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Meta-analysis methods for genome-wide association studies and beyond. Cochran, W. The combination of estimates from different experiments. Biometrics 10 , — Chang, C. Gigascience 4 , 7 McLaren, W. The Ensembl Variant Effect Predictor. Genome Biol. Kerimov, N. Sun, B. Genomic atlas of the human plasma proteome.

Nature , 73—79 Buniello, A. Bulik-Sullivan, B. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Partitioning heritability by functional annotation using genome-wide association summary statistics. People with certain medical conditions. Williamson, E. Zhou, T. Educational attainment and drinking behaviors: Mendelian randomization study in UK Biobank. A map of human genome variation from population-scale sequencing.

Hemani, G. The MR-Base platform supports systematic causal inference across the human phenome. Verbanck, M. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.

Bowden, J. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I 2 statistic. Slob, E. A comparison of robust Mendelian randomization methods using summary data.

Download references. The work of the contributing studies was supported by numerous grants from governmental and charitable bodies. Acknowledgements specific to contributing studies are provided in Supplementary Table We thank G. Butler-Laporte, G. Wojcik, M. Hollm-Delgado, C. Willer and G. Davey Smith for their extensive feedback and discussion. These authors contributed equally: Mari E. Niemi, Juha Karjalainen, Benjamin M. Neale, Mark Daly, Andrea Ganna.

Mari E. Rachel G. Green, Mark J. Karczewski, Elizabeth G. Walters, Duncan S. Daly, Benjamin M. Neale, F. Gita A. Pathak, Frank R. Shea J. Pasquale Striano, Domenico A. Guillaume Butler-Laporte, J. Joseph D. Buxbaum, Laura G. Sloofman, Brett L. Collins, Tess Levy, Slayton J. Hilary Finucane, Alicia R. Brent Richards. Mark Lathrop. Kenneth Baillie, Andrew D. Caulfield, Richard H. Elena Carnero-Montoro, Marta E. University of Groningen, Groningen, The Netherlands.

Adam Auton, Janie F. Shelton, Anjali J. Alexander P. Grobusch, Vanessa Harris, Sabine M. Hermans, Joppe W. Harm Jan Bogaard, Peter I. Lea K. Davis, Nancy J. Cox, Jennifer E. Below, Julia M. Sealock, Annika B. Faucon, Megan M. Shuey, Hannah G. Polikowsky, Lauren E. Petty, Douglas M. Kerstin U. Christopher R. Gignoux, Stephen J. Wicks, Kristy Crooks, Kathleen C.

Muredach P. Gharavi, Ning Shang, Sheila M. Tobias L. Helbig, Julia Kraft, Lena J. Granada, Granada, Spain. Jan C. Holter, Johannes R. Hov, Tom H. Johannes R. Luis Bujanda, Jesus M. Maria J. Enrique J. Bochud, C. Rivolta, S. Bibert, M. Quinodoz, D. Kamdar, D. Neofytos, V. Erard, C. Voide, R. Friolet, P. Vollenweider, J. Pagani, M. Oddo, F. Meyer zu Bentrup, A. Conen, O. Clerc, O. Marchetti, A. Guillet, C.

Guyat-Jacques, S. Foucras, M. Rime, J. Chassot, M. Jaquet, R. Merlet Viollet, Y. Lannepoudenx, L. Bochud, P. Pagani, F. Desgranges, P. Filippidis, B. Haefliger, E. Kampouri, O. Manuel, A. Munting, M. Papadimitriou-Olivgeris, J. Regina, L.

Rochat-Stettler, V. Suttels, E. Tadini, J. Tschopp, M. Van Singer, B. Boillat-Blanco, T. Brahier, O. Tomas Pumarola, S. Gonseth Nussle, M. Bochud, V. Estoppey Younes. Bochud, W. Albrich, N. Suh, A. Cerny, L. Bochud, M. Frischknecht, G. Kleger, M. Filipovic, C. Kahlert, H.

Wozniak, T. Rochat Negro, J. Pugin, K. Bouras, C. Knapp, T. Egger, A. Perret, P. Montillier, C. Laboratorio di Genetica Umana, Genova, Italy. Fondazione per la ricerca Ospedale di Bergamo, Bergamo, Italy. Trembath, Bhavi Trivedi, David A. Albert V. Smith, Andrew P. Boughton, Kevin W. Cinthia E. Jannes, Jose E. Krieger, Alexandre C. Peter J. Malcolm G. Semple, Tom Solomon, Lance C. Clark D. Cynthia M. Drake, Cameron J.

Fairfield, Stephen R. Knight, Kenneth A. Mclean, Derek Murphy, Catherine A. Shaw, Annemarie B. Semple, Lance C. Jaime Fernandez-Roman, David O. Waite, Victoria Waugh, Ingeborg D. Ian Clement, Bijal Patel, A. Gulati, Carole Hays, K. Gillian Andrew, J. Prisca Gondo, B. Allan, T. Geary, Gordon Houston, A.

Robertson, Janet T. Scott, Emma C. Shona C. Moore, William A. Marc-Emmanuel Dumas, Julian L. Matthew R. Sarah Cole, Katie A. Ahmed, Jane A. Armstrong, Milton Ashworth, Innocent G. Asiimwe, Siddharth Bakshi, Samantha L. Catterall, Jordan J. Clark, Emily A. Jensen, Christopher B. Jones, Trevor R. Moore, Ellen G. Shaw, Rebecca K. Shears, Benjamin Small, Krishanthi S.

Elizabeth T. Cirulli, Kelly M. Washington, James T. Luis A. Ke Hao, Robert P. Sebra, Daniel M. Schadt, Judy H. Steven Ascolillo, Ryan C. Thompson, Noam D. Cho, Yuval Itan, Ruth J. Loos, Girish N. Gillian M. Belbin, Noura S. Manuel A. Ferreira, Goncalo R. Abecasis, Michael N. Cantor, Jack A. Kosmicki, Julie E. Anne E. Anurag Verma, Marylyn D. Ritchie, Daniel Rader, Shefali S. Daniel J. Wilson, Sarah G. Jennifer E. Huffman, Christopher J.

Kelly Cho, J. Xiao Chang, Joseph R. Unit of Infectious Diseases, S. Annunziata Hospital, Florence, Italy. Instituto de Biomedicina de Sevilla, Sevilla, Spain. Carmen de la Horra, Enrique J. Correspondence to Benjamin M. Neale , Mark Daly or Andrea Ganna. Peer reviewer reports are available. Using the analytical plan set by the COVID HGI, each individual study runs their analyses and uploads the results to the Initiative, who then runs the meta-analysis.

The phenotypic criteria used to define cases are listed in the dark grey boxes, along with the numbers of cases N included in the final all-ancestries meta-analysis. Controls were defined in the same way across all three analyses as everybody that is not a case—for example, population controls light grey box. Sample number N of control individuals differed between the analyses due to the difference in the number of studies contributing data to these. We asked participating studies to perform a PC projection using the Genomes Project and Human Genome Diversity Project as a reference, with a common set of variants.

For each panel except for the reference , coloured points correspond to contributed samples from each cohort, whereas grey points correspond to the reference samples from the Genomes Project. Colour represents a genetic population that each cohort specified. Here, the severity lead variant rs chr. The susceptibility lead variant rs chr.

The locus on chromosome 6 is the HLA locus, which was removed from the list of reported loci in Supplementary Table 2 due to the high heterogeneity in effect size estimated between studies included in the analysis. The locus on chromosome 7 was also not reported in Supplementary Table 2 due to missingness across studies—that is, the high number of studies in the meta-analysis that did not report summary statistics for this region.

There are two association peaks on chromosome Filled dots indicate variants that showed genome-wide significance in the full meta-analysis of critical illness due to COVID, and empty dots represent variants that were not significant for critical illness but were significant for either hospitalization due to COVID or reported SARS-CoV-2 infection.

The left blue peak in c , which is uncorrelated with the lead variants in the region, indicates that there are independent signals. Sample sizes for hospitalized cases of COVID versus population controls differ between a and b due to differences in the sampling of studies selected for the analysis. This selection included all studies that were able to contribute data to the respective analyses that the data were compared to shown on the y axis in each panel.

The colour represents the z -scores of correlated risk increasing alleles for the trait. The traits that the genetic correlation is run against are listed on the left. Two-sided P values were calculated using LDSC for genetic correlations and exact estimates, unadjusted standard errors and two-sided P values are available in Supplementary Table RBC, red blood cell count. Quantile-quantile plots for GWAS from all individual studies that contributed data.

QQ-plots showing the expected -log 10 P -values on the x-axis and the observed unadjusted P -values values from two-tailed inverse variance weighted meta-analysis on the y-axis red line showing no deviation from the expected for each study contributing data to the analyses. Sample size of cases and controls is listed for each study in the plot title, as well as the median lambda value. LozusZoom plots to visualise the meta-analysis results at the loci passing genome-wide significance.

For each genome-wide significant locus in three meta-analyses: meta-analysis of critical illness, hospitalization, and reported infection, we showed 1 a manhattan plot of each locus where a color represents a weighted-average r 2 value see Methods to a lead variant unadjusted P -values from the two-tailed inverse variance weighted meta-analysis ; 2 r 2 values to a lead variant across gnomAD v2 populations, i.

Note that the COVID lead variants were chosen across all the meta-analyses Supplementary Table 2; see Methods and were not necessarily a variant with the most significant P -value from each inverse variance weighted meta-analysis. Scatter plots show the exposure variant effect size against the COVID outcome variant effect size and corresponding standard errors.

Funnel plots show the Mendelian randomization MR causal estimates for each variant against their precision, with asymmetry in the plot indicating potential violations of the assumptions of MR. Reprints and Permissions. Download citation.

Received : 02 March Accepted : 23 June Published : 08 July Issue Date : 16 December Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. European Journal of Human Genetics By submitting a comment you agree to abide by our Terms and Community Guidelines.

If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Advanced search. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. Download PDF. Abstract The genetic make-up of an individual contributes to the susceptibility and response to viral infection.

Full size image. Variant effects on severity and susceptibility We found no genome-wide significant sex-specific effects at the 13 loci. Gene prioritization and association with other traits To better understand the potential biological mechanism of each locus, we applied several approaches to prioritize candidate causal genes and explore additional associations with other diseases and traits.

Polygenic architecture of COVID To further investigate the genetic architecture of COVID, we used results from meta-analyses including samples from European ancestries sample sizes are described in the Methods and Supplementary Table 1 to estimate the heritability explained by common single-nucleotide polymorphisms—that is, the proportion of variation in the two phenotypes that was attributable to common genetic variants—and to determine whether heritability of COVID phenotypes was enriched in genes that were specifically expressed in certain tissues 30 from the GTEx dataset Methods Contributing studies All of the participants were recruited following protocols approved by local Institutional Review Boards; this information is collected in Supplementary Table 1 for all 46 studies.

Principal component projection To project every GWAS participant into the same principal component PC space, we used pre-computed PC loadings and reference allele frequencies. Gene prioritization To prioritize candidate causal genes reported in full in Supplementary Table 2 , we used various gene prioritization approaches using both locus-based and similarity-based methods. Phenome-wide association study To investigate the evidence of shared effects of 15 index variants for COVID and previously reported phenotypes, we performed a phenome-wide association study.

Heritability LD score regression v. Genome-wide association summary statistics We obtained genome-wide association summary statistics for 43 complex-disease, neuropsychiatric, behavioural or biomarker phenotypes Supplementary Table Genetic correlation LD score regression 50 was also used to estimate the genetic correlations between our COVID meta-analysis phenotypes reported using samples of only European ancestry, and between these and the curated set of 38 summary statistics.

Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this paper. References Docherty, A. Google Scholar Zhou, F. Google Scholar Zhang, Q. Google Scholar Shelton, J. Google Scholar Couturier, N. Google Scholar Hao, K. Google Scholar Zhou, W.

Google Scholar Chang, C. Google Scholar Hemani, G. Author information Author notes These authors contributed equally: Mari E. Gaede Popgen 2. Niemi , Juha Karjalainen , Rachel G. Liao , Benjamin M. Niemi , Gita A. Pathak , Shea J. Buxbaum Analysis group Manuscript analyses team leader Juha Karjalainen Manuscript analyses team member: meta-analysis Juha Mehtonen Manuscript analyses team member: heritability, methods and supplements Mari E.

Pathak Manuscript analyses team member: Mendelian randomization Shea J. Andrews Manuscript analyses team member: PC projection and gene prioritization Masahiro Kanai Manuscript analyses team member: gene prioritization Hilary Finucane Manuscript analyses team member: sensitivity analysis Mattia Cordioli Manuscript analyses team members: PC projection Alicia R.

Buxbaum Project management group Project management leader Rachel G. Andrews , Sophie Limou , Rachel G. Brouwer , Alexander P. Hovius , Menno D. Minnaar , Adrie Kromhout , Kees W. Kaufmann , G. Davis , Nancy J. Below Analysis team members Julia M. Sealock , Annika B.

Faucon , Megan M. Shuey , Hannah G. Polikowsky , Lauren E. Petty , Douglas M. Gignoux Data collection leaders Stephen J. Hov , Tom H. Rodrigues , Pedro P. Bochud , C. Rivolta , S. Bibert , M. Quinodoz , D. Kamdar , D. Neofytos , V. Erard , C. Voide , R. Friolet , P. Vollenweider , J. Pagani , M. Oddo , F. Meyer zu Bentrup , A. Conen , O. Clerc , O. Marchetti , A. Guillet , C. Guyat-Jacques , S. Foucras , M. Rime , J.

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