1932

Abstract

Scalable sequence–function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

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2022-11-30
2024-04-21
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Literature Cited

  1. 1.
    1000 Genomes Project Consort 2015. A global reference for human genetic variation. Nature 526:68–74
    [Google Scholar]
  2. 2.
    Adamson B, Norman TM, Jost M, Cho MY, Nuñez JK et al. 2016. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167:1867–82.e21
    [Google Scholar]
  3. 3.
    Adkar BV, Tripathi A, Sahoo A, Bajaj K, Goswami D et al. 2012. Protein model discrimination using mutational sensitivity derived from deep sequencing. Structure 20:371–81
    [Google Scholar]
  4. 4.
    Adzhubei I, Jordan DM, Sunyaev SR. 2013. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 76:720.1–41
    [Google Scholar]
  5. 5.
    Aitman TJ, Boone C, Churchill GA, Hengartner MO, Mackay TFC, Stemple DL. 2011. The future of model organisms in human disease research. Nat. Rev. Genet. 12:575–82
    [Google Scholar]
  6. 6.
    All Us Res. Program Investig Denny JC, Rutter JL, Goldstein DB, Philippakis A et al. 2019. The “All of Us” Research Program. N. Engl. J. Med. 381:668–76
    [Google Scholar]
  7. 7.
    Amorosi CJ, Chiasson MA, McDonald MG, Wong LH, Sitko KA et al. 2021. Massively parallel characterization of CYP2C9 variant enzyme activity and abundance. Am. J. Hum. Genet. 108:1735–51
    [Google Scholar]
  8. 8.
    Anzalone AV, Randolph PB, Davis JR, Sousa AA, Koblan LW et al. 2019. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576:149–57
    [Google Scholar]
  9. 9.
    Arnold CD, Gerlach D, Stelzer C, Boryń ŁM, Rath M, Stark A. 2013. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339:1074–77
    [Google Scholar]
  10. 10.
    Ashuach T, Fischer DS, Kreimer A, Ahituv N, Theis FJ, Yosef N. 2019. MPRAnalyze: statistical framework for massively parallel reporter assays. Genome Biol 20:1183
    [Google Scholar]
  11. 11.
    AVE Alliance Found. Members 2021. The Atlas of Variant Effects (AVE) Alliance: understanding genetic variation at nucleotide resolution. Zenodo. https://doi.org/10.5281/zenodo.4989960
    [Crossref]
  12. 12.
    Aviv R, Teichmann SA, Lander ES, Ido A, Christophe B 2017. The Human Cell Atlas. eLife 6:e27041
    [Google Scholar]
  13. 13.
    Avsec Ž, Agarwal V, Visentin D, Ledsam JR, Grabska-Barwinska A et al. 2021. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18:1196–203
    [Google Scholar]
  14. 14.
    Avsec Ž, Weilert M, Shrikumar A, Krueger S, Alexandari A et al. 2021. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53:354–66
    [Google Scholar]
  15. 15.
    Baeza-Centurion P, Miñana B, Valcárcel J, Lehner B 2020. Mutations primarily alter the inclusion of alternatively spliced exons. eLife 9:e59959
    [Google Scholar]
  16. 16.
    Bai X-C, McMullan G, Scheres SHW. 2015. How cryo-EM is revolutionizing structural biology. Trends Biochem. Sci. 40:49–57
    [Google Scholar]
  17. 17.
    Bloom JD. 2015. Software for the analysis and visualization of deep mutational scanning data. BMC Bioinform 16:168
    [Google Scholar]
  18. 18.
    Bolognesi B, Faure AJ, Seuma M, Schmiedel JM, Tartaglia GG, Lehner B. 2019. The mutational landscape of a prion-like domain. Nat. Commun. 10:4162
    [Google Scholar]
  19. 19.
    Bonder MJ, Smail C, Gloudemans MJ, Frésard L, Jakubosky D et al. 2021. Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics. Nat. Genet. 53:313–21
    [Google Scholar]
  20. 20.
    Braun S, Enculescu M, Setty ST, Cortés-López M, de Almeida BP et al. 2018. Decoding a cancer-relevant splicing decision in the RON proto-oncogene using high-throughput mutagenesis. Nat. Commun. 9:3315
    [Google Scholar]
  21. 21.
    Bridgford JL, Lee SM, Lee CMM, Guglielmelli P, Rumi E et al. 2020. Novel drivers and modifiers of MPL-dependent oncogenic transformation identified by deep mutational scanning. Blood 135:287–92
    [Google Scholar]
  22. 22.
    Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS et al. 2020. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med 12:3
    [Google Scholar]
  23. 23.
    Brnich SE, Rivera-Muñoz EA, Berg JS 2018. Quantifying the potential of functional evidence to reclassify variants of uncertain significance in the categorical and Bayesian interpretation frameworks. Hum. Mutat. 39:1531–41
    [Google Scholar]
  24. 24.
    Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J et al. 2019. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47:D1005–12
    [Google Scholar]
  25. 25.
    Bycroft C, Freeman C, Petkova D, Band G, Elliott LT et al. 2018. The UK Biobank resource with deep phenotyping and genomic data. Nature 562:203–9
    [Google Scholar]
  26. 26.
    Cadwell RC, Joyce GF. 1994. Mutagenic PCR. Genome Res 3:6S136–40
    [Google Scholar]
  27. 27.
    Canver MC, Smith EC, Sher F, Pinello L, Sanjana NE et al. 2015. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527:192–97
    [Google Scholar]
  28. 28.
    Chan KK, Dorosky D, Sharma P, Abbasi SA, Dye JM et al. 2020. Engineering human ACE2 to optimize binding to the spike protein of SARS coronavirus 2. Science 369:1261–65
    [Google Scholar]
  29. 29.
    Chen KM, Wong AK, Troyanskaya OG, Zhou J. 2022. A sequence-based global map of regulatory activity for deciphering human genetics. Nat. Genet. 54:940–49
    [Google Scholar]
  30. 30.
    Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT. 2005. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437:1365–69
    [Google Scholar]
  31. 31.
    Chiasson MA, Rollins NJ, Stephany JJ, Sitko KA, Matreyek KA et al. 2020. Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact. eLife 9:e58026
    [Google Scholar]
  32. 32.
    Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. 2012. Predicting the functional effect of amino acid substitutions and indels. PLOS ONE 7:e46688
    [Google Scholar]
  33. 33.
    Cirulli ET, Goldstein DB. 2010. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. 11:415–25
    [Google Scholar]
  34. 34.
    Claussnitzer M, Dankel SN, Kim K-H, Quon G, Meuleman W et al. 2015. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373:895–907
    [Google Scholar]
  35. 34a.
    Coelho MA, Karakoc E, Bhosle S, Gonçalves E, Burgold Tet al 2022. Base editing screens map mutations affecting IFNγ signalling in cancer. bioRxiv 2022.03.29.486051. https://doi.org/10.1101/2022.03.29.486051
    [Crossref] [Google Scholar]
  36. 35.
    Coyote-Maestas W, Nedrud D, Suma A, He Y, Matreyek KA et al. 2021. Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling. Nat. Commun. 12:7114
    [Google Scholar]
  37. 36.
    Cox DBT, Platt RJ, Zhang F. 2015. Therapeutic genome editing: prospects and challenges. Nat. Med. 21:121–31
    [Google Scholar]
  38. 37.
    Crotti L, Johnson CN, Graf E, De Ferrari GM, Cuneo BF et al. 2013. Calmodulin mutations associated with recurrent cardiac arrest in infants. Circulation 127:1009–17
    [Google Scholar]
  39. 38.
    Da K, Weile J, Kishore N, Rubin AF, Fields S et al. 2021. MaveRegistry: a collaboration platform for multiplexed assays of variant effect. Bioinformatics 37:3382–83
    [Google Scholar]
  40. 39.
    Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P et al. 2017. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14:297–301
    [Google Scholar]
  41. 40.
    de Almeida BP, Reiter F, Pagani M, Stark A. 2022. DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers. Nat. Genet. 54:613–24
    [Google Scholar]
  42. 41.
    Dekker J, Belmont AS, Guttman M, Leshyk VO, Lis JT et al. 2017. The 4D Nucleome Project. Nature 549:219–26
    [Google Scholar]
  43. 42.
    Diao Y, Fang R, Li B, Meng Z, Yu J et al. 2017. A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells. Nat. Methods 14:629–35
    [Google Scholar]
  44. 43.
    Diss G, Lehner B 2018. The genetic landscape of a physical interaction. eLife 7:e32472
    [Google Scholar]
  45. 44.
    Dixit A, Parnas O, Li B, Chen J, Fulco CP et al. 2016. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:1853–66.e17
    [Google Scholar]
  46. 45.
    Doud M, Bloom J 2016. Accurate measurement of the effects of all amino-acid mutations on influenza hemagglutinin. Viruses 8:155
    [Google Scholar]
  47. 46.
    Durfee T, Becherer K, Chen PL, Yeh SH, Yang Y et al. 1993. The retinoblastoma protein associates with the protein phosphatase type 1 catalytic subunit. Genes Dev 7:555–69
    [Google Scholar]
  48. 47.
    Ellingford JM, Ahn JW, Bagnall RD, Baralle D, Barton S et al. 2021. Recommendations for clinical interpretation of variants found in non-coding regions of the genome. medRxiv 2021.12.28.21267792. https://doi.org/10.1101/2021.12.28.21267792
    [Crossref]
  49. 48.
    ENCODE Proj. Consort 2012. An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74
    [Google Scholar]
  50. 49.
    Ernst J, Melnikov A, Zhang X, Wang L, Rogov P et al. 2016. Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions. Nat. Biotechnol. 34:1180–90
    [Google Scholar]
  51. 50.
    Erwood S, Bily TMI, Lequyer J, Yan J, Gulati N et al. 2022. Saturation variant interpretation using CRISPR prime editing. Nat. Biotechnol. 40:6885–895
    [Google Scholar]
  52. 51.
    Fahed AC, Wang M, Homburger JR, Patel AP, Bick AG et al. 2020. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat. Commun. 11:3635
    [Google Scholar]
  53. 52.
    FANTOM Consort., RIKEN PMI, CLST (DGT) 2014. A promoter-level mammalian expression atlas. Nature 507:462–70
    [Google Scholar]
  54. 53.
    Faure AJ, Schmiedel JM, Baeza-Centurion P, Lehner B. 2020. DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies. Genome Biol 21:207
    [Google Scholar]
  55. 54.
    Fayer S, Horton C, Dines JN, Rubin AF, Richardson ME et al. 2021. Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN. Am. J. Hum. Genet. 108:2248–58
    [Google Scholar]
  56. 55.
    Feldman D, Singh A, Schmid-Burgk JL, Carlson RJ, Mezger A et al. 2019. Optical pooled screens in human cells. Cell 179:787–99.e17
    [Google Scholar]
  57. 56.
    Fields S, Song O-K. 1989. A novel genetic system to detect protein–protein interactions. Nature 340:245–46
    [Google Scholar]
  58. 56a.
    Findlay GM 2021. Linking genome variants to disease: scalable approaches to test the functional impact of human mutations. Hum. Mol. Genet 30:R18797
    [Google Scholar]
  59. 57.
    Findlay GM, Boyle EA, Hause RJ, Klein JC, Shendure J. 2014. Saturation editing of genomic regions by multiplex homology-directed repair. Nature 513:120–23
    [Google Scholar]
  60. 58.
    Findlay GM, Daza RM, Martin B, Zhang MD, Leith AP et al. 2018. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562:217–22
    [Google Scholar]
  61. 59.
    Firnberg E, Ostermeier M. 2012. PFunkel: efficient, expansive, user-defined mutagenesis. PLOS ONE 7:e52031
    [Google Scholar]
  62. 60.
    Floyd B, Weile J, Kannankeril P, Glazer A, Reuter C et al. 2022. Proactive variant effect mapping to accelerate genetic diagnosis for pediatric cardiac arrest. Preprints 2022010177. https://www.preprints.org/manuscript/202201.0177/v1
  63. 61.
    Flynn E, Lappalainen T. 2022. Functional characterization of genetic variant effects on expression. Annu. Rev. Biomed. Data Sci. 5:119–39
    [Google Scholar]
  64. 62.
    Fowler DM, Araya CL, Fleishman SJ, Kellogg EH, Stephany JJ et al. 2010. High-resolution mapping of protein sequence-function relationships. Nat. Methods 7:741–46
    [Google Scholar]
  65. 63.
    Fowler DM, Araya CL, Gerard W, Fields S 2011. Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics 27:3430–31
    [Google Scholar]
  66. 64.
    Fowler DM, Fields S. 2014. Deep mutational scanning: a new style of protein science. Nat. Methods 11:801–7
    [Google Scholar]
  67. 65.
    Frazer J, Notin P, Dias M, Gomez A, Min JK et al. 2022. Publisher correction: Disease variant prediction with deep generative models of evolutionary data. Nature 601:E7
    [Google Scholar]
  68. 66.
    Freund MK, Burch KS, Shi H, Mancuso N, Kichaev G et al. 2018. Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits. Am. J. Hum. Genet. 103:535–52
    [Google Scholar]
  69. 67.
    Fulco CP, Munschauer M, Anyoha R, Munson G, Grossman SR et al. 2016. Systematic mapping of functional enhancer–promoter connections with CRISPR interference. Science 354:769–73
    [Google Scholar]
  70. 68.
    Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT et al. 2019. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51:1664–69
    [Google Scholar]
  71. 69.
    Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S et al. 2019. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell 176:377–90.E19
    [Google Scholar]
  72. 69a.
    Gasperini M, Starita L, Shendure J 2016. The power of multiplexed functional analysis of genetic variants. Nat. Protoc 11:178287
    [Google Scholar]
  73. 69b.
    Geck RC, Boyle G, Amorosi CJ, Fowler DM, Dunham MJ 2022. Measuring pharmacogene variant function at scale using multiplexed assays. Annu. Rev. Pharm. Tox 62:53150
    [Google Scholar]
  74. 70.
    Gelman H, Dines JN, Berg J, Berger AH, Brnich S et al. 2019. Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation. Genome Med 11:85
    [Google Scholar]
  75. 71.
    Genolet O, Ravid Lustig L, Schulz EG 2022. Dissecting molecular phenotypes through FACS-based pooled CRISPR screens. Methods Mol. Biol. https://doi.org/10.1007/7651_2021_457
    [Crossref] [Google Scholar]
  76. 72.
    Gergics P, Smith C, Bando H, Jorge AAL, Rockstroh-Lippold D et al. 2021. High-throughput splicing assays identify missense and silent splice-disruptive POU1F1 variants underlying pituitary hormone deficiency. Am. J. Hum. Genet. 108:81526–39
    [Google Scholar]
  77. 73.
    Gersing S, Cagiada M, Gebbia M, Gjesing AP, Cote AG et al. 2022. A comprehensive map of human glucokinase variant activity. bioRxiv 2022.05.04.490571. https://doi.org/10.1101/2022.05.04.490571
    [Crossref]
  78. 74.
    Giacomelli AO, Yang X, Lintner RE, McFarland JM, Duby M et al. 2018. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat. Genet. 50:1381–87
    [Google Scholar]
  79. 75.
    Gilbert LA, Horlbeck MA, Adamson B, Villalta JE, Chen Y et al. 2014. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159:647–61
    [Google Scholar]
  80. 76.
    Gilbert LA, Larson MH, Morsut L, Liu Z, Brar GA et al. 2013. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154:442–51
    [Google Scholar]
  81. 77.
    Glazer AM, Kroncke BM, Matreyek KA, Yang T, Wada Y et al. 2020. Deep mutational scan of an SCN5A voltage sensor. Circ. Genom. Precis. Med. 13:1e002786
    [Google Scholar]
  82. 78.
    Glazer AM, Wada Y, Li B, Muhammad A, Kalash OR et al. 2020. High-throughput reclassification of SCN5A variants. Am. J. Hum. Genet. 107:111–23
    [Google Scholar]
  83. 79.
    Gordon MG, Inoue F, Martin B, Schubach M, Agarwal V et al. 2020. lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements. Nat. Protoc. 15:2387–412
    [Google Scholar]
  84. 80.
    Gray VE, Hause RJ, Luebeck J, Shendure J, Fowler DM. 2018. Quantitative missense variant effect prediction using large-scale mutagenesis data. Cell Syst 6:116–24.e3
    [Google Scholar]
  85. 81.
    Gray VE, Sitko K, Kameni FZN, Williamson M, Stephany JJ et al. 2019. Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning. G3 9:3683–89
    [Google Scholar]
  86. 82.
    GTEX Consort 2020. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369:1318–30
    [Google Scholar]
  87. 83.
    Gupta RM, Hadaya J, Trehan A, Zekavat SM, Roselli C et al. 2017. A genetic variant associated with five vascular diseases is a distal regulator of Endothelin-1 gene expression. Cell 170:522–33.e15
    [Google Scholar]
  88. 84.
    Hanna RE, Hegde M, Fagre CR, DeWeirdt PC, Sangree AK et al. 2021. Massively parallel assessment of human variants with base editor screens. Cell 184:1064–80.e20
    [Google Scholar]
  89. 85.
    Harrison SM, Dolinsky JS, Knight Johnson AE, Pesaran T, Azzariti DR et al. 2017. Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar. Genet. Med. 19:1096–104
    [Google Scholar]
  90. 86.
    Hasle N, Cooke A, Srivatsan S, Huang H, Stephany JJ et al. 2020. High-throughput, microscope-based sorting to dissect cellular heterogeneity. Mol. Syst. Biol. 16:e9442
    [Google Scholar]
  91. 87.
    Hazelbaker DZ, Beccard A, Angelini G, Mazzucato P, Messana A et al. 2020. A multiplexed gRNA piggyBac transposon system facilitates efficient induction of CRISPRi and CRISPRa in human pluripotent stem cells. Sci. Rep. 10:635
    [Google Scholar]
  92. 88.
    Heredia JD, Park J, Brubaker RJ, Szymanski SK, Gill KS, Procko E. 2018. Mapping interaction sites on human chemokine receptors by deep mutational scanning. J. Immunol. 200:3825–39
    [Google Scholar]
  93. 89.
    Hietpas RT, Jensen JD, Bolon DNA. 2011. Experimental illumination of a fitness landscape. PNAS 108:7896–901
    [Google Scholar]
  94. 90.
    Hilton SK, Huddleston J, Black A, North K, Dingens AS et al. 2020. dms-view: interactive visualization tool for deep mutational scanning data. J. Open Sour. Softw. 5:522353
    [Google Scholar]
  95. 91.
    HuBMAP Consort 2019. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574:187–92
    [Google Scholar]
  96. 92.
    Igartua C, Mozaffari SV, Nicolae DL, Ober C. 2017. Rare non-coding variants are associated with plasma lipid traits in a founder population. Sci. Rep. 7:16415
    [Google Scholar]
  97. 93.
    Inoue F, Kreimer A, Ashuach T, Ahituv N, Yosef N. 2019. Identification and massively parallel characterization of regulatory elements driving neural induction. Cell Stem Cell 25:713–27.e10
    [Google Scholar]
  98. 94.
    Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK et al. 2016. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99:877–85
    [Google Scholar]
  99. 95.
    Jain PC, Varadarajan R. 2014. A rapid, efficient, and economical inverse polymerase chain reaction-based method for generating a site saturation mutant library. Anal. Biochem. 449:90–98
    [Google Scholar]
  100. 96.
    Jia X, Burugula BB, Chen V, Lemons RM, Jayakody S et al. 2021. Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk. Am. J. Hum. Genet. 108:163–75
    [Google Scholar]
  101. 96a.
    Jones EM, Lubock NB, Venkatakrishnan AJ, Wang J, Tseng AMet al 2020. Structural and functional characterization of G protein-coupled receptors with deep mutational scanning. eLife 9:e54895
    [Google Scholar]
  102. 97.
    Julien P, Miñana B, Baeza-Centurion P, Valcárcel J, Lehner B. 2016. The complete local genotype–phenotype landscape for the alternative splicing of a human exon. Nat. Commun. 7:11558
    [Google Scholar]
  103. 98.
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596:583–89
    [Google Scholar]
  104. 99.
    Kanavy DM, McNulty SM, Jairath MK, Brnich SE, Bizon C et al. 2019. Comparative analysis of functional assay evidence use by ClinGen Variant Curation Expert Panels. Genome Med 11:77
    [Google Scholar]
  105. 100.
    Kanfer G, Sarraf SA, Maman Y, Baldwin H, Dominguez-Martin E et al. 2021. Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes. J. Cell Biol. 220:e202006180
    [Google Scholar]
  106. 101.
    Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J et al. 2020. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581:434–43
    [Google Scholar]
  107. 102.
    Ke S, Anquetil V, Zamalloa JR, Maity A, Yang A et al. 2018. Saturation mutagenesis reveals manifold determinants of exon definition. Genome Res 28:11–24
    [Google Scholar]
  108. 103.
    Kelley DR, Snoek J, Rinn JL. 2016. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26:990–99
    [Google Scholar]
  109. 104.
    Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L et al. 2013. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay. Genome Res 23:800–11
    [Google Scholar]
  110. 105.
    Kilpinen H, Goncalves A, Leha A, Afzal V, Alasoo K et al. 2017. Common genetic variation drives molecular heterogeneity in human iPSCs. Nature 546:370–75
    [Google Scholar]
  111. 106.
    Kim J, Koo B-K, Knoblich JA. 2020. Human organoids: model systems for human biology and medicine. Nat. Rev. Mol. Cell Biol. 21:571–84
    [Google Scholar]
  112. 106a.
    Kinney JB, McCandlish DM 2019. Massively parallel assays and quantitative sequence–function relationships. Annu. Rev. Genom. Hum. Genet 20:99127
    [Google Scholar]
  113. 107.
    Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. 2014. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46:310–15
    [Google Scholar]
  114. 108.
    Kircher M, Xiong C, Martin B, Schubach M, Inoue F et al. 2019. Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution. Nat. Commun. 10:3583
    [Google Scholar]
  115. 109.
    Kitzman JO, Starita LM, Lo RS, Fields S, Shendure J. 2015. Massively parallel single-amino-acid mutagenesis. Nat. Methods 12:203–6
    [Google Scholar]
  116. 110.
    Klein JC, Agarwal V, Inoue F, Keith A, Martin B et al. 2020. A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nat. Methods 17:1083–91
    [Google Scholar]
  117. 111.
    Komor AC, Kim YB, Packer MS, Zuris JA, Liu DR. 2016. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533:420–24
    [Google Scholar]
  118. 112.
    Kozek KA, Glazer AM, Ng C-A, Blackwell D, Egly CL et al. 2020. High-throughput discovery of trafficking-deficient variants in the cardiac potassium channel KV11.1. Heart Rhythm 17:2180–89
    [Google Scholar]
  119. 113.
    Kreimer A, Ashuach T, Inoue F, Khodaverdian A, Deng C et al. 2022. Massively parallel reporter perturbation assays uncover temporal regulatory architecture during neural differentiation. Nat. Commun. 13:1504
    [Google Scholar]
  120. 114.
    Kuang D, Truty R, Weile J, Johnson B, Nykamp K et al. 2021. Prioritizing genes for systematic variant effect mapping. Bioinformatics 36:5448–55
    [Google Scholar]
  121. 115.
    Kuang D, Weile J, Li R, Ouellette TW, Barber JA, Roth FP. 2020. MaveQuest: a web resource for planning experimental tests of human variant effects. Bioinformatics 36:3938–40
    [Google Scholar]
  122. 116.
    Kunkel TA. 1985. Rapid and efficient site-specific mutagenesis without phenotypic selection. PNAS 82:488–92
    [Google Scholar]
  123. 117.
    Kweon J, Jang A-H, Shin HR, See J-E, Lee W et al. 2020. A CRISPR-based base-editing screen for the functional assessment of BRCA1 variants. Oncogene 39:30–35
    [Google Scholar]
  124. 118.
    Laber S, Strobel S, Mercader J-M, Dashti H, Ainbinder A et al. 2021. Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler. bioRxiv 2021.07.17.452050. https://doi.org/10.1101/2021.07.17.452050
    [Crossref]
  125. 119.
    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:860–921
    [Google Scholar]
  126. 120.
    Landrum MJ, Lee JM, Benson M, Brown GR, Chao C et al. 2018. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res 46:D1062–67
    [Google Scholar]
  127. 121.
    Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E et al. 2016. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285–91
    [Google Scholar]
  128. 122.
    Liu X, Li C, Mou C, Dong Y, Tu Y. 2020. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med 12:103
    [Google Scholar]
  129. 123.
    MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J et al. 2014. Guidelines for investigating causality of sequence variants in human disease. Nature 508:469–76
    [Google Scholar]
  130. 124.
    Majithia AR, Tsuda B, Agostini M, Gnanapradeepan K, Rice R et al. 2016. Prospective functional classification of all possible missense variants in PPARG. Nat. Genet. 48:1570–75
    [Google Scholar]
  131. 125.
    Marini NJ, Gin J, Ziegle J, Keho KH, Ginzinger D et al. 2008. The prevalence of folate-remedial MTHFR enzyme variants in humans. PNAS 105:8055–60
    [Google Scholar]
  132. 126.
    Marshall JL, Doughty BR, Subramanian V, Guckelberger P, Wang Q et al. 2020. HyPR-seq: single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes. PNAS 117:33404–13
    [Google Scholar]
  133. 127.
    Matreyek KA, Starita LM, Stephany JJ, Martin B, Chiasson MA et al. 2018. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat. Genet. 50:874–82
    [Google Scholar]
  134. 128.
    Matreyek KA, Stephany JJ, Chiasson MA, Hasle N, Fowler DM. 2020. An improved platform for functional assessment of large protein libraries in mammalian cells. Nucleic Acids Res 48:e1
    [Google Scholar]
  135. 129.
    Matreyek KA, Stephany JJ, Fowler DM. 2017. A platform for functional assessment of large variant libraries in mammalian cells. Nucleic Acids Res 45:e102
    [Google Scholar]
  136. 130.
    Mattiazzi Usaj M, Sahin N, Friesen H, Pons C, Usaj M et al. 2020. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol. Syst. Biol. 16:e9243
    [Google Scholar]
  137. 131.
    Mavor D, Barlow K, Thompson S, Barad BA, Bonny AR et al. 2016. Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting. eLife 5:e15802
    [Google Scholar]
  138. 132.
    McDonald D, Wu Y, Dailamy A, Tat J, Parekh U et al. 2020. Defining the teratoma as a model for multi-lineage human development. Cell 183:1402–19.e18
    [Google Scholar]
  139. 133.
    McGary KL, Park TJ, Woods JO, Cha HJ, Wallingford JB, Marcotte EM. 2010. Systematic discovery of nonobvious human disease models through orthologous phenotypes. PNAS 107:6544–49
    [Google Scholar]
  140. 134.
    Meitlis I, Allenspach EJ, Bauman BM, Phan IQ, Dabbah G et al. 2020. Multiplexed functional assessment of genetic variants in CARD11. Am. J. Hum. Genet. 107:1029–43
    [Google Scholar]
  141. 135.
    Melnikov A, Murugan A, Zhang X, Tesileanu T, Wang L et al. 2012. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30:271–77
    [Google Scholar]
  142. 136.
    Melnikov A, Rogov P, Wang L, Gnirke A, Mikkelsen TS. 2014. Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes. Nucleic Acids Res 42:e112
    [Google Scholar]
  143. 137.
    Mighell TL, Evans-Dutson S, O'Roak BJ. 2018. A saturation mutagenesis approach to understanding PTEN lipid phosphatase activity and genotype-phenotype relationships. Am. J. Hum. Genet. 102:943–55
    [Google Scholar]
  144. 138.
    Mimitou EP, Cheng A, Montalbano A, Hao S, Stoeckius M et al. 2019. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16:409–12
    [Google Scholar]
  145. 139.
    Mitchell JM, Nemesh J, Ghosh S, Handsaker RE, Mello CJ et al. 2020. Mapping genetic effects on cellular phenotypes with “cell villages.”. bioRxiv 2020.06.29.174383. https://doi.org/10.1101/2020.06.29.174383
    [Crossref]
  146. 140.
    Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T et al. 2010. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466:714–19
    [Google Scholar]
  147. 141.
    Nagai A, Hirata M, Kamatani Y, Muto K, Matsuda K et al. 2017. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27:S2–8
    [Google Scholar]
  148. 142.
    Nasser J, Bergman DT, Fulco CP, Guckelberger P, Doughty BR et al. 2021. Genome-wide enhancer maps link risk variants to disease genes. Nature 593:238–43
    [Google Scholar]
  149. 143.
    Newberry RW, Leong JT, Chow ED, Kampmann M, DeGrado WF. 2020. Deep mutational scanning reveals the structural basis for α-synuclein activity. Nat. Chem. Biol. 16:653–59
    [Google Scholar]
  150. 144.
    Ng PC, Henikoff S. 2001. Predicting deleterious amino acid substitutions. Genome Res 11:863–74
    [Google Scholar]
  151. 145.
    Nyegaard M, Overgaard MT, Søndergaard MT, Vranas M, Behr ER et al. 2012. Mutations in calmodulin cause ventricular tachycardia and sudden cardiac death. Am. J. Hum. Genet. 91:703–12
    [Google Scholar]
  152. 146.
    Ota S, Horisaki R, Kawamura Y, Ugawa M, Sato I et al. 2018. Ghost cytometry. Science 360:1246–51
    [Google Scholar]
  153. 147.
    Parsi KM, Hennessy E, Kearns N, Maehr R. 2017. Using an inducible CRISPR-dCas9-KRAB effector system to dissect transcriptional regulation in human embryonic stem cells. Methods Mol. Biol. 1507:221–33
    [Google Scholar]
  154. 148.
    Patwardhan RP, Hiatt JB, Witten DM, Kim MJ, Smith RP et al. 2012. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30:265–70
    [Google Scholar]
  155. 149.
    Patwardhan RP, Lee C, Litvin O, Young DL, Pe'er D, Shendure J 2009. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis. Nat. Biotechnol. 27:1173–75
    [Google Scholar]
  156. 150.
    Perez-Pinera P, Kocak DD, Vockley CM, Adler AF, Kabadi AM et al. 2013. RNA-guided gene activation by CRISPR-Cas9–based transcription factors. Nat. Methods 10:973–76
    [Google Scholar]
  157. 150a.
    Qiu C, Kaplan CD 2019. Functional assays for transcription mechanisms in high-throughput. Methods 159–60:115123
    [Google Scholar]
  158. 151.
    Ramilowski JA, Yip CW, Agrawal S, Chang J-C, Ciani Y et al. 2020. Functional annotation of human long noncoding RNAs via molecular phenotyping. Genome Res 30:1060–72
    [Google Scholar]
  159. 152.
    Reilly SK, Gosai SJ, Gutierrez A, Mackay-Smith A, Ulirsch JC et al. 2021. Direct characterization of cis-regulatory elements and functional dissection of complex genetic associations using HCR-FlowFISH. Nat. Genet. 53:1166–76
    [Google Scholar]
  160. 153.
    Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. 2019. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res 47:D886–94
    [Google Scholar]
  161. 154.
    Richards S, Aziz N, Bale S, Bick D, Das S et al. 2015. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17:405–24
    [Google Scholar]
  162. 155.
    Riesselman AJ, Ingraham JB, Marks DS. 2018. Deep generative models of genetic variation capture the effects of mutations. Nat. Methods 15:816–22
    [Google Scholar]
  163. 156.
    Rivera-Muñoz EA, Milko LV, Harrison SM, Azzariti DR, Kurtz CL et al. 2018. ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation. Hum. Mutat. 39:1614–22
    [Google Scholar]
  164. 157.
    Roadmap Epigenomics Consort., Kundaje A, Meuleman W, Ernst J, Bilenky M et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature 518:317–30
    [Google Scholar]
  165. 158.
    Rollins NJ, Brock KP, Poelwijk FJ, Stiffler MA, Gauthier NP et al. 2019. Inferring protein 3D structure from deep mutation scans. Nat. Genet. 51:1170–76
    [Google Scholar]
  166. 159.
    Rubin AF, Gelman H, Lucas N, Bajjalieh SM, Papenfuss AT et al. 2017. A statistical framework for analyzing deep mutational scanning data. Genome Biol 18:1150
    [Google Scholar]
  167. 160.
    Rubin AF, Lucas N, Bajjalieh SM, Papenfuss AT, Speed TP, Fowler DM. 2016. Enrich2: a statistical framework for analyzing deep mutational scanning data. bioRxiv 075150. https://doi.org/10.1101/075150
    [Crossref]
  168. 161.
    Rubin AF, Min JK, Rollins NJ, Da EY, Esposito D et al. 2021. MaveDB v2: a curated community database with over three million variant effects from multiplexed functional assays. bioRxiv 2021.11.29.470445. https://doi.org/10.1101/2021.11.29.470445
    [Crossref]
  169. 162.
    Rusu V, Hoch E, Mercader JM, Tenen DE, Gymrek M et al. 2017. Type 2 diabetes variants disrupt function of SLC16A11 through two distinct mechanisms. Cell 170:199–212.e20
    [Google Scholar]
  170. 163.
    Sample PJ, Wang B, Reid DW, Presnyak V, McFadyen IJ et al. 2019. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay. Nat. Biotechnol. 37:803–9
    [Google Scholar]
  171. 164.
    Schmiedel JM, Lehner B. 2019. Determining protein structures using deep mutagenesis. Nat. Genet. 51:1177–86
    [Google Scholar]
  172. 165.
    Schraivogel D, Gschwind AR, Milbank JH, Leonce DR, Jakob P et al. 2020. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat. Methods 17:629–35
    [Google Scholar]
  173. 166.
    Schraivogel D, Kuhn TM, Rauscher B, Rodríguez-Martínez M, Paulsen M et al. 2022. High-speed fluorescence image-enabled cell sorting. Science 375:315–20
    [Google Scholar]
  174. 167.
    Shan X, Wang L, Hoffmaster R, Kruger WD. 1999. Functional characterization of human methylenetetrahydrofolate reductase in Saccharomyces cerevisiae. J. Biol. Chem. 274:32613–18
    [Google Scholar]
  175. 168.
    Smedley D, Schubach M, Jacobsen JOB, Köhler S, Zemojtel T et al. 2016. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in Mendelian disease. Am. J. Hum. Genet. 99:595–606
    [Google Scholar]
  176. 169.
    Smemo S, Tena JJ, Kim K-H, Gamazon ER, Sakabe NJ et al. 2014. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507:371–75
    [Google Scholar]
  177. 170.
    Sobreira DR, Joslin AC, Zhang Q, Williamson I, Hansen GT et al. 2021. Extensive pleiotropism and allelic heterogeneity mediate metabolic effects of IRX3 and IRX5. Science 372:1085–91
    [Google Scholar]
  178. 171.
    Starita LM, Ahituv N, Dunham MJ, Kitzman JO, Roth FP et al. 2017. Variant interpretation: functional assays to the rescue. Am. J. Hum. Genet. 101:315–25
    [Google Scholar]
  179. 172.
    Starita LM, Islam MM, Banerjee T, Adamovich AI, Gullingsrud J et al. 2018. A multiplex homology-directed DNA repair assay reveals the impact of more than 1,000 BRCA1 missense substitution variants on protein function. Am. J. Hum. Genet. 103:498–508
    [Google Scholar]
  180. 173.
    Starita LM, Young DL, Islam M, Kitzman JO, Gullingsrud J et al. 2015. Massively parallel functional analysis of BRCA1 RING domain variants. Genetics 200:413–22
    [Google Scholar]
  181. 174.
    Starling AL, Ortega JM, Gollob KJ, Vicente EJ, Andrade-Nóbrega GM, Rodriguez MB. 2003. Evaluation of alternative reporter genes for the yeast two-hybrid system. Genet. Mol. Res. 2:124–35
    [Google Scholar]
  182. 175.
    Starr TN, Greaney AJ, Hilton SK, Ellis D, Crawford KHD et al. 2020. Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell 182:1295–310.e20
    [Google Scholar]
  183. 176.
    Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA et al. 2003. Human Gene Mutation Database (HGMD): 2003 update. Hum. Mutat. 21:6577–81
    [Google Scholar]
  184. 177.
    Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP et al. 2007. Population genomics of human gene expression. Nat. Genet. 39:1217–24
    [Google Scholar]
  185. 178.
    Suiter CC, Moriyama T, Matreyek KA, Yang W, Scaletti ER et al. 2020. Massively parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity. PNAS 117:5394–401
    [Google Scholar]
  186. 179.
    Sun S, Weile J, Verby M, Wu Y, Wang Y et al. 2020. A proactive genotype-to-patient-phenotype map for cystathionine beta-synthase. Genome Med 12:13
    [Google Scholar]
  187. 180.
    Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y et al. 2018. Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet. 50:1161–70
    [Google Scholar]
  188. 181.
    Tcheandjieu C, Zhu X, Hilliard AT, Clarke SL, Napolioni V et al. 2022. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat. Med. https://doi.org/10.1038/s41591-022-01891-3
    [Crossref] [Google Scholar]
  189. 182.
    Thakore PI, D'Ippolito AM, Song L, Safi A, Shivakumar NK et al. 2015. Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12:1143–49
    [Google Scholar]
  190. 183.
    Thompson MC, Yeates TO, Rodriguez JA. 2020. Advances in methods for atomic resolution macromolecular structure determination. F1000Research 9:Faculty Rev667
    [Google Scholar]
  191. 184.
    Thomsen SK, Ceroni A, van de Bunt M, Burrows C, Barrett A et al. 2016. Systematic functional characterization of candidate causal genes for type 2 diabetes risk variants. Diabetes 65:3805–11
    [Google Scholar]
  192. 185.
    Tomaselli PJ, Rossor AM, Horga A, Jaunmuktane Z, Carr A et al. 2017. Mutations in noncoding regions of GJB1 are a major cause of X-linked CMT. Neurology 88:1445–53
    [Google Scholar]
  193. 186.
    Ursu O, Neal JT, Shea E, Thakore PI, Jerby-Arnon L et al. 2022. Massively parallel phenotyping of coding variants in cancer with Perturb-seq. Nat. Biotechnol. 40:6896–905
    [Google Scholar]
  194. 187.
    van Arensbergen J, Pagie L, FitzPatrick VD, de Haas M, Baltissen MP et al. 2019. High-throughput identification of human SNPs affecting regulatory element activity. Nat. Genet. 51:1160–69
    [Google Scholar]
  195. 188.
    van der Wijst MGP, Brugge H, de Vries DH, Deelen P, Swertz MA et al. 2018. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50:493–97
    [Google Scholar]
  196. 189.
    Vanhille L, Griffon A, Maqbool MA, Zacarias-Cabeza J, Dao LTM et al. 2015. High-throughput and quantitative assessment of enhancer activity in mammals by CapStarr-seq. Nat. Commun. 6:6905
    [Google Scholar]
  197. 190.
    Varadi M, Anyango S, Deshpande M, Nair S, Natassia C et al. 2022. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50:D439–44
    [Google Scholar]
  198. 191.
    Venter JC, Adams MD, Myers EW, Li PW, Mural RJ et al. 2001. The sequence of the human genome. Science 291:1304–51
    [Google Scholar]
  199. 192.
    Vockley CM, D'Ippolito AM, McDowell IC, Majoros WH, Safi A et al. 2016. Direct GR binding sites potentiate clusters of TF binding across the human genome. Cell 166:1269–81.e19
    [Google Scholar]
  200. 193.
    Wang X, He L, Goggin SM, Saadat A, Wang L et al. 2018. High-resolution genome-wide functional dissection of transcriptional regulatory regions and nucleotides in human. Nat. Commun. 9:5380
    [Google Scholar]
  201. 194.
    Weile J, Kishore N, Sun S, Maaieh R, Verby M et al. 2021. Shifting landscapes of human MTHFR missense-variant effects. Am. J. Hum. Genet. 108:1283–300
    [Google Scholar]
  202. 195.
    Weile J, Roth FP. 2018. Multiplexed assays of variant effects contribute to a growing genotype–phenotype atlas. Hum. Genet. 137:665–78
    [Google Scholar]
  203. 196.
    Weile J, Sun S, Cote AG, Knapp J, Verby M et al. 2017. A framework for exhaustively mapping functional missense variants. Mol. Syst. Biol. 13:957
    [Google Scholar]
  204. 197.
    Wong MS, Kinney JB, Krainer AR. 2018. Quantitative activity profile and context dependence of all human 5′ splice sites. Mol. Cell. 71:1012–26.e3
    [Google Scholar]
  205. 198.
    Wright CF, Quaife NM, Ramos-Hernández L, Danecek P, Ferla MP et al. 2021. Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms. Am. J. Hum. Genet. 108:1083–94
    [Google Scholar]
  206. 199.
    Wu Y, Liu H, Li R, Sun S, Weile J, Roth FP. 2021. Improved pathogenicity prediction for rare human missense variants. Am. J. Hum. Genet. 108:1891–906
    [Google Scholar]
  207. 200.
    Wu Y, Weile J, Cote AG, Sun S, Knapp J et al. 2019. A web application and service for imputing and visualizing missense variant effect maps. Bioinformatics 35:3191–93
    [Google Scholar]
  208. 201.
    wwPDB Consort 2019. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res 47:D520–28
    [Google Scholar]
  209. 202.
    Yeo NC, Chavez A, Lance-Byrne A, Chan Y, Menn D et al. 2018. An enhanced CRISPR repressor for targeted mammalian gene regulation. Nat. Methods 15:611–16
    [Google Scholar]
  210. 202a.
    Zhang L, Sarangi V, Moon I, Yu J, Liu Det al 2020. CYP2C9 and CYP2C19: deep mutational scanning and functional characterization of genomic missense variants. Clin. Transl. Sci 13:72742
    [Google Scholar]
  211. 203.
    Zhao W, Pollack JL, Blagev DP, Zaitlen N, McManus MT, Erle DJ. 2014. Massively parallel functional annotation of 3′ untranslated regions. Nat. Biotechnol. 32:387–91
    [Google Scholar]
  212. 204.
    Zhou J, Troyanskaya OG. 2015. Predicting effects of noncoding variants with deep learning–based sequence model. Nat. Methods 12:931–34
    [Google Scholar]
  213. 205.
    Zinkus-Boltz J, DeValk C, Dickinson BC. 2019. A phage-assisted continuous selection approach for deep mutational scanning of protein–protein interactions. ACS Chem. Biol. 14:2757–67
    [Google Scholar]
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