Whole genome sequencing and its applications in medical genetics

Abstract

Fundamental improvement was made for genome sequencing since the next-generation sequencing (NGS) came out in the 2000s. The newer technologies make use of the power of massively-parallel short-read DNA sequencing, genome alignment and assembly methods to digitally and rapidly search the genomes on a revolutionary scale, which enable large-scale whole genome sequencing (WGS) accessible and practical for researchers. Nowadays, whole genome sequencing is more and more prevalent in detecting the genetics of diseases, studying causative relations with cancers, making genome-level comparative analysis, reconstruction of human population history, and giving clinical implications and instructions. In this review, we first give a typical pipeline of whole genome sequencing, including the lab template preparation, sequencing, genome assembling and quality control, variants calling and annotations. We compare the difference between whole genome and whole exome sequencing (WES), and explore a wide range of applications of whole genome sequencing for both mendelian diseases and complex diseases in medical genetics. We highlight the impact of whole genome sequencing in cancer studies, regulatory variant analysis, predictive medicine and precision medicine, as well as discuss the challenges of the whole genome sequencing.

Graphical abstract

Keywords

whole genome sequencing / whole exome sequencing / next-generation sequencing / non-coding / regulatory variant

References

[1] Veeramah, K. R. and Hammer, M. F. ( 2014 ) The impact of whole-genome sequencing on the reconstruction of human population history. Nat. Rev. Genet. , 15 , 149 – 162

[2] Hendrix, R. W. ( 2003 ) Bacteriophage genomics. Curr. Opin. Microbiol. , 6 , 506 – 511

[3] Metzker, M. L. ( 2010 ) Sequencing technologies—the next generation. Nat. Rev. Genet. , 11 , 31 – 46

[4] Zimmermann, J. , Voss, H. , Schwager, C. , Stegemann, J. and Ansorge, W. ( 1988 ) Automated Sanger dideoxy sequencing reaction protocol. FEBS Lett. , 233 , 432 – 436

[5] Watson, J. D. ( 1990 ) The human genome project: past, present, and future. Science , 248 , 44 – 49

[6] Fleischmann, R. , Adams, M. , White, O. , Clayton, R. , Kirkness, E. , Kerlavage, A. , Bult, C. , Tomb, J. , Dougherty, B. , Merrick, J. , ( 1995 ) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd . Science , 269 , 496 – 512

[7] Pabinger, S. , Dander, A. , Fischer, M. , Snajder, R. , Sperk, M. , Efremova, M. , Krabichler, B. , Speicher, M. R. , Zschocke, J. and Trajanoski, Z. ( 2014 ) A surveyof tools for variant analysis of next-generation genome sequencing data. Brief. Bioinform. , 15 , 256 – 278

[8] Liu, L. , Li, Y. H. , Li, S. L. , Hu, N. , He, Y. M. , Pong, R. , Lin, D. N. , Lu, L. H. and Law, M. ( 2012 ) Comparison of next-generation sequencing systems . J. BioMed. Biotech. , 251364

[9] Voelkerding, K. V. , Dames, S. A. and Durtschi, J. D. ( 2009 ) Next-generation sequencing: from basic research to diagnostics. Clin. Chem. , 55 , 641 – 658

[10] Ng, P. C. and Kirkness, E. F. ( 2010 ) Whole Genome Sequencing. In Genetic Variation , pp. 215 – 226 , Springer

[11] Hurd, P. J. and Nelson, C. J. ( 2009 ) Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief. Funct. Genomics , 8, 174 – 183

[12] Lam, H. Y. , Clark, M. J. , Chen, R. , Chen, R. , Natsoulis, G. , O’Huallachain, M. , Dewey, F. E. , Habegger, L. , Ashley, E. A. , Gerstein, M. B. , ( 2012 ) Performance comparison of whole-genome sequencing platforms. Nat. Biotechnol. , 30 , 78 – 82

[13] Carlton, J. M. , Angiuoli, S. V. , Suh, B. B. , Kooij, T. W. , Pertea, M. , Silva, J. C. , Ermolaeva, M. D. , Allen, J. E. , Selengut, J. D. , Koo, H. L. , ( 2002 ) Genome sequence and comparative analysis of the model rodent malaria parasite Plasmodium yoelii yoelii . Nature , 419 , 512 – 519

[14] Herring, C. D. , Raghunathan, A. , Honisch, C. , Patel, T. , Applebee, M. K. , Joyce, A. R. , Albert, T. J. , Blattner, F. R. , van den Boom, D. , Cantor, C. R. , ( 2006 ) Comparative genome sequencing of Escherichia coli allows observationof bacterial evolution on a laboratory timescale. Nat. Genet. , 38 , 1406 – 1412

[15] Lupski, J. R. ( 2015 ) Structural variation mutagenesis of the human genome: impact on disease and evolution. Environ. Mol. Mutagen. , 56 , 419 – 436

[16] Saunders, C. J. , Miller, N. A. , Soden, S. E. , Dinwiddie, D. L. , Noll, A. , Alnadi, N. A. , Andraws, N. , Patterson, M. L. , Krivohlavek L. A. , Fellis, J. , ( 2012 ) Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci. Trans. Med. 4 , 154ra135

[17] Cirulli, E. T. and Goldstein, D. B. ( 2010 ) Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. , 11 , 415 – 425

[18] Foley, S. B. , Rios, J. J. , Mgbemena, V. E. , Robinson, L. S. , Hampel, H. L. , Toland, A. E. , Durham, L. and Ross, T. S. ( 2015 ) Use of whole genome sequencing for diagnosis and discovery in the cancer genetics clinic. EBioMedicine , 2 , 74 – 81

[19] Chen, K. and Meric-Bernstam, F. ( 2015 ) Whole genome sequencing in cancer clinics. EBioMedicine , 2 , 15 – 16

[20] Berg, J. S. , Khoury, M. J. and Evans, J. P. ( 2011 ) Deploying whole genome sequencing in clinical practice and public health: meeting the challenge one bin at a time. Genet.Med. , 13 , 499 – 504

[21] Dewey, F. E. , Grove, M. E. , Pan, C. , Goldstein, B. A. , Bernstein, J. A. , Chaib, H. , Merker, J. D. , Goldfeder, R. L. , Enns, G. M. , David, S. P. , ( 2014 ) Clinical interpretation and implications of whole-genome sequencing. JAMA , 311 , 1035 – 1045

[22] Belkadi, A. , Bolze, A. , Itan, Y. , Cobat, A. , Vincent, Q. B. , Antipenko, A. , Shang, L. , Boisson, B. , Casanova, J.-L. and Abel, L. ( 2015 ) Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proc. Natl. Acad. Sci. USA , 112 , 5473 – 5478

[23] Ekblom, R. and Wolf, J. B. ( 2014 ) A field guide to whole-genome sequencing, assembly and annotation. Evol. Appl. , 7 , 1026 – 1042

[24]

[25] van Dijk, E. L. , Jaszczyszyn, Y. and Thermes, C. ( 2014 ) Library preparation methods for next-generation sequencing: tone down the bias. Exp. Cell Res. , 322 , 12 – 20

[26] Miyamoto, M. , Motooka, D. , Gotoh, K. , Imai, T. , Yoshitake, K. , Goto, N. , Iida, T. , Yasunaga, T. , Horii, T. , Arakawa, K. , ( 2014 ) Performance comparison of second-and third-generation sequencers using a bacterial genome with two chromosomes. BMC Genomics , 15 , 699

[27]

[28] Bao, S. , Jiang, R. , Kwan, W. K. , Wang, B. B. , Ma, X. and Song, Y.-Q. ( 2011 ) Evaluation of next-generation sequencing software in mapping and assembly. J. Hum. Genet. , 56 , 406 – 414

[29] Swindell, S. R. and Plasterer, €T. N. ( 1997 ) SEQMAN. In Sequence Data Analysis Guidebook , pp. 75 – 89 , New York : Springer

[30] Sundquist, A. , Ronaghi, M. , Tang, H. , Pevzner, P. and Batzoglou, S. ( 2007 ) Whole-genome sequencing and assembly with high-throughput, short-read technologies. PLoS One , 2 , e484

[31] Paszkiewicz, K. and Studholme, D. J. ( 2010 ) De novo assembly of short sequence reads. Brief. Bioinform. , 11, 457 – 472

[32] Hunt, M. , Kikuchi, T. , Sanders, M. , Newbold, C. , Berriman, M. and Otto, T. D. ( 2013 ) REAPR: a universal tool for genome assembly evaluation. Genome Biol. , 14 , R47

[33] Liu, X. , Han, S. , Wang, Z. , Gelernter, J. and Yang, B.-Z. ( 2013 ) Variant callers for next-generation sequencing data: a comparison study. PLoS One , 8 , e75619

[34] Altmann, A. , Weber, P. , Bader, D. , Preuß M. , Binder, E. B. and Müller-Myhsok, B. ( 2012 ) A beginners guide to SNP calling from high-throughput DNA-sequencing data. Hum. Genet. , 131 , 1541 – 1554

[35] Pirooznia, M. , Goes, F. S. and Zandi, P. P ( 2015 ) Whole-genome CNV analysis: advances in computational approaches. Front.Genet. , 6, 138

[36] DePristo, M. A. , Banks, E. , Poplin, R. , Garimella, K. V. , Maguire, J. R. , Hartl, C. , Philippakis, A. A. , del Angel, G. , Rivas, M. A. , Hanna, M. , ( 2011 ) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. , 43 , 491 – 498

[37] Kircher, M. , Witten, D. M. , Jain, P. , O’Roak, B. J. , Cooper, G. M. and Shendure, J. ( 2014 ) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. , 46 , 310 – 315

[38] Liu, X. , Jian, X. and Boerwinkle, E. ( 2011 ) dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum. Mutat. , 32 , 894 – 899

[39] McKenna, A. , Hanna, M. , Banks, E. , Sivachenko, A. , Cibulskis, K. , Kernytsky, A. , Garimella, K. , Altshuler, D. , Gabriel, S. , Daly, M. , ( 2010 ) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencingdata. Genome Res. , 20 , 1297 – 1303

[40] Paila, U. , Chapman, B. A. , Kirchner, R. and Quinlan, A. R. ( 2013 ) GEMINI: integrative exploration of genetic variation and genome annotations. PLoS Comput. Biol. , 9 , e1003153

[41] Wu, J. , Li, Y. and Jiang, R. ( 2014 ) Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies. PLoS Genet. , 10 , e1004237

[42] Makarov, V. , O’Grady, T. , Cai, G. , Lihm, J. , Buxbaum, J. D. and Yoon, S. ( 2012 ) AnnTools: a comprehensive and versatile annotation toolkit for genomic variants. Bioinformatics , 28 , 724 – 725

[43] Wang, K. , Li, M. and Hakonarson, H. ( 2010 ) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. , 38 , e164

[44] Zhao, M. and Zhao, Z. ( 2013 ) CNVannotator: a comprehensive annotation server for copy number variation in the human genome . 8 , e80170

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

[46] Bick, D. and Dimmock, D. ( 2011 ) Whole exome and whole genome sequencing. Curr. Opin. Pediatr. , 23 , 594 – 600

[47] Gilchrist, C. A. , Turner, S. D. , Riley, M. F. , Petri, W. A. Jr and Hewlett, E. L. ( 2015 ) Whole-genome sequencing in outbreak analysis. Clin. Microbiol. Rev. , 28 , 541 – 563

[48]

[49] Online Mendelian Inheritance in Man . Johns Hopkins University (Baltimore,MD) ,

[50] Botstein, D. and Risch, N. ( 2003 ) Discovering genotypes underlying human phenotypes: pastsuccesses for mendelian disease, future approaches for complex disease. Nat. Genet. , 33 , 228 – 237

[51] Ku, C.-S. , Naidoo, N. and Pawitan, Y. ( 2011 ) Revisiting Mendelian disorders through exome sequencing. Hum. Genet. , 129 , 351 – 370

[52] Bamshad, M. J. , Ng, S. B. , Bigham, A. W. , Tabor, H. K. , Emond, M. J. , Nickerson, D. A. and Shendure, J. ( 2011 ) Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. , 12 , 745 – 755

[53] Ng, S. B. , Buckingham, K. J. , Lee, C. , Bigham, A. W. , Tabor, H. K. , Dent, K. M. , Huff, C. D. , Shannon, P. T. , Jabs, E. W. , Nickerson, D. A. , ( 2010 ) Exome sequencing identifies the cause of a mendelian disorder. Nat. Genet. , 42 , 30 – 35

[54] Yang, Y. , Muzny, D. M. , Reid, J. G. , Bainbridge, M. N. , Willis, A. , Ward, P. A. , Braxton, A. , Beuten, J. , Xia, F. , Niu, Z. , ( 2013 ) Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N. Engl. J. Med. , 369 , 1502 – 1511

[55] Ng, S. B. , Bigham, A. W. , Buckingham, K. J. , Hannibal, M. C. , McMillin, M. J. , Gildersleeve, H.I. , Beck, A. E. , Tabor, H. K. , Cooper, G. M. , Mefford, H. C. , ( 2010 ) Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat. Genet. , 42 , 790 – 793

[56] Roach, J. C. , Glusman, G. , Smit, A. F. A. , Huff, C. D. , Hubley, R. , Shannon, P. T. , Rowen, L. , Pant, K. P. , Goodman, N. , Bamshad, M. , ( 2010 ) Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science , 328 , 636 – 639

[57] Lupski, J. R. , Reid, J. G. , Gonzaga-Jauregui, C. , Rio Deiros, D. , Chen, D. C. Y. , Nazareth, L. , Bainbridge, M. , Dinh, H. , Jing, C. , Wheeler, D. A. , ( 2010 ) Whole-genome sequencing in a patient with Charcot–Marie–Tooth neuropathy. N. Engl. J. Med. , 362 , 1181 – 1191

[58] Cooper, G. M. , ( 2005 ) Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. , 15 , 901 – 913

[59] Taylor, J. C. , Martin, H. C. , Lise, S. , Broxholme, J. , Cazier, J.-B. , Rimmer, A. , Kanapin, A. , Lunter, G. , Fiddy, S. , Allan, C. , ( 2015 ) Factors influencing successof clinical genome sequencing across a broad spectrum of disorders. Nat. Genet. , 47 , 717 – 726

[60] Kumar, P. , Henikoff, S. and Ng, P. C. ( 2009 ) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. , 4 , 1073 – 1081

[61] Adzhubei, I. A. , Schmidt, S. , Peshkin, L. , Ramensky, V. E. , Gerasimova, A. , Bork, P. , Kondrashov, A. S. and Sunyaev, S. R. ( 2010 ) A method and server for predicting damaging missense mutations. Nat. Methods , 7 , 248 – 249

[62] Kircher, M. , Witten, D. M. , Jain, P. , O’Roak, B. J. , Cooper, G. M. and Shendure, J. ( 2014 ) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. , 46 , 310 – 315

[63] Quang, D. , Chen, Y. and Xie, X. ( 2015 ) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics , 31 , 761 – 763

[64] Shihab, H. A. , Rogers, M. F. , Gough, J. , Mort, M. , Cooper, D. N. , Day, I. N. M ., Gaunt, T. R. and Campbell C. ( 2015 ) An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics , 10.1093/bioinformatics/btv009

[65] Fu, Y. , Liu, Z. , Lou, S. , Bedford, J. , Mu, X. J. , Yip, K. Y. , Khurana, E. and Gerstein, M. ( 2014 ) FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol. , 15 , 480

[66] Khurana, E. , Fu, Y. , Colonna, V. , Mu, X. J. , Kang, H. M. , Lappalainen, T. , Sboner, A. , Lochovsky, L. , Chen, J. , Harmanci, A. , ( 2013 ) Integrative annotation of variants from 1092 humans: application to cancer genomics. Science , 342 , 1235587

[67] Lehmann, K.-V. and Chen, T. ( 2013 ) Exploring functional variant discovery in non-coding regions with SInBaD. Nucleic Acids Res. , 41 , e7

[68] Lee, D. , Gorkin, D. U. , Baker, M. , Strober, B. J. , Asoni, A. L. , McCallion, A. S. and Beer, M. A. ( 2015 ) A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. , 47 , 955 – 961

[69] Ritchie, G. R. , Dunham, I. , Zeggini, E. and Flicek, P. ( 2014 ) Functional annotation of noncoding sequence variants. Nat. Methods , 11 , 294 – 296

[70] Zhang, F. and Lupski, J. R. ( 2015 ) Noncoding genetic variants in human disease. Hum. Mol. Genet. ,

[71] Lupski, J. R. ( 1998 ) Genomic disorders: structural features of the genome can lead to DNA rearrangements and human disease traits. Trends Genet. , 14 , 417 – 422

[72] Lee, C. and Morton, C. C. ( 2008 ) Structural genomic variation and personalized medicine. N. Engl. J. Med. , 358 , 740 – 741

[73] Lupski, J. R. ( 2009 ) Genomic disorders ten years on. Genome Med. , 1 , 42

[74] Sathirapongsasuti, J. F. , Lee, H. , Horst, B. A. J. , Brunner, G. , Cochran, A. J. , Binder, S. , Quackenbush, J. and Nelson, S. F. ( 2011 ) Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics , 27 , 2648 – 2654

[75] Fromer, M. , Moran, J. L. , Chambert, K. , Banks, E. , Bergen, S. E. , Ruderfer, D. M. , Handsaker, R. E. , McCarroll, S. A. , O’Donovan, M. C. , Owen, M. J. , ( 2012 ) Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am. J. Hum. Genet. , 91 , 597 – 607

[76] Zong, C. , Lu, S. , Chapman, A. R. and Xie, X. S. ( 2012 ) Genome-wide detectionof single-nucleotide and copy-number variations of a single humancell. Science , 338 , 1622 – 1626

[77] Cardon, L. R. and Bell, J. I. ( 2001 ) Association study designs for complex diseases. Nat. Rev. Genet. , 2 , 91 – 99

[78] Manolio, T. A. , Collins, F. S. , Cox, N. J. , Goldstein, D. B. , Hindorff, L. A. , Hunter, D. J. , McCarthy, M. I. , Ramos, E. M. , Cardon, L. R. , Chakravarti, A. , ( 2009 ) Finding the missing heritability of complex diseases. Nature , 461 , 747 – 753

[79] Schork, N. J. , Murray, S. S. , Frazer, K. A. and Topol, E. J. ( 2009 ) Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Dev. , 19 , 212 – 219

[80] Spain, S. L. and Barrett, J. C. ( 2015 ) Strategies for fine-mapping complex traits. Hum. Mol. Genet. ,

[81] CONVERGE consortium ( 2015 ) Sparse whole-genome sequencing identifiestwo loci for major depressive disorder . Nature , 523 , 588 –591.

[82] Lippert, C. , Listgarten, J. , Liu, Y. , Kadie, C. M. , Davidson, R. I. and Heckerman, D. ( 2011 ) FaST linear mixed models for genome-wide association studies. Nat. Methods , 8 , 833 – 835

[83] Widmer, C. , Lippert, C. , Weissbrod, O. , Fusi, N. , Kadie, C. , Davidson, R. , Listgarten, J . and Heckerman , D. ( 2014 ) Further improvements to linear mixed models for genome-wide association studies. Sci. Rep. , 4 , 6874

[84] Wu, M. C. , Lee, S. , Cai, T. , Li, Y. , Boehnke, M. and Lin, X. ( 2011 ) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. , 89 , 82 – 93

[85] Taylor, P. N. , Porcu, E. , Chew, S. , Campbell, P. J. , Traglia, M. , Brown, S. J. , Mullin, B. H. , Shihab, H. A. , Min, J. , Walter, K. , ( 2015 ) Whole-genome sequence-based analysis of thyroid function. Nat. Commun. , 6 , 5681

[86] Morrison, A. C. , Voorman, A. , Johnson, A. D. , Liu, X. M. , Yu, J. , Li, A. , Muzny, D. , Yu, F. L. , Rice, K. , Zhu, C. S. , ( 2013 ) Whole genome sequence-based analysis of a model complex trait, high density lipoprotein cholesterol. Nat. Genet. , 45, 899–901

[87] Gibson, G. ( 2012 ) Rare and common variants: twenty arguments. Nat. Rev. Genet. , 13 , 135 – 145

[88] Yang, J. , Benyamin, B. , McEvoy, B. P. , Gordon, S. , Henders, A. K. , Nyholt, D. R. , Madden, P. A. , Heath, A. C. , Martin, N. G. , Montgomery, G. W. , ( 2010 ) Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. , 42 , 565 – 569

[89] Li, Y. , Sidore, C. , Kang, H. M. , Boehnke, M. and Abecasis, G. R. ( 2011 ) Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. , 21 , 940 – 951

[90] Edwards, S. L. , Beesley, J. , French, J. D. and Dunning, A. M. ( 2013 ) Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. , 93 , 779 – 797

[91] Farh, K. K.-H. , Marson, A. , Zhu, J. , Kleinewietfeld, M. , Housley, W. J. , Beik, S. , Shoresh, N. , Whitton, H. , Ryan, R. J. H. , Shishkin, A. A. , ( 2015 ) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature , 518 , 337 – 343

[92] Maller, J. B. , McVean, G. , Byrnes, J. , Vukcevic, D. , Palin, K. , Su, Z. , Howson, J. M. M. , Auton, A. , Myers, S. , Morris, A. , ( 2012 ) Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. , 44 , 1294 – 1301

[93] Barbieri, C. E. , Baca, S. C. , Lawrence, M. S. , Demichelis, F. , Blattner, M. , Theurillat, J.-P. , White, T. A. , Stojanov, P. , Van Allen, E. , Stransky, N. , ( 2012 ) Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. , 44 , 685 – 689

[94] Wang, K. , Kan, J. , Yuen, S. T. , Shi, S. T. , Chu, K. M. , Law, S. , Chan, T. L. , Kan, Z. , Chan, A. S. Y. , Tsui, W. Y. , ( 2011 ) Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer. Nat. Genet. , 43 , 1219 – 1223

[95] Nakagawa, H. , Wardell, C. P. , Furuta, M. , Taniguchi, H. and Fujimoto, A. ( 2015 ) Cancer whole-genome sequencing: present and future. Oncogene , 34 , 5943 – 5950

[96] Huang, F. W. , Hodis, E. , Xu, M. J. , Kryukov, G. V. , Chin, L. and Garraway, L. A. ( 2013 ) Highly recurrent TERT promoter mutations in human melanoma. Science , 339 , 957 – 959

[97] Vinagre, J. , Almeida, A. , Pópulo, H. , Batista, R. , Lyra, J. , Pinto, V. , Coelho, R. , Celestino, R. , Prazeres, H. , Lima, L. , ( 2013 ) Frequency of TERT promoter mutations in human cancers. Nat.Commun. , 4 , 2185

[98] Mansour, M. R. , Abraham, B. J. , Anders, L. , Berezovskaya, A. , Gutierrez, A. , Durbin, A. D. , Etchin, J. , Lawton, L. , Sallan, S. E. , Silverman, L. B. , ( 2014 ) An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science , 346 , 1373 – 1377

[99] Weinhold, N. , Jacobsen, A. , Schultz, N. , Sander, C. and Lee, W. ( 2014 ) Genome-wide analysis of noncoding regulatory mutationsin cancer. Nat. Genet. , 46 , 1160 – 1165

[100] Fredriksson, N. J. , Ny, L. , Nilsson, J. A. and Larsson, E. ( 2014 ) Systematic analysisof noncoding somatic mutations and gene expression alterations across 14 tumor types. Nat. Genet. , 46 , 1258 – 1263

[101] Melton, C. , Reuter, J. A. , Spacek, D. V. and Snyder, M. ( 2015 ) Recurrent somatic mutations in regulatory regions of human cancer genomes. Nat. Genet. , 47 , 710 – 716

[102] Li, B. and Leal, S. M. ( 2008 ) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. , 83 , 311 – 321

[103] Lin, D.-Y. and Tang, Z.-Z. ( 2011 ) A general framework for detecting disease associations with rare variants in sequencing studies. Am. J. Hum. Genet. , 89 , 354 – 367

[104] Gonzalez-Perez, A. , Mustonen, V. , Reva, B. , Ritchie, G. R. S. , Creixell, P. , Karchin, R. , Vazquez, M. , Fink, J. L. , Kassahn, K. S. , Pearson, J. V. , ( 2013 ) Computational approaches to identify functional genetic variants in cancer genomes. Nat. Methods , 10 , 723 – 729

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

[106] Li, M. J. , Yan, B. , Sham, P. C. , and Wang, J.W. ( 2014 ) Exploring the function of genetic variants in the non-codinggenomic regions: approaches for identifying human regulatory variants affecting gene expression. Brief. Bioinform. , 16 , 393 – 412

[107] Ward, L. D. and Kellis, M. ( 2012 ) Interpreting noncoding genetic variation in complex traitsand human disease. Nat. Biotechnol. , 30 , 1095 – 1106

[108] Rockman, M. V. and Kruglyak, L. ( 2006 ) Genetics of global gene expression. Nat. Rev. Genet. , 7 , 862 – 872

[109] Degner, J. F. , Pai, A. A. , Pique-Regi, R. , Veyrieras, J.-B. , Gaffney, D. J. , Pickrell, J. K. , De Leon, S. , Michelini, K. , Lewellen, N. , Crawford, G. E. , ( 2012 ) DNase I sensitivity QTLs area major determinant of human expression variation. Nature , 482 , 390 – 394

[110] Monlong, J. , Calvo, M. , Ferreira, P. G. and Guigó R. ( 2014 ) Identification of genetic variants associated with alternative splicing using sQTLseekeR . Nat. Commun. , 5 , 4698

[111] Koren, A. , Handsaker, R. E. , Kamitaki, N. , Karlić R. , Ghosh, S. , Polak, P. , Eggan, K. and McCarroll, S. A. ( 2014 ) Genetic variation in human DNA replication timing. Cell , 159 , 1015 – 1026

[112] del Rosario, R. C.-H. , Poschmann, J. , Rouam, S. L. , Png, E. , Khor, C. C. , Hibberd, M. L. and Prabhakar, S. ( 2015 ) Sensitive detection of chromatin-altering polymorphisms reveals autoimmune diseasemechanisms. Nat. Methods , 12 , 458 – 464

[113] Gibbs, J. R. , van der Brug, M. P. , Hernandez, D. G. , Traynor, B. J. , Nalls, M. A. , Lai, S.-L. , Arepalli, S. , Dillman, A. , Rafferty, I. P. , Troncoso, J. , ( 2010 ) Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. , 6 , e1000952

[114] Lappalainen, T. , Sammeth, M. , Friedländer, M. R. , ‘t Hoen, P. A. C. , Monlong, J. , Rivas, M. A. , Gonzàlez-Porta, M. , Kurbatova, N. , Griebel, T. , Ferreira, P. G. , , ( 2013 ) Transcriptome and genome sequencing uncovers functional variationin humans. Nature , 501 , 506 – 511

[115] Maurano, M. T. , Humbert, R. , Rynes, E. , Thurman, R. E. , Haugen, E. , Wang, H. , Reynolds, A. P. , Sandstrom, R. , Qu, H. , Brody, J. , ( 2012 ) Systematic localization of common disease-associated variation in regulatory DNA. Science , 337 , 1190 – 1195

[116] Maurano, M. T. , Haugen, E. , Sandstrom, R. , Vierstra, J. , Shafer, A. , Kaul, R. and Stamatoyannopoulos, J. A. ( 2015 ) Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo . Nat. Genet. , 47 , 1393 – 1401

[117] Kasowski, M. , Grubert, F. , Heffelfinger, C. , Hariharan, M. , Asabere, A. , Waszak, S. M. , Habegger, Lukas. , Rozowsky, J. , Shi, M. , Urban, A. E. , ( 2010 ) Variation in transcription factor binding among humans. Science 328 , 232 – 235

[118] Karczewski, K. J. , Dudley, J. T. , Kukurba, K. R. , Chen, R. , Butte, A. J. , Montgomery, S. B. and Snyder, M. ( 2013 ) Systematic functional regulatory assessment of disease-associated variants. Proc. Natl. Acad. Sci. USA , 110 , 9607 – 9612

[119] Chiang, C. , Layer, R. M. , Faust, G. G. , Lindberg, M. R. , Rose, D. B. , Garrison, E. P. , Marth, G. T. , Quinlan, A. R. and Hall, I. M. ( 2014 ) SpeedSeq: Ultra-fast personal genome analysis and interpretation. Nat. Meth., 12 , 966 – 968

[120] Park, P. J. ( 2009 ) ChIP–seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. , 10 , 669 – 680

[121] Song, L. and Crawford, G. E. ( 2010 ) DNase-seq: a high-resolution technique for mapping activegene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. pdb . prot5384

[122] Buenrostro, J. D. , Giresi, P. G. , Zaba, L. C. , Chang, H. Y. and Greenleaf, W. J. ( 2013 ) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods , 10 , 1213 – 1218

[123] dbWGFP :

[124] Biesecker, L. G. ( 2013 ) Hypothesis-generating research and predictive medicine. Genome Res. , 23 , 1051 – 1053

[125] Simon, R. ( 2011 ) Genomic biomarkers in predictive medicine. An interim analysis. EMBO Mol. Med. , 3 , 429 – 435

[126] Matsui, S. , Simon, R. , Qu, P. , Shaughnessy, J. D. , Barlogie, B. and Crowley, J. ( 2012 ) Developing and validating continuous genomic signatures in randomized clinical trials for predictive medicine. Clin. Cancer Res. , 18 , 6065 – 6073

[127] Collins, F. S. and Varmus, H. ( 2015 ) A new initiative on precision medicine. N. Engl. J. Med. , 372 , 793 – 795

[128] Rubin, M. A. ( 2015 ) Health: Make precision medicine work for cancer care. Nature , 520 , 290 – 291

[129] Bellmunt, J. , Orsola, A. and Sonpavde, G. ( 2015 ) Precision and predictive medicine in urothelial cancer: Are we making progress? Eur. Urol. , 68 , 547 – 549

[130] Geschwind, D. H. and State, M. W. ( 2015 ) Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. , 14 , 1109 – 1120

[131] Mak, H. C. ( 2012 ) Genome interpretation and assembly—recent progress and next steps. Nat. Biotechnol. , 30 , 1081 – 1083

[132] Shendure, J. and Aiden, E. L. ( 2012 ) The expanding scope of DNA sequencing. Nat. Biotechnol. , 30 , 1084 – 1094

[133] Fujimoto, A. , Nakagawa, H. , Hosono, N. , Nakano, K. , Abe, T. , Boroevich, K. A. , Nagasaki, M. , Yamaguchi, R. , Shibuya, T. , Kubo, M. , ( 2010 ) Whole-genome sequencing and comprehensive variant analysis of a Japanese individual using massively parallel sequencing. Nat. Genet. , 42 , 931 – 936

[134] Gonzaga-Jauregui, C. , Lupski, J. R. and Gibbs, R. A. ( 2012 ) Human genome sequencing in health and disease. Annu. Rev. Med. , 63 , 35 – 61

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg