Tools for predicting the functional impact of nonsynonymous genetic variation

Genetics

Volumn 203, Issue 2, 2016, Pages 635-647

  Tang, Haiming ^a   Thomas, Paul D ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Genetic variation; Human disease; Phenotypic effects; Protein mutation

Indexed keywords

ACCURACY; AMINO ACID SEQUENCE; GENE FUNCTION; GENE SEQUENCE; GENETIC VARIATION; HUMAN; MACHINE LEARNING; PHENOTYPE; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN STRUCTURE; QUANTITATIVE ANALYSIS; REVIEW; SEQUENCE ANALYSIS; STRUCTURE ANALYSIS; GENETIC POLYMORPHISM; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMAN GENOME; PROCEDURES;

GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; POLYMORPHISM, GENETIC;

EID: 84979900842     PISSN: 00166731     EISSN: 19432631     Source Type: Journal    
DOI: 10.1534/genetics.116.190033     Document Type: Review

References (121)

1. Abecasis, G. R., D. Altshuler, A. Auton, L. D. Brooks, R. M. Durbin et al., 2010 A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073.
2. Abecasis, G. R., A. Auton, L. D. Brooks, M. A. DePristo, R. M. Durbin et al., 2012 An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65.
3. Adzhubei, I. A., S. Schmidt, L. Peshkin, V. E. Ramensky, A. Gerasimova et al., 2010 A method and server for predicting damaging missense mutations. Nat. Methods 7: 248–249.
4. Altschul, SF, W Gish, W Miller, E W Myers, and D J Lipman, 1990 Basic local alignment search tool. J. Mol. Biol. 215(3): 403–410
5. Altschul, S. F., T. L. Madden, A. A. Schaffer, J. Zhang, Z. Zhang et al., 1997 Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.
6. Ashburner, M., C. A. Ball, J. A. Blake, D. Botstein, H. Butler et al., 2000 Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25: 25–29.
7. Baker, M., 2012 One-stop shop for disease genes. Nature 491: 171.
8. Barenboim, M., and T. Manke, 2013 ChroMoS: an integrated web tool for SNP classification, prioritization and functional interpretation. Bioinformatics 29: 2197–2198.
9. Barenboim, M., B. J. Zoltick, Y. Guo, and D. R. Weinberger, 2010 MicroSNiPer: a web tool for prediction of SNP effects on putative microRNA targets. Hum. Mutat. 31: 1223–1232.
10. Barrett, C., R. Hughey, and K. Karplus, 1997 Scoring hidden Markov models. Comput. Appl. Biosci. 13: 191–199.
11. Bendl, J., J. Stourac, O. Salanda, A. Pavelka, E. D. Wieben et al., 2014 PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLOS Comput. Biol. 10: e1003440.
12. Benedix, A., C. M. Becker, B. L. de Groot, A. Caflisch, and R. A. Bockmann, 2009 Predicting free energy changes using structural ensembles. Nat. Methods 6: 3–4.
13. Boeckmann, B., A. Bairoch, R. Apweiler, M. C. Blatter, A. Estreicher et al., 2003 The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31: 365–370.
14. Bridgham, J. T., E. A. Ortlund, and J. W. Thornton, 2009 An epistatic ratchet constrains the direction of glucocorticoid receptor evolution. Nature 461: 515–519.
15. Bromberg, Y., and B. Rost, 2007 SNAP: predict effect of nonsynonymous polymorphisms on function. Nucleic Acids Res. 35: 3823–3835.
16. Buetow, K. H., M. N. Edmonson, and A. B. Cassidy, 1999 Reliable identification of large numbers of candidate SNPs from public EST data. Nat. Genet. 21: 323–325.
17. Cai, Z., E. F. Tsung, V. D. Marinescu, M. F. Ramoni, A. Riva et al., 2004 Bayesian approach to discovering pathogenic SNPs in conserved protein domains. Hum. Mutat. 24: 178–184.
18. Calabrese, R., E. Capriotti, P. Fariselli, P. L. Martelli, and R. Casadio, 2009 Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat. 30: 1237–1244.
19. Campbell, I. M., C. A. Shaw, P. Stankiewicz, and J. R. Lupski, 2015 Somatic mosaicism: implications for disease and transmission genetics. Trends Genet. 31: 382–392.
20. Cancer Genome Atlas Research Network, 2008 Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455: 1061–1068.
21. Capriotti, E., P. Fariselli, and R. Casadio, 2005 I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33: W306–W310.
22. Capriotti, E., R. Calabrese, and R. Casadio, 2006 Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22: 2729–2734.
23. Capriotti, E., N. L. Nehrt, M. G. Kann, and Y. Bromberg, 2012 Bioinformatics for personal genome interpretation. Brief. Bioinform. 13: 495–512.
24. Capriotti, E., R. B. Altman, and Y. Bromberg, 2013a Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics 14(Suppl 3): S2.
25. Capriotti, E., R. Calabrese, P. Fariselli, P. L. Martelli, R. B. Altman et al., 2013b WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics 14(Suppl 3): S6.
26. Cargill, M., D. Altshuler, J. Ireland, P. Sklar, K. Ardlie et al., 1999 Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat. Genet. 22: 231–238.
27. Chasman, D., and R. M. Adams, 2001 Predicting the functional consequences of non-synonymous single nucleotide polymorphisms: structure-based assessment of amino acid variation. J. Mol. Biol. 307: 683–706.
28. Chen, Y. C., C. Douville, C. Wang, N. Niknafs, G. Yeo et al., 2014 A probabilistic model to predict clinical phenotypic traits from genome sequencing. PLOS Comput. Biol. 10: e1003825.
29. Cline, M. S., and R. Karchin, 2011 Using bioinformatics to predict the functional impact of SNVs. Bioinformatics 27: 441–448.
30. Collins, F. S., L. D. Brooks, and A. Chakravarti, 1998 A DNA polymorphism discovery resource for research on human genetic variation. Genome Res. 8: 1229–1231.
31. Compiani, M., and E. Capriotti, 2013 Computational and theoretical methods for protein folding. Biochemistry 52: 8601–8624.
32. Cooper, G. M., E. A. Stone, G. Asimenos, E. D. Green, S. Batzoglou et al., 2005 Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15: 901–913.
33. Cotton, R. G., A. D. Auerbach, M. Axton, C. I. Barash, S. F. Berkovic et al., 2008 GENETICS. The Human Variome Project. Science 322: 861–862.
34. Dayhoff, M. O., R. M. Schwartz, and B. C. Orcutt, 1978 A model of evolutionary change in proteins. Atlas Prot. Sequence Struct. 5: 345–351.
35. Dehouck, Y., J. Kwasigroch, M. Rooman, and D. Gilis, 2013 BeAtMuSiC: prediction of changes in protein-protein binding affinity on mutations. Nucleic Acids Res. 41: W333–W339.
36. Deo, R. C., G. Musso, M. Tasan, P. Tang, A. Poon et al., 2014 Prioritizing causal disease genes using unbiased genomic features. Genome Biol. 15: 534.
37. Dimster-Denk, D., K. W. Tripp, N. J. Marini, S. Marqusee, and J. Rine, 2013 Mono and dual cofactor dependence of human cystathionine beta-synthase enzyme variants in vivo and in vitro. G3 (Bethesda) 3: 1619–1628.
38. Dong, C., P. Wei, X. Jian, R. Gibbs, E. Boerwinkle et al., 2015 Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24: 2125–2137.
39. Encode Project Consortium, 2012 An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57–74.
40. Fernald, G. H., E. Capriotti, R. Daneshjou, K. J. Karczewski, and R. B. Altman, 2011 Bioinformatics challenges for personalized medicine. Bioinformatics 27: 1741–1748.
41. Ferrer-Costa, C., J. L. Gelpi, L. Zamakola, I. Parraga, X. de la Cruz et al., 2005 PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics 21: 3176– 3178.
42. Fitch, W. M., 1970 Distinguishing homologous from analogous proteins. Syst. Zool. 19: 99–113.
43. Frousios, K., C. S. Iliopoulos, T. Schlitt, and M. A. Simpson, 2013 Predicting the functional consequences of non-synonymous DNA sequence variants: evaluation of bioinformatics tools and development of a consensus strategy. Genomics 102: 223–228.
44. Fu, W., T. D. O’Connor, G. Jun, H. M. Kang, G. Abecasis et al., 2013 Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493: 216–220.
45. Giardine, B., C. Riemer, T. Hefferon, D. Thomas, F. Hsu et al., 2007 PhenCode: connecting ENCODE data with mutations and phenotype. Hum. Mutat. 28: 554–562.
46. Goldgar, D. E., D. F. Easton, A. M. Deffenbaugh, A. N. A. Monteiro, S. V. Tavtigian et al., 2004 Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2. Am. J. Hum. Genet. 75: 535–544.
47. Gonzalez-Perez, A., and N. Lopez-Bigas, 2011 Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am. J. Hum. Genet. 88: 440–449.
48. Grantham, R., 1974 Amino acid difference formula to help explain protein evolution. Science 185: 862–864.
49. Greenblatt, M. S., L. C. Brody, W. D. Foulkes, M. Genuardi, R. M. Hofstra et al., 2008 Locus-specific databases and recommendations to strengthen their contribution to the classification of variants in cancer susceptibility genes. Hum. Mutat. 29: 1273– 1281.
50. Greenman, C., P. Stephens, R. Smith, G. L. Dalgliesh, C. Hunter et al., 2007 Patterns of somatic mutation in human cancer genomes. Nature 446: 153–158.
51. Gribskov, M., 1994 Profile analysis. Methods Mol. Biol. 25: 247– 266.
52. Gribskov, M., and N. L. Robinson, 1996 Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comput. Chem. 20: 25–33.
53. Grimm, D. G., C. A. Azencott, F. Aicheler, U. Gieraths, D. G. Mac Arthur et al., 2015 The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum. Mutat. 36: 513–523.
54. Guerois, R., J. E. Nielsen, and L. Serrano, 2002 Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J. Mol. Biol. 320: 369–387.
55. Halushka, M. K., J. B. Fan, K. Bentley, L. Hsie, N. Shen et al., 1999 Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat. Genet. 22: 239–247.
56. Hamosh, A., A. F. Scott, J. S. Amberger, C. A. Bocchini, and V. A. McKusick, 2005 Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33: D514–D517.
57. Hecht, M., Y. Bromberg, and B. Rost, 2015 Better prediction of functional effects for sequence variants. BMC Genomics 16: S1.
58. Henikoff, S., and J. G. Henikoff, 1992 Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89: 10915–10919.
59. Henikoff, S., and J. G. Henikoff, 1994 Position-based sequence weights. J. Mol. Biol. 243: 574–578.
60. Hicks, S., D. A. Wheeler, S. E. Plon, and M. Kimmel, 2011 Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum. Mutat. 32: 661–668.
61. International HapMap Consortium, 2005 A haplotype map of the human genome. Nature 437: 1299–1320.
62. Jones, D. T., W. R. Taylor, and J. M. Thornton, 1992 The rapid generation of mutation data matrices from protein sequences. Comput. Appl. Biosci. 8: 275–282.
63. Karchin, R., 2009 Next generation tools for the annotation of human SNPs. Brief. Bioinform. 10: 35–52.
64. Kawabata, T., M. Ota, and K. Nishikawa, 1999 The Protein Mutant Database. Nucleic Acids Res. 27: 355–357.
65. Kellogg, E. H., A. Leaver-Fay, and D. Baker, 2011 Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins 79: 830–838.
66. Kircher, M., D. M. Witten, P. Jain, B. J. O’Roak and G. M. Cooper, 2014 A general framework for estimating the relative pathogenicity of human genetic variants. 46: 310–315.
67. Kondrashov, A. S., S. Sunyaev, and F. A. Kondrashov, 2002 Dobzhansky-Muller incompatibilities in protein evolution. Proc. Natl. Acad. Sci. USA 99: 14878–14883.
68. Kulathinal, R. J., B. R. Bettencourt, and D. L. Hartl, 2004 Compensated deleterious mutations in insect genomes. Science 306: 1553–1554.
69. Lewinger, J. P., D. V. Conti, J. W. Baurley, T. J. Triche, and D. C. Thomas, 2007 Hierarchical Bayes prioritization of marker associations from a genome-wide association scan for further investigation. Genet. Epidemiol. 31: 871–882.
70. Li, B., V. G. Krishnan, M. E. Mort, F. Xin, K. K. Kamati et al., 2009 Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 25: 2744– 2750.
71. Liao, B. Y., and J. Zhang, 2007 Mouse duplicate genes are as essential as singletons. Trends Genet. 23: 378–381.
72. Liu, X., X. Jian, and E. Boerwinkle, 2011 dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum. Mutat. 32: 894–899.
73. Loeb, D. D., R. Swanstrom, L. Everitt, M. Manchester, S. E. Stamper et al., 1989 Complete mutagenesis of the HIV-1 protease. Nature 340: 397–400.
74. Lopes, M. C., C. Joyce, G. R. Ritchie, S. L. John, F. Cunningham et al., 2012 A combined functional annotation score for nonsynonymous variants. Hum. Hered. 73: 47–51.
75. Macintyre, G., J. Bailey, I. Haviv, and A. Kowalczyk, 2010 is-rSNP: a novel technique for in silico regulatory SNP detection. Bioinformatics 26: i524–i530.
76. Marini, N. J., P. D. Thomas, and J. Rine, 2010 The use of orthologous sequences to predict the impact of amino acid substitutions on protein function. PLoS Genet. 6: e1000968.
77. Markiewicz, P., L. G. Kleina, C. Cruz, S. Ehret, and J. H. Miller, 1994 Genetic studies of the lac repressor. XIV. Analysis of 4000 altered Escherichia coli lac repressors reveals essential and non-essential residues, as well as “spacers” which do not require a specific sequence. J. Mol. Biol. 240: 421–433.
78. Masso, M., and I. I. Vaisman, 2010 AUTO-MUTE: web-based tools for predicting stability changes in proteins due to single amino acid replacements. Protein Eng. Des. Sel. 23: 683–687.
79. Miller, M. P., and S. Kumar, 2001 Understanding human disease mutations through the use of interspecific genetic variation. Hum. Mol. Genet. 10: 2319–2328.
80. Mottaz, A., F. P. David, A. L. Veuthey, and Y. L. Yip, 2010 Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar. Bioinformatics 26: 851–852.
81. Moult, J., K. Fidelis, A. Kryshtafovych, T. Schwede, and A. Tramontano, 2014 Critical assessment of methods of protein structure prediction (CASP): round x. Proteins 82(Suppl 2): 1–6.
82. Ng, P. C., and S. Henikoff, 2001 Predicting deleterious amino acid substitutions. Genome Res. 11: 863–874.
83. Ng, P. C., and S. Henikoff, 2006 Predicting the effects of amino acid substitutions on protein function. Annu. Rev. Genomics Hum. Genet. 7: 61–80.
84. Niroula, A., S. Urolagin, and M. Vihinen, 2015 PON-P2: prediction method for fast and reliable identification of harmful variants. PLoS One 10: e0117380.
85. O’Fallon, B. D., W. Wooderchak-Donahue, P. Bayrak-Toydemir, and D. Crockett, 2013 VarRanker: rapid prioritization of sequence variations associated with human disease. BMC Bioinformatics 14(Suppl 13): S1.
86. Ohno, S., 1970 Evolution by Gene Duplication. Springer-Verlag, New York.
87. Olatubosun, A., J. Valiaho, J. Harkonen, J. Thusberg, and M. Vihinen, 2012 PON-P: integrated predictor for pathogenicity of missense variants. Hum. Mutat. 33: 1166–1174.
88. Olivier, M., R. Eeles, M. Hollstein, M. A. Khan, C. C. Harris et al., 2002 The IARC TP53 database: new online mutation analysis and recommendations to users. Hum. Mutat. 19: 607–614.
89. Pazdrak, K., T. Adachi, and R. Alam, 1997 Src homology 2 protein tyrosine phosphatase (SHPTP2)/Src homology 2 phosphatase 2 (SHP2) tyrosine phosphatase is a positive regulator of the interleukin 5 receptor signal transduction pathways leading to the prolongation of eosinophil survival. J. Exp. Med. 186: 561–568.
90. Pearson, WR, and D J Lipman, 1988 Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. USA 85: 2444–2448
91. Piirila, H., J. Valiaho, and M. Vihinen, 2006 Immunodeficiency mutation databases (IDbases). Hum. Mutat. 27: 1200–1208.
92. Pokala, N., and T. M. Handel, 2005 Energy functions for protein design: adjustment with protein-protein complex affinities, models for the unfolded state, and negative design of solubility and specificity. J. Mol. Biol. 347: 203–227.
93. Pollard, K. S., M. J. Hubisz, K. R. Rosenbloom, and A. Siepel, 2010 Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20: 110–121.
94. Potapov, V., M. Cohen, and G. Schreiber, 2009 Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng. Des. Sel. 22: 553–560.
95. Ramensky, V., P. Bork, and S. Sunyaev, 2002 Human nonsynonymous SNPs: server and survey. Nucleic Acids Res. 30: 3894–3900.
96. Reva, B., Y. Antipin, and C. Sander, 2011 Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39: e118.
97. Rohl, C. A., C. E. Strauss, K. M. Misura, and D. Baker, 2004 Protein structure prediction using Rosetta. Methods Enzymol. 383: 66–93.
98. Sasidharan Nair, P., and M. Vihinen, 2013 VariBench: a benchmark database for variations. Hum. Mutat. 34: 42–49.
99. Schaefer, C., A. Meier, B. Rost, and Y. Bromberg, 2012 SNPdbe: constructing an nsSNP functional impacts database. Bioinformatics 28: 601–602.
100. Schwarz, J. M., C. Rodelsperger, M. Schuelke, and D. Seelow, 2010 MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7: 575–576.
101. Shendure, J., and J. M. Akey, 2015 The origins, determinants, and consequences of human mutations. Science 349: 1478–1483.
102. Sherry, S. T., M. H. Ward, M. Kholodov, J. Baker, L. Phan et al., 2001 dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29: 308–311.
103. Shihab, H. A., J. Gough, D. N. Cooper, P. D. Stenson, G. L. Barker et al., 2013 Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum. Mutat. 34: 57–65.
104. Siepel, A., G. Bejerano, J. S. Pedersen, A. S. Hinrichs, M. Hou et al., 2005 Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15: 1034–1050.
105. Sippl, M. J., 1990 Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213: 859–883.
106. Sjoblom, T., S. Jones, L. D. Wood, D. W. Parsons, J. Lin et al., 2006 The consensus coding sequences of human breast and colorectal cancers. Science 314: 268–274.
107. Sjolander, K., K. Karplus, M. Brown, R. Hughey, A. Krogh et al., 1996 Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology. Comput. Appl. Biosci. 12: 327–345.
108. Stenson, P. D., E. V. Ball, M. Mort, A. D. Phillips, J. A. Shiel et al., 2003 Human Gene Mutation Database (HGMD): 2003 update. Hum. Mutat. 21: 577–581.
109. Stone, E. A., and A. Sidow, 2005 Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res. 15: 978–986.
110. Sunyaev, S. R., F. Eisenhaber, I. V. Rodchenkov, B. Eisenhaber, V. G. Tumanyan et al., 1999 PSIC: profile extraction from sequence alignments with position-specific counts of independent observations. Protein Eng. 12: 387–394.
111. Tang, H., and P. D. Thomas, 2016 PANTHER-PSEP: predicting disease-causing mutations using position-specific evolutionary preservation. Bioinformatics.
112. Thomas, P. D., and A. Kejariwal, 2004 Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: evolutionary evidence for differences in molecular effects. Proc. Natl. Acad. Sci. USA 101: 15398–15403.
113. Thomas, P. D., M. J. Campbell, A. Kejariwal, H. Mi, B. Karlak et al., 2003 PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13: 2129–2141.
114. Thusberg, J., A. Olatubosun, and M. Vihinen, 2011 Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mutat. 32: 358–368.
115. UniProt Consortium, 2011 Ongoing and future developments at the Universal Protein Resource. Nucleic Acids Res. 39: D214– D219.
116. Wang, Z., and J. Moult, 2001 SNPs, protein structure, and disease. Hum. Mutat. 17: 263–270.
117. Wei, Q., L. Wang, Q. Wang, W. D. Kruger, and R. L. Dunbrack, Jr.., 2010 Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase. Proteins 78: 2058–2074.
118. Yang, Z., 1997 PAML: a program package for phylogenetic analysis by maximum likelihood. Comput. Appl. Biosci. 13: 555– 556.
119. Yue, P., E. Melamud, and J. Moult, 2006 SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 7: 166.
120. Zhao, Y., W. T. Clark, M. Mort, D. N. Cooper, P. Radivojac et al., 2011 Prediction of functional regulatory SNPs in monogenic and complex disease. Hum. Mutat. 32: 1183–1190.
121. Zuckerkandl, E., and L. Pauling, 1962 Molecular disease, evolution and genic heterogenetity, pp. 189–225 in Horizons in Biochemistry, edited by M. Kasha and B. Pullman. Academic Press, New York.