Refinement of evolutionary medicine predictions based on clinical evidence for the manifestations of Mendelian diseases
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
31819097
PubMed Central
PMC6901466
DOI
10.1038/s41598-019-54976-4
PII: 10.1038/s41598-019-54976-4
Knihovny.cz E-zdroje
- MeSH
- algoritmy MeSH
- ektodysplasiny genetika MeSH
- fenotyp MeSH
- genetická variace MeSH
- genetické nemoci vrozené genetika MeSH
- genomika MeSH
- glukosa-6-fosfátdehydrogenasa genetika MeSH
- hemoglobiny genetika MeSH
- hepatocytární jaderný faktor 4 genetika MeSH
- lidé MeSH
- missense mutace MeSH
- modely genetické * MeSH
- molekulární evoluce MeSH
- mutace * MeSH
- pravděpodobnostní funkce MeSH
- proteomika MeSH
- tyrosinfosfatasa nereceptorového typu 11 genetika MeSH
- výpočetní biologie metody MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- EDA protein, human MeSH Prohlížeč
- ektodysplasiny MeSH
- G6PD protein, human MeSH Prohlížeč
- glukosa-6-fosfátdehydrogenasa MeSH
- hemoglobin B MeSH Prohlížeč
- hemoglobiny MeSH
- hepatocytární jaderný faktor 4 MeSH
- HNF4A protein, human MeSH Prohlížeč
- PTPN11 protein, human MeSH Prohlížeč
- tyrosinfosfatasa nereceptorového typu 11 MeSH
Prediction methods have become an integral part of biomedical and biotechnological research. However, their clinical interpretations are largely based on biochemical or molecular data, but not clinical data. Here, we focus on improving the reliability and clinical applicability of prediction algorithms. We assembled and curated two large non-overlapping large databases of clinical phenotypes. These phenotypes were caused by missense variations in 44 and 63 genes associated with Mendelian diseases. We used these databases to establish and validate the model, allowing us to improve the predictions obtained from EVmutation, SNAP2 and PoPMuSiC 2.1. The predictions of clinical effects suffered from a lack of specificity, which appears to be the common constraint of all recently used prediction methods, although predictions mediated by these methods are associated with nearly absolute sensitivity. We introduced evidence-based tailoring of the default settings of the prediction methods; this tailoring substantially improved the prediction outcomes. Additionally, the comparisons of the clinically observed and theoretical variations led to the identification of large previously unreported pools of variations that were under negative selection during molecular evolution. The evolutionary variation analysis approach described here is the first to enable the highly specific identification of likely disease-causing missense variations that have not yet been associated with any clinical phenotype.
Zobrazit více v PubMed
Biesecker LG, Green RC. Diagnostic clinical genome and exome sequencing. N. Engl. J. Med. 2014;371:1170. doi: 10.1056/NEJMc1409040. PubMed DOI
Simm F, et al. Identification of SLC20A1 and SLC15A4 among other genes as potential risk factors for combined pituitary hormone deficiency. Genet. Med. 2018;20:728–736. doi: 10.1038/gim.2017.165. PubMed DOI
Tennessen JA, et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science. 2012;337:64–69. doi: 10.1126/science.1219240. PubMed DOI PMC
The 1000 Genomes Project Consortium An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65. doi: 10.1038/nature11632. PubMed DOI PMC
Ioannidis NM, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 2016;99:877–885. doi: 10.1016/j.ajhg.2016.08.016. PubMed DOI PMC
Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. 2010;11:415–425. doi: 10.1038/nrg2779. PubMed DOI
Bamshad MJ, et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 2011;12:745–755. doi: 10.1038/nrg3031. PubMed DOI
Šimčíková D, Kocková L, Vackářová K, Těšínský M, Heneberg P. Evidence-based tailoring of bioinformatics approaches to optimize methods that predict the effects of nonsynonymous amino acid substitutions in glucokinase. Sci. Rep. 2017;7:9499. doi: 10.1038/s41598-017-09810-0. PubMed DOI PMC
Hayat S, Sander C, Marks DS, Elofsson A. All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences. Proc. Natl. Acad. Sci. USA. 2015;110:5413–5418. doi: 10.1073/pnas.1419956112. PubMed DOI PMC
Wang Y, Barth P. Evolutionary-guided de novo structure prediction of self-associated transmembrane helical proteins with near-atomic accuracy. Nat. Commun. 2015;6:7196. doi: 10.1038/ncomms8196. PubMed DOI PMC
Peled S, et al. De-novo protein function prediction using DNA binding and RNA binding proteins as a test case. Nat. Commun. 2016;7:13424. doi: 10.1038/ncomms13424. PubMed DOI PMC
Huang YF, Gulko B, Siepel A. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat. Genet. 2017;49:618–624. doi: 10.1038/ng.3810. PubMed DOI PMC
Dehouck Y, Kwasigroch JM, Gilis D, Rooman M. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform. 2011;12:151. doi: 10.1186/1471-2105-12-151. PubMed DOI PMC
Hopf TA, et al. Mutation effects predicted from sequence co-variation. Nat. Biotechnol. 2017;35:128–135. doi: 10.1038/nbt.3769. PubMed DOI PMC
Bromberg Y, Kahn PC, Rost B. Neutral and weakly nonneutral sequence variants may define individuality. Proc. Natl. Acad. Sci. USA. 2013;110:14255–14260. doi: 10.1073/pnas.1216613110. PubMed DOI PMC
Vaser R, Adusumalli S, Leng SN, Sikic M, Ng PC. SIFT missense predictions for genomes. Nat. Protoc. 2016;11:1–9. doi: 10.1038/nprot.2015.123. PubMed DOI
Libbrecht MW. Machine learning in genetics and genomics. Nat. Rev. Genet. 2015;16:321–332. doi: 10.1038/nrg3920. PubMed DOI PMC
Sela I, Ashkenazy H, Katoh K, Pupko T. GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters. Nucl. Acids Res. 2015;43:W7–W14. doi: 10.1093/nar/gkv318. PubMed DOI PMC
Adebali O, Reznik AO, Ory DS, Zhulin IB. Establishing the precise evolutionary history of a gene improves prediction of disease-causing missense mutations. Genet. Med. 2016;18:1029–1036. doi: 10.1038/gim.2015.208. PubMed DOI PMC
Hecht M, Bromberg Y, Rost B. Better prediction of functional effects for sequence variants. BMC Genom. 2015;16(Suppl 8):S1. doi: 10.1186/1471-2164-16-S8-S1. PubMed DOI PMC
DePristo MA, Weinreich DM, Hartl DL. Missense meanderings in sequence space: a biophysical view of protein evolution. Nat. Rev. Genet. 2005;6:678–687. doi: 10.1038/nrg1672. PubMed DOI
de Visser JA, Krug J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 2014;15:480–490. doi: 10.1038/nrg3744. PubMed DOI
Breen MS, Kemena C, Vlasov PK, Notredame C, Kondrashov FA. Epistasis as the primary factor in molecular evolution. Nature. 2012;490:535–538. doi: 10.1038/nature11510. PubMed DOI
Nykamp K, et al. Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria. Genet. Med. 2017;19:1105–1117. doi: 10.1038/gim.2017.37. PubMed DOI PMC
Richards S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 2015;17:405–424. doi: 10.1038/gim.2015.30. PubMed DOI PMC
Stenson PD, et al. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 2014;133:1–9. doi: 10.1007/s00439-013-1358-4. PubMed DOI PMC
Liu L, et al. High-density SNP genotyping to define beta-globin locus haplotypes. Blood Cells Mol. Dis. 2009;42:16–24. doi: 10.1016/j.bcmd.2008.07.002. PubMed DOI PMC
Steele AM, et al. The previously reported T342P GCK missense variant is not a pathogenic mutation causing MODY. Diabetologia. 2011;54:2202–2205. doi: 10.1007/s00125-011-2194-5. PubMed DOI
Chellapa K, et al. Src tyrosine kinase phosphorylation of nuclear receptor HNF4α correlates with isoform-specific loss of HNF4α in human colon cancer. Proc. Natl. Acad. Sci. USA. 2012;109:2302–2307. doi: 10.1073/pnas.1106799109. PubMed DOI PMC
Houlleberghs H, et al. Oligonucleotide-directed mutagenesis screen to identify pathogenic Lynch syndrome-associated MSH2 DNA mismatch repair gene variants. Proc. Natl. Acad. Sci. USA. 2016;113:4128–4133. doi: 10.1073/pnas.1520813113. PubMed DOI PMC
Maxwell KN, et al. Evaluation of ACMG-guideline based variant classification of cancer susceptibility and non-cancer-associated genes in families affected by breast cancer. Am. J. Hum. Genet. 2016;98:801–817. doi: 10.1016/j.ajhg.2016.02.024. PubMed DOI PMC
Walsh R, et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet. Med. 2016;19:192–203. doi: 10.1038/gim.2016.90. PubMed DOI PMC
Hicks S, Wheeler DA, Plon SE, Kimmel M. Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum. Mutat. 2011;32:661–668. doi: 10.1002/humu.21490. PubMed DOI PMC
Riera C, Padilla N, de la Cruz X. The complementarity between protein-specific and general pathogenicity predictors for amino acid substitutions. Hum. Mutat. 2016;37:1013–1024. doi: 10.1002/humu.23048. PubMed DOI
Mathe E, et al. Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Nucl. Acids Res. 2006;34:1317–1325. doi: 10.1093/nar/gkj518. PubMed DOI PMC
Pawson T. Protein modules and signaling networks. Nature. 1995;373:573–580. doi: 10.1038/373573a0. PubMed DOI
Aronson HE, Royer WE, Hendrickson WA. Quantification of tertiary structural conservation despite primary sequence drift in the globin fold. Protein Sci. 1994;3:1706–1711. doi: 10.1002/pro.5560031009. PubMed DOI PMC
Rost B. Enzyme function less conserved than anticipated. J. Mol. Biol. 2002;318:595–608. doi: 10.1016/S0022-2836(02)00016-5. PubMed DOI
Miller ML, et al. Pan-cancer analysis of mutation hotspots in protein domains. Cell Systems. 2015;1:197–209. doi: 10.1016/j.cels.2015.08.014. PubMed DOI PMC
Salgado D, et al. UMD-Predictor: a high-throughput sequencing compliant system for pathogenicity prediction of any human cDNA substitution. Hum. Mutat. 2016;37:439–446. doi: 10.1002/humu.22965. PubMed DOI PMC
Havrilla JM, Pedersen BS, Layer RM, Quinlan AR. A map of constrained coding regions in the human genome. Nat. Genet. 2019;51:88–95. doi: 10.1038/s41588-018-0294-6. PubMed DOI PMC
Bastarache L, et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science. 2018;359:1233–1239. doi: 10.1126/science.aal4043. PubMed DOI PMC
Romeo S, et al. Rare loss-of-function mutations in ANGPTL family members contribute to plasma triglyceride levels in humans. J. Clin. Invest. 2009;119:70–79. PubMed PMC
Rost B, Radivojac P, Bromberg Y. Protein function in precision medicine: deep understanding with machine learning. FEBS Lett. 2016;590:2327–2341. doi: 10.1002/1873-3468.12307. PubMed DOI PMC
Fowler DM, Fields S. Deep mutational scanning: a new style of protein science. Nat. Methods. 2014;11:801–807. doi: 10.1038/nmeth.3027. PubMed DOI PMC
Boucher JI, Bolon DN, Tawfik DS. Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature. Protein Sci. 2016;25:1219–1226. doi: 10.1002/pro.2928. PubMed DOI PMC
Singleton MV, et al. Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families. Am. J. Hum. Genet. 2014;94:599–610. doi: 10.1016/j.ajhg.2014.03.010. PubMed DOI PMC
Bone WP, et al. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genet. Med. 2016;18:608–617. doi: 10.1038/gim.2015.137. PubMed DOI PMC
Simonti CN, et al. The phenotypic legacy of admixture between modern humans and Neadertals. Science. 2016;351:737–741. doi: 10.1126/science.aad2149. PubMed DOI PMC
Posey JE, et al. Resolution of disease phenotypes resulting from multilocus genomic variation. N. Engl. J. Med. 2017;376:21–31. doi: 10.1056/NEJMoa1516767. PubMed DOI PMC
Bendl J, et al. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput. Biol. 2014;10:e1003440. doi: 10.1371/journal.pcbi.1003440. PubMed DOI PMC
Bendl J, et al. PredictSNP2: a unified platform for accurately evaluating SNP effects by exploiting the different characteristics of variants in distinct genomic regions. PLoS Comput. Biol. 2016;12:e1004962. doi: 10.1371/journal.pcbi.1004962. PubMed DOI PMC
Kircher M, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 2014;46:310–315. doi: 10.1038/ng.2892. PubMed DOI PMC
Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31:761–763. doi: 10.1093/bioinformatics/btu703. PubMed DOI PMC
Sarkozy A, et al. Clinical and molecular analysis of 30 patients with multiple lentigines LEOPARD syndrome. J. Med. Genet. 2004;41:e68. doi: 10.1136/jmg.2003.013466. PubMed DOI PMC
Yoshida R, et al. Two novel and one recurrent PTPN11 mutations in LEOPARD syndrome. Am. J. Med. Genet. A. 2004;130A:432–434. doi: 10.1002/ajmg.a.30281. PubMed DOI
Osawa R, et al. A novel PTPN11 missense mutation in a patient with LEOPARD syndrome. Br. J. Dermatol. 2009;161:1202–1204. doi: 10.1111/j.1365-2133.2009.09385.x. PubMed DOI
Digilio MC, et al. Grouping of multiple-lentigines/LEOPARD and Noonan syndromes on the PTPN11 gene. Am. J. Hum. Genet. 2002;71:389–394. doi: 10.1086/341528. PubMed DOI PMC
Seishima M, et al. Malignant melanoma in a woman with LEOPARD syndrome: identification of a germline PTPN11 mutation and a somatic BRAF mutation. Br. J. Dermatol. 2007;157:1297–1299. doi: 10.1111/j.1365-2133.2007.08229.x. PubMed DOI
Conti E, et al. A novel PTPN11 mutation in LEOPARD syndrome. Hum. Mutat. 2003;21:654. doi: 10.1002/humu.9149. PubMed DOI
Keren B, et al. PTPN11 mutations in patients with LEOPARD syndrome: a French multicentric experience. J. Med. Genet. 2004;41:e117. doi: 10.1136/jmg.2004.021451. PubMed DOI PMC
Sarkozy A, et al. Correlation between PTPN11 gene mutations and congenital heart defects in Noonan and LEOPARD syndromes. J. Med. Genet. 2003;40:704–708. doi: 10.1136/jmg.40.9.704. PubMed DOI PMC
Atik T, et al. Mutation spectrum and phenotypic features in Noonan syndrome with PTPN11 mutations: definition of two novel mutations. Indian J. Pediatr. 2016;83:517–521. doi: 10.1007/s12098-015-1998-6. PubMed DOI
Tartaglia M, et al. PTPN11 mutations in Noonan syndrome: molecular spectrum, genotype-phenotype correlation, and phenotypic heterogeneity. Am. J. Hum. Genet. 2002;70:1555–1563. doi: 10.1086/340847. PubMed DOI PMC
Al-Gazali L, Ali BR. Mutations of a country: a mutation review of single gene disorders in the United Arab Emirates (UAE) Hum. Mutat. 2010;31:505–520. doi: 10.1002/humu.21232. PubMed DOI
Knott M, et al. Novel and Mediterranean beta thalassemia mutations in the indigenous Northern Ireland population. Blood Cells Mol. Dis. 2006;36:265–268. doi: 10.1016/j.bcmd.2005.12.005. PubMed DOI
Colah R, et al. Regional heterogeneity of beta-thalassemia mutations in the multi ethnic Indian population. Blood Cells Mol. Dis. 2009;42:241–246. doi: 10.1016/j.bcmd.2008.12.006. PubMed DOI
Villegas A, et al. Hb Santander [beta34(B16)Val–> Asp (GTC–> GAC)]: a new unstable variant found as a de novo mutation in a Spanish patient. Hemoglobin. 2003;27:31–35. doi: 10.1081/HEM-120016378. PubMed DOI
Henderson SJ, et al. Ten years of routine α- and β-globin gene sequencing in UK hemoglobinopathy referrals reveals 60 novel mutations. Hemoglobin. 2016;40:75–84. doi: 10.3109/03630269.2015.1113990. PubMed DOI
Zanella-Cleon I, et al. Strategy for identification by mass spectrometry of a new human hemoglobin variant with two mutations in Cis in the beta-globin chain: Hb S-Clichy [beta6(A3)Glu–>Val; beta8(A5)Lys–>Thr] Hemoglobin. 2009;33:177–187. doi: 10.1080/03630260903061184. PubMed DOI
Wajcman H, et al. Two new hemoglobin variants with increased oxygen affinity: Hb Nantes [beta34(B16)Val–>Leu] and Hb Vexin [beta116(G18)His–>Leu] Hemoglobin. 2003;27:191–199. doi: 10.1081/HEM-120023384. PubMed DOI
McClure RF, Hoyer JD, Mai M. The JAK2 V617F mutation is absent in patients with erythrocytosis due to high oxygen affinity hemoglobin variants. Hemoglobin. 2006;30:487–489. doi: 10.1080/03630260600868147. PubMed DOI
Shin SY, Bang SM, Kim HJ. A novel hemoglobin variant associated with congenital erythrocytosis: Hb Seoul [β86(F2)Ala→Thr] (HBB:c.259G>A) Ann. Clin. Lab. Sci. 2016;46:312–314. PubMed
Vulliamy T, Beutler E, Luzzatto L. Variants of glucose-6-phosphate dehydrogenase are due to missense mutations spread throughout the coding region of the gene. Hum. Mutat. 1993;2:159–167. doi: 10.1002/humu.1380020302. PubMed DOI
Bulliamy T, Luzzatto L, Hirono A, Beutler E. Hematologically important mutations: glucose-6-phosphate dehydrogenase. Blood Cells Mol. Dis. 1997;23:302–313. doi: 10.1006/bcmd.1997.0147. PubMed DOI
Yan T, et al. Incidence and complete molecular characterization of glucose-6-phosphate dehydrogenase deficiency in the Guangxi Zhuang autonomous region of southern China: description of four novel mutations. Haematologica. 2006;91:1321–1328. PubMed
McGlacken-Byrne SM, et al. The evolving course of HNF4A hyperinsulinaemic hypoglycaemia–a case series. Diabet. Med. 2014;31:e1–e5. doi: 10.1111/dme.12259. PubMed DOI
Flanagan SE, et al. Diazoxide-responsive hyperinsulinemic hypoglycemia caused by HNF4A gene mutations. Eur. J. Endocrinol. 2010;162:987–992. doi: 10.1530/EJE-09-0861. PubMed DOI PMC
Colclough K, Bellanne-Chantelot C, Saint-Martin C, Flanagan SE, Ellard S. Mutations in the genes encoding the transcription factors hepatocyte nuclear factor 1 alpha and 4 alpha in maturity-onset diabetes of the young and hyperinsulinemic hypoglycemia. Hum. Mutat. 2013;34:669–685. doi: 10.1002/humu.22279. PubMed DOI
Urbanová J, et al. Positivity for islet cell autoantibodies in patients with monogenic diabetes is associated with later diabetes onset and higher HbA1c level. Diabet. Med. 2014;31:466–471. doi: 10.1111/dme.12314. PubMed DOI
Harries LW, et al. The diabetic phenotype in HNF4A mutation carriers is moderated by the expression of HNF4A isoforms from the P1 promoter during fetal development. Diabetes. 2008;57:1745–1752. doi: 10.2337/db07-1742. PubMed DOI
Song S, et al. EDA gene mutations underlie non-syndromic oligodontia. J. Dent. Res. 2009;88:126–131. doi: 10.1177/0022034508328627. PubMed DOI PMC
Lee KE, et al. Oligodontia and curly hair occur with ectodysplasin-a mutations. J. Dent. Res. 2014;93:371–375. doi: 10.1177/0022034514522059. PubMed DOI
Ruiz-Heiland G, et al. Novel missense mutation in the EDA gene in a family affected by oligodontia. J. Orofac. Orthop. 2016;77:31–38. doi: 10.1007/s00056-015-0005-1. PubMed DOI
Cluzeau C, et al. Only four genes (EDA1, EDAR, EDARADD, and WNT10A) account for 90% of hypohidrotic/anhidrotic ectodermal dysplasia cases. Hum. Mutat. 2011;32:70–72. doi: 10.1002/humu.21384. PubMed DOI
Guazzarotti L, et al. Phenotypic heterogeneity and mutational spectrum in a cohort of 45 Italian males subjects with X-linked ectodermal dysplasia. Clin. Genet. 2015;87:338–342. doi: 10.1111/cge.12404. PubMed DOI
Clauss F, et al. X-linked and autosomal recessive Hypohidrotic Ectodermal Dysplasia: genotypic-dental phenotypic findings. Clin. Genet. 2010;78:257–266. doi: 10.1111/j.1399-0004.2010.01376.x. PubMed DOI
Monreal AW, Zonana J, Ferguson B. Identification of a new splice form of the EDA1 gene permits detection of nearly all X-linked hypohidrotic ectodermal dysplasia mutations. Am. J. Hum. Genet. 1998;63:380–389. doi: 10.1086/301984. PubMed DOI PMC
Schneider P, et al. Mutations leading to X-linked hypohidrotic ectodermal dysplasia affect three major functional domains in the tumor necrosis factor family member ectodysplasin-A. J. Biol. Chem. 2001;276:18819–18827. doi: 10.1074/jbc.M101280200. PubMed DOI
Pääkkönen K, et al. The mutation spectrum of the EDA gene in X-linked anhidrotic ectodermal dysplasia. Hum. Mutat. 2001;17:349. doi: 10.1002/humu.33. PubMed DOI