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A Bayesian method to estimate variant-induced disease penetrance


Autoři: Brett M. Kroncke aff001;  Derek K. Smith aff004;  Yi Zuo aff004;  Andrew M. Glazer aff001;  Dan M. Roden aff001;  Jeffrey D. Blume aff004
Působiště autorů: Department of Medicine Vanderbilt University Medical Center, Nashville, Tennessee, United States of America aff001;  Vanderbilt Center for Arrhythmia Research and Therapeutics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America aff002;  Department of Pharmacology Vanderbilt University, Nashville, Tennessee, United States of America aff003;  Department of Biostatistics Vanderbilt University, Nashville, Tennessee, United States of America aff004;  Department of Biomedical Informatics Vanderbilt University Medical Center, Nashville, Tennessee, United States of America aff005
Vyšlo v časopise: A Bayesian method to estimate variant-induced disease penetrance. PLoS Genet 16(6): e32767. doi:10.1371/journal.pgen.1008862
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008862

Souhrn

A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance.

Klíčová slova:

Arrhythmia – Forecasting – Genetics of disease – Genomic medicine – Pathogenesis – Phenotypes – Probability distribution – Sodium channels


Zdroje

1. Tennessen JA, Bigham AW, O'Connor TD, Fu WQ, Kenny EE, Gravel S, et al. Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes. Science. 2012;337(6090):64–9. doi: 10.1126/science.1219240 22604720

2. Dewey FE, Murray MF, Overton JD, Habegger L, Leader JB, Fetterolf SN, et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR Study. Science. 2016;354(6319).

3. Van Hout CV, Tachmazidou I, Backman JD, Hoffman JX, Ye B, Pandey AK, et al. Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. bioRxiv. 2019.

4. Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. bioRxiv. 2019.

5. Chen R, Shi L, Hakenberg J, Naughton B, Sklar P, Zhang J, et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat Biotechnol. 2016;34(5):531–8. doi: 10.1038/nbt.3514 27065010

6. Krier J, Barfield R, Green RC, Kraft P. Reclassification of genetic-based risk predictions as GWAS data accumulate. Genome Med. 2016;8.

7. Cooper GM. Parlez-vous VUS? Genome Res. 2015;25(10):1423–6. doi: 10.1101/gr.190116.115 26430151

8. Hoffman-Andrews L. The known unknown: the challenges of genetic variants of uncertain significance in clinical practice. J Law Biosci. 2017;4(3):648–57. doi: 10.1093/jlb/lsx038 29868193

9. Ackerman MJ. Genetic purgatory and the cardiac channelopathies: Exposing the variants of uncertain/unknown significance issue. Heart Rhythm. 2015;12(11):2325–31. doi: 10.1016/j.hrthm.2015.07.002 26144349

10. Manolio TA, Fowler DM, Starita LM, Haendel MA, MacArthur DG, Biesecker LG, et al. Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research. Cell. 2017;169(1):6–12. doi: 10.1016/j.cell.2017.03.005 28340351

11. 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. Genet Med. 2015;17(5):405–24. doi: 10.1038/gim.2015.30 25741868

12. Tavtigian SV, Greenblatt MS, Harrison SM, Nussbaum RL, Prabhu SA, Boucher KM, et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med. 2018;20(9):1054–60. doi: 10.1038/gim.2017.210 29300386

13. Cooper DN, Krawczak M, Polychronakos C, Tyler-Smith C, Kehrer-Sawatzki H. Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Hum Genet. 2013;132(10):1077–130. doi: 10.1007/s00439-013-1331-2 23820649

14. Kapplinger JD, Tester DJ, Alders M, Benito B, Berthet M, Brugada J, et al. An international compendium of mutations in the SCN5A-encoded cardiac sodium channel in patients referred for Brugada syndrome genetic testing. Heart Rhythm. 2010;7(1):33–46. doi: 10.1016/j.hrthm.2009.09.069 20129283

15. Chen Q, Kirsch GE, Zhang D, Brugada R, Brugada J, Brugada P, et al. Genetic basis and molecular mechanism for idiopathic ventricular fibrillation. Nature. 1998;392(6673):293–6. doi: 10.1038/32675 9521325

16. Hong K, Berruezo-Sanchez A, Poungvarin N, Oliva A, Vatta M, Brugada J, et al. Phenotypic characterization of a large European family with Brugada syndrome displaying a sudden unexpected death syndrome mutation in SCN5A. J Cardiovasc Electrophysiol. 2004;15(1):64–9. doi: 10.1046/j.1540-8167.2004.03341.x 15028074

17. Potet F, Mabo P, Le Coq G, Probst V, Schott JJ, Airaud F, et al. Novel brugada SCN5A mutation leading to ST segment elevation in the inferior or the right precordial leads. J Cardiovasc Electrophysiol. 2003;14(2):200–3. doi: 10.1046/j.1540-8167.2003.02382.x 12693506

18. Kroncke BM, Glazer AM, Smith DK, Blume JD, Roden DM. SCN5A (NaV1.5) Variant Functional Perturbation and Clinical Presentation: Variants of a Certain Significance. Circ Genom Precis Med. 2018;11(5):e002095. doi: 10.1161/CIRCGEN.118.002095 29728395

19. Kroncke BM, Mendenhall J, Smith DK, Sanders CR, Capra JA, George AL, et al. Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1. Computational and Structural Biotechnology Journal. 2019;17:206–14. doi: 10.1016/j.csbj.2019.01.008 30828412

20. Kapplinger JD, Giudicessi JR, Ye D, Tester DJ, Callis TE, Valdivia CR, et al. Enhanced Classification of Brugada Syndrome-Associated and Long-QT Syndrome-Associated Genetic Variants in the SCN5A-Encoded Na(v)1.5 Cardiac Sodium Channel. Circ Cardiovasc Genet. 2015;8(4):582–95. doi: 10.1161/CIRCGENETICS.114.000831 25904541

21. Glazer AM, Wada Y, Li B, et al. High-Throughput Reclassification of SCN5A Variants [published online ahead of print, 2020 Jun 5]. Am J Hum Genet. 2020;S0002-9297(20)30162-2. doi: 10.1016/j.ajhg.2020.05.015

22. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016;44(D1):D862–8. doi: 10.1093/nar/gkv1222 26582918

23. Wright CF, West B, Tuke M, Jones SE, Patel K, Laver TW, et al. Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting. The American Journal of Human Genetics. 2019;104(2):275–86. doi: 10.1016/j.ajhg.2018.12.015 30665703

24. Katsanis N. The continuum of causality in human genetic disorders. Genome Biol. 2016;17(1):233. doi: 10.1186/s13059-016-1107-9 27855690

25. Tuke MA, Ruth KS, Wood AR, Beaumont RN, Tyrrell J, Jones SE, et al. Mosaic Turner syndrome shows reduced penetrance in an adult population study. Genetics in Medicine. 2018.

26. Shah N, Hou YC, Yu HC, Sainger R, Caskey CT, Venter JC, et al. Identification of Misclassified ClinVar Variants via Disease Population Prevalence. Am J Hum Genet. 2018;102(4):609–19. doi: 10.1016/j.ajhg.2018.02.019 29625023

27. Schwartz PJ, Crotti L, George AL Jr. Modifier genes for sudden cardiac death. Eur Heart J. 2018;39(44):3925–31. doi: 10.1093/eurheartj/ehy502 30215713

28. Hosseini SM, Kim R, Udupa S, Costain G, Jobling R, Liston E, et al. Reappraisal of Reported Genes for Sudden Arrhythmic Death. Circulation. 2018;138(12):1195–205. doi: 10.1161/CIRCULATIONAHA.118.035070 29959160

29. Oetjens MT, Kelly MA, Sturm AC, Martin CL, Ledbetter DH. Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders. Nature communications. 2019;10(1):4897. doi: 10.1038/s41467-019-12869-0 31653860

30. Mizusawa Y, Wilde AA. Brugada syndrome. Circ Arrhythm Electrophysiol. 2012;5(3):606–16. doi: 10.1161/CIRCEP.111.964577 22715240

31. Copas JB. Regression, Prediction and Shrinkage. J R Stat Soc B. 1983;45(3):311–54.

32. Dempster A, Laird N, Rdin D, editors. {M}aximum {L}ikelihood from {I}ncomplete {D}ata via the {EM} {A}lgorithm. JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B; 1977.

33. 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):285–91. doi: 10.1038/nature19057 27535533

34. Postema PG. About Brugada syndrome and its prevalence. Europace. 2012;14(7):925–8. doi: 10.1093/europace/eus042 22417721

35. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073–81. doi: 10.1038/nprot.2009.86 19561590

36. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–9. doi: 10.1038/nmeth0410-248 20354512

37. 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):e46688. doi: 10.1371/journal.pone.0046688 23056405

38. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402. doi: 10.1093/nar/25.17.3389 9254694

39. Pupko T, Bell RE, Mayrose I, Glaser F, Ben-Tal N. Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues. Bioinformatics. 2002;18 Suppl 1:S71–7.

40. Li B, Mendenhall JL, Kroncke BM, Taylor KC, Huang H, Smith DK, et al. Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance. Circ Cardiovasc Genet. 2017;10(5).

41. Dayhoff MO, Schwartz RM, Orcutt BC. A model of evolutionary change in proteins. Atlas of protein sequence and structure. 1978;5(suppl 3):345–51.

42. Fletcher Mercaldo S, Blume JD. Missing data and prediction: the pattern submodel. Biostatistics. 2018.


Článek vyšel v časopise

PLOS Genetics


2020 Číslo 6
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