GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Stevelink, Remi; Campbell, Ciarán; Chen, Siwei; Abou-Khalil, Bassel; Adesoji, Oluyomi M.; Afawi, Zaid; Amadori, Elisabetta; Anderson, Alison; Anderson, Joseph; Andrade, Danielle M.; Annesi, Grazia; Auce, Pauls; Avbersek, Andreja; Bahlo, Melanie; Baker, Mark D.; Balagura, Ganna; Balestrini, Simona; Barba, Carmen; Barboza, Karen; Bartolomei, Fabrice; Bast, Thomas; Baum, Larry; Baumgartner, Tobias; Baykan, Betül; Bebek, Nerses; Becker, Albert J.; Becker, Felicitas; Bennett, Caitlin A.; Berghuis, Bianca; Berkovic, Samuel F.; Beydoun, Ahmad; Bianchini, Claudia; Bisulli, Francesca; Blatt, Ilan; Bobbili, Dheeraj Reddy; Borggraefe, Ingo; Bosselmann, Christian; Braatz, Vera; Bradfield, Jonathan P.; Brockmann, Knut; Brody, Lawrence C.; Buono, Russell J.; Busch, Robyn M.; Caglayan, Hande; Campbell, Ellen; Canafoglia, Laura; Canavati, Christina; Cascino, Gregory D.; Castellotti, Barbara; Catarino, Claudia B.; Cavalleri, Gianpiero L.; Cerrato, Felecia; Chassoux, Francine; Cherny, Stacey S.; Cheung, Ching-Lung; Chinthapalli, Krishna; Chou, I.-Jun; Chung, Seo-Kyung; Churchhouse, Claire; Clark, Peggy O.; Cole, Andrew J.; Compston, Alastair; Coppola, Antonietta; Cosico, Mahgenn; Cossette, Patrick; Craig, John J.; Cusick, Caroline; Daly, Mark J.; Davis, Lea K.; de Haan, Gerrit-Jan; Delanty, Norman; Depondt, Chantal; Derambure, Philippe; Devinsky, Orrin; Di Vito, Lidia; Dlugos, Dennis J.; Doccini, Viola; Doherty, Colin P.; El-Naggar, Hany; Elger, Christian E.; Ellis, Colin A.; Eriksson, Johan G.; Faucon, Annika; Feng, Yen-Chen A.; Ferguson, Lisa; Ferraro, Thomas N.; Ferri, Lorenzo; Feucht, Martha; Fitzgerald, Mark; Fonferko-Shadrach, Beata; Fortunato, Francesco; Franceschetti, Silvana; Franke, Andre; French, Jacqueline A.; Freri, Elena; Gagliardi, Monica; Gambardella, Antonio; Geller, Eric B.; Giangregorio, Tania; Gjerstad, Leif; Glauser, Tracy; Goldberg, Ethan; Goldman, Alicia; Granata, Tiziana; Greenberg, David A.; Guerrini, Renzo; Gupta, Namrata; Haas, Kevin F.; Hakonarson, Hakon; Hallmann, Kerstin; Hassanin, Emadeldin Saeed Fathy Sayed; Hegde, Manu; Heinzen, Erin L.; Helbig, Ingo; Hengsbach, Christian; Heyne, Henrike O.; Hirose, Shinichi; Hirsch, Edouard; Hjalgrim, Helle; Howrigan, Daniel P.; Hucks, Donald; Hung, Po-Cheng; Iacomino, Michele; Imbach, Lukas L.; Inoue, Yushi; Ishii, Atsushi; Jamnadas-Khoda, Jennifer; Jehi, Lara; Johnson, Michael R.; Kälviäinen, Reetta; Kamatani, Yoichiro; Kanaan, Moien; Kanai, Masahiro; Kantanen, Anne-Mari; Kara, Bülent; Kariuki, Symon M.; Kasperavičiūte, Dalia; Kasteleijn-Nolst Trenite, Dorothee; Kato, Mitsuhiro; Kegele, Josua; Kesim, Yeşim; Khoueiry-Zgheib, Nathalie; King, Chontelle; Kirsch, Heidi E.; Klein, Karl M.; Kluger, Gerhard; Knake, Susanne; Knowlton, Robert C.; Koeleman, Bobby P. C.; Korczyn, Amos D.; Koupparis, Andreas; Kousiappa, Ioanna; Krause, Roland; Krenn, Martin; Krestel, Heinz; Krey, Ilona; Kunz, Wolfram S.; Kurki, Mitja I.; Kurlemann, Gerhard; Kuzniecky, Ruben; Kwan, Patrick; Labate, Angelo; Lacey, Austin; Lal, Dennis; Landoulsi, Zied; Lau, Yu-Lung; Lauxmann, Stephen; Leech, Stephanie L.; Lehesjoki, Anna-Elina; Lemke, Johannes R.; Lerche, Holger; Lesca, Gaetan; Leu, Costin; Lewin, Naomi; Lewis-Smith, David; Li, Gloria H.-Y.; Li, Qingqin S.; Licchetta, Laura; Lin, Kuang-Lin; Lindhout, Dick; Linnankivi, Tarja; Lopes-Cendes, Iscia; Lowenstein, Daniel H.; Lui, Colin H. T.; Madia, Francesca; Magnusson, Sigurdur; Marson, Anthony G.; May, Patrick; McGraw, Christopher M.; Mei, Davide; Mills, James L.; Minardi, Raffaella; Mirza, Nasir; Møller, Rikke S.; Molloy, Anne M.; Montomoli, Martino; Mostacci, Barbara; Muccioli, Lorenzo; Muhle, Hiltrud; Müller-Schlüter, Karen; Najm, Imad M.; Nasreddine, Wassim; Neale, Benjamin M.; Neubauer, Bernd; Newton, Charles R. J. C.; Nöthen, Markus M.; Nothnagel, Michael; Nürnberg, Peter; O’Brien, Terence J.; Okada, Yukinori; Ólafsson, Elías; Oliver, Karen L.; Özkara, Çiğdem; Palotie, Aarno; Pangilinan, Faith; Papacostas, Savvas S.; Parrini, Elena; Pato, Carlos N.; Pato, Michele T.; Pendziwiat, Manuela; Petrovski, Slavé; Pickrell, William O.; Pinsky, Rebecca; Pippucci, Tommaso; Poduri, Annapurna; Pondrelli, Federica; Powell, Rob H. W.; Privitera, Michael; Rademacher, Annika; Radtke, Rodney; Ragona, Francesca; Rau, Sarah; Rees, Mark I.; Regan, Brigid M.; Reif, Philipp S.; Rhelms, Sylvain; Riva, Antonella; Rosenow, Felix; Ryvlin, Philippe; Saarela, Anni; Sadleir, Lynette G.; Sander, Josemir W.; Sander, Thomas; Scala, Marcello; Scattergood, Theresa; Schachter, Steven C.; Schankin, Christoph J.; Scheffer, Ingrid E.; Schmitz, Bettina; Schoch, Susanne; Schubert-Bast, Susanne; Schulze-Bonhage, Andreas; Scudieri, Paolo; Sham, Pak; Sheidley, Beth R.; Shih, Jerry J.; Sills, Graeme J.; Sisodiya, Sanjay M.; Smith, Michael C.; Smith, Philip E.; Sonsma, Anja C. M.; Speed, Doug; Sperling, Michael R.; Stefansson, Hreinn; Stefansson, Kári; Steinhoff, Bernhard J.; Stephani, Ulrich; Stewart, William C.; Stipa, Carlotta; Striano, Pasquale; Stroink, Hans; Strzelczyk, Adam; Surges, Rainer; Suzuki, Toshimitsu; Tan, K. Meng; Taneja, R. S.; Tanteles, George A.; Taubøll, Erik; Thio, Liu Lin; Thomas, G. Neil; Thomas, Rhys H.; Timonen, Oskari; Tinuper, Paolo; Todaro, Marian; Topaloğlu, Pınar; Tozzi, Rossana; Tsai, Meng-Han; Tumiene, Birute; Turkdogan, Dilsad; Unnsteinsdóttir, Unnur; Utkus, Algirdas; Vaidiswaran, Priya; Valton, Luc; van Baalen, Andreas; Vetro, Annalisa; Vining, Eileen P. G.; Visscher, Frank; von Brauchitsch, Sophie; von Wrede, Randi; Epilepsies, International League Against Epilepsy Consortium On Complex

doi:10.1038/s41588-023-01485-w

Article (Scientific journals)

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Stevelink, Remi; Campbell, Ciarán; Chen, Siwei et al.

2023 • In Nature Genetics

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/10993/56044

DOI
10.1038/s41588-023-01485-w

PubMed
37653029

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

ILAE.GWAS.NatGenet.2023.pdf

Publisher postprint (2.75 MB)

Download

All documents in ORBilu are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Epilepsy; GWAS; sub-phenotypes

Abstract :

[en] Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6 and 90 of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

Research center :

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)

Disciplines :

Genetics & genetic processes
Neurology

Author, co-author :

Stevelink, Remi

Campbell, Ciarán

Chen, Siwei

Abou-Khalil, Bassel

Adesoji, Oluyomi M.

Afawi, Zaid

Amadori, Elisabetta

Anderson, Alison

Anderson, Joseph

Andrade, Danielle M.

More authors (290 more)

External co-authors :

yes

Language :

English

Title :

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Publication date :

31 July 2023

Journal title :

Nature Genetics

ISSN :

1546-1718

Publisher :

Nature Publishing Group, New York, United Kingdom

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Systems Biomedicine

Additional URL :

https://doi.org/10.1038/s41588-023-01485-w

FnR Project :

FNR16394868 - Epileptogenesis Of Genetic Epilepsies, 2021 (01/10/2021-...) - Alexander Skupin

Funders :

FNR - Fonds National de la Recherche [LU]
BMBF - Bundesministerium für Bildung und Forschung [DE]

Available on ORBilu :

since 01 October 2023

Statistics

Number of views

25 (1 by Unilu)

Number of downloads

8 (0 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

Bibliography

Fisher, R. S. et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55, 475–482 (2014). DOI: 10.1111/epi.12550
Fiest, K. M. et al. Prevalence and incidence of epilepsy: a systematic review and meta-analysis of international studies. Neurology 88, 296–303 (2017). DOI: 10.1212/WNL.0000000000003509
Scheffer, I. E. et al. ILAE classification of the epilepsies: position paper of the ILAE Commission for Classification and Terminology. Epilepsia 58, 512–521 (2017). DOI: 10.1111/epi.13709
International League Against Epilepsy Consortium on Complex Epilepsies. Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nat. Commun. 9, 5269 (2018). DOI: 10.1038/s41467-018-07524-z
Epi4K Consortium & Epilepsy Phenome/Genome Project. Ultra-rare genetic variation in common epilepsies: a case–control sequencing study. Lancet Neurol. 16, 135–143 (2017).
Leu, C. et al. Polygenic burden in focal and generalized epilepsies. Brain 142, 3473–3481 (2019). DOI: 10.1093/brain/awz292
Koko, M. et al. Distinct gene-set burden patterns underlie common generalized and focal epilepsies. EBioMedicine 72, 103588 (2021). DOI: 10.1016/j.ebiom.2021.103588
McTague, A., Howell, K. B., Cross, J. H., Kurian, M. A. & Scheffer, I. E. The genetic landscape of the epileptic encephalopathies of infancy and childhood. Lancet Neurol. 15, 304–316 (2016). DOI: 10.1016/S1474-4422(15)00250-1
Speed, D. et al. Describing the genetic architecture of epilepsy through heritability analysis. Brain 137, 2680–2689 (2014). DOI: 10.1093/brain/awu206
Motelow, J. E. et al. Sub-genic intolerance, ClinVar, and the epilepsies: a whole-exome sequencing study of 29,165 individuals. Am. J. Hum. Genet. 108, 965–982 (2021). DOI: 10.1016/j.ajhg.2021.04.009
Chen, Z., Brodie, M. J., Liew, D. & Kwan, P. Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: a 30-year longitudinal cohort study. JAMA Neurol. 75, 279–286 (2018). DOI: 10.1001/jamaneurol.2017.3949
Devinsky, O. et al. Epilepsy. Nat. Rev. Dis. Primers 4, 18024 (2018). DOI: 10.1038/nrdp.2018.24
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). DOI: 10.1038/ng.3211
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012). DOI: 10.1038/nmeth.1906
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014). DOI: 10.1038/ng.2892
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013). DOI: 10.1016/j.cell.2013.09.053
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018). DOI: 10.1038/s41588-017-0009-4
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017). DOI: 10.1038/s41467-017-01261-5
De Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). DOI: 10.1371/journal.pcbi.1004219
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016). DOI: 10.1038/ng.3506
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018). DOI: 10.1126/science.aat8127
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016). DOI: 10.1038/ng.3538
Xu, C. et al. Knockdown of RMI1 impairs DNA repair under DNA replication stress. Biochem. Biophys. Res. Commun. 494, 158–164 (2017). DOI: 10.1016/j.bbrc.2017.10.062
International League Against Epilepsy Consortium on Complex Epilepsies. Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies. Lancet Neurol. 13, 893–903 (2014). DOI: 10.1016/S1474-4422(14)70171-1
Yoshida, M. et al. Identification of novel BCL11A variants in patients with epileptic encephalopathy: expanding the phenotypic spectrum. Clin. Genet. 93, 368–373 (2018). DOI: 10.1111/cge.13067
Cook, S. et al. Accurate imputation of human leukocyte antigens with CookHLA. Nat. Commun. 12, 1264 (2021). DOI: 10.1038/s41467-021-21541-5
Holland, D. et al. Beyond SNP heritability: polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet. 16, e1008612 (2020). DOI: 10.1371/journal.pgen.1008612
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015). DOI: 10.1038/ng.3406
Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019). DOI: 10.1038/s41562-019-0566-x
Ruano, D. et al. Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability. Am. J. Hum. Genet. 86, 113–125 (2010). DOI: 10.1016/j.ajhg.2009.12.006
Lukyanetz, E. A., Shkryl, V. M. & Kostyuk, P. G. Selective blockade of N-type calcium channels by levetiracetam. Epilepsia 43, 9–18 (2002). DOI: 10.1046/j.1528-1157.2002.24501.x
Wang, S. J., Huang, C. C., Hsu, K. S., Tsai, J. J. & Gean, P. W. Inhibition of N-type calcium currents by lamotrigine in rat amygdalar neurones. Neuroreport 7, 3037–3040 (1996). DOI: 10.1097/00001756-199611250-00048
Marson, A. et al. The SANAD II study of the effectiveness and cost-effectiveness of levetiracetam, zonisamide, or lamotrigine for newly diagnosed focal epilepsy: an open-label, non-inferiority, multicentre, phase 4, randomised controlled trial. Lancet 397, 1363–1374 (2021). DOI: 10.1016/S0140-6736(21)00247-6
Christensen, J., Kjeldsen, M. J., Andersen, H., Friis, M. L. & Sidenius, P. Gender differences in epilepsy. Epilepsia 46, 956–960 (2005). DOI: 10.1111/j.1528-1167.2005.51204.x
Magi, R., Lindgren, C. M. & Morris, A. P. Meta-analysis of sex-specific genome-wide association studies. Genet. Epidemiol. 34, 846–853 (2010). DOI: 10.1002/gepi.20540
Gaborit, N. et al. Gender-related differences in ion-channel and transporter subunit expression in non-diseased human hearts. J. Mol. Cell. Cardiol. 49, 639–646 (2010). DOI: 10.1016/j.yjmcc.2010.06.005
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019). DOI: 10.1093/nar/gky1120
Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat. Commun. 10, 2417 (2019). DOI: 10.1038/s41467-019-10310-0
Mirza, N. et al. Using common genetic variants to find drugs for common epilepsies. Brain Commun. 3, fcab287 (2021). DOI: 10.1093/braincomms/fcab287
Bourgeois, B. F. D. Chronic management of seizures in the syndromes of idiopathic generalized epilepsy. Epilepsia 44, 27–32 (2003). DOI: 10.1046/j.1528-1157.44.s.2.1.x
Marson, A. G. et al. The SANAD study of effectiveness of valproate, lamotrigine, or topiramate for generalised and unclassifiable epilepsy: an unblinded randomised controlled trial. Lancet 369, 1016–1026 (2007). DOI: 10.1016/S0140-6736(07)60461-9
Punetha, J. et al. Biallelic CACNA2D2 variants in epileptic encephalopathy and cerebellar atrophy. Ann. Clin. Transl. Neurol. 6, 1395–1406 (2019). DOI: 10.1002/acn3.50824
Fariello, R. G. Safinamide. Neurotherapeutics 4, 110–116 (2007). DOI: 10.1016/j.nurt.2006.11.011
Alsaegh, H., Eweis, H., Kamal, F. & Alrafiah, A. Celecoxib decrease seizures susceptibility in a rat model of inflammation by inhibiting HMGB1 translocation. Pharmaceuticals 14, 380 (2021). DOI: 10.3390/ph14040380
Johannesen, K. M. et al. Genotype-phenotype correlations in SCN8A-related disorders reveal prognostic and therapeutic implications. Brain 145, 2991–3009 (2021). DOI: 10.1093/brain/awab321
Ma, M.-G. et al. RYR2 mutations are associated with benign epilepsy of childhood with centrotemporal spikes with or without arrhythmia. Front. Neurosci. 15, 629610 (2021). DOI: 10.3389/fnins.2021.629610
Yap, S. M. & Smyth, S. Ryanodine receptor 2 (RYR2) mutation: a potentially novel neurocardiac calcium channelopathy manifesting as primary generalised epilepsy. Seizure 67, 11–14 (2019). DOI: 10.1016/j.seizure.2019.02.017
EPICURE Consortium. et al. Genome-wide association analysis of genetic generalized epilepsies implicates susceptibility loci at 1q43, 2p16.1, 2q22.3 and 17q21.32. Hum. Mol. Genet. 21, 5359–5372 (2012). DOI: 10.1093/hmg/dds373
Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018). DOI: 10.1038/s41588-018-0248-z
Heinzen, E. L. Somatic variants in epilepsy—advancing gene discovery and disease mechanisms. Curr. Opin. Genet. Dev. 65, 1–7 (2020). DOI: 10.1016/j.gde.2020.04.004
Beesley, L. J. et al. The emerging landscape of health research based on biobanks linked to electronic health records: existing resources, statistical challenges, and potential opportunities. Stat. Med. 39, 773–800 (2020). DOI: 10.1002/sim.8445
Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018). DOI: 10.1038/s41467-018-04951-w
Hautakangas, H. et al. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat. Genet. 54, 152–160 (2022). DOI: 10.1038/s41588-021-00990-0
Wightman, D. P. et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 53, 1276–1282 (2021). DOI: 10.1038/s41588-021-00921-z
International Multiple Sclerosis Genetics Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019). DOI: 10.1126/science.aav7188
Wood, M. D. & Gillard, M. Evidence for a differential interaction of brivaracetam and levetiracetam with the synaptic vesicle 2A protein. Epilepsia 58, 255–262 (2017). DOI: 10.1111/epi.13638
Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature 604, 509–516 (2022). DOI: 10.1038/s41586-022-04556-w
Van Rheenen, W. et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat. Genet. 53, 1636–1648 (2021). DOI: 10.1038/s41588-021-00973-1
Reay, W. R. & Cairns, M. J. Advancing the use of genome-wide association studies for drug repurposing. Nat. Rev. Genet. 22, 658–671 (2021). DOI: 10.1038/s41576-021-00387-z
Loh, P.-R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016). DOI: 10.1038/ng.3571
Rubinacci, S., Ribeiro, D. M., Hofmeister, R. J. & Delaneau, O. Efficient phasing and imputation of low-coverage sequencing data using large reference panels. Nat. Genet. 53, 120–126 (2021). DOI: 10.1038/s41588-020-00756-0
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016). DOI: 10.1038/ng.3643
Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018). DOI: 10.1038/s41588-018-0184-y
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010). DOI: 10.1093/bioinformatics/btq340
De Bakker, P. I. W. et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17, R122–R128 (2008). DOI: 10.1093/hmg/ddn288
Han, B. & Eskin, E. Interpreting meta-analyses of genome-wide association studies. PLoS Genet. 8, e1002555 (2012). DOI: 10.1371/journal.pgen.1002555
Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015). DOI: 10.1371/journal.pmed.1001779
Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017). DOI: 10.1016/j.je.2016.12.005
Ishigaki, K. et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat. Genet. 52, 669–679 (2020). DOI: 10.1038/s41588-020-0640-3
Locke, A. E. et al. Exome sequencing of Finnish isolates enhances rare-variant association power. Nature 572, 323–328 (2019). DOI: 10.1038/s41586-019-1457-z
Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015). DOI: 10.1038/ng.3247
Campbell, C. et al. Polygenic risk score analysis reveals shared genetic burden between epilepsy and psychiatric comorbidities. Preprint at medRxiv 10.1101/2023.07.04.23292071 (2023).
Turner, S. D. qqman: an R package for visualizing GWAS results using Q–Q and Manhattan plots. J. Open Source Softw. 3, 731 (2018).
Bhattacharjee, S. et al. A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. Am. J. Hum. Genet. 90, 821–835 (2012). DOI: 10.1016/j.ajhg.2012.03.015
Kim, T.-J. et al. Anti-LGI1 encephalitis is associated with unique HLA subtypes. Ann. Neurol. 81, 183–192 (2017). DOI: 10.1002/ana.24860
Van Sonderen, A. et al. Anti-LGI1 encephalitis is strongly associated with HLA-DR7 and HLA-DRB4. Ann. Neurol. 81, 193–198 (2017). DOI: 10.1002/ana.24858
1000 Genomes Project Consortium. et al.A global reference for human genetic variation. Nature 526, 68–74 2015). DOI: 10.1038/nature15393
Choi, W., Luo, Y., Raychaudhuri, S. & Han, B. HATK: HLA analysis toolkit. Bioinformatics 37, 416–418 (2021). DOI: 10.1093/bioinformatics/btaa684
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015). DOI: 10.1186/s13742-015-0047-8
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010). DOI: 10.1093/nar/gkq603
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019). DOI: 10.1093/nar/gky1016
Roadmap Epigenomics Consortium. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015). DOI: 10.1038/nature14248
De Klein, N. et al. Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases. Nat. Genet. 55, 377–388 (2023). DOI: 10.1038/s41588-023-01300-6
Mägi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010). DOI: 10.1186/1471-2105-11-288
Weeks, E. M. et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat. Genet. 10.1038/s41588-023-01443-6 (2023). DOI: 10.1038/s41588-023-01443-6
GTEx Consortium. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017). DOI: 10.1038/nature24277
Freytag, S., Burgess, R., Oliver, K. L. & Bahlo, M. Brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets. Genome Med. 9, 55 (2017). DOI: 10.1186/s13073-017-0444-y
Rodriguez-Acevedo, A. J., Gordon, L. G., Waddell, N., Hollway, G. & Vadlamudi, L. Developing a gene panel for pharmacoresistant epilepsy: a review of epilepsy pharmacogenetics. Pharmacogenomics 22, 225–234 (2021). DOI: 10.2217/pgs-2020-0145
Oliver, K. L. et al. Genes4Epilepsy: an epilepsy gene resource. Epilepsia 64, 1368–1375 (2023). DOI: 10.1111/epi.17547
Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014). DOI: 10.1038/nature12873
Speed, D. & Balding, D. J. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat. Genet. 51, 277–284 (2019). DOI: 10.1038/s41588-018-0279-5
Speed, D., Holmes, J. & Balding, D. J. Evaluating and improving heritability models using summary statistics. Nat. Genet. 52, 458–462 (2020). DOI: 10.1038/s41588-020-0600-y
Grotzinger, A. D., de la Fuente, J., Nivard, M. G. & Tucker-Drob, E. M. Pervasive downward bias in estimates of liability scale heritability in GWAS meta-analysis: a simple solution. Biol. Psychiatry 93, 29–36 (2023). DOI: 10.1016/j.biopsych.2022.05.029
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018). DOI: 10.1126/science.aat8464
Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018). DOI: 10.1038/nature25980
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018). DOI: 10.1038/s41588-018-0081-4
Brainstorm Consortium. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018). DOI: 10.1126/science.aap8757
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017). DOI: 10.1093/bioinformatics/btw613