Perspectives on Allele-Specific Expression

Literature Cited

1. 1. 

   Knight JC. 2004. Allele-specific gene expression uncovered. Trends Genet. 20:113–16
   [Google Scholar]
2. 2. 

   Buckland PR. 2004. Allele-specific gene expression differences in humans. Hum. Mol. Genet. 13:R255–60
   [Google Scholar]
3. 3. 

   Gregg C. 2014. Known unknowns for allele-specific expression and genomic imprinting effects. F1000Prime Rep. 6:75
   [Google Scholar]
4. 4. 

   Pastinen T. 2010. Genome-wide allele-specific analysis: insights into regulatory variation. Nat. Rev. Genet. 11:533–38
   [Google Scholar]
5. 5. 

   Bader DM, Wilkening S, Lin G, Tekkedil MM, Dietrich K et al. 2015. Negative feedback buffers effects of regulatory variants. Mol. Syst. Biol. 11:785
   [Google Scholar]
6. 6. 

   Li X, Kim Y, Tsang EK, Davis JR, Damani FN et al. 2017. The impact of rare variation on gene expression across tissues. Nature 550:239–43
   [Google Scholar]
7. 7. 

   Lee MP. 2012. Allele-specific gene expression and epigenetic modifications and their application to understanding inheritance and cancer. Biochim. Biophys. Acta Gene Regul. Mech. 1819:739–42
   [Google Scholar]
8. 8. 

   Zhang S, Zhang H, Zhou Y, Qiao M, Zhao S et al. 2020. Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Science 369:561–65
   [Google Scholar]
9. 9. 

   Lo HS, Wang Z, Hu Y, Yang HH, Gere S et al. 2003. Allelic variation in gene expression is common in the human genome. Genome Res. 13:1855–62
   [Google Scholar]
10. 10. 

    Reddy TE, Gertz J, Pauli F, Kucera KS, Varley KE et al. 2012. Effects of sequence variation on differential allelic transcription factor occupancy and gene expression. Genome Res. 22:860–69
    [Google Scholar]
11. 11. 

    Amoah K, Hsiao YHE, Bahn JH, Sun Y, Burghard C et al. 2021. Allele-specific alternative splicing and its functional genetic variants in human tissues. Genome Res 31:359–71
    [Google Scholar]
12. 12. 

    Nembaware V, Wolfe KH, Bettoni F, Kelso J, Seoighe C. 2004. Allele-specific transcript isoforms in human. FEBS Lett. 577:233–38
    [Google Scholar]
13. 13. 

    Nembaware V, Lupindo B, Schouest K, Spillane C, Scheffler K, Seoighe C. 2008. Genome-wide survey of allele-specific splicing in humans. BMC Genom. 9:265
    [Google Scholar]
14. 14. 

    Kim J, Bartel DP. 2009. Allelic imbalance sequencing reveals that single-nucleotide polymorphisms frequently alter microRNA-directed repression. Nat. Biotechnol. 27:472–77
    [Google Scholar]
15. 15. 

    Robert F, Pelletier J. 2018. Exploring the impact of single-nucleotide polymorphisms on translation. Front. Genet. 9:507
    [Google Scholar]
16. 16. 

    Li Q, Makri A, Lu Y, Marchand L, Grabs R et al. 2013. Genome-wide search for exonic variants affecting translational efficiency. Nat. Commun. 4:2260
    [Google Scholar]
17. 17. 

    Zhou ZY, Hu Y, Li A, Li YJ, Zhao H et al. 2018. Genome wide analyses uncover allele-specific RNA editing in human and mouse. Nucleic Acids Res. 46:8888–97
    [Google Scholar]
18. 18. 

    Do C, Shearer A, Suzuki M, Terry MB, Gelernter J et al. 2017. Genetic–epigenetic interactions in cis: a major focus in the post-GWAS era. Genome Biol. 18:120
    [Google Scholar]
19. 19. 

    Cavalli M, Pan G, Nord H, Arzt EW, Wallerman O, Wadelius C. 2016. Allele-specific transcription factor binding in liver and cervix cells unveils many likely drivers of GWAS signals. Genomics 107:248–54
    [Google Scholar]
20. 20. 

    Cavalli M, Pan G, Nord H, Arzt EW, Wallerman O, Wadelius C. 2019. Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases. Sci. Rep. 9:2695
    [Google Scholar]
21. 21. 

    Wang H, Lou D, Wang Z. 2019. Crosstalk of genetic variants, allele-specific DNA methylation, and environmental factors for complex disease risk. Front. Genet. 9:695
    [Google Scholar]
22. 22. 

    Yang HH, Hu N, Wang C, Ding T, Dunn BK et al. 2010. Influence of genetic background and tissue types on global DNA methylation patterns. PLOS ONE 5:e9355
    [Google Scholar]
23. 23. 

    Orjuela S, Machlab D, Menigatti M, Marra G, Robinson MD. 2020. DAMEfinder: a method to detect differential allele-specific methylation. Epigenet. Chromatin 13:25
    [Google Scholar]
24. 24. 

    Onuchic V, Lurie E, Carrero I, Pawliczek P, Patel RY et al. 2018. Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci. Science 361:eaar3146
    [Google Scholar]
25. 25. 

    Ng B, White CC, Klein HU, Sieberts SK, McCabe C et al. 2017. An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome. Nat. Neurosci. 20:1418–26
    [Google Scholar]
26. 26. 

    Hug N, Longman D, Cáceres JF. 2016. Mechanism and regulation of the nonsense-mediated decay pathway. Nucleic Acids Res. 44:1483–95
    [Google Scholar]
27. 27. 

    Kervestin S, Jacobson A. 2012. NMD: a multifaceted response to premature translational termination. Nat. Rev. Mol. Cell Biol. 13:700–12
    [Google Scholar]
28. 28. 

    Alonso CR. 2005. Nonsense-mediated RNA decay: a molecular system micromanaging individual gene activities and suppressing genomic noise. Bioessays 27:463–66
    [Google Scholar]
29. 29. 

    Montgomery SB, Lappalainen T, Gutierrez-Arcelus M, Dermitzakis ET. 2011. Rare and common regulatory variation in population-scale sequenced human genomes. PLOS Genet. 7:e1002144
    [Google Scholar]
30. 30. 

    Park E, Pan Z, Zhang Z, Lin L, Xing Y. 2018. The expanding landscape of alternative splicing variation in human populations. Am. J. Hum. Genet. 102:11–26
    [Google Scholar]
31. 31. 

    Li YI, Van De Geijn B, Raj A, Knowles DA, Petti AA et al. 2016. RNA splicing is a primary link between genetic variation and disease. Science 352:600–4
    [Google Scholar]
32. 32. 

    Sheinberger J, Hochberg H, Lavi E, Kanter I, Avivi S et al. 2017. CD-tagging-MS2: detecting allelic expression of endogenous mRNAs and their protein products in single cells. Biol. Methods Protoc. 2:bpx004
    [Google Scholar]
33. 33. 

    Yang EW, Bahn JH, Hsiao EYH, Tan BX, Sun Y et al. 2019. Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA. Nat. Commun. 10:1338
    [Google Scholar]
34. 34. 

    Bahrami-Samani E, Xing Y. 2019. Discovery of allele-specific protein-RNA interactions in human transcriptomes. Am. J. Hum. Genet. 104:492–502
    [Google Scholar]
35. 35. 

    Messemaker TC, van Leeuwen SM, van den Berg PR, ’t Jong AEJ, Palstra RJ et al. 2018. Allele-specific repression of Sox2 through the long non-coding RNA Sox2ot. Sci. Rep. 8:386
    [Google Scholar]
36. 36. 

    Võsa U, Esko T, Kasela S, Annilo T. 2015. Altered gene expression associated with microRNA binding site polymorphisms. PLOS ONE 10:e0141351
    [Google Scholar]
37. 37. 

    Johnsson PA, Hartmanis L, Ziegenhain C, Hendriks GJ, Hagemann-Jensen M et al. 2020. Deducing transcriptional kinetics and molecular functions of long non-coding RNAs using allele-sensitive single-cell RNA-sequencing. bioRxiv 2020.05.05.079251. https://doi.org/10.1101/2020.05.05.079251
    [Crossref]
38. 38. 

    Liu Y, Fischer AD, Pierre CLS, Macias-Velasco JF, Lawson HA, Dougherty JD. 2020. TRAP-based allelic translation efficiency imbalance analysis to identify genetic regulation of ribosome occupancy in specific cell types in vivo. bioRxiv 2020.08.24.265389. https://doi.org/10.1101/2020.08.24.265389
    [Crossref] [Google Scholar]
39. 39. 

    Soderlund CA, Nelson WM, Goff SA. 2014. Allele workbench: transcriptome pipeline and interactive graphics for allele-specific expression. PLOS ONE 9:e115740
    [Google Scholar]
40. 40. 

    Van De Geijn B, McVicker G, Gilad Y, Pritchard JK. 2015. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12:1061–63
    [Google Scholar]
41. 41. 

    Dumont EL, Tycko B, Do C. 2020. CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation. Bioinformatics 36:3558–60
    [Google Scholar]
42. 42. 

    Younesy H, Möller T, Heravi-Moussavi A, Cheng JB, Costello JF et al. 2014. ALEA: a toolbox for allele-specific epigenomics analysis. Bioinformatics 30:1172–74
    [Google Scholar]
43. 43. 

    Lappalainen T, Sammeth M, Friedländer MR, ’t Hoen PAC, Monlong J et al. 2013. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501:506–11
    [Google Scholar]
44. 44. 

    GTEx Consort 2015. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348:648–60
    [Google Scholar]
45. 45. 

    Castel SE, Levy-Moonshine A, Mohammadi P, Banks E, Lappalainen T. 2015. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 16:195
    [Google Scholar]
46. 46. 

    GTEx Consort 2020. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369:1318–30
    [Google Scholar]
47. 47. 

    Castel SE, Aguet F, Mohammadi P, Ardlie KG, Lappalainen T. 2020. A vast resource of allelic expression data spanning human tissues. Genome Biol. 21:234
    [Google Scholar]
48. 48. 

    Benitez JA, Cheng S, Deng Q. 2017. Revealing allele-specific gene expression by single-cell transcriptomics. Int. J. Biochem. Cell Biol. 90:155–60
    [Google Scholar]
49. 49. 

    Tunnacliffe E, Chubb JR. 2020. What is a transcriptional burst?. Trends Genet. 36:288–97
    [Google Scholar]
50. 50. 

    Reinius B, Mold JE, Ramsköld D, Deng Q, Johnsson P et al. 2016. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq. Nat. Genet. 48:1430–35
    [Google Scholar]
51. 51. 

    Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E et al. 2009. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 25:3207–12
    [Google Scholar]
52. 52. 

    Lee C, Kang EY, Gandal MJ, Eskin E, Geschwind DH. 2019. Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage. Nat. Neurosci. 22:1521–32
    [Google Scholar]
53. 53. 

    Rozowsky J, Abyzov A, Wang J, Alves P, Raha D et al. 2011. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol. Syst. Biol. 7:522
    [Google Scholar]
54. 54. 

    Raghupathy N, Choi K, Vincent MJ, Beane GL, Sheppard KS et al. 2018. Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression. Bioinformatics 34:2177–84
    [Google Scholar]
55. 55. 

    Wood DL, Nones K, Steptoe A, Christ A, Harliwong I et al. 2015. Recommendations for accurate resolution of gene and isoform allele-specific expression in RNA-seq data. PLOS ONE 10:e0126911
    [Google Scholar]
56. 56. 

    Pandey RV, Franssen SU, Futschik A, Schlötterer C. 2013. Allelic imbalance metre (Allim), a new tool for measuring allele-specific gene expression with RNA-seq data. Mol. Ecol. Resour. 13:740–45
    [Google Scholar]
57. 57. 

    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C et al. 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21
    [Google Scholar]
58. 58. 

    Miao Z, Alvarez M, Pajukanta P, Ko A. 2018. ASElux: an ultra-fast and accurate allelic reads counter. Bioinformatics 34:1313–20
    [Google Scholar]
59. 59. 

    Manske HM, Kwiatkowski DP. 2009. SNP-o-matic. Bioinformatics 25:2434–35
    [Google Scholar]
60. 60. 

    Dong L, Wang J, Wang G 2020. BYASE: a python library for estimating gene and isoform level allele-specific expression. Bioinformatics 36:194955–56
    [Google Scholar]
61. 61. 

    Deonovic B, Wang Y, Weirather J, Wang XJ, Au KF. 2017. IDP-ASE: haplotyping and quantifying allele-specific expression at the gene and gene isoform level by hybrid sequencing. Nucleic Acids Res. 45:e32
    [Google Scholar]
62. 62. 

    Harvey CT, Moyerbrailean GA, Davis GO, Wen X, Luca F, Pique-Regi R. 2015. QuASAR: quantitative allele-specific analysis of reads. Bioinformatics 31:1235–42
    [Google Scholar]
63. 63. 

    Kumasaka N, Knights AJ, Gaffney DJ. 2016. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48:206–13
    [Google Scholar]
64. 64. 

    Castel SE, Mohammadi P, Chung WK, Shen Y, Lappalainen T. 2016. Rare variant phasing and haplotypic expression from RNA sequencing with phASER. Nat. Commun. 7:12817
    [Google Scholar]
65. 65. 

    Fan J, Hu J, Xue C, Zhang H, Susztak K et al. 2020. ASEP: gene-based detection of allele-specific expression across individuals in a population by RNA sequencing. PLOS Genet. 16:e1008786
    [Google Scholar]
66. 66. 

    Xie J, Ji T, Ferreira MA, Li Y, Patel BN, Rivera RM 2019. Modeling allele-specific expression at the gene and SNP levels simultaneously by a Bayesian logistic mixed regression model. BMC Bioinformat. 20:530
    [Google Scholar]
67. 67. 

    Liu Z, Gui T, Wang Z, Li H, Fu Y et al. 2016. cisASE: a likelihood-based method for detecting putative cis-regulated allele-specific expression in RNA sequencing data. Bioinformatics 32:3291–97
    [Google Scholar]
68. 68. 

    Edsgärd D, Iglesias MJ, Reilly SJ, Hamsten A, Tornvall P et al. 2016. GeneiASE: detection of condition-dependent and static allele-specific expression from RNA-seq data without haplotype information. Sci. Rep. 6:21134
    [Google Scholar]
69. 69. 

    Knowles DA, Davis JR, Edgington H, Raj A, Favé MJ et al. 2017. Allele-specific expression reveals interactions between genetic variation and environment. Nat. Methods 14:699–702
    [Google Scholar]
70. 70. 

    Oliva M, Muñoz-Aguirre M, Kim-Hellmuth S, Wucher V, Gewirtz AD et al. 2020. The impact of sex on gene expression across human tissues. Science 369:eaba3066
    [Google Scholar]
71. 71. 

    de Santiago I, Liu W, Yuan K, O'Reilly M, Chilamakuri CSR et al. 2017. BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes. Genome Biol. 18:39
    [Google Scholar]
72. 72. 

    Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y et al. 2020. Genomic basis for RNA alterations in cancer. Nature 578:129–36
    [Google Scholar]
73. 73. 

    Li G, Bahn JH, Lee JH, Peng G, Chen Z et al. 2012. Identification of allele-specific alternative mRNA processing via transcriptome sequencing. Nucleic Acids Res. 40:e104
    [Google Scholar]
74. 74. 

    Bielski CM, Donoghue MT, Gadiya M, Hanrahan AJ, Won HH et al. 2018. Widespread selection for oncogenic mutant allele imbalance in cancer. Cancer Cell 34:852–62
    [Google Scholar]
75. 75. 

    Luft J, Young RS, Meynert AM, Taylor MS. 2020. Detecting oncogenic selection through biased allele retention in The Cancer Genome Atlas. bioRxiv 2020.07.03.186593. https://doi.org/10.1101/2020.07.03.186593
    [Crossref]
76. 76. 

    Clayton EA, Khalid S, Ban D, Wang L, Jordan IK, McDonald JF. 2020. Tumor suppressor genes and allele-specific expression: mechanisms and significance. Oncotarget 11:462–79
    [Google Scholar]
77. 77. 

    Batcha AM, Bamopoulos SA, Kerbs P, Kumar A, Jurinovic V et al. 2019. Allelic imbalance of recurrently mutated genes in acute myeloid leukaemia. Sci. Rep. 9:11796
    [Google Scholar]
78. 78. 

    Rhee JK, Lee S, Park WY, Kim YH, Kim TM. 2017. Allelic imbalance of somatic mutations in cancer genomes and transcriptomes. Sci. Rep. 7:1653
    [Google Scholar]
79. 79. 

    Halabi NM, Martinez A, Al-Farsi H, Mery E, Puydenus L et al. 2016. Preferential allele expression analysis identifies shared germline and somatic driver genes in advanced ovarian cancer. PLOS Genet. 12:e1005755
    [Google Scholar]
80. 80. 

    Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM 2011. A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Res. 21:1728–37
    [Google Scholar]
81. 81. 

    McCoy RC, Wakefield J, Akey JM. 2017. Impacts of neanderthal-introgressed sequences on the landscape of human gene expression. Cell 168:916–27
    [Google Scholar]
82. 82. 

    Mayba O, Gilbert HN, Liu J, Haverty PM, Jhunjhunwala S et al. 2014. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 15:405
    [Google Scholar]
83. 83. 

    Lefebvre JF, Vello E, Ge B, Montgomery SB, Dermitzakis ET et al. 2012. Genotype-based test in mapping cis-regulatory variants from allele-specific expression data. PLOS ONE 7:e38667
    [Google Scholar]
84. 84. 

    Sun W. 2012. A statistical framework for eQTL mapping using RNA-seq data. Biometrics 68:1–11
    [Google Scholar]
85. 85. 

    Mohammadi P, Castel SE, Cummings BB, Einson J, Sousa C et al. 2019. Genetic regulatory variation in populations informs transcriptome analysis in rare disease. Science 366:351–56
    [Google Scholar]
86. 86. 

    Pant PK, Tao H, Beilharz EJ, Ballinger DG, Cox DR, Frazer KA. 2006. Analysis of allelic differential expression in human white blood cells. Genome Res. 16:331–39
    [Google Scholar]
87. 87. 

    ’t Hoen PAC, Friedländer MR, Almlöf J, Sammeth M, Pulyakhina I et al. 2013. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31:1015–22
    [Google Scholar]
88. 88. 

    Berger E, Yorukoglu D, Zhang L, Nyquist SK, Shalek AK et al. 2020. Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets. Nat. Commun. 11:4662
    [Google Scholar]
89. 89. 

    Marx V. 2017. How to deduplicate PCR. Nat. Methods 14:473–76
    [Google Scholar]
90. 90. 

    Mendelevich A, Vinogradova S, Gupta S, Mironov AA, Sunyaev S, Gimelbrant AA. 2020. Unexpected variability of allelic imbalance estimates from RNA sequencing. bioRxiv 2020.02.18.948323. https://doi.org/10.1101/2020.02.18.948323
    [Crossref]
91. 91. 

    Almlöf JC, Lundmark P, Lundmark A, Ge B, Maouche S et al. 2012. Powerful identification of cis-regulatory SNPs in human primary monocytes using allele-specific gene expression. PLOS ONE 7:e52260
    [Google Scholar]
92. 92. 

    Mohammadi P, Castel SE, Brown AA, Lappalainen T. 2017. Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change. Genome Res. 27:1872–84
    [Google Scholar]
93. 93. 

    Choi K, Raghupathy N, Churchill GA. 2019. A Bayesian mixture model for the analysis of allelic expression in single cells. Nat. Commun. 10:5188
    [Google Scholar]
94. 94. 

    Shen-Orr SS, Gaujoux R 2013. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr. Opin. Immunol. 25:571–78
    [Google Scholar]
95. 95. 

    Fan J, Wang X, Xiao R, Li M 2020. Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. bioRxiv 2020.08.26.267815. https://doi.org/10.1101/2020.08.26.267815
    [Crossref]
96. 96. 

    Pinter SF, Colognori D, Beliveau BJ, Sadreyev RI, Payer B et al. 2015. Allelic imbalance is a prevalent and tissue-specific feature of the mouse transcriptome. Genetics 200:537–49
    [Google Scholar]
97. 97. 

    Crowley JJ, Zhabotynsky V, Sun W, Huang S, Pakatci IK et al. 2015. Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance. Nat. Genet. 47:353–60
    [Google Scholar]
98. 98. 

    Yan H, Yuan W, Velculescu VE, Vogelstein B, Kinzler KW et al. 2002. Allelic variation in human gene expression. Science 297:1143
    [Google Scholar]
99. 99. 

    Lind L, Fors N, Hall J, Marttala K, Stenborg A. 2005. A comparison of three different methods to evaluate endothelium-dependent vasodilation in the elderly: the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. Arterioscler. Thromb. Vasc. Biol. 25:2368–75
    [Google Scholar]
100. 100. 

     Balliu B, Durrant M, de Goede O, Abell N, Li X et al. 2019. Genetic regulation of gene expression and splicing during a 10-year period of human aging. Genome Biol. 20:230
     [Google Scholar]
101. 101. 

     Gimelbrant A, Hutchinson JN, Thompson BR, Chess A. 2007. Widespread monoallelic expression on human autosomes. Science 318:1136–40
     [Google Scholar]
102. 102. 

     Savova V, Chun S, Sohail M, McCole RB, Witwicki R et al. 2016. Genes with monoallelic expression contribute disproportionately to genetic diversity in humans. Nat. Genet. 48:231–37
     [Google Scholar]
103. 103. 

     Vigneau S, Vinogradova S, Savova V, Gimelbrant A. 2018. High prevalence of clonal monoallelic expression. Nat. Genet. 50:1198–99
     [Google Scholar]
104. 104. 

     Reinius B, Sandberg R. 2018. Reply to ‘High prevalence of clonal monoallelic expression’. . Nat. Genet. 50:1199–200
     [Google Scholar]
105. 105. 

     Izzi B, Pistoni M, Cludts K, Akkor P, Lambrechts D et al. 2016. Allele-specific DNA methylation reinforces PEAR1 enhancer activity. Blood 128:1003–12
     [Google Scholar]
106. 106. 

     McKean DM, Homsy J, Wakimoto H, Patel N, Gorham J et al. 2016. Loss of RNA expression and allele-specific expression associated with congenital heart disease. Nat. Commun. 7:12824
     [Google Scholar]
107. 107. 

     Falkenberg KD, Braverman NE, Moser AB, Steinberg SJ, Klouwer FC et al. 2017. Allelic expression imbalance promoting a mutant PEX6 allele causes Zellweger spectrum disorder. Am. J. Hum. Genet. 101:965–76
     [Google Scholar]
108. 108. 

     de Klein N, van Dijk F, Deelen P, Urzua CG, Claringbould A et al. 2020. Imbalanced expression for predicted high-impact, autosomal-dominant variants in a cohort of 3,818 healthy samples. bioRxiv 2020.09.19.30009. https://doi.org/10.1101/2020.09.19.300095
     [Crossref]
109. 109. 

     Castel SE, Cervera A, Mohammadi P, Aguet F, Reverter F et al. 2018. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat. Genet. 50:1327–34
     [Google Scholar]
110. 110. 

     Huang F, Bielski C, Rinne M, Hahn W, Sellers W et al. 2015. TERT promoter mutations and monoallelic activation of TERT in cancer. Oncogenesis 4:e176
     [Google Scholar]
111. 111. 

     GTEx Consort 2017. Genetic effects on gene expression across human tissues. Nature 550:204–13
     [Google Scholar]
112. 112. 

     Zou J, Hormozdiari F, Jew B, Castel SE, Lappalainen T et al. 2019. Leveraging allelic imbalance to refine fine-mapping for eQTL studies. PLOS Genet. 15:e1008481
     [Google Scholar]
113. 113. 

     Chiba H, Kakuta Y, Kinouchi Y, Kawai Y, Watanabe K et al. 2018. Allele-specific DNA methylation of disease susceptibility genes in Japanese patients with inflammatory bowel disease. PLOS ONE 13:e0194036
     [Google Scholar]