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Principles and methods of in-silico prioritization of non-coding regulatory variants

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Abstract

Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.

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Notes

  1. It though remains an unresolved question how much of the human genome is “actually” functional. Typically, the mutations of “functional” elements are expected to result in a biological consequence. Since the ENCODE study, comparative genomics studies have suggested that at most 8-25% of the human genome may carry a biological function (Graur 2017; Rands et al. 2014). Refer to (Chi 2016) for further discussion of this issue.

References

  • Albert F, Kruglyak L (2015) The role of regulatory variation in complex traits and disease. Nat Rev Genet 16:197–212

    Article  CAS  PubMed  Google Scholar 

  • Backenroth D et al (2017) FUN-LDA: a latent Dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation. https://www.biorxiv.org/content/early/2017/08/02/069229. Accessed 10 Sep 2017

  • Bae J (2013) Perspectives of International Human Epigenome Consortium. Genom Inform 11:7–14

    Article  Google Scholar 

  • Banovich N, Lan X, McVicker G, van de Geijn B, Degner JF, Blischak JD, Roux J, Pritchard JK, Gilad Y (2014) Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet 10:e1004663

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Barenboim M, Manke T (2013) ChroMoS: an integrated web tool for SNP classification, prioritization and functional interpretation. Bioinformatics 29:2197–2198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Barret T et al (2012) BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata. Nucleic Acids Res 40:d57–d63

    Article  CAS  Google Scholar 

  • Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, Haudenschild CD, Beckman KB, Shi J, Mei R, Urban AE, Montgomery SB, Levinson DF, Koller D (2014) Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res 24:14–24

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, Damborsky J (2014) PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput Biol 10(1):e1003440

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, Kellis M, Marra MA, Beaudet AL, Ecker JR, Farnham PJ, Hirst M, Lander ES, Mikkelsen TS, Thomson JA (2010) The NIH roadmap epigenomics mapping consortium. Nat Biotechnol 28:1045–1048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bombard Y, Rozmovits L, Trudeau M, Leighl NB, Deal K, Marshall DA (2014) Access to personalized medicine: factors influencing the use and value of gene expression profiling in breast cancer treatment. Curr Oncol 21:e426–e433

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Boyle A, Hong EL, Hariharan M, Cheng Y, Schaub MA, Maya Kasowski KJK, Park J, Hitz BC, Shuai Weng J, Cherry M (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22:1790–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Browning SR (2008) Estimation of pairwise identity by descent from dense genetic marker data in a population sample of haplotypes. Genetics 178:2123–2132

    Article  PubMed  PubMed Central  Google Scholar 

  • Burgess DJ (2011) Comparative genomics: mammalian alignments reveal human functional elements. Nat Rev Genetics 12:806–807

    CAS  PubMed  Google Scholar 

  • Burgess DJ (2012) Cancer genomics: histone modification at the gene level. Nat Rev Genetics 12:153–156

    Google Scholar 

  • Burska A, Roget K, Blits M, Soto Gomez L, van de Loo F, Hazelwood LD, Verweij CL, Rowe A, Goulielmos GN, van Baarsen LG, Ponchel F (2014) Gene expression analysis in RA: towards personalized medicine. Pharmacogenom J 14:93–106

    Article  CAS  Google Scholar 

  • Cao J (2014) The functional role of long non-coding RNAs and epigenetics. Biol Proced Online 16:1

    Article  CAS  Google Scholar 

  • Chen L, Jin P, Qin ZS (2016) DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles. Genome Biol 17:252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen H, Patterson N, Reich D (2010) Population differentiation as a test for selective sweeps. Genome Res 20:393–402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chi KR (2016) The dark side of the human genome. Nature 538:275–277

    Article  CAS  PubMed  Google Scholar 

  • Chinwalla AT et al (2002) Initial sequencing and comparative analysis of the mouse genome. Nature 420:520–562

    Article  PubMed  CAS  Google Scholar 

  • Coetzee SG, Coetzee GA, Hazelett DJ (2015) motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics 31(23):3847–3849

    CAS  PubMed  PubMed Central  Google Scholar 

  • Coetzee SG, Rhie S, Berman B, Coetzee GA, Noushmehr H (2012) FunciSNP: an R/Bioconductor tool integrating functional non-coding datasets with genetic association studies to identify candidate regulatory SNPs. Nucleic Acids Res 40(18):e139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP ++. PLoS Comput Biol 6:e1001025

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • de Hoon M, Shin JW, Carninci P (2015) Paradigm shifts in genomics through the FANTOM projects. Mamm Genome 26:1–12

    Article  CAS  Google Scholar 

  • Degner JF, Pai AA, Pique-Regi R, Veyrieras JB, Gaffney DJ, Pickrell JK, De Leon S, Michelini K, Lewellen N, Crawford GE et al (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482:390–394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Diehl A, Boyle AP (2016) Deciphering ENCODE. Trends Genet 32:238–249

    Article  CAS  PubMed  Google Scholar 

  • Dunham I, Kulesha E, Iotchkova V, Morganella S, Birney E (2014) FORGE: a tool to discover cell specific enrichments of GWAS associated SNPs in regulatory regions. http://biorxiv.org/content/early/2014/12/20/013045. Accessed 22 Jul 2017

  • ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74

    Article  CAS  Google Scholar 

  • Edwards S, Beesley J, French JD, Dunning AM (2013) Beyond GWASs: illuminating the dark road from association to function. Am J Hum Genet 93:779–797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Eric L, Greer YS (2012) Histone methylation: a dynamic mark in health, disease and inheritance. Nat Rev Genetics 13:343–357

    Google Scholar 

  • Ferlaino M, Rogers MF, Shihab HA, Mort M, Cooper DN, Gaunt TR, Campbell C (2017) An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome. BMC Bioinform 18:442

    Article  Google Scholar 

  • Ghandi M, Lee D, Mohammad-Noori M, Beer MA (2014a) Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol 10(7):e1003711

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gibbs RA, Weinstock GM, Metzker ML, Muzny DM, Sodergren EJ, Scherer S et al (2004) Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428:492–521

    Google Scholar 

  • Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P, Karchin R, Vazquez M, Fink JL, Kassahn KS, Pearson JV, Bader GD, Boutros PC, Muthuswamy L, Ouellette BF, Reimand J, Linding R, Shibata T, Valencia A, Butler A, Dronov S, Flicek P, Shannon NB, Carter H, Ding L, Sander C, Stuart JM, Stein LD, Lopez-Bigas N, International Cancer Genome Consortium Mutation Pathways, Consequences Subgroup of the Bioinformatics Analyses Working Group (2013) Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods 10:723–729

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Grarup N et al (2011) The diabetogenic VPS13C/C2CD4A/C2CD4B rs7172432 variant impairs glucose-stimulated insulin response in 5,722 non-diabetic Danish individuals. Diabetologia 54:789–794

    Article  CAS  PubMed  Google Scholar 

  • Graur D (2017) An upper limit on the functional fraction of the human genome. Genome Biol Evol 9:1880–1885

    Article  PubMed  PubMed Central  Google Scholar 

  • Grubert F, Zaugg JB, Kasowski M, Ursu O, Spacek DV, Martin AR, Greenside P, Srivas R, Phanstiel DH, Pekowska A, Heidari N, Euskirchen G, Huber W, Pritchard JK, Bustamante CD, Steinmetz LM, Kundaje A, Snyder M (2015) Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162:1051–1065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Grundberg E, Meduri E, Sandling JK, Hedman AK, Keildson S, Buli A, Busche S, Yuan W, Nisbet J, Sekowska M, Wilk A, Barrett A, Small KS, Ge B, Caron M, Shin SY, Multiple Tissue Expression Resource Consortium, Lathrop M, Dermitzakis ET, McCarthy MI, Spector TD, Bell JT, Deloukas P (2013) Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am J Hum Genet 93:876–890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gulko B, Hubisz MJ, Gronau I, Siepel A (2015) A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat Genetics 47:276–283

    Article  CAS  PubMed  Google Scholar 

  • Guo L, Du Y, Chang S, Zhang K, Wang J (2013) rSNPBase: a database for curated regulatory SNPs. Nucleic Acids Res 42:d1033–d1039

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Guo Y, Conti DV, Wang K (2014) Enlight: web-based integration of GWAS results with biological annotations. Bioinformatics 31:275–276

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gupta RM et al (2017) A genetic variant associated with five vascular diseases is a distal regulator of endothelin-1 gene expression. Cell 170(522–533):e515

    Google Scholar 

  • Heidari N, Phanstiel DH, He C, Grubert F, Jahanbani F, Kasowski M, Zhang MQ, Snyder MP (2014) Genomewide map of regulatory interactions in the human genome. Genome Res 24:1905–1917

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Holsinger KE, Weir Bruce S (2009) Genetics in geographically structured populations: defining, estimating and interpreting FST. Nat Rev Genet 10:639–650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hoskins R, Repo S, Barsky D, Andreoletti G, Moult J, Brenner SE (2017) Reports from CAGI: the critical assessment of genome interpretation. Human Mutat 38:1039–1041

    Article  Google Scholar 

  • Huang Y-F, Gulko B, Siepel A (2017) Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat Genet 49:618–624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hughes JR, Roberts N, McGowan S, Hay D, Giannoulatou E, Lynch M, De Gobbi M, Taylor S, Gibbons R, Higgs DR (2014) Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat Genet 46:205–212

    Article  CAS  PubMed  Google Scholar 

  • Ionita-Laza I, McCallum K, Xu B, Buxbaum JD (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48:20

    Article  CAS  Google Scholar 

  • Jennifer Harrow AF, Gonzalez Jose M, Tapanari E et al (2012) GENCODE: the reference human genome annotation for ENCODE project. Genome Res 22:1760–1774

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jin B, Li Y, Robertson KD (2011) DNA methylation: superior or subordinate in the epigenetic hierarchy? Genes Cancer 2:607–617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kasowski M, Grubert F, Heffelfinger C, Hariharan M, Asabere A, Waszak SM, Habegger L, Rozowsky J, Shi M, Urban AE, Hong M-Y, Karczewski KJ, Huber W, Weissman SM, Gerstein MB, Korbel JO, Snyder M (2010) Variation in transcription factor binding among humans. Science 328:232–235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kelley DR, Snoek J, Rinn JL (2016) Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26:990–999

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Khurana E, Fu Y, Chakravarty D, Demichelis F, Rubin MA, Gerstein M (2016) Role of non-coding sequence variants in cancer. Nat Rev Genet 17:93–108

    Article  CAS  PubMed  Google Scholar 

  • Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, Sboner A, Lochovsky L, Chen J, Harmanci A, Das J, Abyzov A, Balasubramanian S, Beal K, Chakravarty D, Challis D, Chen Y, Clarke D, Clarke L, Cunningham F, Evani US, Flicek P, Fragoza R, Garrison E, Gibbs R, Gümüs ZH, Herrero J, Kitabayashi N, Kong Y, Lage K, Liluashvili V, Lipkin SM, MacArthur DG, Marth G, Muzny D, Pers TH, Ritchie GR, Rosenfeld JA, Sisu C, Wei X, Wilson M, Xue Y, Yu F, 1000 Genomes Project Consortium, Dermitzakis ET, Yu H, Rubin MA, Tyler-Smith C, Gerstein M (2013) Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342(6154):1235587

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kilpinen H, Waszak SM, Gschwind AR, Raghav SK, Witwicki RM, Orioli A, Migliavacca E, Wiederkehr M, Gutierrez-Arcelus M, Panousis NI, Yurovsky A (2013) Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science 342:744–747

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim T, Hemberg M, Gray JM, Costa AM, Bear DM, Wu J, Harmin DA, Laptewicz M, Barbara-Haley K, Kuersten S, Markenscoff-Papadimitriou E, Kuhl D, Bito H, Worley PF, Kreiman G, Greenberg ME (2010) Widespread transcription at neuronal activity-regulated enhancers. Nature 465:182–187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 46:5

    Article  CAS  Google Scholar 

  • Landers JE et al (2009) Reduced expression of the kinesin-associated protein 3 (KIFAP3) gene increases survival in sporadic amyotrophic lateral sclerosis. Proc Natl Acad Sci 106:9004–9009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  CAS  PubMed  Google Scholar 

  • Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, Beer MA (2015) A method to predict the impact of regulatory variants from DNA sequence. Nat Genet 47(8):955–961

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lettice LA, Heaney S, Purdie LA, Li L, de Beer P, Oostra BA, Goode D, Elgar G, Hill R, de Graaff E (2003) A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyly. Hum Mol Genet 12:1725–1735

    Article  CAS  PubMed  Google Scholar 

  • Leung MKK, Xiong HY, Lee LJ, Frey BJ (2014) Deep learning of the tissue-regulated splicing code. Bioinformatics 30:121–129

    Article  CAS  Google Scholar 

  • Li MJ et al (2016) Predicting regulatory variants with composite statistic. Bioinformatics 32:2729–2736

    Article  CAS  PubMed  Google Scholar 

  • Li MJ et al (2017) cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes. Genome Biol 18:52

    Article  PubMed  PubMed Central  Google Scholar 

  • Li MJ, Sham PC, Wang J (2012) Genetic variant representation, annotation and prioritization in the post-GWAS era. Cell Res 22:1505–1508

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li MJ, Wang J (2015) Current trend of annotating single nucleotide variation in humans–a case study on SNVrap. Methods 79–80:32–40

    Article  PubMed  CAS  Google Scholar 

  • Li MJ, Wang LY, Xia Z, Sham PC, Wang J (2013) GWAS3D: detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications. Nucleic Acids Res 41(W1):W150–W158

    Article  PubMed  PubMed Central  Google Scholar 

  • Lindblad-Toh K, Wade CM, Mikkelsen TS, Karlsson EK, Jaffe DB, Kamal M, Clamp M, Chang JL, Kulbokas EJ, Zody MC et al (2005) Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature 438:803–819

    Article  CAS  PubMed  Google Scholar 

  • Liu X, Wu C, Li C, Boerwinkle E (2016) dbNSFP v3.0: a one-stop database of functional predictions and annotations for human non-synonymous and splice site SNVs. Hum Mutat 37:235–241

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S, Mungall CJ, Arner E, Baillie JK, Bertin N, Bono H, de Hoon M, Diehl AD, Dimont E, Freeman TC, Fujieda K, Hide W, Kaliyaperumal R, Katayama T, Lassmann T, Meehan TF, Nishikata K, Ono H, Rehli M, Sandelin A, Schultes EA, Hoen PAC 't, Tatum Z, Thompson M, Toyoda T, Wright DW, Daub CO, Itoh M, Carninci P, Hayashizaki Y, Forrest ARR, Kawaji H, The FANTOM consortium (2014) Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol 16:22

    Article  CAS  Google Scholar 

  • Loscalzo J, Handy DE (2014) Epigenetic modifications: basic mechanisms and role in cardiovascular disease. Pulm Circ 4(2):169–174

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lu Q, Hu Y, Sun J, Cheng Y, Cheung K-H, Zhao H (2015) A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci Rep 5:10576

    Article  PubMed  PubMed Central  Google Scholar 

  • Lu Q, Powles RL, Wang Q, He BJ, Zhao H (2016) Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLoS Genet 12:e1005947

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Maria Gutierrez-Arcelus HO, Lappalainen T, Montgomery SB, Buil A, Yurovsky A, Bryois J, Padioleau I, Romano L, Planchon A, Falconnet E, Bielser D, Gagnebin M, Giger T, Borel C, Letourneau A, Makrythanasis P, Guipponi M, Gehrig C, Antonarakis SE, Dermitzakis ET (2015) Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet 11:e1004958

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • McVicker G, van de Geijn B, Degner JF, Cain CE, Banovich NE, Raj A, Lewellen N, Myrthil M, Gilad Y, Pritchard JK (2013) Identification of genetic variants that affect histone modifications in human cells. Science 342:747–749

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mercer T, Dinger ME, Mattick JS (2009) Long non-coding RNAs: insights into functions. Nat Rev Genet 10:155–159

    Article  CAS  PubMed  Google Scholar 

  • Monlong J, Calvo M, Ferreira PG, Guigo R (2014) Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat Commun 5:4698. https://doi.org/10.1038/ncomms5698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nainar S, Marshall PR, Tyler CR, Spitale RC, Bredy TW (2016) Evolving insights into RNA modifications and their functional diversity in the brain. Nat Neurosci 19:1292–1298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nair P, Vihinen M (2013) VariBench: a benchmark database for variations. Human Mutat 34:42–49

    Article  CAS  Google Scholar 

  • Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium, O’Dushlaine C, Rossin L, Lee PH, Duncan L, Parikshak NN, Newhouse S, Ripke S, Neale BM, Holmans PA, Breen G et al (2015) Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci 18:199–209

    Article  CAS  Google Scholar 

  • Parts L, Hedman AK, Keildson S, Knights AJ, Abreu-Goodger C, van de Bunt M, Guerra-Assunção JA, Bartonicek N, van Dongen S, Mägi R, Nisbet J, Barrett A, Rantalainen M, Nica AC, Quail MA, Small KS, Glass D, Enright AJ, Winn J, MuTHER Consortium, Deloukas P, Dermitzakis ET, McCarthy MI, Spector TD, Durbin R, Lindgren CM (2012) Extent, causes, and consequences of small RNA expression variation in human adipose tissue. PLoS Genet 8:e1002704

  • Paull D, Sevilla A, Zhou H, Hahn AK, Kim H, Napolitano C, Tsankov A, Shang L, Krumholz K, Jagadeesan P, Woodard CM, Sun B, Vilboux T, Zimmer M, Forero E, Moroziewicz DN, Martinez H, Malicdan MC, Weiss KA, Vensand LB, Dusenberry CR, Polus H, Sy KT, Kahler DJ, Gahl WA, Solomon SL, Chang S, Meissner A, Eggan K, Noggle SA (2015) Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells. Nat Methods 12:885–892

    Article  CAS  PubMed  Google Scholar 

  • Peterson TA, Mort M, Cooper DN, Radivojac P, Kann MG, Mooney SD (2016) regulatory single-nucleotide variant predictor increases predictive performance of functional regulatory variants. Human Mutat 37:1137–1143

    Article  CAS  Google Scholar 

  • Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, Fischer J, Maatz H, Kren V, Pravenec M, Hubner N, Aitman TJ (2006) Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet 2:e172

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Popadin K, Gutierrez-Arcelus M, Dermitzakis ET, Antonaraki SE (2013) Genetic and epigenetic regulation of human lincRNA gene expression. Am J Human Genet 93:1015–1026

    Article  CAS  Google Scholar 

  • Quang D, Chen Y, Xie X (2015) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31:3

    Article  CAS  Google Scholar 

  • Quang D, Xie X (2016) DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res 44(11):e107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Rands CM, Meader S, Ponting CP, Lunter G (2014) 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS Genet 10:e1004525

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES et al (2014) A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159:1665–1680

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rogers MF, Shihab HA, Mort M, Cooper DN, Gaunt TR, Campbell C (2017b) FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx536

  • Schizophrenia Working Group of the Psychiatric Genomics Consortium, Ripke S, Neale BM, Corvin A, Walters JT, Farh KH, Holmans PA, Lee P, Bulik-Sullivan B, Collier DA, Huang H, Pers TH, Agartz I, Agerbo E, Albus M, Alexander M, Amin F, Bacanu SA, Begemann M, Belliveau RA Jr, Bene J, Bergen SE, Bevilacqua E, Bigdeli TB, Black DW, Bruggeman R, Buccola NG, Buckner RL, Byerley W, Cahn W, Cai G, Campion D, Cantor RM, Carr VJ, Carrera N, Catts SV, Chambert KD, Chan RC, Chen RY, Chen EY, Cheng W, Cheung EF, Chong SA, Cloninger CR, Cohen D, Cohen N, Cormican P, Craddock N, Crowley JJ, Curtis D, Davidson M, Davis KL, Degenhardt F, Del Favero J, Demontis D, Dikeos D, Dinan T, Djurovic S, Donohoe G, Drapeau E, Duan J, Dudbridge F, Durmishi N, Eichhammer P, Eriksson J, Escott-Price V, Essioux L, Fanous AH, Farrell MS, Frank J, Franke L, Freedman R, Freimer NB, Friedl M, Friedman JI, Fromer M, Genovese G, Georgieva L, Giegling I, Giusti-Rodríguez P, Godard S, Goldstein JI, Golimbet V, Gopal S, Gratten J, de Haan L, Hammer C, Hamshere ML, Hansen M, Hansen T, Haroutunian V, Hartmann AM, Henskens FA, Herms S, Hirschhorn JN, Hoffmann P, Hofman A, Hollegaard MV, Hougaard DM, Ikeda M, Joa I, Julià A, Kahn RS, Kalaydjieva L, Karachanak-Yankova S, Karjalainen J, Kavanagh D, Keller MC, Kennedy JL, Khrunin A, Kim Y, Klovins J, Knowles JA, Konte B, Kucinskas V, Ausrele Kucinskiene Z, Kuzelova-Ptackova H, Kähler AK, Laurent C, Keong JL, Lee SH, Legge SE, Lerer B, Li M, Li T, Liang KY, Lieberman J, Limborska S, Loughland CM, Lubinski J, Lönnqvist J, Macek M Jr, Magnusson PK, Maher BS, Maier W, Mallet J, Marsal S, Mattheisen M, Mattingsdal M, McCarley RW, McDonald C, McIntosh AM, Meier S, Meijer CJ, Melegh B, Melle I, Mesholam-Gately RI, Metspalu A, Michie PT, Milani L, Milanova V, Mokrab Y, Morris DW, Mors O, Murphy KC, Murray RM, Myin-Germeys I, Müller-Myhsok B, Nelis M, Nenadic I, Nertney DA, Nestadt G, Nicodemus KK, Nikitina-Zake L, Nisenbaum L, Nordin A, O’Callaghan E, O’Dushlaine C, O’Neill FA, Oh SY, Olincy A, Olsen L, Van Os J, Pantelis C, Papadimitriou GN, Papiol S, Parkhomenko E, Pato MT, Paunio T, Pejovic-Milovancevic M, Perkins DO, Pietiläinen O, Pimm J, Pocklington AJ, Powell J, Price A, Pulver AE, Purcell SM, Quested D, Rasmussen HB, Reichenberg A, Reimers MA, Richards AL, Roffman JL, Roussos P, Ruderfer DM, Salomaa V, Sanders AR, Schall U, Schubert CR, Schulze TG, Schwab SG, Scolnick EM, Scott RJ, Seidman LJ, Shi J, Sigurdsson E, Silagadze T, Silverman JM, Sim K, Slominsky P, Smoller JW, So HC, Spencer CA, Stahl EA, Stefansson H, Steinberg S, Stogmann E, Straub RE, Strengman E, Strohmaier J, Stroup TS, Subramaniam M, Suvisaari J, Svrakic DM, Szatkiewicz JP, Söderman E, Thirumalai S, Toncheva D, Tosato S, Veijola J, Waddington J, Walsh D, Wang D, Wang Q, Webb BT, Weiser M, Wildenauer DB, Williams NM, Williams S, Witt SH, Wolen AR, Wong EH, Wormley BK, Xi HS, Zai CC, Zheng X, Zimprich F, Wray NR, Stefansson K, Visscher PM, Adolfsson R, Andreassen OA, Blackwood DH, Bramon E, Buxbaum JD, Børglum AD, Cichon S, Darvasi A, Domenici E, Ehrenreich H, Esko T, Gejman PV, Gill M, Gurling H, Hultman CM, Iwata N, Jablensky AV, Jönsson EG, Kendler KS, Kirov G, Knight J, Lencz T, Levinson DF, Li QS, Liu J, Malhotra AK, McCarroll SA, McQuillin A, Moran JL, Mortensen PB, Mowry BJ, Nöthen MM, Ophoff RA, Owen MJ, Palotie A, Pato CN, Petryshen TL, Posthuma D, Rietschel M, Riley BP, Rujescu D, Sham PC, Sklar P, St Clair D, Weinberger DR, Wendland JR, Werge T, Daly MJ, Sullivan PF, O’Donovan MC (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427

    Article  PubMed Central  CAS  Google Scholar 

  • Ritchie GR, Dunham I, Zeggini E, Flicek P (2014) Functional annotation of noncoding sequence variants. Nat Methods 11:6

    Article  CAS  Google Scholar 

  • Rogers MF, Shihab HA, Gaunt TR, Campbell C (2017a) CScape: a tool for predicting oncogenic single-point mutations in the cancer genome. Sci Rep 7:11597

    Article  PubMed  PubMed Central  Google Scholar 

  • Ryan NM, Morris SW, Porteous DJ, Taylor MS, Evans KL (2014) SuRFing the genomics wave: an R package for prioritising SNPs by functionality. Genome Med 6:79

    PubMed  PubMed Central  Google Scholar 

  • Schmidt D, Wilson MD, Ballester B, Schwalie PC, Brown GD, Marshall A, Odom DT (2010) Five vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 328:1036–1040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schmidt EM, Zhang J, Zhou W, Chen J, Mohlke KL, Chen YE, Willer CJ (2015) GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31:2601–2606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sebastian M, Waszak OD, Gschwind AR, Kilpinen H, Raghav SK, Witwicki RM, Orioli A, Wiederkehr M, Panousis NI, Yurovsky A, Romano-Palumbo L, Planchon A, Bielser D, Padioleau I, Udin G, Thurnheer S, Hacker D, Hernandez N, Reymond A, Deplancke B, Dermitzakis ET (2015) Population variation and genetic control of modular chromatin architecture in humans. Cell 162:1039–1050

    Article  CAS  Google Scholar 

  • Shalem O, Sanjana NE, Zhang F (2015) High-throughput functional genomics using CRISPR-Cas9. Nat Rev Genet 16:299–311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shihab HA, Rogers MF, Gough J, Mort M, Cooper DN, Day INM, Campbell C (2015) An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics 31:1536–1543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Siepel A, Bajerano G, Pedersen JS, Hinrichs Angie S, Hou Minmei, Kate Rosenbloom HC, Spieth John, Hillier LaDeana W, Stephen Richards GMW, Wilson Richard K, Gibbs RA, James Kent WWM, Haussler D (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 15:1034–1050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Smedley D, Schubach M, Jacobsen JO, Köhler S, Zemojtel T, Spielmann M, Jäger M, Hochheiser H, Washington NL, McMurry JA, Haendel MA, Mungall CJ, Lewis SE, Groza T, Valentini G, Robinson PN (2016) A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am J Hum Genet 99:595–606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Taher L, Narlikar L, Ovcharenko I (2015) Identification and computational analysis of gene regulatory elements. Cold Spring Harb Protoc 1:24–51

    Google Scholar 

  • Takata A, Ionita-Laza I, Gogos JA, Xu B, Karayiorgou M (2016) De novo synonymous mutations in regulatory elements contribute to the genetic etiology of autism and schizophrenia. Neuron 89:940–947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Taudt A, Colomé-Tatché M, Johannes F (2016) Genetic sources of population epigenomic variation. Nat Rev Genet 17:319–332

    Article  CAS  PubMed  Google Scholar 

  • The GTEX Consortium (2013) The genotype-tissue expression (GTEx) project. Nat Genet 45:580–585

    Article  PubMed Central  CAS  Google Scholar 

  • Thomas-Chollier M, Hufton A, Heinig M, O’Keeffe S, El Masri N, Roider HG, Manke T, Vingron M (2011) Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat Protoc 6:1860–1869

    Article  CAS  PubMed  Google Scholar 

  • Trynka G, Sandor C, Han B, Xu H, Stranger BE, Liu XS, Raychaudhuri S (2013) Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 45:124–130

    Article  CAS  PubMed  Google Scholar 

  • van der Velde KJ et al (2017) GAVIN: Gene-Aware Variant INterpretation for medical sequencing. Genome Biol 18:6

    Article  PubMed  PubMed Central  Google Scholar 

  • Vihinen M (2012) How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis. BMC Genomics 13:S2

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang K, Yang YW, Liu B, Sanyal A, Corces-Zimmerman R, Chen Y, Lajoie BR, Protacio A, Flynn RA, Gupta RA, Wysocka J, Lei M, Dekker J, Helms IA, Chang HY (2011) A long noncoding RNA maintains active chromatin to coordinate homeotic gene expression. Nature 472:120–124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ward L, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40:d930–d934

    Article  CAS  PubMed  Google Scholar 

  • West A, Gaszner M, Felsenfeld G (2003) Insulators: many functions, many mechanisms. Genes Dev 16:271–288

    Article  CAS  Google Scholar 

  • Wittkopp PJ, Kalay G (2011) Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat Rev Genet 13:59–69. https://doi.org/10.1038/nrg3095

    Article  PubMed  CAS  Google Scholar 

  • Yamauchi T et al (2010) A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B. Nat Genet 42:864–868

    Article  CAS  PubMed  Google Scholar 

  • Yang TT et al (2006) Role of transcription factor NFAT in glucose and insulin homeostasis. Mol Cell Biol 26:7372–7387

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang F, Lupski JR (2015) Non-coding genetic variants in human disease. Hum Mol Genet 24:R102–R110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhong M, Zhang Y, Lange K, Fan R (2011) A cross-population extended haplotype-based homozygosity score test to detect positive selection in genome-wide scans. Stat Interface 4:51

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhou J, Troyanskaya O (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12:931–934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by NIMH grants R00MH101367 (PHL) and U01MH111660 (MJD).

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Authors and Affiliations

  1. Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA

    Phil H. Lee, Christian Lee, Brian Wee, Tushar Dwivedi & Mark Daly

  2. Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Phil H. Lee & Xihao Li

  3. Department of Life Sciences, Harvard University, Cambridge, MA, USA

    Christian Lee

  4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Xihao Li

  5. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Tushar Dwivedi

  6. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA

    Mark Daly

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Lee, P.H., Lee, C., Li, X. et al. Principles and methods of in-silico prioritization of non-coding regulatory variants. Hum Genet 137, 15–30 (2018). https://doi.org/10.1007/s00439-017-1861-0

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