Abstract
Most genotype–phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most well known of these new cohort efforts is arguably All of Us, formerly known as the Precision Medicine Cohort Initiative Program. All of Us intends to ascertain at least one million participants in the United States representative of the multiple dimensions of diversity. As an incentive to participate, All of Us is offering the return of research results, including whole genome sequencing data, as well as the opportunity to contribute to the scientific process as non-scientists. The scale and scope of the proposed return of research results are unprecedented. Here, we briefly review possible return of genetic data models, including the likely data file formats and modes of data transfer or access. We also review the resources required to access and interpret the genetic or genomic data once received by the average participant, highlighting the nuanced anticipated barriers that will challenge both the digitally, computationally literate and illiterate participant alike. This inventory of resources required to receive, process, and interpret return of research results exposes the potential for access disparities and warns the scientific community to mind the gap so that all participants have equal access and understanding of the benefits of human genetic research.
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References
Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, Chilton J, Clements D, Coraor N, Grüning BA, Guerler A, Hillman-Jackson J, Hiltemann S, Jalili V, Rasche H, Soranzo N, Goecks J, Taylor J, Nekrutenko A, Blankenberg D (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46:W537–W544. https://doi.org/10.1093/nar/gky379
Agurs-Collins T, Ferrer R, Ottenbacher A, Waters EA, O’Connell ME, Hamilton JG (2015) Public awareness of direct-to-consumer genetic tests: findings from the 2013 US Health Information National Trends Survey. J Cancer Educ 30:799–807. https://doi.org/10.1007/s13187-014-0784-x
Anderson C (2017) AZ partners with DNAnexus for 2 million patient sequencing project. Clin OMICs 4:32. https://doi.org/10.1089/clinomi.04.04.23
Angrist M (2011) You never call, you never write: why return of ‘omic’ results to research participants is both a good idea and a moral imperative. Personal Med 8:651–657. https://doi.org/10.2217/pme.11.62
Apathy NC, Menser T, Keeran LM, Ford EW, Harle CA, Huerta TR (2018) Trends and gaps in awareness of direct-to-consumer genetic tests from 2007 to 2014. Am J Prev Med 54:806–813. https://doi.org/10.1016/j.amepre.2018.02.013
Aungst H, Fishman JR, McGowan ML (2017) Participatory genomic research: ethical issues from the bottom up to the top down. Annu Rev Genomics Hum Genet 18:357–367. https://doi.org/10.1146/annurev-genom-091416-035230
Badalato L, Kalokairinou L, Borry P (2017) Third party interpretation of raw genetic data: an ethical exploration. Eur J Hum Genet 25:1189. https://doi.org/10.1038/ejhg.2017.126
Baker M (2010) Next-generation sequencing: adjusting to data overload. Nat Methods 7:495. https://doi.org/10.1038/nmeth0710-495
Bates M (2018) Direct-to-consumer genetic testing: is the public ready for simple, at-home DNA tests to detect disease risk? IEEE Pulse 9:11–14. https://doi.org/10.1109/MPUL.2018.2869315
Beck S, Berner AM, Bignell G, Bond M, Callanan MJ, Chervova O, Conde L, Corpas M, Ecker S, Elliott HR, Fioramonti SA, Flanagan AM, Gaentzsch R, Graham D, Gribbin D, Guerra-Assunção JA, Hamoudi R, Harding V, Harrison PL, Herrero J, Hofmann J, Jones E, Khan S, Kaye J, Kerr P, Libertini E, Marks L, McCormack L, Moghul I, Pontikos N, Rajanayagam S, Rana K, Semega-Janneh M, Smith CP, Strom L, Umur S, Webster AP, Williams EH, Wint K, Wood JN (2018) Personal genome project UK (PGP-UK): a research and citizen science hybrid project in support of personalized medicine. BMC Med Genom 11:108. https://doi.org/10.1186/s12920-018-0423-1
Bonfield JK (2014) The Scrmble conversion tool. Bioinformatics 30:2818–2819. https://doi.org/10.1093/bioinformatics/btu390
Borry P, van Hellemondt RE, Sprumont D, Jales CF, Rial-Sebbag E, Spranger TM, Curren L, Kaye J, Nys H, Howard H (2012) Legislation on direct-to-consumer genetic testing in seven European countries. Eur J Hum Genet 20:715–721. https://doi.org/10.1038/ejhg.2011.278
Branch M (2013) The FDA and me. Nature 504:7–8
Cariaso M, Lennon G (2012) SNPedia: a wiki supporting personal genome annotation, interpretation and analysis. Nucleic Acids Res 40:D1308–D1312. https://doi.org/10.1093/nar/gkr798
Chiang C, Layer RM, Faust GG, Lindberg MR, Rose DB, Garrison EP, Marth GT, Quinlan AR, Hall IM (2015) SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Methods 12:966. https://doi.org/10.1038/nmeth.3505
Clarke L, Zheng-Bradley X, Smith R, Kulesha E, Xiao C, Toneva I, Vaughan B, Preuss D, Leinonen R, Shumway M, Sherry S, Flicek P, 1000 Genomes Project Analysis Group (2012) The 1000 genomes project: data management and community access. Nat Methods 9:459. https://doi.org/10.1038/nmeth.1974
Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38:1767–1771. https://doi.org/10.1093/nar/gkp1137
Cooke Bailey JN, Crawford DC, Goldenberg A, Slaven A, Pencak J, Schachere M, Bush WS, Sedor JR, O’Toole JF (2018) Willingness to participate in a national precision medicine cohort: attitudes of chronic kidney disease patients at a Cleveland public hospital. J Personal Med 8:21. https://doi.org/10.3390/jpm8030021
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R, Group GPA (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158. https://doi.org/10.1093/bioinformatics/btr330
Desai S, Jena AB (2016) Do celebrity endorsements matter? Observational study of BRCA gene testing and mastectomy rates after Angelina Jolie’s New York Times editorial. BMJ 355:i6357. https://doi.org/10.1136/bmj.i6357
Desvignes J-P, Bartoli M, Delague V, Krahn M, Miltgen M, Béroud C, Salgado D (2018) VarAFT: a variant annotation and filtration system for human next generation sequencing data. Nucleic Acids Res 46:W545–W553. https://doi.org/10.1093/nar/gky471
DNAnexus (2019) DNAnexus. https://www.dnanexus.com/. Accessed 20 May 2019
Dougherty MJ, Pleasants C, Solow L, Wong A, Zhang H (2011) A comprehensive analysis of high school genetics standards: are states keeping pace with modern genetics? CBE Life Sci Educ 10:318–327. https://doi.org/10.1187/cbe.10-09-0122
Erlich Y, Shor T, Pe’er I, Carmi S (2018) Identity inference of genomic data using long-range familial searches. Science 362:690. https://doi.org/10.1126/science.aau4832
Facio FM, Eidem H, Fisher T, Brooks S, Linn A, Kaphingst KA, Biesecker LG, Biesecker BB (2012) Intentions to receive individual results from whole-genome sequencing among participants in the ClinSeq study. Eur J Hum Genet 21:261. https://doi.org/10.1038/ejhg.2012.179
Federal Communications Commission (2015) 2015 Broadband Progress Report. https://www.fcc.gov/reports-research/reports/broadband-progress-reports/2015-broadband-progress-report. Accessed 1 Feb 2019
Finney Rutten LJ, Gollust SE, Naveed S, Moser RP (2012) Increasing public awareness of direct-to-consumer genetic tests: health care access, internet use, and population density correlates. J Cancer Epidemiol 2012:309109. https://doi.org/10.1155/2012/309109
Gailly J-l, Adler M (1992) GNU Gzip. GNU. https://www.gnu.org/software/gzip/. Accessed 8 Jan 2019
Garrison E, Marth G (2012) Haplotype-based variant detection from short-read sequencing. arxiv:1207.3907
Greshake B, Bayer PE, Rausch H, Reda J (2014) openSNP–a crowdsourced web resource for personal genomics. PLoS One 9:e89204. https://doi.org/10.1371/journal.pone.0089204
Guerrini CJ, Robinson JO, Petersen D, McGuire AL (2018) Should police have access to genetic genealogy databases? Capturing the golden state killer and other criminals using a controversial new forensic technique. PLoS Biol 16:e2006906. https://doi.org/10.1371/journal.pbio.2006906
Haga SB, Barry WT, Mills R, Ginsburg GS, Svetkey L, Sullivan J, Willard HF (2013) Public knowledge of and attitudes toward genetics and genetic testing. Genet Test Mol Biomarkers 17:327–335. https://doi.org/10.1089/gtmb.2012.0350
Hansen NF (2016) Variant Calling From Next Generation Sequence Data. In: Mathé E, Davis S (eds) Statistical genomics: methods and protocols. Springer, New York, pp 209–224
Hansen T, Keeler J (2018) The NIH is bypassing tribal sovereignty to harvest genetic data from Native Americans. Motherboard. https://motherboard.vice.com/en_us/article/8xp33a/the-nih-is-bypassing-tribal-sovereignty-to-harvest-genetic-data-from-native-americans. Accessed 10 Jan 2019
Hart SN, Duffy P, Quest DJ, Hossain A, Meiners MA, Kocher JP (2016) VCF-Miner: gUI-based application for mining variants and annotations stored in VCF files. Brief Bioinform 17:346–351. https://doi.org/10.1093/bib/bbv051
Hazel JW, Clayton EW, Malin BA, Slobogin C (2018) Is it time for a universal genetic forensic database? Science 362:898–900. https://doi.org/10.1126/science.aav5475
Hsi-Yang Fritz M, Leinonen R, Cochrane G, Birney E (2011) Efficient storage of high throughput DNA sequencing data using reference-based compression. Genome Res 21:734–740. https://doi.org/10.1101/gr.114819.110
Illumina (2018) BaseSpace sequence hub. Illumina, San Diego, pp 1–6
Jarvik GP, Amendola LM, Berg JS, Brothers K, Clayton EW, Chung W, Evans BJ, Evans JP, Fullerton SM, Gallego CJ, Garrison NA, Gray SW, Holm IA, Kullo IJ, Lehmann LS, McCarty C, Prows CA, Rehm HL, Sharp RR, Salama J, Sanderson S, Van- Driest SL, Williams MS, Wolf SM, Wolf WA, Burke W (2014) Return of genomic results to research participants: the floor, the ceiling, and the choices in between. Am J Hum Genet 94:818–826. https://doi.org/10.1016/j.ajhg.2014/04.009
Johns Hopkins University (2016) Command line tools for genomic data science. Coursera. https://www.coursera.org/learn/genomic-tools. Accessed 8 Jan 2019
Johnson JA, Caudle KE, Gong L, Whirl-Carrillo M, Stein CM, Scott SA, Lee MT, Gage BF, Kimmel SE, Perera MA, Anderson JL, Pirmohamed M, Klein TE, Limdi NA, Cavallari LH, Wadelius M, Clinical pharmacogenetics implementation Consortium (CPIC) (2017) Guideline for pharmacogenetics-guided warfarin dosing: 2017 update. Clin Pharmacol Ther 102:397–404. https://doi.org/10.1002/cpt.668
Jolie A (2013) My medical choice. The New York Times, New York
Kalia SS, Adelman K, Bale SJ, Chung WK, Eng C, Evans JP, Herman GE, Hufnagel SB, Klein TE, Korf BR, McKelvey KD, Ormond KE, Richards CS, Vlangos CN, Watson M, Martin CL, Miller DT (2016) Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med 19:249. https://doi.org/10.1038/gim.2016.190
Karow J (2018) All of Us Program plans to return disease variants, PGx results, primary genomic data. GenomeWeb. https://allofus.nih.gov/sites/default/files/all_of_us_program_plans_to_return_disease_variants_pgx_results_primary_genomic_data.pdf. Accessed 2 Jan 2019
Kaufman D, Murphy J, Erby L, Hudson K, Scott J (2009) Veterans’ attitudes regarding a database for genomic research. Genet Med 11:329. https://doi.org/10.1097/GIM.0b013e31819994f8
Kaufman DJ, Baker R, Milner LC, Devaney S, Hudson KL (2016) A survey of US adults’ opinions about conduct of a Nationwide Precision Medicine Initiative® Cohort study of genes and environment. PLoS One 11:e0160461. https://doi.org/10.1371/journal.pone.0160461
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Meintjes P, Drummond A (2012) Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649. https://doi.org/10.1093/bioinformatics/bts199
Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, Maglott DR (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42:D980–D985. https://doi.org/10.1093/nar/gkt1113
Lewis KL, Han PKJ, Hooker GW, Klein WMP, Biesecker LG, Biesecker BB (2015) Characterizing participants in the ClinSeq genome sequencing cohort as early adopters of a new health technology. PLoS One 10:e0132690. https://doi.org/10.1371/journal.pone.0132690
Li H (2011) A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27:2987–2993. https://doi.org/10.1093/bioinformatics/btr509
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Subgroup GPDP (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079. https://doi.org/10.1093/bioinformatics/btp352
Liede A, Cai M, Crouter TF, Niepel D, Callaghan F, Evans DG (2018) Risk-reducing mastectomy rates in the US: a closer examination of the Angelina Jolie effect. Breast Cancer Res Treat 171:435–442. https://doi.org/10.1007/s10549-018-4824-9
Lontok KS, Zhang H, Dougherty MJ (2015) Assessing the genetics content in the next generation science standards. PLoS One 10:e0132742. https://doi.org/10.1371/journal.pone.0132742
MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, Junkins H, McMahon A, Milano A, Morales J, Pendlington ZM, Welter D, Burdett T, Hindorff L, Flicek P, Cunningham F, Parkinson H (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 45:D896–D901. https://doi.org/10.1093/nar/gkw1133
Mamedova S, Pawlowski E (2018) A description of US adults who are not digitally literate. In: Hudson L (ed) Stats in Brief. US Department of Education, Washington DC
Marcon AR, Bieber M, Caulfield T (2018) Representing a “revolution”: how the popular press has portrayed personalized medicine. Genet Med 20:950–956. https://doi.org/10.1038/gim.2017.217
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303. https://doi.org/10.1101/gr.107524.110
Middleton A, Wright CF, Morley KI, Bragin E, Firth HV, Hurles ME, Parker M (2015) Potential research participants support the return of raw sequence data. J Med Genet 52:571–574. https://doi.org/10.1136/jmedgenet-2015-103119
Milicchio F, Rose R, Bian J, Min J, Prosperi M (2016) Visual programming for next-generation sequencing data analytics. BioData Min 9:16. https://doi.org/10.1186/s13040-016-0095-3
Moscarello T, Murray B, Reuter CM, Demo E (2019) Direct-to-consumer raw genetic data and third-party interpretation services: more burden than bargain? Genet Med 21:539–541. https://doi.org/10.1038/s41436-018-0097-2
Müller A, Dalzotto A (2018) GATTACA and genetic determinism. Nurse Educ Today 70:94–95. https://doi.org/10.1016/j.nedt.2018.08.004
Muller H, Jimenez-Heredia R, Krolo A, Hirschmugl T, Dmytrus J, Boztug K, Bock C (2017) VCF.Filter: interactive prioritization of disease-linked genetic variants from sequencing data. Nucleic Acids Res 45:W567–W572. https://doi.org/10.1093/nar/gkx425
National Institutes of Health (2018a) All of Us Research Program Genome Centers (OT2). OT-PM-18-002. https://allofus.nih.gov/sites/default/files/fa_genome_centers_OT_18.pdf. Accessed 9 Dec 2018
National Institutes of Health (2018b) NIH Strategic Plan for Data Science. https://datascience.nih.gov/sites/default/files/NIH_Strategic_Plan_for_Data_Science_Final_508.pdf. Accessed 4 Apr 2019
Navale V, Bourne PE (2018) Cloud computing applications for biomedical science: a perspective. PLoS Comput Biol 14:e1006144. https://doi.org/10.1371/journal.pcbi.1006144
Pew Research Center (2018) Internet/broadband fact sheet. http://www.pewinternet.org/fact-sheet/internet-broadband/. Accessed 2 Jan 2019
Popejoy AB, Ritter DI, Crooks K, Currey E, Fullerton SM, Hindorff LA, Koenig B, Ramos EM, Sorokin EP, Wand H, Wright MW, Zou J, Gignoux CR, Bonham VL, Plon SE, Bustamante CD, Clinical Genome Resource A, Diversity Working G (2018) The clinical imperative for inclusivity: race, ethnicity, and ancestry (REA) in genomics. Hum Mutat 39:1713–1720. https://doi.org/10.1002/humu.23644
Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL, Ramirez AH, Delaney JT, Bowton E, Brothers K, Johnson K, Crawford DC, Schildcrout J, Masys DR, Dilks HH, Wilke RA, Clayton EW, Shultz E, Laposata M, McPherson J, Jirjis JN, Roden DM (2012) Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin Pharmacol Ther 92:87–95. https://doi.org/10.1038/clpt.2011.371
Ram N, Guerrini CJ, McGuire AL (2018) Genealogy databases and the future of criminal investigation. Science 360:1078–1079. https://doi.org/10.1126/science.aau1083
Ray T (2018) Public awareness of personalized medicine not growing in step with industry, survey shows. GenomeWeb. https://www.genomeweb.com/molecular-diagnostics/public-awareness-personalized-medicine-not-growing-step-industry-survey-shows#.XPKvso97leU. Accessed 9 Dec 2018
Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, Ledbetter DH, Maglott DR, Martin CL, Nussbaum RL, Plon SE, Ramos EM, Sherry ST, Watson MS (2015) ClinGen—the clinical genome resource. N Engl J Med 372:2235–2242. https://doi.org/10.1056/NEJMsr1406261
Relling M, Krauss R, Roden D, Klein T, Fowler D, Terada N, Lin L, Riel-Mehan M, Do T, Kubo M, Yee S, Johnson G, Giacomini K (2017) New pharmacogenomics research network: an open community catalyzing research and translation in precision medicine. Clin Pharmacol Ther 102:897–902. https://doi.org/10.1002/cpt.755
Ryan C, Lewis JM (2017) Computer and internet use in the United States: 2015. https://www.census.gov/content/dam/Census/library/publications/2017/acs/acs-37.pdf. Accessed 2 Jan 2019
Salloum RG, George TJ, Silver N, Markham MJ, Hall JM, Guo Y, Bian J, Shenkman EA (2018) Rural-urban and racial-ethnic differences in awareness of direct-to-consumer genetic testing. BMC Public Health 18:277. https://doi.org/10.1186/s12889-018-5190-6
Schwartz MLB, McCormick CZ, Lazzeri AL, Lindbuchler DAM, Hallquist MLG, Manickam K, Buchanan AH, Rahm AK, Giovanni MA, Frisbie L, Flansburg CN, Davis FD, Sturm AC, Nicastro C, Lebo MS, Mason-Suares H, Mahanta LM, Carey DJ, Williams JL, Williams MS, Ledbetter DH, Faucett WA, Murray MF (2018) A model for genome-first care: returning secondary genomic findings to participants and their healthcare providers in a large research cohort. Am J Hum Genet 103:328–337. https://doi.org/10.1016/j.ajhg.2018.07.009
Schweitzer NJ, Saks MJ (2007) The CSI effect: popular fiction about forensic science affects the public’s expectations about real forensic science. Jurimetr J 47:357–364
Seward J (1996) bzip2. GitLab. https://gitlab.com/bzip/bzip2. Accessed 8 Jan 2019
Sirugo G, Williams SM, Tishkoff SA (2019) The missing diversity in human genetic studies. Cell 177:26–31. https://doi.org/10.1016/j.cell.2019.02.048
Smith DR (2015) Buying into bioinformatics: an introduction to commercial sequence analysis software. Brief Bioinform 16:700–709. https://doi.org/10.1093/bib/bbu030
Tabor HK, Jamal SM, Yu JH, Crouch JM, Shankar AG, Dent KM, Anderson N, Miller DA, Futral BT, Bamshad MJ (2017) My46: a web-based tool for self-guided management of genomic test results in research and clinical settings. Genet Med 19:467–475. https://doi.org/10.1038/gim.2016.133
Tandy-Connor S, Guiltinan J, Krempely K, LaDuca H, Reineke P, Gutierrez S, Gray P, Tippin Davis B (2018) False-positive results released by direct-to-consumer genetic tests highlight the importance of clinical confirmation testing for appropriate patient care. Genet Med 20:1515–1521. https://doi.org/10.1038/gim.2018.38
Teer JK, Bonnycastle LL, Chines PS, Hansen NF, Aoyama N, Swift AJ, Abaan HO, Albert TJ, Program NCS, Margulies EH, Green ED, Collins FS, Mullikin JC, Biesecker LG (2010) Systematic comparison of three genomic enrichment methods for massively parallel DNA sequencing. Genome Res 20:1420–1431. https://doi.org/10.1101/gr.106716.110
The Tribal Collaboration Working Group Report to the All of Us Rsearch Program Advisory Panel (2018) Considerations for meaningful collaboration with tribal pouplations. All of Us Research Program Advisory Panel. https://allofus.nih.gov/sites/default/files/tribal_collab_work_group_rept.pdf. Accessed 10 Jan 2019
Thieme N (2018) After Hurricane Maria, Puerto Rico’s Internet Problems Go from Bad to Worse. Public Broadcasting Service. https://www.pbs.org/wgbh/nova/article/puerto-rico-hurricane-maria-internet/. Accessed 2 Jan 2019
Thorogood A, Bobe J, Prainsack B, Middleton A, Scott E, Nelson S, Corpas M, Bonhomme N, Rodriguez LL, Murtagh M, Kleiderman E, on behalf of the Participant Values Task Team of the Global Alliance for Genomics and Health (2018) APPLaUD: access for patients and participants to individual level uninterpreted genomic data. Hum Genom 12:7. https://doi.org/10.1186/s40246-018-0139-5
Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella KV, Altshuler D, Gabriel S, DePristo MA (2013) From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinform 43:1–33. https://doi.org/10.1002/0471250953.bi1110s43
Woolley JP, McGowan ML, Teare HJA, Coathup V, Fishman JR, Settersten RA, Sterckx S, Kaye J, Juengst ET (2016) Citizen science or scientific citizenship? Disentangling the uses of public engagement rhetoric in national research initiatives. BMC Med Ethics 17:33. https://doi.org/10.1186/s12910-016-0117-1
Yu J-H, Crouch J, Jamal SM, Bamshad MJ, Tabor HK (2014) Attitudes of non-African American focus group participants toward return of results from exome and whole genome sequencing. Am J Med Genet Part A 164:2153–2160. https://doi.org/10.1002/ajmg.a.36610
Zalunin V (2012) CRAM. European Nucleotide Archive. https://www.ebi.ac.uk/ena/software/cram-toolkit. Accessed 8 Jan 2019
Zhu Z, Zhang Y, Ji Z, He S, Yang X (2015) High-throughput DNA sequence data compression. Brief Bioinform 16:1–15. https://doi.org/10.1093/bib/bbt087
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This research was funded by institutional funds from the CWRU Cleveland Institute for Computational Biology as well as the Clinical and Translational Science Collaborative of Cleveland UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCATS or NIH. JNCB is supported by the Clinical and Translational Science Collaborative of Cleveland (KL2TR002547).
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Crawford, D.C., Cooke Bailey, J.N. & Briggs, F.B.S. Mind the gap: resources required to receive, process and interpret research-returned whole genome data. Hum Genet 138, 691–701 (2019). https://doi.org/10.1007/s00439-019-02033-5
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DOI: https://doi.org/10.1007/s00439-019-02033-5