmaz, Z., Thornton, L. M., Hübel, C., Coleman, J. R. I., Gaspar, H. A., Bryois, J., Hinney, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0439-2 · Cited by 1,109
Self-citationThe genetic architecture of type 2 diabetes
Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature · doi:10.1038/nature18642 · Cited by 1,051
Self-citationDIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Mahajan, A., Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Consortium, Go, M. J., Zhang, W., Below, J. E., Gaulton, K. J., et al. (2014) · Nature Genetics · doi:10.1038/ng.2897 · Cited by 1,034
Self-citationExome sequencing and analysis of 454,787 UK Biobank participants
Backman, J., Li, A. H., Marcketta, A., Sun, D., Mbatchou, J. S., Kessler, M. D., Benner, C., Liu, D., Locke, A. E., Balasubramanian, S., et al. (2021) · Nature · doi:10.1038/s41586-021-04103-z · Cited by 955
Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases
Chen, Y., Lu, T., Pettersson, U., Stewart, I. D., Butler‐Laporte, G., Nakanishi, T., Cerani, A., Liang, K. Y. H., Yoshiji, S., Willett, J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01270-1 · Cited by 1,030
Causal associations between risk factors and common diseases inferred from GWAS summary data
Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., Robinson, M. R., McGrath, J. J., Visscher, P. M., Wray, N. R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02317-2 · Cited by 951
Self-citationXue, A., Wu, Y., Zhu, Z., Zhang, F., Kemper, K. E., Zheng, Z., Yengo, L., Lloyd‐Jones, L. R., Sidorenko, J., Wu, Y., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04951-w · Cited by 897
Self-citationFrom genome-wide associations to candidate causal variants by statistical fine-mapping
Schaid, D. J., Chen, W., & Larson, N. B. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0016-z · Cited by 927
Biobank-driven genomic discovery yields new insight into atrial fibrillation biology
Nielsen, J. B., Thorolfsdottir, R. B., Fritsche, L. G., Zhou, W., Skov, M. W., Graham, S. E., Herron, T. J., McCarthy, S., Schmidt, E. M., Sveinbjornsson, G., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0171-3 · Cited by 876
A saturated map of common genetic variants associated with human height
Yengo, L., Vedantam, S., Marouli, E., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A. U., Jiang, Y., Raghavan, S., et al. (2022) · Nature · doi:10.1038/s41586-022-05275-y · Cited by 825
Self-citation23and Me Research Team, Karlsson Linnér, R., eQTLgen Consortium, International Cannabis Consortium, Social Science Genetic Association Consortium, Biroli, P., Kong, E., Meddens, S. F. W., Wedow, R., Fontana, M. A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0309-3 · Cited by 756
Self-citationGWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., Westra, H.-J., Shakhbazov, K., Abdellaoui, A., Agrawal, A., et al. (2013) · Science · doi:10.1126/science.1235488 · Cited by 827
Self-citationZheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., et al. (2019) · doi:10.1101/627398 · Cited by 812
The trans-ancestral genomic architecture of glycemic traits
Chen, J., Spracklen, C. N., Marenne, G., Varshney, A., Corbin, L. J., Luan, J., Willems, S. M., Wu, Y., Zhang, X., Horikoshi, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00852-9 · Cited by 776
An atlas of genetic influences on osteoporosis in humans and mice
23andMe Research Team, Morris, J. A., Kemp, J. P., Youlten, S. E., Laurent, L., Logan, J. G., Chai, R. C., Vulpescu, N. A., Forgetta, V., Kleinman, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0302-x · Cited by 773
Multi-ethnic genome-wide association study for atrial fibrillation
Roselli, C., Chaffin, M. D., Weng, L.-C. C., Aeschbacher, S., Ahlberg, G., Albert, C. M., Almgren, P., Alonso, Á., Anderson, C. D., Aragam, K. G., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0133-9 · Cited by 761
Zhao, J. H., Stacey, D., Eriksson, N., Macdonald-Dunlop, E., Hedman, Å. K., Kalnapenkis, A., Enroth, S., Cozzetto, D., Digby-Bell, J. L., Marten, J., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01588-w · Cited by 781
Kettunen, J., Demirkan, A., Würtz, P., Draisma, H. H. M., Haller, T., Rawal, R., Vaarhorst, A., Kangas, A. J., Lyytikäinen, L.-P., Pirinen, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11122 · Cited by 738
An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans
Scott, R. A., Scott, L. J., Mägi, R., Marullo, L., Gaulton, K. J., Kaakinen, M., Pervjakova, N., Pers, T. H., Johnson, A. D., Eicher, J. D., et al. (2017) · Diabetes · doi:10.2337/db16-1253 · Cited by 708
Self-citationAragam, K. G., Jiang, T., Goel, A., Kanoni, S., Wolford, B. N., Atri, D. S., Weeks, E. M., Wang, M., Hindy, G., Zhou, W., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01233-6 · Cited by 738
The transcriptional landscape of age in human peripheral blood
Peters, M. J., Joehanes, R., Pilling, L. C., Schurmann, C., Conneely, K. N., Powell, J., Reinmaa, E., Sutphin, G. L., Zhernakova, A., Schramm, K., et al. (2015) · Nature Communications · doi:10.1038/ncomms9570 · Cited by 725
Self-citationMahajan, A., Spracklen, C. N., Zhang, W., Ng, M. C. Y., Petty, L. E., Kitajima, H., Yu, G. Z., Rüeger, S., Speidel, L., Kim, Y. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01058-3 · Cited by 704
Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals
Folkersen, L., Gustafsson, S., Wang, Q., Hansen, D. H., Hedman, Å. K., Schork, A., Page, K., Zhernakova, D. V., Wu, Y., Peters, J., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00287-2 · Cited by 705
Self-citationAssociation analyses based on false discovery rate implicate new loci for coronary artery disease
Nelson, C. P., CARDIoGRAMplusC4D, Goel, A., Butterworth, A. S., Kanoni, S., Webb, T. R., Marouli, E., Zeng, L., Ντάλλα, Ι., Lai, F. Y., et al. (2017) · Nature Genetics · doi:10.1038/ng.3913 · Cited by 672
Self-citationPitfalls of predicting complex traits from SNPs
Wray, N. R., Yang, J., Hayes, B. J., Price, A. L., Goddard, M. E., & Visscher, P. M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3457 · Cited by 690
Self-citationOkbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01016-z · Cited by 654
Berndt, S. I., Gustafsson, S., Mägi, R., Ganna, A., Wheeler, E., Feitosa, M. F., Justice, A. E., Monda, K. L., Croteau-Chonka, D. C., Day, F. R., et al. (2013) · Nature Genetics · doi:10.1038/ng.2606 · Cited by 646
Self-citationGhoussaini, M., Mountjoy, E., Carmona, M., Peat, G., Schmidt, E. M., Hercules, A., Fumis, L., Miranda, A., Carvalho‐Silva, D., Buniello, A., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa840 · Cited by 593
Paternoster, L., Kemp, J. P., Zheng, J., Smith, G. D., Relton, C. L., Evans, D. M., McArdle, W. L., Henderson, A. J., Standl, M., Tiesler, C. M. T., et al. (2015) · Nature Genetics · doi:10.1038/ng.3424 · Cited by 620
Dashti, H. S., Jones, S. E., Wood, A. R., Lane, J. M., van Hees, V. T., Wang, H., Rhodes, J. A., Song, Y., Patel, K., Anderson, S. G., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08917-4 · Cited by 609
Self-citationRare and low-frequency coding variants alter human adult height
Marouli, E., Graff, M., Medina-Gomez, C., Lo, K. S., Wood, A. R., Kjaer, T. R., Fine, R. S., Lu, Y., Schurmann, C., Highland, H. M., et al. (2017) · Nature · doi:10.1038/nature21039 · Cited by 610
Self-citationStroke genetics informs drug discovery and risk prediction across ancestries
Mishra, A., Malik, R., Hachiya, T., Jürgenson, T., Namba, S., Posner, D. C., Kamanu, F., Koido, M., Grand, Q. L., Shi, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05165-3 · Cited by 623
Self-citationParent-of-origin-specific allelic associations among 106 genomic loci for age at menarche
Study, A. O. C., Perry, J. R. B., The GENICA Network, kConFab, Study, T. L. C., The InterAct Consortium, Early Growth Genetics (EGG) Consortium, Day, F., Elks, C. E., Sulem, P., et al. (2014) · Nature · doi:10.1038/nature13545 · Cited by 604
Self-citationWhole‐genome sequencing identifies EN1 as a determinant of bone density and fracture
Zheng, H., Forgetta, V., Hsu, Y., Estrada, K., Roselló‐Díez, A., Leo, P. J., Dahia, C. L., Park‐Min, K. H., Tobias, J. H., Kooperberg, C. L., et al. (2015) · Nature · doi:10.1038/nature14878 · Cited by 596
Systematic identification of genetic influences on methylation across the human life course
Gaunt, T. R., Shihab, H. A., Hemani, G., Min, J. L., Woodward, G., Lyttleton, O., Zheng, J., Duggirala, A., McArdle, W. L., Ho, K., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0926-z · Cited by 598
Meta-analysis methods for genome-wide association studies and beyond
Evangelou, E., & Ioannidis, J. P. A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3472 · Cited by 596
Warrington, N. M., Beaumont, R. N., Horikoshi, M., Day, F. R., Helgeland, Ø., Laurin, C., Bačelis, J., Peng, S., Hao, K., Feenstra, B., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0403-1 · Cited by 590
Qin, Y., Havulinna, A. S., Liu, Y., Jousilahti, P., Ritchie, S. C., Tokolyi, A., Sanders, J. G., Valsta, L., Brożyńska, M., Zhu, Q., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00991-z · Cited by 586
Mountjoy, E., Schmidt, E. M., Carmona, M., Schwartzentruber, J., Peat, G., Miranda, A., Fumis, L., Hayhurst, J., Buniello, A., Karim, M. A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00945-5 · Cited by 581
Genetic drug target validation using Mendelian randomisation
Schmidt, A. F., Finan, C., Gordillo‐Marañón, M., Asselbergs, F. W., Freitag, D. F., Patel, R. S., Tyl, B., Chopade, S., Faraway, R., Zwierzyna, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16969-0 · Cited by 590
Identifying Causal Variants at Loci with Multiple Signals of Association
Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B., & Eskin, E. (2014) · Genetics · doi:10.1534/genetics.114.167908 · Cited by 465
A more accurate method for colocalisation analysis allowing for multiple causal variants
Wallace, C. (2021) · PLOS Genetics · doi:10.1371/journal.pgen.1009440 · Cited by 567
van Rheenen, W., van der Spek, R. A. A., Bakker, M. K., van Vugt, J. J. F. A., Hop, P. J., Zwamborn, R. A. J., de Klein, N., Westra, H.-J., Bakker, O. B., Deelen, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00973-1 · Cited by 542
Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology
23andMe Research Team, Ferreira, M. A., collaborators, A., BIOS consortium, Study, L. C., Vonk, J. M., Baurecht, H., Marenholz, I., Tian, C., Hoffman, J. D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3985 · Cited by 550
Self-citationDémenais, F., Margaritte‐Jeannin, P., Barnes, K. C., Cookson, W., Altmüller, J., Ang, W., Barr, R. G., Beaty, T. H., Becker, A. B., Beilby, J. P., et al. (2017) · Nature Genetics · doi:10.1038/s41588-017-0014-7 · Cited by 547
Self-citationShrine, N. R. G., Guyatt, A. L., Erzurumluoglu, A. M., Jackson, V. E., Hobbs, B. D., Melbourne, C., Batini, C., Fawcett, K. A., Song, K. S., Sakornsakolpat, P., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0321-7 · Cited by 521
Self-citationJoint Analysis of Functional Genomic Data and Genome-wide Association Studies of 18 Human Traits
Pickrell, J. K. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.03.004 · Cited by 526
Approximately independent linkage disequilibrium blocks in human populations
Berisa, T., & Pickrell, J. K. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv546 · Cited by 500
Improved polygenic prediction by Bayesian multiple regression on summary statistics
Lloyd-Jones, L. R., Zeng, J., Sidorenko, J., Yengo, L., Moser, G., Kemper, K. E., Wang, H., Zheng, Z., Magi, R., Esko, T., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12653-0 · Cited by 528
Self-citationSchwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532
Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus
Perez, R. K., Gordon, M. G., Subramaniam, M., Kim, M. C., Hartoularos, G. C., Targ, S., Sun, Y., Ogorodnikov, A., Bueno, R., Lu, A. C., et al. (2022) · Science · doi:10.1126/science.abf1970 · Cited by 535
Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
Min, J. L., Hemani, G., Hannon, E., Dekkers, K. F., Castillo-Fernandez, J., Luijk, R., Carnero-Montoro, E., Lawson, D. J., Burrows, K., Suderman, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00923-x · Cited by 506
Self-citationGenome-wide associations for birth weight and correlations with adult disease
Horikoshi, M., Beaumont, R. N., Day, F. R., Warrington, N. M., Kooijman, M. N., Fernandez-Tajes, J. J., Feenstra, B., van Zuydam, N. R., Gaulton, K. J., Grarup, N., et al. (2016) · Nature · doi:10.1038/nature19806 · Cited by 504
Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies
Kichaev, G., Yang, W.-Y., Lindström, S., Hormozdiari, F., Eskin, E., Price, A. L., Kraft, P. L., & Paşaniuc, B. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004722 · Cited by 486
Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0092-1 · Cited by 491
Liu, Y., & Xie, J. H. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1554485 · Cited by 483
Kemp, J. P., Morris, J. A., Medina‐Gómez, C., Forgetta, V., Warrington, N. M., Youlten, S. E., Zheng, J., Gregson, C. L., Grundberg, E., Trajanoska, K., et al. (2017) · Nature Genetics · doi:10.1038/ng.3949 · Cited by 470
Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data
Shi, H., Kichaev, G., & Pasaniuc, B. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.05.013 · Cited by 387
Dissecting the genetics of complex traits using summary association statistics
Paşaniuc, B., & Price, A. L. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.142 · Cited by 461
Fine-mapping from summary data with the “Sum of Single Effects” model
Zou, Y., Carbonetto, P., Wang, G., & Stephens, M. (2022) · PLOS Genetics · doi:10.1371/journal.pgen.1010299 · Cited by 429
Gupta, R. M., Hadaya, J., Trehan, A., Zekavat, S. M., Roselli, C., Klarin, D., Emdin, C. A., Hilvering, C. R. E., Bianchi, V., Mueller, C., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.049 · Cited by 450
Gharahkhani, P., Jorgenson, E., Hysi, P. G., Khawaja, A. P., Pendergrass, S. A., Han, X., Ong, J., Hewitt, A. W., Segrè, A. V., Rouhana, J. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20851-4 · Cited by 459
Wheeler, E., Leong, A., Liu, C.-T., Hivert, M.-F., Strawbridge, R. J., Podmore, C., Li, M., Yao, J., Sim, X., Hong, J., et al. (2017) · PLOS Medicine · doi:10.1371/journal.pmed.1002383 · Cited by 408
Genetic determinants of daytime napping and effects on cardiometabolic health
Dashti, H. S., Daghlas, I., Lane, J. M., Huang, Y., Udler, M. S., Wang, H., Ollila, H. M., Jones, S. E., Kim, J., Wood, A. R., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20585-3 · Cited by 559
Self-citationLo, M., Hinds, D. A., Tung, J. Y., Franz, C. E., Fan, C.-C., Wang, Y., Smeland, O. B., Schork, A. J., Holland, D., Kauppi, K., et al. (2016) · Nature Genetics · doi:10.1038/ng.3736 · Cited by 462
Identification of type 2 diabetes loci in 433,540 East Asian individuals
Spracklen, C. N., Horikoshi, M., Kim, Y. J., Lin, K., Bragg, F., Moon, S., Ken, S., Tam, C. H. T., Tabara, Y., Kwak, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2263-3 · Cited by 446
Self-citationEliciting priors and relaxing the single causal variant assumption in colocalisation analyses
Wallace, C. (2020) · PLOS Genetics · doi:10.1371/journal.pgen.1008720 · Cited by 441
Clarke, T.-K., Adams, M. J., Davies, G., Howard, D. M., Hall, L. S., Padmanabhan, S., Murray, A. D., Smith, B. H., Campbell, A., Hayward, C., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.153 · Cited by 440
Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations
Boer, C. G., Hatzikotoulas, K., Southam, L., Stefánsdóttir, L., Zhang, Y., de Almeida, R. C., Wu, T. T., Zheng, J., Hartley, A. E., Teder‐Laving, M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.038 · Cited by 453
Mahajan, A., Wessel, J., Willems, S. M., Zhao, W., Robertson, N. R., Chu, A. Y., Gan, W., Kitajima, H., Taliun, D., Rayner, N. W., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0084-1 · Cited by 442
Metadata
- Authors
- Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., Weedon, M. N., Loos, R. J., Frayling, T. M., McCarthy, M. I., Hirschhorn, J. N., Goddard, M. E., & Visscher, P. M.
- Affiliations
- QIMR Berghofer Medical Research Institute (AU)
- Centre for Human Genetics (GB)
- Washington University in St. Louis (US)
- University of Exeter (GB)
- Addenbrooke's Hospital (GB)
- Broad Institute (US)
- The University of Melbourne (AU)
- The University of Queensland (AU)
- Corresponding author
- Visscher, Peter M
- ORCID
- Venue
- Nature Genetics
- Published
- 2012-03-18
- Volume
- 44
- Issue
- 4
- Pages
- 369-375
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3593158
- OpenAlex
- W2152885121
- Cited by
- 1,712
- References
- 37
- Citations by year
- 2026: 144 · 2025: 168 · 2024: 230 · 2023: 186 · 2022: 172 · 2021: 159 · 2020: 124 · 2019: 127 · 2018: 104 · 2017: 92 · 2016: 61 · 2015: 43 · 2014: 42 · 2013: 44 · 2012: 16
- FWCI
- 27.95
- 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)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Site cant be reached (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- Wellcome Trust (WT083270, 090532)
- U.S. Public Health Service (HHSN 268200625226C)
- National Institute on Alcohol Abuse and Alcoholism (AA-10248, K05AA017688, R01AA013321, AA13320, AA13321, AA-014041, R01AA013326, AA13326, R01AA013320, R01AA014041, R01AA007535)
- National Heart, Lung, and Blood Institute (HHSN268201 100012C, HHSN268201100007I, HHSN268201100007C, HHSN2682 01100008C, HH-SN-268201100006C, HHSN268201100011C, HHSN2682011-00005I, HHSN2682011-00009I, R01 HL-59367, HHSN268201100005C, HHSN268201100005G, HHSN268201100010C, HHSN268201100011I, R01HL087641, R01HL059367, HHSN26820 1100009C, HHSN2682011-00008I, R01HL086694)
- National Institute on Drug Abuse (R56DA012854, DA12854, R01DA012854)
- National Human Genome Research Institute (U01HG004402)
- National Center for Research Resources (UL1RR025005)
- 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
- GWAS summary statistics, linkage disequilibrium, height, body mass index, type 2 diabetes, heritability, complex traits, case-control data
- MeSH terms
- Algorithms, Body Height, Data Interpretation, Statistical, Diabetes Mellitus, Type 2, Genotype, Humans, Phenotype, Genetic Variation, Meta-Analysis as Topic, Linkage Disequilibrium, Genome, Human, Body Mass Index, Case-Control Studies, Polymorphism, Single Nucleotide, Cyclin-Dependent Kinase Inhibitor p15, Genome-Wide Association Study, Genetic Loci
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
