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Separating the wheat from the chaff: systematic identification of functionally relevant noncoding variants in ADHD

Abstract

Attention deficit hyperactivity disorder (ADHD) is a highly heritable psychiatric condition with negative lifetime outcomes. Uncovering its genetic architecture should yield important insights into the neurobiology of ADHD and assist development of novel treatment strategies. Twenty years of candidate gene investigations and more recently genome-wide association studies have identified an array of potential association signals. In this context, separating the likely true from false associations (‘the wheat’ from ‘the chaff’) will be crucial for uncovering the functional biology of ADHD. Here, we defined a set of 2070 DNA variants that showed evidence of association with ADHD (or were in linkage disequilibrium). More than 97% of these variants were noncoding, and were prioritised for further exploration using two tools—genome-wide annotation of variants (GWAVA) and Combined Annotation-Dependent Depletion (CADD)—that were recently developed to rank variants based upon their likely pathogenicity. Capitalising on recent efforts such as the Encyclopaedia of DNA Elements and US National Institutes of Health Roadmap Epigenomics Projects to improve understanding of the noncoding genome, we subsequently identified 65 variants to which we assigned functional annotations, based upon their likely impact on alternative splicing, transcription factor binding and translational regulation. We propose that these 65 variants, which possess not only a high likelihood of pathogenicity but also readily testable functional hypotheses, represent a tractable shortlist for future experimental validation in ADHD. Taken together, this study brings into sharp focus the likely relevance of noncoding variants for the genetic risk associated with ADHD, and more broadly suggests a bioinformatics approach that should be relevant to other psychiatric disorders.

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References

  1. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 2009; 106: 9362–9367.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature 2012; 489: 57–74.

    Article  CAS  Google Scholar 

  3. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol 2010; 28: 1045–1048.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Stranger BE, Stahl EA, Raj T . Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 2011; 187: 367–383.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Visscher PM, Brown MA, McCarthy MI, Yang J . Five years of GWAS discovery. Am J Hum Genet 2012; 90: 7–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010; 42: 565–569.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. International Schizophrenia C Purcell SM Wray NR Stone JL Visscher PM O'Donovan MC et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009; 460: 748–752.

    Google Scholar 

  8. Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 2011; 473: 43–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS . Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 2012; 9: 473–476.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hoffman MM, Ernst J, Wilder SP, Kundaje A, Harris RS, Libbrecht M et al. Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res 2013; 41: 827–841.

    Article  CAS  PubMed  Google Scholar 

  11. Ward LD, Kellis M . HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 2012; 40: 4.

    Article  CAS  Google Scholar 

  12. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 2012; 22: 1790–1797.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ritchie GR, Dunham I, Zeggini E, Flicek P . Functional annotation of noncoding sequence variants. Nat Methods 2014; 11: 294–296.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J . A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 2014; 46: 310–315.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Levy F, Hay DA, McStephen M, Wood C, Waldman I . Attention-deficit hyperactivity disorder: a category or a continuum? Genetic analysis of a large-scale twin study. J Am Acad Child Adolesc Psychiatry 1997; 36: 737–744.

    Article  CAS  PubMed  Google Scholar 

  16. Stevenson J . Evidence for a genetic etiology in hyperactivity in children. Behav Genet 1992; 22: 337–344.

    Article  CAS  PubMed  Google Scholar 

  17. Qian Q, Wang Y, Zhou R, Yang L, Faraone SV . Family-based and case-control association studies of DRD4 and DAT1 polymorphisms in Chinese attention deficit hyperactivity disorder patients suggest long repeats contribute to genetic risk for the disorder. Am J Med Genet B Neuropsychiatr Genet 2004; 128B: 84–89.

    Article  PubMed  Google Scholar 

  18. Hawi Z, Lowe N, Kirley A, Gruenhage F, Nothen M, Greenwood T et al. Linkage disequilibrium mapping at DAT1, DRD5 and DBH narrows the search for ADHD susceptibility alleles at these loci. Mol Psychiatry 2003; 8: 299–308.

    Article  CAS  PubMed  Google Scholar 

  19. Brookes KJ, Mill J, Guindalini C, Curran S, Xu X, Knight J et al. A common haplotype of the dopamine transporter gene associated with attention-deficit/hyperactivity disorder and interacting with maternal use of alcohol during pregnancy. Arch Gen Psychiatry 2006; 63: 74–81.

    CAS  PubMed  Google Scholar 

  20. Genro JP, Zeni C, Polanczyk GV, Roman T, Rohde LA, Hutz MH . A promoter polymorphism (-839 C>T) at the dopamine transporter gene is associated with attention deficit/hyperactivity disorder in Brazilian children. Am J Med Genet B Neuropsychiatr Genet 2007; 144B: 215–219.

    Article  CAS  PubMed  Google Scholar 

  21. Daly G, Hawi Z, Fitzgerald M, Gill M . Mapping susceptibility loci in attention deficit hyperactivity disorder: preferential transmission of parental alleles at DAT1, DBH and DRD5 to affected children. Mol Psychiatry 1999; 4: 192–196.

    Article  CAS  PubMed  Google Scholar 

  22. LaHoste GJ, Swanson JM, Wigal SB, Glabe C, Wigal T, King N et al. Dopamine D4 receptor gene polymorphism is associated with attention defict hyperactvity disorder. Mol Psychiatry 1996; 1: 121–124.

    CAS  PubMed  Google Scholar 

  23. Lowe N, Kirley A, Mullins C, Fitzgerald M, Gill M, Hawi Z . Multiple marker analysis at the promoter region of the DRD4 gene and ADHD: evidence of linkage and association with the SNP-616. Am J Med Genet B Neuropsychiatr Genet 2004; 131B: 33–37.

    Article  PubMed  Google Scholar 

  24. Kent L, Doerry U, Hardy E, Parmar R, Gingell K, Hawi Z et al. Evidence that variation at the serotonin transporter gene influences susceptibility to attention deficit hyperactivity disorder (ADHD): analysis and pooled analysis. Mol Psychiatry 2002; 7: 908–912.

    Article  CAS  PubMed  Google Scholar 

  25. Hawi Z, Dring M, Kirley A, Foley D, Kent L, Craddock N et al. Serotonergic system and attention deficit hyperactivity disorder (ADHD): a potential susceptibility locus at the 5-HT(1B) receptor gene in 273 nuclear families from a multi-centre sample. Mol Psychiatry 2002; 7: 718–725.

    Article  CAS  PubMed  Google Scholar 

  26. Hawi Z, Matthews N, Wagner J, Wallace RH, Butler TJ, Vance A et al. DNA variation in the SNAP25 gene confers risk to ADHD and is associated with reduced expression in prefrontal cortex. PLoS One 2013; 8: e60274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gizer IR, Ficks C, Waldman ID . Candidate gene studies of ADHD: a meta-analytic review. Hum Genet 2009; 126: 51–90.

    Article  CAS  PubMed  Google Scholar 

  28. Yang L, Neale BM, Liu L, Lee SH, Wray NR, Ji N et al. Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants. Am J Med Genet B Neuropsychiatr Genet 2013; 162B: 419–430.

    Article  CAS  PubMed  Google Scholar 

  29. Lasky-Su J, Neale BM, Franke B, Anney RJ, Zhou K, Maller JB et al. Genome-wide association scan of quantitative traits for attention deficit hyperactivity disorder identifies novel associations and confirms candidate gene associations. Am J Med Genet B Neuropsychiatr Genet 2008; 147B: 1345–1354.

    Article  CAS  PubMed  Google Scholar 

  30. Neale BM, Medland S, Ripke S, Anney RJ, Asherson P, Buitelaar J et al. Case-control genome-wide association study of attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2010; 49: 906–920.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hinney A, Scherag A, Jarick I, Albayrak O, Putter C, Pechlivanis S et al. Genome-wide association study in German patients with attention deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 2011; 156B: 888–897.

    Article  PubMed  Google Scholar 

  32. Stergiakouli E, Hamshere M, Holmans P, Langley K, Zaharieva I, de CG et al. Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. Am J Psychiatry 2012; 169: 186–194.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ebejer JL, Duffy DL, van der Werf J, Wright MJ, Montgomery G, Gillespie NA et al. Genome-wide association study of inattention and hyperactivity-impulsivity measured as quantitative traits. Twin Res Hum Genet 2013; 16: 560–574.

    Article  PubMed  Google Scholar 

  34. Lesch KP, Timmesfeld N, Renner TJ, Halperin R, Roser C, Nguyen TT et al. Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J Neural Transm 2008; 115: 1573–1585.

    Article  CAS  PubMed  Google Scholar 

  35. Mick E, Todorov A, Smalley S, Hu X, Loo S, Todd RD et al. Family-based genome-wide association scan of attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2010; 49: 898–905.e893.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Neale BM, Medland SE, Ripke S, Asherson P, Franke B, Lesch K-PP et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry 2010; 49: 884–897.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Schizophrenia Psychiatric Genome-Wide Association Study C. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 2011; 43: 969–976.

    Article  CAS  Google Scholar 

  38. Hamshere ML, Walters JTR, Smith R, Richards AL, Green E, Grozeva D et al. Genome-wide significant associations in schizophrenia to ITIH3/4, CACNA1C and SDCCAG8, and extensive replication of associations reported by the Schizophrenia PGC. Mol Psychiatry 2012; 18: 708–712.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. King SR, Smith AG, Alpy F, Tomasetto C, Ginsberg SD, Lamb DJ . Characterization of the putative cholesterol transport protein metastatic lymph node 64 in the brain. Neuroscience 2006; 139: 1031–1038.

    Article  CAS  PubMed  Google Scholar 

  40. Arcos-Burgos M, Jain M, Acosta MT, Shively S, Stanescu H, Wallis D et al. A common variant of the latrophilin 3 gene, LPHN3, confers susceptibility to ADHD and predicts effectiveness of stimulant medication. Mol Psychiatry 2010; 15: 1053–1066.

    Article  CAS  PubMed  Google Scholar 

  41. Brookes K, Xu X, Chen W, Zhou K, Neale B, Lowe N et al. The analysis of 51 genes in DSM-IV combined type attention deficit hyperactivity disorder: association signals in DRD4, DAT1 and 16 other genes. Mol Psychiatry 2006; 11: 934–953.

    Article  CAS  PubMed  Google Scholar 

  42. The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56–65.

    Article  CAS  PubMed Central  Google Scholar 

  43. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI . SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008; 24: 2938–2939.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wang K, Li M, Hakonarson H . ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010; 38: e164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 2005; 15: 1034–1050.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A . Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 2010; 20: 110–121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ernst J, Kellis M . ChromHMM: automating chromatin-state discovery and characterization. Nature methods 2012; 9: 215–216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F . Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 2010; 26: 2069–2070.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Desmet FO, Hamroun D, Lalande M, Collod-Beroud G, Claustres M, Beroud C . Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 2009; 37: e67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yeo G, Burge CB . Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. Journal of computational biology: a journal of computational molecular cell biology 2004; 11: 377–394.

    Article  CAS  Google Scholar 

  51. Reese MG, Eeckman FH, Kulp D, Haussler D . Improved splice site detection in Genie. Journal of computational biology: a journal of computational molecular cell biology 1997; 4: 311–323.

    Article  CAS  Google Scholar 

  52. Pertea M, Lin X, Salzberg SL . GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res 2001; 29: 1185–1190.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kheradpour P, Kellis M . Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res 2014; 42: 2976–2987.

    Article  CAS  PubMed  Google Scholar 

  54. Grillo G, Turi A, Licciulli F, Mignone F, Liuni S, Banfi S et al. UTRdb and UTRsite (RELEASE 2010): a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucleic Acids Res 2010; 38: D75–D80.

    Article  CAS  PubMed  Google Scholar 

  55. Bhattacharya A, Ziebarth JD, Cui Y . PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res 2014; 42: D86–D91.

    Article  CAS  PubMed  Google Scholar 

  56. Betel D, Koppal A, Agius P, Sander C, Leslie C . Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 2010; 11: R90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM et al. The Human Genome Browser at UCSC. Genome Res 2002; 12: 996–1006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kozomara A, Griffiths-Jones S . miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic acids research 2011; 39: 7.

    Article  CAS  Google Scholar 

  59. Garcia D, Baek D, Shin C, Bell G, Grimson A, Bartel D . Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nature structural & molecular biology 2011; 18: 1139–1146.

    Article  CAS  Google Scholar 

  60. Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M et al. DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res 2013; 41: W169–W173.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Wong N, Wang X . miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 2015; 43: D146–D152.

    Article  CAS  PubMed  Google Scholar 

  62. Beissbarth T, Speed TP . GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 2004; 20: 1464–1465.

    Article  CAS  PubMed  Google Scholar 

  63. Skol AD, Scott LJ, Abecasis GR, Boehnke M . Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nature Genetics 2006; 38: 209–213.

    Article  CAS  PubMed  Google Scholar 

  64. Bellgrove MA, Hawi Z, Gill M, Robertson IH . The cognitive genetics of attention deficit hyperactivity disorder (ADHD): sustained attention as a candidate phenotype. Cortex; a journal devoted to the study of the nervous system and behavior 2006; 42: 838–845.

    Article  PubMed  Google Scholar 

  65. Schmidt D, Wilson MD, Ballester B, Schwalie PC, Brown GD, Marshall A et al. Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 2010; 328: 1036–1040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lindblad-Toh K, Garber M, Zuk O, Lin MF, Parker BJ, Washietl S et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 2011; 478: 476–482.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Krawczak M, Reiss J, Cooper DN . The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: causes and consequences. Hum Genet 1992; 90: 41–54.

    Article  CAS  PubMed  Google Scholar 

  68. Crowe ML, Wang XQ, Rothnagel JA . Evidence for conservation and selection of upstream open reading frames suggests probable encoding of bioactive peptides. BMC Genomics 2006; 7: 16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Iacono M, Mignone F, Pesole G . uAUG and uORFs in human and rodent 5'untranslated mRNAs. Gene 2005; 349: 97–105.

    Article  CAS  PubMed  Google Scholar 

  70. Liu L, Dilworth D, Gao L, Monzon J, Summers A, Lassam N et al. Mutation of the CDKN2A 5' UTR creates an aberrant initiation codon and predisposes to melanoma. Nat Genet 1999; 21: 128–132.

    Article  CAS  PubMed  Google Scholar 

  71. Nicoloso MS, Sun H, Spizzo R, Kim H, Wickramasinghe P, Shimizu M et al. Single-nucleotide polymorphisms inside microRNA target sites influence tumor susceptibility. Cancer Res 2010; 70: 2789–2798.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Chin LJ, Ratner E, Leng S, Zhai R, Nallur S, Babar I et al. A SNP in a let-7 microRNA complementary site in the KRAS 3' untranslated region increases non-small cell lung cancer risk. Cancer Res 2008; 68: 8535–8540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Fuke S, Sasagawa N, Ishiura S . Identification and Characterization of the Hesr1/Hey1 as a Candidate trans-Acting Factor on Gene Expression through the 3' Non-Coding Polymorphic Region of the Human Dopamine Transporter (DAT1) Gene. J Biochem 2005; 137: 205–216.

    Article  CAS  PubMed  Google Scholar 

  74. Han J, Lee Y, Yeom K-HH, Nam J-WW, Heo I, Rhee J-KK et al. Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex. Cell 2006; 125: 887–901.

    Article  CAS  PubMed  Google Scholar 

  75. Hinske LC, Franca GS, Torres HA, Ohara DT, Lopes-Ramos CM, Heyn J et al. miRIAD-integrating microRNA inter- and intragenic data. Database (Oxford) 2014; 2014.

  76. An Y, Amr SS, Torres A, Weissman L, Raffalli P, Cox G et al. SOX12 and NRSN2 are candidate genes for 20p13 subtelomeric deletions associated with developmental delay. Am J Med Genet B Neuropsychiatr Genet 2013; 162B: 832–840.

    Article  CAS  PubMed  Google Scholar 

  77. Pruunsild P, Kazantseva A, Aid T, Palm K, Timmusk T . Dissecting the human BDNF locus: bidirectional transcription, complex splicing, and multiple promoters. Genomics 2007; 90: 397–406.

    Article  CAS  PubMed  Google Scholar 

  78. Stevens LJ, Zentall SS, Deck JL, Abate ML, Watkins BA, Lipp SR et al. Essential fatty acid metabolism in boys with attention-deficit hyperactivity disorder. Am J Clin Nutr 1995; 62: 761–768.

    Article  CAS  PubMed  Google Scholar 

  79. Rubia K, Alegria AA, Cubillo AI, Smith AB, Brammer MJ, Radua J . Effects of stimulants on brain function in attention-deficit/hyperactivity disorder: a systematic review and meta-analysis. Biol Psychiatry 2014; 76: 616–628.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Bellgrove MA, Hawi Z, Lowe N, Kirley A, Robertson IH, Gill M . DRD4 gene variants and sustained attention in attention deficit hyperactivity disorder (ADHD): effects of associated alleles at the VNTR and -521 SNP. Am J Med Genet B Neuropsychiatr Genet 2005; 136B: 81–86.

    Article  PubMed  Google Scholar 

  81. Cooper TA . Use of minigene systems to dissect alternative splicing elements. Methods 2005; 37: 331–340.

    Article  CAS  PubMed  Google Scholar 

  82. Kwon HS, Lee DK, Lee JJ, Edenberg HJ, Ahn YH, Hur MW . Posttranscriptional regulation of human ADH5/FDH and Myf6 gene expression by upstream AUG codons. Arch Biochem Biophys 2001; 386: 163–171.

    Article  CAS  PubMed  Google Scholar 

  83. Nemeth N, Kovacs-Nagy R, Szekely A, Sasvari-Szekely M, Ronai Z . Association of impulsivity and polymorphic microRNA-641 target sites in the SNAP-25 gene. PLoS One 2013; 8: e84207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Kovacs-Nagy R, Sarkozy P, Hu J, Guttman A, Sasvari-Szekely M, Ronai Z . Haplotyping of putative microRNA-binding sites in the SNAP-25 gene. Electrophoresis 2011; 32: 2013–2020.

    Article  CAS  PubMed  Google Scholar 

  85. Hu X-ZZ, Lipsky RH, Zhu G, Akhtar LA, Taubman J, Greenberg BD et al. Serotonin transporter promoter gain-of-function genotypes are linked to obsessive-compulsive disorder. Am J Hum Genet 2006; 78: 815–826.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Genro JP, Polanczyk GV, Zeni C, Oliveira AS, Roman T, Rohde LA et al. A common haplotype at the dopamine transporter gene 5' region is associated with attention-deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 2008; 147B: 1568–1575.

    Article  CAS  PubMed  Google Scholar 

  87. de Azeredo LA, Rovaris DL, Mota NR, Polina ER, Marques FZ, Contini V et al. Further evidence for the association between a polymorphism in the promoter region of SLC6A3/DAT1 and ADHD: findings from a sample of adults. Eur Arch Psychiatry Clin Neurosci 2014; 264: 401–408.

    Article  PubMed  Google Scholar 

  88. Mitchell PJ, Timmons PM, Hebert JM, Rigby PW, Tjian R . Transcription factor AP-2 is expressed in neural crest cell lineages during mouse embryogenesis. Genes Dev 1991; 5: 105–119.

    Article  CAS  PubMed  Google Scholar 

  89. Mochida GH, Ganesh VS, de Michelena MI, Dias H, Atabay KD, Kathrein KL et al. CHMP1A encodes an essential regulator of BMI1-INK4A in cerebellar development. Nat Genet 2012; 44: 1260–1264.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Xiang C, Baubet V, Pal S, Holderbaum L, Tatard V, Jiang P et al. RP58/ZNF238 directly modulates proneurogenic gene levels and is required for neuronal differentiation and brain expansion. Cell Death Differ 2012; 19: 692–702.

    Article  CAS  PubMed  Google Scholar 

  91. Helwak A, Kudla G, Dudnakova T, Tollervey D . Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 2013; 153: 654–665.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Petryszak R, Burdett T, Fiorelli B, Fonseca NA, Gonzalez-Porta M, Hastings E et al. Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments. Nucleic Acids Res 2014; 42: D926–932.

    Article  CAS  PubMed  Google Scholar 

  93. Burset M, Seledtsov IA, Solovyev VV . SpliceDB: database of canonical and noncanonical mammalian splice sites. Nucleic Acids Res 2001; 29: 255–259.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We acknowledge the financial support of the following agencies: the Australian National Health and Medical Research Council (NHMRC) (Project IDs 1064591 and 1065677, and Fellowship ID 520574 (KCP)), Australian Research Council (Future Fellowship (MAB)), Royal Australasian College of Physicians and the Menzies Foundation. We would also like to acknowledge the Queensland Brain Bank and the Victorian Brain Bank for providing the non-pathological brain tissue samples. These centres are funded by the NHMRC. Tissues were also obtained from the New South Wales Tissue Resource Centre at the University of Sydney, which is supported by the NHMRC, Schizophrenia Research Institute and National Institute of Alcohol Abuse and Alcoholism (NIH (NIAAA) R24AA012725).

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Tong, J., Hawi, Z., Dark, C. et al. Separating the wheat from the chaff: systematic identification of functionally relevant noncoding variants in ADHD. Mol Psychiatry 21, 1589–1598 (2016). https://doi.org/10.1038/mp.2016.2

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