GAVIN: Gene-Aware Variant INterpretation for medical sequencing

Genome Biology

Volumn 18, Issue 1, 2017, Pages

  van der Velde, K Joeri ^a   de Boer, Eddy N ^a   van Diemen, Cleo C ^a   Sikkema Raddatz, Birgit ^a   Abbott, Kristin M ^a   Knopperts, Alain ^a   Franke, Lude ^a   Sijmons, Rolf H ^a   de Koning, Tom J ^a   Wijmenga, Cisca ^a   Sinke, Richard J ^a   Swertz, Morris A ^a  

^a UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

Author keywords

Allele frequency; Automated protocol; Clinical next generation sequencing; Gene specific calibration; Pathogenicity prediction; Protein impact; Variant classification

Indexed keywords

CALIBRATION; CLASSIFICATION; DATA BASE; DIAGNOSTIC TEST ACCURACY STUDY; GENE FREQUENCY; HUMAN; HUMAN EXPERIMENT; NEXT GENERATION SEQUENCING; PATHOGENICITY; PIPELINE; PREDICTION; BIOLOGY; GENETIC ASSOCIATION STUDY; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; PROCEDURES; SOFTWARE;

COMPUTATIONAL BIOLOGY; GENE FREQUENCY; GENETIC ASSOCIATION STUDIES; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; HUMANS; SOFTWARE;

EID: 85010014418     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-016-1141-7     Document Type: Article

References (37)

1. Berg JS, Khoury MJ, Evans JP. Deploying whole genome sequencing in clinical practice and public health: Meeting the challenge one bin at a time. Genet Med. 2011;13:499-504.
2. Cooper GM, Shendure J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat Rev Genet. 2011;12:628-40.
3. 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. Nat Genet. 2014;46:310-5.
4. van der Velde KJ, Kuiper J, Thompson BA, Plazzer JP, van Valkenhoef G, de Haan M, et al. Evaluation of CADD scores in curated mismatch repair gene variants yields a model for clinical validation and prioritization. Hum Mutat. 2015;36:712-9.
5. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4:1073-81.
6. Combined Annotation Dependent Depletion (CADD). Available: http://cadd.gs.washington.edu/info. [Accessed 1 Oct 2015]
7. Itan Y, Shang L, Boisson B, Ciancanelli MJ, Markle JG, Martinez-Barricarte R, et al. The mutation significance cutoff: gene-level thresholds for variant predictions. Nat Methods. 2016;13:109-10.
8. 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;17:405-23.
9. Lek M, Karczewski K, Minikel E, Samocha K, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285-91.
10. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w(1118); iso-2; iso-3. Fly (Austin). 2012;6:80-92.
11. 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.
12. Sasidharan Nair P, Vihinen M. VariBench: A Benchmark Database for Variations. Hum Mutat. 2013;34:42-9.
13. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Meth. 2014;11:361-2.
14. Solomon BD, Nguyen A-D, Bear KA, Wolfsberg TG. Clinical Genomic Database. Proc Natl Acad Sci. 2013;110(24):9851-5.
15. 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;7:248-9.
16. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One. 2012;7, e46688.
17. González-Pérez A, López-Bigas N. Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score. Condel Am J Hum Genet. 2011;88:440-9.
18. Niroula A, Urolagin S, Vihinen M. PON-P2: Prediction method for fast and reliable identification of harmful variants. PLoS One. 2015;10:1-17.
19. Bendl J, Musil M, Štourač J, Zendulka J, Damborský J, Brezovský J. 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.
20. Shihab HA, Rogers MF, Gough J, Mort M, Cooper DN, Day INM, et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinforma. 2015;31(10):1536-43.
21. Ritchie GRS, Dunham I, Zeggini E, Flicek P. Functional annotation of noncoding sequence variants. Nat Meth. 2014;11:294-6.
22. Fu Y, Liu Z, Lou S, Bedford J, Mu XJ, Yip KY, et al. FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol. 2014;15:1-15.
23. Quang D, Chen Y, Xie X. DANN: A deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31:761-3.
24. Stenson PD, Mort M, Ball EV, Shaw K, Phillips AD, Cooper DN. 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.
25. Mather CA, Mooney SD, Salipante SJ, Scroggins S, Wu D, Pritchard CC, et al. CADD score has limited clinical validity for the identification of pathogenic variants in noncoding regions in a hereditary cancer panel. Genet Med. 2016;18:1269-75.
26. 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. Proc Natl Acad Sci. 2015;112(44):13615-20.
27. Song W, Gardner SA, Hovhannisyan H, Natalizio A, Weymouth KS, Chen W, et al. Exploring the landscape of pathogenic genetic variation in the ExAC population database: insights of relevance to variant classification. Genet Med. 2016;18:850-4.
28. 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;32:1077-130.
29. Zhang F, Lupski JR. Non-coding genetic variants in human disease. Hum Mol Genet. 2015;24:R102-10.
30. Ionita-Laza I, McCallum K, Xu B, Buxbaum JD. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet. 2016;48:214-20.
31. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Meth. 2015;12:931-4.
32. Swertz MA, Dijkstra M, Adamusiak T, van der Velde JK, Kanterakis A, Roos ET, et al. The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button. BMC Bioinformatics. 2010;11 Suppl 1:S12.
33. Giardine B, Riemer C, Hefferon T, Thomas D, Hsu F, Zielenski J, et al. PhenCode: connecting ENCODE data with mutations and phenotype. Hum Mutat. 2007;28:554-62.
34. Piirilä H, Väliaho J, Vihinen M. Immunodeficiency mutation databases (IDbases). Hum Mutat. 2006;27:1200-8.
35. Association of Clinical Genetics Netherlands. Available: http://vkgn.org/vakinformatie/richtlijnen-en-protocollen/. [Accessed 15 March 2016]
36. The 1000 Genomes Project. A global reference for human genetic variation. Nature. 2015;526:68-74.
37. The Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat Genet. 2014;46:818-25.