Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility.
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
13 Jun 2024
13 Jun 2024
Historique:
received:
16
05
2023
accepted:
03
06
2024
medline:
14
6
2024
pubmed:
14
6
2024
entrez:
13
6
2024
Statut:
epublish
Résumé
Genomic data holds huge potential for medical progress but requires strict safety measures due to its sensitive nature to comply with data protection laws. This conflict is especially pronounced in genome-wide association studies (GWAS) which rely on vast amounts of genomic data to improve medical diagnoses. To ensure both their benefits and sufficient data security, we propose a federated approach in combination with privacy-enhancing technologies utilising the findings from a systematic review on federated learning and legal regulations in general and applying these to GWAS.
Identifiants
pubmed: 38872191
doi: 10.1186/s13059-024-03296-6
pii: 10.1186/s13059-024-03296-6
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
154Informations de copyright
© 2024. The Author(s).
Références
General Data Protection Legislation. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC Apr 27, 2016. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679 .
California Legislative Information. California Consumer Privacy Act of 2018. Available from: https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201720180SB1121 .
Shabani M, Borry P. Rules for processing genetic data for research purposes in view of the new EU General Data Protection Regulation. Eur J Hum Genet. 2018;26:149–56.
pubmed: 29187736
doi: 10.1038/s41431-017-0045-7
Pardau SL, Edwards B. The FTC, the unfairness doctrine, and privacy by design: new legal frontiers in cybersecurity. J Business Technol Law. 2017;12:227–76.
Wan Z, Hazel JW, Clayton EW, Vorobeychik Y, Kantarcioglu M, Malin BA. Sociotechnical safeguards for genomic data privacy. Nat Rev Genet. 2022;23:429–45.
pubmed: 35246669
pmcid: 8896074
doi: 10.1038/s41576-022-00455-y
Bednar K, Spiekermann S, Langheinrich M. Engineering privacy by design: are engineers ready to live up to the challenge?. arXiv [cs.CY]. 2020. Available from: http://arxiv.org/abs/2006.04579 .
Berger B, Cho H. Emerging technologies towards enhancing privacy in genomic data sharing. Genome Biol. 2019;20:128.
pubmed: 31262363
pmcid: 6604426
doi: 10.1186/s13059-019-1741-0
Erlich Y, Narayanan A. Routes for breaching and protecting genetic privacy. Nat Rev Genet. 2014;15:409–21.
pubmed: 24805122
pmcid: 4151119
doi: 10.1038/nrg3723
Bonomi L, Huang Y, Ohno-Machado L. Privacy challenges and research opportunities for genomic data sharing. Nat Genet. 2020;52:646–54.
pubmed: 32601475
pmcid: 7761157
doi: 10.1038/s41588-020-0651-0
Shabani M, Marelli L. Re-identifiability of genomic data and the GDPR: assessing the re-identifiability of genomic data in light of the EU General Data Protection Regulation. EMBO Rep. 2019;20:e48316. https://doi.org/10.15252/embr.201948316 .
doi: 10.15252/embr.201948316
pubmed: 31126909
pmcid: 6549023
Colin Mitchell, Johan Ordish, Emma Johnson, Tanya Brigden and Alison Hall. The GDPR and genomic data. PHG Foundation; 2020 May. Available from: https://www.phgfoundation.org/report/the-gdpr-and-genomic-data .
Quinn P, Quinn L. Big genetic data and its big data protection challenges. Comput Law Secur Rev. 2018;34:1000–18.
doi: 10.1016/j.clsr.2018.05.028
Brauneck A, Schmalhorst L, Kazemi Majdabadi MM, Bakhtiari M, Völker U, Baumbach J, et al. Federated machine learning, privacy-enhancing technologies, and data protection laws in medical research: scoping review. J Med Internet Res. 2023;25:e41588.
pubmed: 36995759
pmcid: 10131784
doi: 10.2196/41588
Wang X, Dervishi L, Li W, Ayday E, Jiang X, Vaidya J. Privacy-preserving federated genome-wide association studies via dynamic sampling. Bioinformatics. 2023;39:btad639. https://doi.org/10.1093/bioinformatics/btad639 .
doi: 10.1093/bioinformatics/btad639
pubmed: 37856329
pmcid: 10612407
Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. Plos Genet. 2008;4:e1000167.
pubmed: 18769715
pmcid: 2516199
doi: 10.1371/journal.pgen.1000167
Wang R, Li YF, Wang X, Tang H, Zhou X. Learning your identity and disease from research papers: information leaks in genome wide association study. Proceedings of the 16th ACM conference on Computer and communications security. New York, NY, USA: Association for Computing Machinery; 2009. p. 534–44.
Humbert M, Ayday E, Hubaux J-P, Telenti A. Telenti A. Addressing the concerns of the lacks family: quantification of kin genomic privacy. Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security. New York, NY, USA: Association for Computing Machinery; 2013. p. 1141–52.
Mizas C, Sirakoulis GC, Mardiris V, Karafyllidis I, Glykos N, Sandaltzopoulos R. Reconstruction of DNA sequences using genetic algorithms and cellular automata: towards mutation prediction? Biosystems. 2008;92:61–8.
pubmed: 18243517
doi: 10.1016/j.biosystems.2007.12.002
Bossé Y, Amos CI. A decade of GWAS results in lung cancer. Cancer Epidemiol Biomarkers Prev. 2018;27:363–79.
pubmed: 28615365
doi: 10.1158/1055-9965.EPI-16-0794
Constable SD, Tang Y, Wang S, Jiang X, Chapin S. Privacy-preserving GWAS analysis on federated genomic datasets. BMC Med Inform Decis Mak. 2015;15(Suppl 5):S2.
pubmed: 26733045
pmcid: 4699163
doi: 10.1186/1472-6947-15-S5-S2
Nasirigerdeh R, Torkzadehmahani R, Matschinske J, Frisch T, List M, Späth J, et al. sPLINK: a federated, privacy-preserving tool as a robust alternative to meta-analysis in genome-wide association studies. bioRxiv. 2022. p. 2020.06.05.136382. Available from: https://www.biorxiv.org/content/10.1101/2020.06.05.136382v2 . Cited 2022 Aug 2.
Psychiatric Genomics Consortium. Available from: https://pgc.unc.edu/about-us/ . Cited 2023 Feb 15.
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.
pmcid: 4112379
doi: 10.1038/nature13595
Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PIW, Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331–6.
pubmed: 17463246
doi: 10.1126/science.1142358
Federal Trade Commission. Protecting Consumer Privacy in an Era of Rapid Change. Federal Trade Commission; 2012 Mar. Available from: https://www.ftc.gov/sites/default/files/documents/reports/federal-trade-commission-report-protecting-consumer-privacy-era-rapid-change-recommendations/120326privacyreport.pdf .
Act on the Protection of Personal Information - English - Japanese Law Translation. Available from: https://www.japaneselawtranslation.go.jp/en/laws/view/2781/en . Cited 2023 Feb 15.
González G, Van Brakel R, De Hert P. Research handbook on privacy and data protection law: values, norms and global politics. Cheltenham: Edward Elgar Publishing; 2022.
Regalado A. More than 26 million people have taken an at-home ancestry test. MIT Technology Review. 2019. Available from: https://www.technologyreview.com/2019/02/11/103446/more-than-26-million-people-have-taken-an-at-home-ancestry-test/ . Cited 2024 Jan 30.
Naveed M, Ayday E, Clayton EW, Fellay J, Gunter CA, Hubaux J-P, et al. Privacy in the genomic era. ACM Comput Surv. 2015;48:1. https://doi.org/10.1145/2767007 .
doi: 10.1145/2767007
Carballo R. Data Breach at 23andMe Affects 6.9 Million Profiles, Company Says. The New York Times. 2023. Available from: https://www.nytimes.com/2023/12/04/us/23andme-hack-data.html . Cited 2024 Jan 31.
Bucher A. 23andMe hit with another class action lawsuit over data breach. Top Class Actions. 2023. Available from: https://topclassactions.com/lawsuit-settlements/privacy/data-breach/23andme-hit-with-another-class-action-lawsuit-over-data-breach/ . Cited 2024 Jan 31.
Jon Styf AJ. 23andMe reportedly blames data breach on victims. Top Class Actions. 2024. Available from: https://topclassactions.com/lawsuit-settlements/privacy/data-breach/23andme-confirms-oct-breach-compromised-data-from-6-9m-users/ . Cited 2024 Jan 31.
Pinheiro PP, Battaglini HB. Artificial intelligence and data protection: a comparative analysis of AI regulation through the lens of data protection in the EU and Brazil. GRUR Int. 2022;71:924–32.
doi: 10.1093/grurint/ikac049
Thouvenin F. informational self-determination: a convincing rationale for data protection law? J Intell Prop Info Tech & Elec Com L. 2021;12:246–56.
Malgieri G. The concept of fairness in the GDPR: a linguistic and contextual interpretation. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York, NY, USA: Association for Computing Machinery; 2020. p. 154–66.
Froelicher D, Troncoso-Pastoriza JR, Raisaro JL, Cuendet MA, Sousa JS, Cho H, et al. Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. Nat Commun. 2021;12:5910.
pubmed: 34635645
pmcid: 8505638
doi: 10.1038/s41467-021-25972-y
Blatt M, Gusev A, Polyakov Y, Goldwasser S. Secure large-scale genome-wide association studies using homomorphic encryption. Proc Natl Acad Sci U S A. 2020;117:11608–13.
pubmed: 32398369
pmcid: 7261120
doi: 10.1073/pnas.1918257117
Sudlow C. Trusted Research Environments. HDR UK. 2021. Available from: https://www.hdruk.ac.uk/access-to-health-data/trusted-research-environments/ . Cited 2023 Feb 13.
Waind E. Multi-party trusted research environment federation: Establishing infrastructure for secure analysis across different clinical-genomic datasets. DARE UK. 2022. Available from: https://dareuk.org.uk/multi-party-trusted-research-environment-federation-clinical-genomic-datasets/ . Cited 2023 Feb 13.
Buchmann J, Geihs M, Hamacher K, Katzenbeisser S, Stammler S. Long-term integrity protection of genomic data. EURASIP J Inf Secur. 2019;2019:1–14.
Kuru T. Genetic data: the Achilles’ heel of the GDPR? Eur Data Prot Law Rev. 2021;7:45–58.
doi: 10.21552/edpl/2021/1/8
Kuru T, de Beriain IM. Your genetic data is my genetic data: unveiling another enforcement issue of the GDPR. Comp Law Sec Rev. 2022;47:105752.
doi: 10.1016/j.clsr.2022.105752
McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.
pubmed: 27548312
pmcid: 5388176
doi: 10.1038/ng.3643
Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed program. Nature. 2021;590:290–9.
pubmed: 33568819
pmcid: 7875770
doi: 10.1038/s41586-021-03205-y
Wienbrandt L, Prieß C, Kässens JC, Franke A, Uhing F, Ellinghaus D. EagleImp-Web: a fast and secure genotype phasing and imputation web service using field-programmable gate arrays. bioRxiv. 2022. p. 2022.02.24.481790. Available from: https://www.biorxiv.org/content/10.1101/2022.02.24.481790v1 . Cited 2022 Oct 6.
Judgment of the Court (Grand Chamber) of 6 October 2015 (Schrems I). Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A62014CJ0362 . Cited 2023 Nov 14.
Judgment of the Court (Grand Chamber) of 16 July 2020 (Schrems II). Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:62018CJ0311 . Cited 2022 Oct 6.
Marko R, Sekanina J. The new transatlantic data privacy framework. Transatlantic Law Journal. 2023;2:63–5.
Miño V. What does the Data Privacy Framework Self-Certification mean for your company?. datenschutz notizen | News-Blog der DSN GROUP. 2023. Available from: https://www.datenschutz-notizen.de/what-does-the-data-privacy-framework-self-certification-mean-for-your-company-0545511/ . Cited 2024 Jan 18.
Phillips M. International data-sharing norms: from the OECD to the General Data Protection Regulation (GDPR). Hum Genet. 2018;137:575–82.
pubmed: 30069638
pmcid: 6132662
doi: 10.1007/s00439-018-1919-7
New Standard Contractual Clauses - Questions and Answers overview. European Commission. Available from: https://commission.europa.eu/law/law-topic/data-protection/international-dimension-data-protection/new-standard-contractual-clauses-questions-and-answers-overview_en . Cited 2024 Feb 6.
Gürsoy G, Chielle E, Brannon CM, Maniatakos M, Gerstein M. Privacy-preserving genotype imputation with fully homomorphic encryption. Cell Syst. 2022;13:173-82.e3.
pubmed: 34758288
doi: 10.1016/j.cels.2021.10.003
Kim M, Harmanci AO, Bossuat J-P, Carpov S, Cheon JH, Chillotti I, et al. Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation. Cell Syst. 2021;12:1108-20.e4.
pubmed: 34464590
pmcid: 9898842
doi: 10.1016/j.cels.2021.07.010
Dokmai N, Kockan C, Zhu K, Wang X, Sahinalp SC, Cho H. Privacy-preserving genotype imputation in a trusted execution environment. Cell Syst. 2021;12:983-93.e7.
pubmed: 34450045
pmcid: 8542641
doi: 10.1016/j.cels.2021.08.001
Sherman MA. Paving the path toward genomic privacy with secure imputation. Cell Syst. 2021;12:950–2.
pubmed: 34672957
doi: 10.1016/j.cels.2021.09.006
Sabt M, Achemlal M, Bouabdallah A. Trusted execution environment: what it is, and what it is not. 2015 IEEE Trustcom/BigDataSE/ISPA. New York City: IEEE; 2015. p. 57–64.
Heinz C, Wall N, Wansch AH, Grimm C. Privacy, GDPR, and homomorphic encryption. In: Zivkovic C, Guan Y, Grimm C, editors. IoT Platforms, Use Cases, Privacy, and Business Models: With Hands-on Examples Based on the VICINITY Platform. Cham: Springer International Publishing; 2021. p. 165–84.
doi: 10.1007/978-3-030-45316-9_8
Johnson A, Shmatikov V. Privacy-preserving data exploration in genome-wide association studies. KDD. 2013;2013:1079–87.
pubmed: 26691928
pmcid: 4681528
Uhlerop C, Slavković A, Fienberg SE. Privacy-preserving data sharing for genome-wide association studies. J Priv Confid. 2013;5:137–66.
pubmed: 26525346
pmcid: 4623434
Ficek J, Wang W, Chen H, Dagne G, Daley E. Differential privacy in health research: a scoping review. J Am Med Inform Assoc. 2021;28:2269–76.
pubmed: 34333623
pmcid: 8449619
doi: 10.1093/jamia/ocab135
Mugunthan V, Byrd D, Balch TH, Morgan JP. SMPAI: Secure Multi-Party Computation for Federated Learning. 2019; Available from: https://www.jpmorgan.com/content/dam/jpm/cib/complex/content/technology/ai-research-publications/pdf-9.pdf . Cited 2022 Mar 9.
Truong N, Sun K, Wang S, Guitton F, Guo Y. Privacy preservation in federated learning: an insightful survey from the GDPR perspective. Computer Security. 2021;110. Available from: https://www.sciencedirect.com/science/article/pii/S0167404821002261 .
Information Commissioner’s Office. Privacy-enhancing technologies (PETs). 2023. Available from: https://ico.org.uk/media/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/privacy-enhancing-technologies-1-0.pdf .
Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E, Sakaue S, et al. A saturated map of common genetic variants associated with human height. Nature. 2022;610:704–12.
pubmed: 36224396
pmcid: 9605867
doi: 10.1038/s41586-022-05275-y
Metzler I, Ferent L-M, Felt U. On samples, data, and their mobility in biobanking: How imagined travels help to relate samples and data. Big Data Soc. 2023;10:20539517231158636.
doi: 10.1177/20539517231158635
Goisauf M, Martin G, Bentzen HB, Budin-Ljøsne I, Ursin L, Durnová A, et al. Data in question: a survey of European biobank professionals on ethical, legal and societal challenges of biobank research. Plos One. 2019;14:e0221496.
pubmed: 31532777
pmcid: 6750647
doi: 10.1371/journal.pone.0221496
Hallinan D. Broad consent under the GDPR: an optimistic perspective on a bright future. Life Sci Soc Pol. 2020;16:1–18.
doi: 10.1186/s40504-019-0096-3
Richter G, Krawczak M, Lieb W, Wolff L, Schreiber S, Buyx A. Broad consent for health care-embedded biobanking: understanding and reasons to donate in a large patient sample. Genet Med. 2018;20:76–82.
pubmed: 28640237
doi: 10.1038/gim.2017.82
Hansson MG. Striking a balance between personalised genetics and privacy protection from the perspective of GDPR. In: Slokenberga S, Tzortzatou O, Reichel J, editors. GDPR and Biobanking: Individual Rights, Public Interest and Research Regulation across Europe. Cham: Springer International Publishing; 2021. p. 31–42.
doi: 10.1007/978-3-030-49388-2_3
Politou E, Alepis E, Patsakis C. Forgetting personal data and revoking consent under the GDPR: challenges and proposed solutions. J Cyber Secur. 2018;4. Available from: https://academic.oup.com/cybersecurity/article-pdf/4/1/tyy001/27126900/tyy001.pdf . Cited 2022 Aug 10.
de Wert G, Dondorp W, Clarke A, Dequeker EMC, Cordier C, Deans Z, et al. Opportunistic genomic screening. Recommendations of the European society of human genetics. Eur J Hum Genet. 2021;29:365–77.
pubmed: 33223530
doi: 10.1038/s41431-020-00758-w
Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, Gil L, et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 2023;51:D977–85.
pubmed: 36350656
doi: 10.1093/nar/gkac1010
King A, Wu L, Deng H-W, Shen H, Wu C. Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease. BMC Med. 2022;20:385.
pubmed: 36336692
pmcid: 9639312
doi: 10.1186/s12916-022-02583-y
Haga SB. Impact of limited population diversity of genome-wide association studies. Genet Med. 2010;12:81–4.
pubmed: 20057316
doi: 10.1097/GIM.0b013e3181ca2bbf
Wauters A, Van Hoyweghen I. Global trends on fears and concerns of genetic discrimination: a systematic literature review. J Hum Genet. 2016;61:275–82.
pubmed: 26740237
doi: 10.1038/jhg.2015.151
Renieris E. Why PETs (privacy-enhancing technologies) may not always be our friends. Available from: https://www.adalovelaceinstitute.org/blog/privacy-enhancing-technologies-not-always-our-friends/ . Cited 2024 Jan 18.
Jordan S, Fontaine C, Hendricks-Sturrup R. Selecting privacy-enhancing technologies for managing health data use. Front Public Health. 2022;10:814163.
pubmed: 35372185
pmcid: 8967420
doi: 10.3389/fpubh.2022.814163
Malin B, Loukides G, Benitez K, Clayton EW. Identifiability in biobanks: models, measures, and mitigation strategies. Hum Genet. 2011;130:383–92.
pubmed: 21739176
pmcid: 3621020
doi: 10.1007/s00439-011-1042-5
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–9.
pubmed: 30305743
pmcid: 6786975
doi: 10.1038/s41586-018-0579-z
Zolotareva O, Nasirigerdeh R, Matschinske J, Torkzadehmahani R, Bakhtiari M, Frisch T, et al. Flimma: a federated and privacy-aware tool for differential gene expression analysis. Genome Biol. 2021;22:338.
pubmed: 34906207
pmcid: 8670124
doi: 10.1186/s13059-021-02553-2
Yadav P, Ellinghaus D, Rémy G, Freitag-Wolf S, Cesaro A, Degenhardt F, et al. Genetic factors interact with tobacco smoke to modify risk for inflammatory bowel disease in humans and mice. Gastroenterology. 2017;153:550–65.
pubmed: 28506689
doi: 10.1053/j.gastro.2017.05.010
Cho H, Wu DJ, Berger B. Secure genome-wide association analysis using multiparty computation. Nat Biotechnol. 2018;36:547–51.
pubmed: 29734293
pmcid: 5990440
doi: 10.1038/nbt.4108
David Froelicher, Hyunghoon Cho, Manaswitha Edupalli, Joao Sa Sousa, Jean-Philippe Bossuat, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, Bonnie Berger and Jean-Pierre Hubaux. Scalable and privacy-preserving federated principal component analysis. IEEE Symposium on Security and Privacy. 2023; Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10179350 .
von Thenen N, Ayday E, Cicek AE. Re-identification of individuals in genomic data-sharing beacons via allele inference. Bioinformatics. 2019;35:365–71.
doi: 10.1093/bioinformatics/bty643
Cai R, Hao Z, Winslett M, Xiao X, Yang Y, Zhang Z, et al. Deterministic identification of specific individuals from GWAS results. Bioinformatics. 2015;31:1701–7.
pubmed: 25630377
pmcid: 4443672
doi: 10.1093/bioinformatics/btv018
Venkatesaramani R, Malin BA, Vorobeychik Y. Re-identification of individuals in genomic datasets using public face images. Sci Adv. 2021;7:eabg3296.
pubmed: 34788101
pmcid: 8597988
doi: 10.1126/sciadv.abg3296
Heidt CM, Hund H, Fegeler C. A federated record linkage algorithm for secure medical data sharing. Stud Health Technol Inform. 2021;278:142–9.
pubmed: 34042887
Alvarellos M, Sheppard HE, Knarston I, Davison C, Raine N, Seeger T, et al. Democratizing clinical-genomic data: how federated platforms can promote benefits sharing in genomics. Front Genet. 2022;13:1045450.
pubmed: 36704354
doi: 10.3389/fgene.2022.1045450
Olowu M, Yinka-Banjo C, Misra S, Florez H. A secured private-cloud computing system. Applied Informatics. Madrid: Springer International Publishing; 2019. p. 373–84.
Technical University of Denmark. Computerome. Available from: https://www.computerome.dk/solutions/secure-private-cloud . Cited 2023 Feb 27.
Cookbook for eQTLGen phase II analyses - eQTLGen Phase II. Available from: https://eqtlgen.github.io/eqtlgen-web-site/eQTLGen-p2-cookbook.html . Cited 2023 Mar 16.