Unified inference of missense variant effects and gene constraints in the human genome


Autoři: Yi-Fei Huang aff001
Působiště autorů: Department of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America aff001;  Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America aff002
Vyšlo v časopise: Unified inference of missense variant effects and gene constraints in the human genome. PLoS Genet 16(7): e32767. doi:10.1371/journal.pgen.1008922
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008922

Souhrn

A challenge in medical genomics is to identify variants and genes associated with severe genetic disorders. Based on the premise that severe, early-onset disorders often result in a reduction of evolutionary fitness, several statistical methods have been developed to predict pathogenic variants or constrained genes based on the signatures of negative selection in human populations. However, we currently lack a statistical framework to jointly predict deleterious variants and constrained genes from both variant-level features and gene-level selective constraints. Here we present such a unified approach, UNEECON, based on deep learning and population genetics. UNEECON treats the contributions of variant-level features and gene-level constraints as a variant-level fixed effect and a gene-level random effect, respectively. The sum of the fixed and random effects is then combined with an evolutionary model to infer the strength of negative selection at both variant and gene levels. Compared with previously published methods, UNEECON shows improved performance in predicting missense variants and protein-coding genes associated with autosomal dominant disorders, and feature importance analysis suggests that both gene-level selective constraints and variant-level predictors are important for accurate variant prioritization. Furthermore, based on UNEECON, we observe a low correlation between gene-level intolerance to missense mutations and that to loss-of-function mutations, which can be partially explained by the prevalence of disordered protein regions that are highly tolerant to missense mutations. Finally, we show that genes intolerant to both missense and loss-of-function mutations play key roles in the central nervous system and the autism spectrum disorders. Overall, UNEECON is a promising framework for both variant and gene prioritization.

Klíčová slova:

Autosomal dominant diseases – Evolutionary genetics – Forecasting – Gene prediction – Human genomics – Missense mutation – Mutation – Structural genomics


Zdroje

1. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, 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. Genetics in Medicine. 2015;17(5):405–423. doi: 10.1038/gim.2015.30 25741868

2. Maxwell K, Hart S, Vijai J, Schrader K, Slavin T, Thomas T, et al. Evaluation of ACMG-Guideline-Based Variant Classification of Cancer Susceptibility and Non-Cancer-Associated Genes in Families Affected by Breast Cancer. The American Journal of Human Genetics. 2016;98(5):801–817. doi: 10.1016/j.ajhg.2016.02.024 27153395

3. Eilbeck K, Quinlan A, Yandell M. Settling the score: variant prioritization and Mendelian disease. Nature Reviews Genetics. 2017;18(10):599–612. doi: 10.1038/nrg.2017.52

4. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Research. 2011;39(17):e118. doi: 10.1093/nar/gkr407

5. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Research. 2003;31(13):3812–3814. doi: 10.1093/nar/gkg509

6. Cooper GM, Shendure J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nature Reviews Genetics. 2011;12(9):628–640. doi: 10.1038/nrg3046

7. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLOS ONE. 2012;7(10):1–13.

8. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics. 2014;46(3):310–315. doi: 10.1038/ng.2892

9. Gulko B, Hubisz MJ, Gronau I, Siepel A. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nature Genetics. 2015;47(3):276–283. doi: 10.1038/ng.3196

10. Huang YF, Gulko B, Siepel A. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nature Genetics. 2017;49(4):618–624. doi: 10.1038/ng.3810

11. Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, et al. Predicting the clinical impact of human mutation with deep neural networks. Nature Genetics. 2018;50(8):1161–1170. doi: 10.1038/s41588-018-0167-z 30038395

12. Huang YF, Siepel A. Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease. Genome Research. 2019;29(8):1310–1321. doi: 10.1101/gr.245522.118

13. Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, et al. Integrative annotation of variants from 1092 humans: application to cancer genomics. Science. 2013;342(6154):1235587. doi: 10.1126/science.1235587 24092746

14. Fu Y, Liu Z, Lou S, Bedford J, Mu X, Yip K, et al. FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biology. 2014;15(10):480. doi: 10.1186/s13059-014-0480-5 25273974

15. Gulko B, Siepel A. An evolutionary framework for measuring epigenomic information and estimating cell-type-specific fitness consequences. Nature Genetics. 2019;51(2):335–342. doi: 10.1038/s41588-018-0300-z

16. Petrovski S, Wang Q, Heinzen EL, Allen AS, Goldstein DB. Genic intolerance to functional variation and the interpretation of personal genomes. PLOS Genetics. 2013;9(8):e1003709. doi: 10.1371/journal.pgen.1003709

17. Samocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A, McGrath LM, et al. A framework for the interpretation of de novo mutation in human disease. Nature Genetics. 2014;46(9):944–950. doi: 10.1038/ng.3050 25086666

18. Petrovski S, Gussow AB, Wang Q, Halvorsen M, Han Y, Weir WH, et al. The intolerance of regulatory sequence to genetic variation predicts gene dosage sensitivity. PLoS Genet. 2015;11(9):e1005492. doi: 10.1371/journal.pgen.1005492 26332131

19. Itan Y, Shang L, Boisson B, Patin E, Bolze A, Moncada-Vélez M, et al. The human gene damage index as a gene-level approach to prioritizing exome variants. Proceedings of the National Academy of Sciences. 2015;112(44):13615–13620. doi: 10.1073/pnas.1518646112

20. Gussow A, Petrovski S, Wang Q, Allen A, Goldstein D. The intolerance to functional genetic variation of protein domains predicts the localization of pathogenic mutations within genes. Genome Biology. 2016;17(1):9. doi: 10.1186/s13059-016-0869-4

21. Pérez-Palma E, May P, Iqbal S, Niestroj LM, Du J, Heyne H, et al. Identification of pathogenic variant enriched regions across genes and gene families. bioRxiv. 2019;

22. Havrilla JM, Pedersen BS, Layer RM, Quinlan AR. A map of constrained coding regions in the human genome. Nature Genetics. 2019;51(1):88–95. doi: 10.1038/s41588-018-0294-6

23. Silk M, Petrovski S, Ascher DB. MTR-Viewer: identifying regions within genes under purifying selection. Nucleic Acids Research. 2019;47(W1):W121–W126. doi: 10.1093/nar/gkz457

24. Iossifov I, Levy D, Allen J, Ye K, Ronemus M, Lee Yh, et al. Low load for disruptive mutations in autism genes and their biased transmission. Proceedings of the National Academy of Sciences. 2015;112(41):E5600–E5607. doi: 10.1073/pnas.1516376112

25. Samocha KE, Kosmicki JA, Karczewski KJ, O’Donnell-Luria AH, Pierce-Hoffman E, MacArthur DG, et al. Regional missense constraint improves variant deleteriousness prediction. bioRxiv. 2017;

26. Jagadeesh KA, Wenger AM, Berger MJ, Guturu H, Stenson PD, Cooper DN, et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nature Genetics. 2016;48(12):1581–1586. doi: 10.1038/ng.3703 27776117

27. Evans P, Wu C, Lindy A, McKnight DA, Lebo M, Sarmady M, et al. Genetic variant pathogenicity prediction trained using disease-specific clinical sequencing data sets. Genome Research. 2019;29(7):1144–1151. doi: 10.1101/gr.240994.118 31235655

28. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–291. doi: 10.1038/nature19057 27535533

29. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. bioRxiv. 2019;

30. Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Research. 2014;42(D1):D980–D985. doi: 10.1093/nar/gkt1113 24234437

31. Deciphering Developmental Disorders Study, McRae JF, Clayton S, Fitzgerald TW, Kaplanis J, Prigmore E, et al. Prevalence and architecture of de novo mutations in developmental disorders. Nature. 2017;542:433–438. doi: 10.1038/nature21062

32. Hart T, Brown KR, Sircoulomb F, Rottapel R, Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Molecular Systems Biology. 2014;10(7):733. doi: 10.15252/msb.20145216

33. Blake JA, Bult CJ, Kadin JA, Richardson JE, Eppig JT, the Mouse Genome Database Group. The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics. Nucleic Acids Research. 2010;39(suppl1):D842–D848.

34. Georgi B, Voight BF, Bucan M. From mouse to human: evolutionary genomics analysis of human orthologs of essential genes. PLOS Genetics. 2013;9(5):e1003484. doi: 10.1371/journal.pgen.1003484

35. Blekhman R, Man O, Herrmann L, Boyko AR, Indap A, Kosiol C, et al. Natural Selection on Genes that Underlie Human Disease Susceptibility. Current Biology. 2008;18(12):883–889. doi: 10.1016/j.cub.2008.04.074 18571414

36. Berg JS, Adams M, Nassar N, Bizon C, Lee K, Schmitt CP, et al. An informatics approach to analyzing the incidentalome. Genetics In Medicine. 2012;15:36. doi: 10.1038/gim.2012.112 22995991

37. Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, et al. ClinGen—the clinical genome resource. New England Journal of Medicine. 2015;372(23):2235–2242. doi: 10.1056/NEJMsr1406261 26014595

38. Armon A, Graur D, Ben-Tal N. ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information. Journal of Molecular Biology. 2001;307(1):447–463. doi: 10.1006/jmbi.2000.4474

39. Cooper GM, Stone EA, Asimenos G, Green ED, Batzoglou S, Sidow A. Distribution and intensity of constraint in mammalian genomic sequence. Genome Research. 2005;15(7):901–913. doi: 10.1101/gr.3577405

40. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Research. 2010;20(1):110–121. doi: 10.1101/gr.097857.109

41. Huang YF, Golding GB. Phylogenetic Gaussian process model for the inference of functionally important regions in protein tertiary structures. PLoS Computational Biology. 2014;10(1):e1003429. doi: 10.1371/journal.pcbi.1003429

42. Huang YF, Golding GB. FuncPatch: a web server for the fast Bayesian inference of conserved functional patches in protein 3D structures. Bioinformatics. 2015;31(4):523–531. doi: 10.1093/bioinformatics/btu673

43. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research. 2014;15:1929–1958.

44. Bengio Y. Practical recommendations for gradient-based training of deep architectures. In: Neural networks: tricks of the trade. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 437–478.

45. Mainland JD, Li YR, Zhou T, Liu WLL, Matsunami H. Human olfactory receptor responses to odorants. Scientific Data. 2015;2:150002. doi: 10.1038/sdata.2015.2

46. Gilad Y, Bustamante CD, Lancet D, Pääbo S. Natural selection on the olfactory receptor gene family in humans and chimpanzees. The American Journal of Human Genetics. 2003;73(3):489–501. doi: 10.1086/378132

47. McGarvey PB, Nightingale A, Luo J, Huang H, Martin MJ, Wu C, et al. UniProt genomic mapping for deciphering functional effects of missense variants. Human mutation. 2019;40(6):694–705. doi: 10.1002/humu.23738 30840782

48. Piovesan D, Tabaro F, Paladin L, Necci M, Mičetić I, Camilloni C, et al. MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins. Nucleic Acids Research. 2017;46(D1):D471–D476. doi: 10.1093/nar/gkx1071

49. The UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Research. 2018;47(D1):D506–D515. doi: 10.1093/nar/gky1049

50. Ionita-Laza I, McCallum K, Xu B, Buxbaum JD. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nature Genetics. 2016;48(2):214–220. doi: 10.1038/ng.3477

51. Grimm DG, Azencott CA, Aicheler F, Gieraths U, MacArthur DG, Samocha KE, et al. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Human Mutation. 2015;36(5):513–523. doi: 10.1002/humu.22768 25684150

52. Turner TN, Yi Q, Krumm N, Huddleston J, Hoekzema K, F Stessman HA, et al. denovo-db: a compendium of human de novo variants. Nucleic Acids Research. 2016;45(D1):D804–D811. doi: 10.1093/nar/gkw865 27907889

53. Ziegler A, Colin E, Goudenège D, Bonneau D. A snapshot of some pLI score pitfalls. Human Mutation. 2019;40(7):839–841.

54. Wright PE, Dyson HJ. Intrinsically disordered proteins in cellular signalling and regulation. Nature Reviews Molecular Cell Biology. 2015;16:18. doi: 10.1038/nrm3920

55. Brown CJ, Takayama S, Campen AM, Vise P, Marshall TW, Oldfield CJ, et al. Evolutionary rate heterogeneity in proteins with long disordered regions. Journal of Molecular Evolution. 2002;55(1):104–110. doi: 10.1007/s00239-001-2309-6 12165847

56. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome athway Knowledgebase. Nucleic Acids Research. 2018;46(D1):D649–D655. doi: 10.1093/nar/gkx1132 29145629

57. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics. 2000;25(1):25–29. doi: 10.1038/75556 10802651

58. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research. 2019;47(D1):D330–D338. doi: 10.1093/nar/gky1055

59. Abrahams BS, Arking DE, Campbell DB, Mefford HC, Morrow EM, Weiss LA, et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Molecular Autism. 2013;4(1):36. doi: 10.1186/2040-2392-4-36 24090431

60. Fuller ZL, Berg JJ, Mostafavi H, Sella G, Przeworski M. Measuring intolerance to mutation in human genetics. Nature Genetics. 2019;51(5):772–776. doi: 10.1038/s41588-019-0383-1

61. Wainschtein P, Jain DP, Yengo L, Zheng Z, Cupples LA, Shadyab AH, et al. Recovery of trait heritability from whole genome sequence data. bioRxiv. 2019;

62. Starita LM, Ahituv N, Dunham MJ, Kitzman JO, Roth FP, Seelig G, et al. Variant interpretation: functional assays to the rescue. The American Journal of Human Genetics. 2017;101(3):315–325. doi: 10.1016/j.ajhg.2017.07.014 28886340

63. Kinney JB, McCandlish DM. Massively parallel assays and quantitative sequence-function pelationships. Annual Review of Genomics and Human Genetics. 2019;20:99–127. doi: 10.1146/annurev-genom-083118-014845

64. Massingham T, Goldman N. Detecting amino acid sites under positive selection and purifying selection. Genetics. 2005;169(3):1753–1762. doi: 10.1534/genetics.104.032144

65. Grantham R. Amino acid difference formula to help explain protein evolution. Science. 1974;185(4154):862–864. doi: 10.1126/science.185.4154.862

66. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nature Methods. 2010;7:248–249. doi: 10.1038/nmeth0410-248 20354512

67. Chun S, Fay JC. Identification of deleterious mutations within three human genomes. Genome Research. 2009;19(9):1553–1561. doi: 10.1101/gr.092619.109

68. Wong WC, Kim D, Carter H, Diekhans M, Ryan MC, Karchin R. CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer. Bioinformatics. 2011;27(15):2147–2148. doi: 10.1093/bioinformatics/btr357

69. Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RKC, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015;347:1254806. doi: 10.1126/science.1254806 25525159

70. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–330. doi: 10.1038/nature14248 25693563

71. Arbiza L, Gronau I, Aksoy BA, Hubisz MJ, Gulko B, Keinan A, et al. Genome-wide inference of natural selection on human transcription factor binding sites. Nature Genetics. 2013;45(7):723–729. doi: 10.1038/ng.2658 23749186

72. Gronau I, Arbiza L, Mohammed J, Siepel A. Inference of natural selection from interspersed genomic elements based on polymorphism and divergence. Molecular Biology and Evolution. 2013;30(5):1159–1171. doi: 10.1093/molbev/mst019

73. Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Research. 2012;22(9):1760–1774. doi: 10.1101/gr.135350.111 22955987

74. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Research. 2005;15(8):1034–1050. doi: 10.1101/gr.3715005 16024819

75. Team RDC. R: a language and environment for statistical computing; 2008. Available from: http://www.R-project.org.

76. Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. ICML’10. USA: Omnipress; 2010. p. 807–814.

77. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Teh YW, Titterington M, editors. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. vol. 9 of Proceedings of Machine Learning Research. Chia Laguna Resort, Sardinia, Italy: PMLR; 2010. p. 249–256.

78. Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv:14126980. 2014;.

79. Liu X, Jian X, Eric B. dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Human Mutation. 2013;34(9):E2393–E2402. doi: 10.1002/humu.22376

80. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21(20):3940–3941. doi: 10.1093/bioinformatics/bti623

81. Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515:216–221. doi: 10.1038/nature13908 25363768

82. Krumm N, Turner TN, Baker C, Vives L, Mohajeri K, Witherspoon K, et al. Excess of rare, inherited truncating mutations in autism. Nature Genetics. 2015;47(6):582–588. doi: 10.1038/ng.3303 25961944

83. Turner T, Hormozdiari F, Duyzend M, McClymont S, Hook P, Iossifov I, et al. Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. The American Journal of Human Genetics. 2016;98(1):58–74. doi: 10.1016/j.ajhg.2015.11.023 26749308

84. Yuen RKC, Merico D, Bookman M, L Howe J, Thiruvahindrapuram B, Patel RV, et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nature Neuroscience. 2017;20:602–611. doi: 10.1038/nn.4524

85. Werling DM, Brand H, An JY, Stone MR, Zhu L, Glessner JT, et al. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. Nature Genetics. 2018;50(5):727–736. doi: 10.1038/s41588-018-0107-y 29700473

86. Rauch A, Wieczorek D, Graf E, Wieland T, Endele S, Schwarzmayr T, et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. The Lancet. 2012;380(9854):1674–1682. doi: 10.1016/S0140-6736(12)61480-9

87. Gulsuner S, Walsh T, Watts A, Lee M, Thornton A, Casadei S, et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell. 2013;154(3):518–529. doi: 10.1016/j.cell.2013.06.049 23911319

88. The 1000 Genomes Project, Conrad DF, Keebler JEM, DePristo MA, Lindsay SJ, Zhang Y, et al. Variation in genome-wide mutation rates within and between human families. Nature Genetics. 2011;43(7):712–714. doi: 10.1038/ng.862 21666693

89. Ramu A, Noordam MJ, Schwartz RS, Wuster A, Hurles ME, Cartwright RA, et al. DeNovoGear: de novo indel and point mutation discovery and phasing. Nature Methods. 2013;10(1):985–987. doi: 10.1038/nmeth.2611 23975140

90. The Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nature Genetics. 2014;46:818–825. doi: 10.1038/ng.3021

91. Besenbacher S, Liu S, Izarzugaza JMG, Grove J, Belling K, Bork-Jensen J, et al. Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios. Nature Communications. 2015;6:5969. doi: 10.1038/ncomms6969 25597990

92. Ho DE, Imai K, King G, Stuart EA. MatchIt: nonparametric preprocessing for parametric causal inference. Journal of Statistical Software. 2011;42(8):1–28.

93. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, et al. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Research. 2017;45(D1):D183–D189. doi: 10.1093/nar/gkw1138 27899595


Článek vyšel v časopise

PLOS Genetics


2020 Číslo 7

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

VIRTUÁLNÍ ČEKÁRNA ČR Jste praktický lékař nebo pediatr? Zapojte se! Jste praktik nebo pediatr? Zapojte se!

×