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Whole genome sequencing and its applications in medical genetics |
Jiaxin Wu, Mengmeng Wu, Ting Chen, Rui Jiang( ) |
MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China |
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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.
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Author Summary
Whole genome sequencing is prevalent in detecting the genetics of diseases, studying causative relations with cancers, making genome-level comparative analysis, and giving clinical implications and instructions. In this review, we give a typical pipeline of whole genome sequencing, compare the difference between whole genome and whole exome sequencing, and explore a wide range of applications of whole genome sequencing in medical genetics. We highlight the impact of whole genome sequencing in cancer studies, regulatory variant analysis, predictive medicine and precision medicine, and discuss the challenges of the whole genome sequencing. |
Keywords
whole genome sequencing
whole exome sequencing
next-generation sequencing
non-coding
regulatory variant
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Corresponding Author(s):
Rui Jiang
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About author: Tongcan Cui and Yizhe Hou contributed equally to this work. |
Just Accepted Date: 21 March 2016
Online First Date: 11 May 2016
Issue Date: 26 May 2016
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|
1 |
K. R. Veeramah, and M. F. Hammer, ( 2014 ) The impact of whole-genome sequencing on the reconstruction of human population history. Nat. Rev. Genet. , 15 , 149 – 162
https://doi.org/10.1038/nrg3625
|
2 |
R. W. Hendrix, ( 2003 ) Bacteriophage genomics. Curr. Opin. Microbiol. , 6 , 506 – 511
https://doi.org/10.1016/j.mib.2003.09.004
|
3 |
M. L. Metzker, ( 2010 ) Sequencing technologies—the next generation. Nat. Rev. Genet. , 11 , 31 – 46
https://doi.org/10.1038/nrg2626
|
4 |
J. Zimmermann, , H. Voss, , C. Schwager, , J. Stegemann, and W. Ansorge, ( 1988 ) Automated Sanger dideoxy sequencing reaction protocol. FEBS Lett. , 233 , 432 – 436
https://doi.org/10.1016/0014-5793(88)80477-0
|
5 |
J. D. Watson, ( 1990 ) The human genome project: past, present, and future. Science , 248 , 44 – 49
https://doi.org/10.1126/science.2181665
|
6 |
R. Fleischmann, , M. Adams, , O. White, , R. Clayton, , E. Kirkness, , A. Kerlavage, , C. Bult, , J. Tomb, , B. Dougherty, , J. Merrick, , et al. ( 1995 ) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd . Science , 269 , 496 – 512
https://doi.org/10.1126/science.7542800
|
7 |
S. Pabinger, , A. Dander, , M. Fischer, , R. Snajder, , M. Sperk, , M. Efremova, , B. Krabichler, , M. R. Speicher, , J. Zschocke, and Z. Trajanoski, ( 2014 ) A surveyof tools for variant analysis of next-generation genome sequencing data. Brief. Bioinform. , 15 , 256 – 278
https://doi.org/10.1093/bib/bbs086
|
8 |
L. Liu, , Y. H. Li, , S. L. Li, , N. Hu, , Y. M. He, , R. Pong, , D. N. Lin, , L. H. Lu, and M. Law, ( 2012 ) Comparison of next-generation sequencing systems . J. BioMed. Biotech. , 251364
|
9 |
K. V. Voelkerding, , S. A. Dames, and J. D. Durtschi, ( 2009 ) Next-generation sequencing: from basic research to diagnostics. Clin. Chem. , 55 , 641 – 658
https://doi.org/10.1373/clinchem.2008.112789
|
10 |
P. C. Ng, and E. F. Kirkness, ( 2010 ) Whole Genome Sequencing. In Genetic Variation , pp. 215 – 226 , Springer
|
11 |
P. J. Hurd, and C. J. Nelson, ( 2009 ) Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief. Funct. Genomics , 8, 174 – 183
|
12 |
H. Y. Lam, , M. J. Clark, , R. Chen, , R. Chen, , G. Natsoulis, , M. O’Huallachain, , F. E. Dewey, , L. Habegger, , E. A. Ashley, , M. B. Gerstein, , et al. ( 2012 ) Performance comparison of whole-genome sequencing platforms. Nat. Biotechnol. , 30 , 78 – 82
https://doi.org/10.1038/nbt.2065
|
13 |
J. M. Carlton, , S. V. Angiuoli, , B. B. Suh, , T. W. Kooij, , M. Pertea, , J. C. Silva, , M. D. Ermolaeva, , J. E. Allen, , J. D. Selengut, , H. L. Koo, , et al. ( 2002 ) Genome sequence and comparative analysis of the model rodent malaria parasite Plasmodium yoelii yoelii . Nature , 419 , 512 – 519
https://doi.org/10.1038/nature01099
|
14 |
C. D. Herring, , A. Raghunathan, , C. Honisch, , T. Patel, , M. K. Applebee, , A. R. Joyce, , T. J. Albert, , F. R. Blattner, , D. van den Boom, , C. R. Cantor, , et al. ( 2006 ) Comparative genome sequencing of Escherichia coli allows observationof bacterial evolution on a laboratory timescale. Nat. Genet. , 38 , 1406 – 1412
https://doi.org/10.1038/ng1906
|
15 |
J. R. Lupski, ( 2015 ) Structural variation mutagenesis of the human genome: impact on disease and evolution. Environ. Mol. Mutagen. , 56 , 419 – 436
https://doi.org/10.1002/em.21943
|
16 |
C. J. Saunders, , N. A. Miller, , S. E. Soden, , D. L. Dinwiddie, , A. Noll, , N. A. Alnadi, , N. Andraws, , M. L. Patterson, , L. A. Krivohlavek , J. Fellis, , et al. ( 2012 ) Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci. Trans. Med. 4 , 154ra135
|
17 |
E. T. Cirulli, and D. B. Goldstein, ( 2010 ) Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. , 11 , 415 – 425
https://doi.org/10.1038/nrg2779
|
18 |
S. B. Foley, , J. J. Rios, , V. E. Mgbemena, , L. S. Robinson, , H. L. Hampel, , A. E. Toland, , L. Durham, and T. S. Ross, ( 2015 ) Use of whole genome sequencing for diagnosis and discovery in the cancer genetics clinic. EBioMedicine , 2 , 74 – 81
https://doi.org/10.1016/j.ebiom.2014.12.003
|
19 |
K. Chen, and F. Meric-Bernstam, ( 2015 ) Whole genome sequencing in cancer clinics. EBioMedicine , 2 , 15 – 16
https://doi.org/10.1016/j.ebiom.2014.12.007
|
20 |
J. S. Berg, , M. J. Khoury, and J. P. Evans, ( 2011 ) Deploying whole genome sequencing in clinical practice and public health: meeting the challenge one bin at a time. Genet.Med. , 13 , 499 – 504
https://doi.org/10.1097/GIM.0b013e318220aaba
|
21 |
F. E. Dewey, , M. E. Grove, , C. Pan, , B. A. Goldstein, , J. A. Bernstein, , H. Chaib, , J. D. Merker, , R. L. Goldfeder, , G. M. Enns, , S. P. David, , et al. ( 2014 ) Clinical interpretation and implications of whole-genome sequencing. JAMA , 311 , 1035 – 1045
https://doi.org/10.1001/jama.2014.1717
|
22 |
A. Belkadi, , A. Bolze, , Y. Itan, , A. Cobat, , Q. B. Vincent, , A. Antipenko, , L. Shang, , B. Boisson, , J.-L. Casanova, and L. Abel, ( 2015 ) Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proc. Natl. Acad. Sci. USA , 112 , 5473 – 5478
https://doi.org/10.1073/pnas.1418631112
|
23 |
R. Ekblom, and J. B. Wolf, ( 2014 ) A field guide to whole-genome sequencing, assembly and annotation. Evol. Appl. , 7 , 1026 – 1042
https://doi.org/10.1111/eva.12178
|
24 |
|
25 |
E. L. van Dijk, , Y. Jaszczyszyn, and C. Thermes, ( 2014 ) Library preparation methods for next-generation sequencing: tone down the bias. Exp. Cell Res. , 322 , 12 – 20
https://doi.org/10.1016/j.yexcr.2014.01.008
|
26 |
M. Miyamoto, , D. Motooka, , K. Gotoh, , T. Imai, , K. Yoshitake, , N. Goto, , T. Iida, , T. Yasunaga, , T. Horii, , K. Arakawa, , et al.( 2014 ) Performance comparison of second-and third-generation sequencers using a bacterial genome with two chromosomes. BMC Genomics , 15 , 699
https://doi.org/10.1186/1471-2164-15-699
|
27 |
|
28 |
S. Bao, , R. Jiang, , W. K. Kwan, , B. B. Wang, , X. Ma, and Y.-Q. Song, ( 2011 ) Evaluation of next-generation sequencing software in mapping and assembly. J. Hum. Genet. , 56 , 406 – 414
https://doi.org/10.1038/jhg.2011.43
|
29 |
S. R. Swindell, and T. N. Plasterer, ( 1997 ) SEQMAN. In Sequence Data Analysis Guidebook , pp. 75 – 89 , New York : Springer
|
30 |
A. Sundquist, , M. Ronaghi, , H. Tang, , P. Pevzner, and S. Batzoglou, ( 2007 ) Whole-genome sequencing and assembly with high-throughput, short-read technologies. PLoS One , 2 , e484
https://doi.org/10.1371/journal.pone.0000484
|
31 |
K. Paszkiewicz, and D. J. Studholme, ( 2010 ) De novo assembly of short sequence reads. Brief. Bioinform. , 11, 457 – 472
|
32 |
M. Hunt, , T. Kikuchi, , M. Sanders, , C. Newbold, , M. Berriman, and T. D. Otto, ( 2013 ) REAPR: a universal tool for genome assembly evaluation. Genome Biol. , 14 , R47
https://doi.org/10.1186/gb-2013-14-5-r47
|
33 |
X. Liu, , S. Han, , Z. Wang, , J. Gelernter, and B.-Z. Yang, ( 2013 ) Variant callers for next-generation sequencing data: a comparison study. PLoS One , 8 , e75619
https://doi.org/10.1371/journal.pone.0075619
|
34 |
A. Altmann, , P. Weber, , D. Bader, , M. Preuß, , E. B. Binder, and B. Müller-Myhsok, ( 2012 ) A beginners guide to SNP calling from high-throughput DNA-sequencing data. Hum. Genet. , 131 , 1541 – 1554
https://doi.org/10.1007/s00439-012-1213-z
|
35 |
M. Pirooznia, , F. S. Goes, and P. P Zandi, ( 2015 ) Whole-genome CNV analysis: advances in computational approaches. Front.Genet. , 6, 138
|
36 |
M. A. DePristo, , E. Banks, , R. Poplin, , K. V. Garimella, , J. R. Maguire, , C. Hartl, , A. A. Philippakis, , G. del Angel, , M. A. Rivas, , M. Hanna, , et al. ( 2011 ) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. , 43 , 491 – 498
https://doi.org/10.1038/ng.806
|
37 |
M. Kircher, , D. M. Witten, , P. Jain, , B. J. O’Roak, , G. M. Cooper, and J. Shendure, ( 2014 ) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. , 46 , 310 – 315
https://doi.org/10.1038/ng.2892
|
38 |
X. Liu, , X. Jian, and E. Boerwinkle, ( 2011 ) dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum. Mutat. , 32 , 894 – 899
https://doi.org/10.1002/humu.21517
|
39 |
A. McKenna, , M. Hanna, , E. Banks, , A. Sivachenko, , K. Cibulskis, , A. Kernytsky, , K. Garimella, , D. Altshuler, , S. Gabriel, , M. Daly, , et al. ( 2010 ) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencingdata. Genome Res. , 20 , 1297 – 1303
https://doi.org/10.1101/gr.107524.110
|
40 |
U. Paila, , B. A. Chapman, , R. Kirchner, and A. R. Quinlan, ( 2013 ) GEMINI: integrative exploration of genetic variation and genome annotations. PLoS Comput. Biol. , 9 , e1003153
https://doi.org/10.1371/journal.pcbi.1003153
|
41 |
J. Wu, , Y. Li, and R. Jiang, ( 2014 ) Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies. PLoS Genet. , 10 , e1004237
https://doi.org/10.1371/journal.pgen.1004237
|
42 |
V. Makarov, , T. O’Grady, , G. Cai, , J. Lihm, , J. D. Buxbaum, and S. Yoon, ( 2012 ) AnnTools: a comprehensive and versatile annotation toolkit for genomic variants. Bioinformatics , 28 , 724 – 725
https://doi.org/10.1093/bioinformatics/bts032
|
43 |
K. Wang, , M. Li, and H. Hakonarson, ( 2010 ) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. , 38 , e164
https://doi.org/10.1093/nar/gkq603
|
44 |
M. Zhao, and Z. Zhao, ( 2013 ) CNVannotator: a comprehensive annotation server for copy number variation in the human genome . 8 , e80170
|
45 |
W. McLaren, , B. Pritchard, , D. Rios, , Y. Chen, , P. Flicek, and F. Cunningham, ( 2010 ) Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics , 26 , 2069 – 2070
https://doi.org/10.1093/bioinformatics/btq330
|
46 |
D. Bick, and D. Dimmock, ( 2011 ) Whole exome and whole genome sequencing. Curr. Opin. Pediatr. , 23 , 594 – 600
https://doi.org/10.1097/MOP.0b013e32834b20ec
|
47 |
C. A. Gilchrist, , S. D. Turner, , M. F. Riley, , W. A. Petri, Jr and E. L. Hewlett, ( 2015 ) Whole-genome sequencing in outbreak analysis. Clin. Microbiol. Rev. , 28 , 541 – 563
https://doi.org/10.1128/CMR.00075-13
|
48 |
|
49 |
Online Mendelian Inheritance in Man . Johns Hopkins University (Baltimore,MD) ,
|
50 |
D. Botstein, and N. Risch, ( 2003 ) Discovering genotypes underlying human phenotypes: pastsuccesses for mendelian disease, future approaches for complex disease. Nat. Genet. , 33 , 228 – 237
https://doi.org/10.1038/ng1090
|
51 |
C.-S. Ku, , N. Naidoo, and Y. Pawitan, ( 2011 ) Revisiting Mendelian disorders through exome sequencing. Hum. Genet. , 129 , 351 – 370
https://doi.org/10.1007/s00439-011-0964-2
|
52 |
M. J. Bamshad, , S. B. Ng, , A. W. Bigham, , H. K. Tabor, , M. J. Emond, , D. A. Nickerson, and J. Shendure, ( 2011 ) Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. , 12 , 745 – 755
https://doi.org/10.1038/nrg3031
|
53 |
S. B. Ng, , K. J. Buckingham, , C. Lee, , A. W. Bigham, , H. K. Tabor, , K. M. Dent, , C. D. Huff, , P. T. Shannon, , E. W. Jabs, , D. A. Nickerson, , et al. ( 2010 ) Exome sequencing identifies the cause of a mendelian disorder. Nat. Genet. , 42 , 30 – 35
https://doi.org/10.1038/ng.499
|
54 |
Y. Yang, , D. M. Muzny, , J. G. Reid, , M. N. Bainbridge, , A. Willis, , P. A. Ward, , A. Braxton, , J. Beuten, , F. Xia, , Z. Niu, , et al. ( 2013 ) Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N. Engl. J. Med. , 369 , 1502 – 1511
https://doi.org/10.1056/NEJMoa1306555
|
55 |
S. B. Ng, , A. W. Bigham, , K. J. Buckingham, , M. C. Hannibal, , M. J. McMillin, , H.I. Gildersleeve, , A. E. Beck, , H. K. Tabor, , G. M. Cooper, , H. C. Mefford, , et al. ( 2010 ) Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat. Genet. , 42 , 790 – 793
https://doi.org/10.1038/ng.646
|
56 |
J. C. Roach, , G. Glusman, , A. F. A. Smit, , C. D. Huff, , R. Hubley, , P. T. Shannon, , L. Rowen, , K. P. Pant, , N. Goodman, , M. Bamshad, , et al. ( 2010 ) Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science , 328 , 636 – 639
https://doi.org/10.1126/science.1186802
|
57 |
J. R. Lupski, , J. G. Reid, , C. Gonzaga-Jauregui, , D. Rio Deiros, , D. C. Y. Chen, , L. Nazareth, , M. Bainbridge, , H. Dinh, , C. Jing, , D. A. Wheeler, , et al. ( 2010 ) Whole-genome sequencing in a patient with Charcot–Marie–Tooth neuropathy. N. Engl. J. Med. , 362 , 1181 – 1191
https://doi.org/10.1056/NEJMoa0908094
|
58 |
G. M. Cooper, , et al. ( 2005 ) Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. , 15 , 901 – 913
https://doi.org/10.1101/gr.3577405
|
59 |
J. C. Taylor, , H. C. Martin, , S. Lise, , J. Broxholme, , J.-B. Cazier, , A. Rimmer, , A. Kanapin, , G. Lunter, , S. Fiddy, , C. Allan, , et al. ( 2015 ) Factors influencing successof clinical genome sequencing across a broad spectrum of disorders. Nat. Genet. , 47 , 717 – 726
https://doi.org/10.1038/ng.3304
|
60 |
P. Kumar, , S. Henikoff, and P. C. Ng, ( 2009 ) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. , 4 , 1073 – 1081
https://doi.org/10.1038/nprot.2009.86
|
61 |
I. A. Adzhubei, , S. Schmidt, , L. Peshkin, , V. E. Ramensky, , A. Gerasimova, , P. Bork, , A. S. Kondrashov, and S. R. Sunyaev, ( 2010 ) A method and server for predicting damaging missense mutations. Nat. Methods , 7 , 248 – 249
https://doi.org/10.1038/nmeth0410-248
|
62 |
M. Kircher, , D. M. Witten, , P. Jain, , B. J. O’Roak, , G. M. Cooper, and J. Shendure, ( 2014 ) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. , 46 , 310 – 315
https://doi.org/10.1038/ng.2892
|
63 |
D. Quang, , Y. Chen, and X. Xie, ( 2015 ) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics , 31 , 761 – 763
https://doi.org/10.1093/bioinformatics/btu703
|
64 |
H. A. Shihab, , M. F. Rogers, , J. Gough, , M. Mort, , D. N. Cooper, , I. N. M Day, ., T. R. Gaunt, and C. Campbell ( 2015 ) An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics , 10.1093/bioinformatics/btv009
https://doi.org/10.1093/bioinformatics/btv009
|
65 |
Y. Fu, , Z. Liu, , S. Lou, , J. Bedford, , X. J. Mu, , K. Y. Yip, , E. Khurana, and M. Gerstein, ( 2014 ) FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol. , 15 , 480
https://doi.org/10.1186/s13059-014-0480-5
|
66 |
E. Khurana, , Y. Fu, , V. Colonna, , X. J. Mu, , H. M. Kang, , T. Lappalainen, , A. Sboner, , L. Lochovsky, , J. Chen, , A. Harmanci, , et al. ( 2013 ) Integrative annotation of variants from 1092 humans: application to cancer genomics. Science , 342 , 1235587
https://doi.org/10.1126/science.1235587
|
67 |
K.-V. Lehmann, and T. Chen, ( 2013 ) Exploring functional variant discovery in non-coding regions with SInBaD. Nucleic Acids Res. , 41 , e7
https://doi.org/10.1093/nar/gks800
|
68 |
D. Lee, , D. U. Gorkin, , M. Baker, , B. J. Strober, , A. L. Asoni, , A. S. McCallion, and M. A. Beer, ( 2015 ) A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. , 47 , 955 – 961
https://doi.org/10.1038/ng.3331
|
69 |
G. R. Ritchie, , I. Dunham, , E. Zeggini, and P. Flicek, ( 2014 ) Functional annotation of noncoding sequence variants. Nat. Methods , 11 , 294 – 296
https://doi.org/10.1038/nmeth.2832
|
70 |
F. Zhang, and J. R. Lupski, ( 2015 ) Noncoding genetic variants in human disease. Hum. Mol. Genet. ,
https://doi.org/10.1093/hmg/ddv259.
|
71 |
J. R. Lupski, ( 1998 ) Genomic disorders: structural features of the genome can lead to DNA rearrangements and human disease traits. Trends Genet. , 14 , 417 – 422
https://doi.org/10.1016/S0168-9525(98)01555-8
|
72 |
C. Lee, and C. C. Morton, ( 2008 ) Structural genomic variation and personalized medicine. N. Engl. J. Med. , 358 , 740 – 741
https://doi.org/10.1056/NEJMcibr0708452
|
73 |
J. R. Lupski, ( 2009 ) Genomic disorders ten years on. Genome Med. , 1 , 42
https://doi.org/10.1186/gm42
|
74 |
J. F. Sathirapongsasuti, , H. Lee, , B. A. J. Horst, , G. Brunner, , A. J. Cochran, , S. Binder, , J. Quackenbush, and S. F. Nelson, ( 2011 ) Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics , 27 , 2648 – 2654
https://doi.org/10.1093/bioinformatics/btr462
|
75 |
M. Fromer, , J. L. Moran, , K. Chambert, , E. Banks, , S. E. Bergen, , D. M. Ruderfer, , R. E. Handsaker, , S. A. McCarroll, , M. C. O’Donovan, , M. J. Owen, , et al. ( 2012 ) Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am. J. Hum. Genet. , 91 , 597 – 607
https://doi.org/10.1016/j.ajhg.2012.08.005
|
76 |
C. Zong, , S. Lu, , A. R. Chapman, and X. S. Xie, ( 2012 ) Genome-wide detectionof single-nucleotide and copy-number variations of a single humancell. Science , 338 , 1622 – 1626
https://doi.org/10.1126/science.1229164
|
77 |
L. R. Cardon, and J. I. Bell, ( 2001 ) Association study designs for complex diseases. Nat. Rev. Genet. , 2 , 91 – 99
https://doi.org/10.1038/35052543
|
78 |
T. A. Manolio, , F. S. Collins, , N. J. Cox, , D. B. Goldstein, , L. A. Hindorff, , D. J. Hunter, , M. I. McCarthy, , E. M. Ramos, , L. R. Cardon, , A. Chakravarti, , et al. ( 2009 ) Finding the missing heritability of complex diseases. Nature , 461 , 747 – 753
https://doi.org/10.1038/nature08494
|
79 |
N. J. Schork, , S. S. Murray, , K. A. Frazer, and E. J. Topol, ( 2009 ) Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Dev. , 19 , 212 – 219
https://doi.org/10.1016/j.gde.2009.04.010
|
80 |
S. L. Spain, and J. C. Barrett, ( 2015 ) Strategies for fine-mapping complex traits. Hum. Mol. Genet. ,
https://doi.org/10.1093/hmg/ddv260.
|
81 |
CONVERGE consortium ( 2015 ) Sparse whole-genome sequencing identifiestwo loci for major depressive disorder . Nature , 523 , 588 –591.
|
82 |
C. Lippert, , J. Listgarten, , Y. Liu, , C. M. Kadie, , R. I. Davidson, and D. Heckerman, ( 2011 ) FaST linear mixed models for genome-wide association studies. Nat. Methods , 8 , 833 – 835
https://doi.org/10.1038/nmeth.1681
|
83 |
C. Widmer, , C. Lippert, , O. Weissbrod, , N. Fusi, , C. Kadie, , R. Davidson, , J Listgarten, . and , D. Heckerman ( 2014 ) Further improvements to linear mixed models for genome-wide association studies. Sci. Rep. , 4 , 6874
|
84 |
M. C. Wu, , S. Lee, , T. Cai, , Y. Li, , M. Boehnke, and X. Lin, ( 2011 ) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. , 89 , 82 – 93
https://doi.org/10.1016/j.ajhg.2011.05.029
|
85 |
P. N. Taylor, , E. Porcu, , S. Chew, , P. J. Campbell, , M. Traglia, , S. J. Brown, , B. H. Mullin, , H. A. Shihab, , J. Min, , K. Walter, , et al.( 2015 ) Whole-genome sequence-based analysis of thyroid function. Nat. Commun. , 6 , 5681
|
86 |
A. C. Morrison, , A. Voorman, , A. D. Johnson, , X. M. Liu, , J. Yu, , A. Li, , D. Muzny, , F. L. Yu, , K. Rice, , C. S. Zhu, , et al. ( 2013 ) Whole genome sequence-based analysis of a model complex trait, high density lipoprotein cholesterol. Nat. Genet. , 45, 899–901
https://doi.org/10.1038/ng.2671
|
87 |
G. Gibson, ( 2012 ) Rare and common variants: twenty arguments. Nat. Rev. Genet. , 13 , 135 – 145
https://doi.org/10.1038/nrg3118
|
88 |
J. Yang, , B. Benyamin, , B. P. McEvoy, , S. Gordon, , A. K. Henders, , D. R. Nyholt, , P. A. Madden, , A. C. Heath, , N. G. Martin, , G. W. Montgomery, , et al. ( 2010 ) Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. , 42 , 565 – 569
https://doi.org/10.1038/ng.608
|
89 |
Y. Li, , C. Sidore, , H. M. Kang, , M. Boehnke, and G. R. Abecasis, ( 2011 ) Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. , 21 , 940 – 951
https://doi.org/10.1101/gr.117259.110
|
90 |
S. L. Edwards, , J. Beesley, , J. D. French, and A. M. Dunning, ( 2013 ) Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. , 93 , 779 – 797
https://doi.org/10.1016/j.ajhg.2013.10.012
|
91 |
K. K.-H. Farh, , A. Marson, , J. Zhu, , M. Kleinewietfeld, , W. J. Housley, , S. Beik, , N. Shoresh, , H. Whitton, , R. J. H. Ryan, , A. A. Shishkin, , et al. ( 2015 ) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature , 518 , 337 – 343
https://doi.org/10.1038/nature13835
|
92 |
J. B. Maller, , G. McVean, , J. Byrnes, , D. Vukcevic, , K. Palin, , Z. Su, , J. M. M. Howson, , A. Auton, , S. Myers, , A. Morris, , et al. ( 2012 ) Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. , 44 , 1294 – 1301
https://doi.org/10.1038/ng.2435
|
93 |
C. E. Barbieri, , S. C. Baca, , M. S. Lawrence, , F. Demichelis, , M. Blattner, , J.-P. Theurillat, , T. A. White, , P. Stojanov, , E. Van Allen, , N. Stransky, , et al. ( 2012 ) Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. , 44 , 685 – 689
https://doi.org/10.1038/ng.2279
|
94 |
K. Wang, , J. Kan, , S. T. Yuen, , S. T. Shi, , K. M. Chu, , S. Law, , T. L. Chan, , Z. Kan, , A. S. Y. Chan, , W. Y. Tsui, , et al. ( 2011 ) Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer. Nat. Genet. , 43 , 1219 – 1223
https://doi.org/10.1038/ng.982
|
95 |
H. Nakagawa, , C. P. Wardell, , M. Furuta, , H. Taniguchi, and A. Fujimoto, ( 2015 ) Cancer whole-genome sequencing: present and future. Oncogene , 34 , 5943 – 5950
https://doi.org/10.1038/onc.2015.90
|
96 |
F. W. Huang, , E. Hodis, , M. J. Xu, , G. V. Kryukov, , L. Chin, and L. A. Garraway, ( 2013 ) Highly recurrent TERT promoter mutations in human melanoma. Science , 339 , 957 – 959
https://doi.org/10.1126/science.1229259
|
97 |
J. Vinagre, , A. Almeida, , H. Pópulo, , R. Batista, , J. Lyra, , V. Pinto, , R. Coelho, , R. Celestino, , H. Prazeres, , L. Lima, , et al. ( 2013 ) Frequency of TERT promoter mutations in human cancers. Nat.Commun. , 4 , 2185
|
98 |
M. R. Mansour, , B. J. Abraham, , L. Anders, , A. Berezovskaya, , A. Gutierrez, , A. D. Durbin, , J. Etchin, , L. Lawton, , S. E. Sallan, , L. B. Silverman, , et al. ( 2014 ) An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science , 346 , 1373 – 1377
https://doi.org/10.1126/science.1259037
|
99 |
N. Weinhold, , A. Jacobsen, , N. Schultz, , C. Sander, and W. Lee, ( 2014 ) Genome-wide analysis of noncoding regulatory mutationsin cancer. Nat. Genet. , 46 , 1160 – 1165
https://doi.org/10.1038/ng.3101
|
100 |
N. J. Fredriksson, , L. Ny, , J. A. Nilsson, and E. Larsson, ( 2014 ) Systematic analysisof noncoding somatic mutations and gene expression alterations across 14 tumor types. Nat. Genet. , 46 , 1258 – 1263
https://doi.org/10.1038/ng.3141
|
101 |
C. Melton, , J. A. Reuter, , D. V. Spacek, and M. Snyder, ( 2015 ) Recurrent somatic mutations in regulatory regions of human cancer genomes. Nat. Genet. , 47 , 710 – 716
https://doi.org/10.1038/ng.3332
|
102 |
B. Li, and S. M. Leal, ( 2008 ) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. , 83 , 311 – 321
https://doi.org/10.1016/j.ajhg.2008.06.024
|
103 |
D.-Y. Lin, and Z.-Z. Tang, ( 2011 ) A general framework for detecting disease associations with rare variants in sequencing studies. Am. J. Hum. Genet. , 89 , 354 – 367
https://doi.org/10.1016/j.ajhg.2011.07.015
|
104 |
A. Gonzalez-Perez, , V. Mustonen, , B. Reva, , G. R. S. Ritchie, , P. Creixell, , R. Karchin, , M. Vazquez, , J. L. Fink, , K. S. Kassahn, , J. V. Pearson, , et al. ( 2013 ) Computational approaches to identify functional genetic variants in cancer genomes. Nat. Methods , 10 , 723 – 729
https://doi.org/10.1038/nmeth.2562
|
105 |
F. W. Albert, and L. Kruglyak, ( 2015 ) The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. , 16 , 197 – 212
https://doi.org/10.1038/nrg3891
|
106 |
M. J. Li, , B. Yan, , P. C. Sham, , and J.W. Wang, ( 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 |
L. D. Ward, and M. Kellis, ( 2012 ) Interpreting noncoding genetic variation in complex traitsand human disease. Nat. Biotechnol. , 30 , 1095 – 1106
https://doi.org/10.1038/nbt.2422
|
108 |
M. V. Rockman, and L. Kruglyak, ( 2006 ) Genetics of global gene expression. Nat. Rev. Genet. , 7 , 862 – 872
https://doi.org/10.1038/nrg1964
|
109 |
J. F. Degner, , A. A. Pai, , R. Pique-Regi, , J.-B. Veyrieras, , D. J. Gaffney, , J. K. Pickrell, , S. De Leon, , K. Michelini, , N. Lewellen, , G. E. Crawford, , et al. ( 2012 ) DNase I sensitivity QTLs area major determinant of human expression variation. Nature , 482 , 390 – 394
https://doi.org/10.1038/nature10808
|
110 |
J. Monlong, , M. Calvo, , P. G. Ferreira, and R. Guigó, ( 2014 ) Identification of genetic variants associated with alternative splicing using sQTLseekeR . Nat. Commun. , 5 , 4698
|
111 |
A. Koren, , R. E. Handsaker, , N. Kamitaki, , R. Karlić, , S. Ghosh, , P. Polak, , K. Eggan, and S. A. McCarroll, ( 2014 ) Genetic variation in human DNA replication timing. Cell , 159 , 1015 – 1026
https://doi.org/10.1016/j.cell.2014.10.025
|
112 |
R. C.-H. del Rosario, , J. Poschmann, , S. L. Rouam, , E. Png, , C. C. Khor, , M. L. Hibberd, and S. Prabhakar, ( 2015 ) Sensitive detection of chromatin-altering polymorphisms reveals autoimmune diseasemechanisms. Nat. Methods , 12 , 458 – 464
https://doi.org/10.1038/nmeth.3326
|
113 |
J. R. Gibbs, , M. P. van der Brug, , D. G. Hernandez, , B. J. Traynor, , M. A. Nalls, , S.-L. Lai, , S. Arepalli, , A. Dillman, , I. P. Rafferty, , J. Troncoso, , et al. ( 2010 ) Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. , 6 , e1000952
https://doi.org/10.1371/journal.pgen.1000952
|
114 |
T. Lappalainen, , M. Sammeth, , M. R. Friedländer, , P. A. C. ‘t Hoen, , J. Monlong, , M. A. Rivas, , M. Gonzàlez-Porta, , N. Kurbatova, , T. Griebel, , P. G. Ferreira, , et al., ( 2013 ) Transcriptome and genome sequencing uncovers functional variationin humans. Nature , 501 , 506 – 511
https://doi.org/10.1038/nature12531
|
115 |
M. T. Maurano, , R. Humbert, , E. Rynes, , R. E. Thurman, , E. Haugen, , H. Wang, , A. P. Reynolds, , R. Sandstrom, , H. Qu, , J. Brody, , et al. ( 2012 ) Systematic localization of common disease-associated variation in regulatory DNA. Science , 337 , 1190 – 1195
https://doi.org/10.1126/science.1222794
|
116 |
M. T. Maurano, , E. Haugen, , R. Sandstrom, , J. Vierstra, , A. Shafer, , R. Kaul, and J. A. Stamatoyannopoulos, ( 2015 ) Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo . Nat. Genet. , 47 , 1393 – 1401
https://doi.org/10.1038/ng.3432
|
117 |
M. Kasowski, , F. Grubert, , C. Heffelfinger, , M. Hariharan, , A. Asabere, , S. M. Waszak, , Lukas. Habegger, , J. Rozowsky, , M. Shi, , A. E. Urban, , et al. ( 2010 ) Variation in transcription factor binding among humans. Science 328 , 232 – 235
|
118 |
K. J. Karczewski, , J. T. Dudley, , K. R. Kukurba, , R. Chen, , A. J. Butte, , S. B. Montgomery, and M. Snyder, ( 2013 ) Systematic functional regulatory assessment of disease-associated variants. Proc. Natl. Acad. Sci. USA , 110 , 9607 – 9612
https://doi.org/10.1073/pnas.1219099110
|
119 |
C. Chiang, , R. M. Layer, , G. G. Faust, , M. R. Lindberg, , D. B. Rose, , E. P. Garrison, , G. T. Marth, , A. R. Quinlan, and I. M. Hall, ( 2014 ) SpeedSeq: Ultra-fast personal genome analysis and interpretation. Nat. Meth., 12 , 966 – 968
|
120 |
P. J. Park, ( 2009 ) ChIP–seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. , 10 , 669 – 680
https://doi.org/10.1038/nrg2641
|
121 |
L. Song, and G. E. Crawford, ( 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 |
J. D. Buenrostro, , P. G. Giresi, , L. C. Zaba, , H. Y. Chang, and W. J. Greenleaf, ( 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
https://doi.org/10.1038/nmeth.2688
|
123 |
dbWGFP :
|
124 |
L. G. Biesecker, ( 2013 ) Hypothesis-generating research and predictive medicine. Genome Res. , 23 , 1051 – 1053
https://doi.org/10.1101/gr.157826.113
|
125 |
R. Simon, ( 2011 ) Genomic biomarkers in predictive medicine. An interim analysis. EMBO Mol. Med. , 3 , 429 – 435
https://doi.org/10.1002/emmm.201100153
|
126 |
S. Matsui, , R. Simon, , P. Qu, , J. D. Shaughnessy, , B. Barlogie, and J. Crowley, ( 2012 ) Developing and validating continuous genomic signatures in randomized clinical trials for predictive medicine. Clin. Cancer Res. , 18 , 6065 – 6073
https://doi.org/10.1158/1078-0432.CCR-12-1206
|
127 |
F. S. Collins, and H. Varmus, ( 2015 ) A new initiative on precision medicine. N. Engl. J. Med. , 372 , 793 – 795
https://doi.org/10.1056/NEJMp1500523
|
128 |
M. A. Rubin, ( 2015 ) Health: Make precision medicine work for cancer care. Nature , 520 , 290 – 291
https://doi.org/10.1038/520290a
|
129 |
J. Bellmunt, , A. Orsola, and G. Sonpavde, ( 2015 ) Precision and predictive medicine in urothelial cancer: Are we making progress? Eur. Urol. , 68 , 547 – 549
https://doi.org/10.1016/j.eururo.2015.04.025
|
130 |
D. H. Geschwind, and M. W. State, ( 2015 ) Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. , 14 , 1109 – 1120
https://doi.org/10.1016/S1474-4422(15)00044-7
|
131 |
H. C. Mak, ( 2012 ) Genome interpretation and assembly—recent progress and next steps. Nat. Biotechnol. , 30 , 1081 – 1083
https://doi.org/10.1038/nbt.2425
|
132 |
J. Shendure, and E. L. Aiden, ( 2012 ) The expanding scope of DNA sequencing. Nat. Biotechnol. , 30 , 1084 – 1094
https://doi.org/10.1038/nbt.2421
|
133 |
A. Fujimoto, , H. Nakagawa, , N. Hosono, , K. Nakano, , T. Abe, , K. A. Boroevich, , M. Nagasaki, , R. Yamaguchi, , T. Shibuya, , M. Kubo, , et al. ( 2010 ) Whole-genome sequencing and comprehensive variant analysis of a Japanese individual using massively parallel sequencing. Nat. Genet. , 42 , 931 – 936
https://doi.org/10.1038/ng.691
|
134 |
C. Gonzaga-Jauregui, , J. R. Lupski, and R. A. Gibbs, ( 2012 ) Human genome sequencing in health and disease. Annu. Rev. Med. , 63 , 35 – 61
https://doi.org/10.1146/annurev-med-051010-162644
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