Cystic fibrosis testing in a referral laboratory: results and lessons from a six-year period

Journal of Clinical Bioinformatics

Karl V Voelkerding¹  Elaine Lyon¹  Jeffrey J Swensen¹  Rong Mao¹  D Hunter Best¹  Pinar Bayrak-Toydemir¹  Christine Miller¹  Perry G Ridge¹ 

[1] 500 W Chipeta Way, Salt Lake City, UT, 84108, USA

关键词: Interpretation of variants;    Next-generation sequencing;    Novel variants;    CFTR;    Cystic fibrosis;   

Others  :  804169
DOI  :  10.1186/2043-9113-3-3

 received in 2012-07-31, accepted in 2013-01-08,  发布年份 2013

【 摘 要 】

Background

The recent introduction of high throughput sequencing technologies into clinical genetics has made it practical to simultaneously sequence many genes. In contrast, previous technologies limited sequencing based tests to only a handful of genes. While the ability to more accurately diagnose inherited diseases is a great benefit it introduces specific challenges. Interpretation of missense mutations continues to be challenging and the number of variants of uncertain significance continues to grow.

Results

We leveraged the data available at ARUP Laboratories, a major reference laboratory, for the CFTR gene to explore specific challenges related to variant interpretation, including a focus on understanding ethnic-specific variants and an evaluation of existing databases for clinical interpretation of variants. In this study we analyzed 555 patients representing eight different ethnic groups. We observed 184 different variants, most of which were ethnic group specific. Eighty-five percent of these variants were present in the Cystic Fibrosis Mutation Database, whereas the Human Mutation Database and dbSNP/1000 Genomes had far fewer of the observed variants. Finally, 21 of the variants were novel and we report these variants and their clinical classifications.

Conclusions

Based on our analyses of data from six years of CFTR testing at ARUP Laboratories a more comprehensive, clinical grade database is needed for the accurate interpretation of observed variants. Furthermore, there is a particular need for more and better information regarding variants from individuals of non-Caucasian ethnicity.

【 授权许可】

   
2013 Ridge et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Moore B, Hu H, Singleton M, Reese MG, De La Vega FM, Yandell M: Global analysis of disease-related DNA sequence variation in 10 healthy individuals: implications for whole genome-based clinical diagnostics. Genet Med 2011, 13:210-217.
• [2]Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE, et al.: Targeted capture and massively parallel sequencing of 12 human exomes. Nature 2009, 461:272-276.
• [3]Musunuru K, Pirruccello JP, Do R, Peloso GM, Guiducci C, Sougnez C, Garimella KV, Fisher S, Abreu J, Barry AJ, et al.: Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N Engl J Med 2010, 363:2220-2227.
• [4]Bolze A, Byun M, McDonald D, Morgan NV, Abhyankar A, Premkumar L, Puel A, Bacon CM, Rieux-Laucat F, Pang K, et al.: Whole-Exome-Sequencing-Based Discovery of Human FADD Deficiency. Am J Hum Genet 2010, 87:873-881.
• [5]Byun M, Abhyankar A, Lelarge V, Plancoulaine S, Palanduz A, Telhan L, Boisson B, Picard C, Dewell S, Zhao C, et al.: Whole-exome sequencing-based discovery of STIM1 deficiency in a child with fatal classic Kaposi sarcoma. J Exp Med 2010, 207:2307-2312.
• [6]Vissers LE, de Ligt J, Gilissen C, Janssen I, Steehouwer M, de Vries P, van Lier B, Arts P, Wieskamp N, Del Rosario M, et al.: A de novo paradigm for mental retardation. Nat Genet 2010, 42:1109-1112.
• [7]Gilissen C, Arts HH, Hoischen A, Spruijt L, Mans DA, Arts P, van Lier B, Steehouwer M, van Reeuwijk J, Kant SG, et al.: Exome sequencing identifies WDR35 variants involved in Sensenbrenner syndrome. Am J Hum Genet 2010, 87:418-423.
• [8]Sherry ST, Ward M, Sirotkin K: dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res 1999, 9:677-679.
• [9]Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 2001, 29:308-311.
• [10]Bonnefond A, Durand E, Sand O, De Graeve F, Gallina S, Busiah K, Lobbens S, Simon A, Bellanne-Chantelot C, Letourneau L, et al.: Molecular diagnosis of neonatal diabetes mellitus using next-generation sequencing of the whole exome. PLoS One 2010, 5:e13630.
• [11]Richards CS, Bale S, Bellissimo DB, Das S, Grody WW, Hegde MR, Lyon E, Ward BE: ACMG recommendations for standards for interpretation and reporting of sequence variations: Revisions 2007. Genet Med 2008, 10:294-300.
• [12]Bell J, Bodmer D, Sistermans E, Ramsden SC: Practice guidelines for the interpretation and reporting of unclassified variants (UVs) in Clinical Molecular Genetics. Clin Mol Genet Soc 2007.
• [13]Vink GR, van Asperen CJ, Devilee P, Breuning MH, Bakker E: Unclassified variants in disease-causing genes: nonuniformity of genetic testing and counselling, a proposal for guidelines. Eur J Hum Genet 2005, 13:525-527.
• [14]Plon SE, Eccles DM, Easton D, Foulkes WD, Genuardi M, Greenblatt MS, Hogervorst FB, Hoogerbrugge N, Spurdle AB, Tavtigian SV: Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat 2008, 29:1282-1291.
• [15]Wilson JA: Population-based carrier screening for Cystic Fibrosis - clinical, molecular, and practical considerations for the obstetrician. US Respir Dis 2007.
• [16]Grody WW, Cutting GR, Klinger KW, Richards CS, Watson MS, Desnick RJ: Laboratory standards and guidelines for population-based cystic fibrosis carrier screening. Genet Med 2001, 3:149-154.
• [17]Moskowitz SM, Chmiel JF, Sternen DL, Cheng E, Gibson RL, Marshall SG, Cutting GR: Clinical practice and genetic counseling for cystic fibrosis and CFTR-related disorders. Genet Med 2008, 10:851-868.
• [18]Amos J, Feldman GL, Grody WW, Monaghan K, Palomaki GE, Prior TW, Richards CS, Watson MS: Technical Standards and Guidelines for CFTR Mutation Testing. Am Coll Med Genet 2005.
• [19]Watson MS, Cutting GR, Desnick RJ, Driscoll DA, Klinger K, Mennuti M, Palomaki GE, Popovich BW, Pratt VM, Rohlfs EM, et al.: Cystic fibrosis population carrier screening: 2004 revision of American College of Medical Genetics mutation panel. Genet Med 2004, 6:387-391.
• [20]Alper OM, Wong LJ, Young S, Pearl M, Graham S, Sherwin J, Nussbaum E, Nielson D, Platzker A, Davies Z, et al.: Identification of novel and rare mutations in California Hispanic and African American cystic fibrosis patients. Hum Mutat 2004, 24:353.
• [21]McKone EF, Emerson SS, Edwards KL, Aitken ML: Effect of genotype on phenotype and mortality in cystic fibrosis: a retrospective cohort study. Lancet 2003, 361:1671-1676.
• [22]Farrell PM, Rosenstein BJ, White TB, Accurso FJ, Castellani C, Cutting GR, Durie PR, Legrys VA, Massie J, Parad RB, et al.: Guidelines for diagnosis of cystic fibrosis in newborns through older adults: Cystic Fibrosis Foundation consensus report. J Pediatr 2008, 153:S4-S14.
• [23]Grody WW, Cutting GR, Watson MS: The Cystic Fibrosis mutation "arms race": when less is more. Genet Med 2007, 9:739-744.
• [24]Cystic Fibrosis Mutation Database. 2011. Available at http://www.genet.sickkids.on.ca/Home.html webcite
• [25]Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS, Abeysinghe S, Krawczak M, Cooper DN: Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat 2003, 21:577-581.
• [26][http://www.progenysoftware.com] webciteGenetic Pedigree Software.
• [27]Consortium GP: A map of human genome variation from population-scale sequencing. Nature 2010, 467:1061-1073.
• [28]Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR: A method and server for predicting damaging missense mutations. Nat Methods 2010, 7:248-249.
• [29]Kumar P, Henikoff S, Ng PC: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009, 4:1073-1081.
• [30]Crockett DK, Piccolo SR, Ridge PG, Margraf RL, Lyon E, Williams MS, Mitchell JA: Predicting phenotypic severity of uncertain gene variants in the RET proto-oncogene. PLoS One 2011, 6:e18380.
• [31]Desmet FO, Hamroun D, Lalande M, Collod-Beroud G, Claustres M, Beroud C: Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 2009, 37:e67.
• [32]Yeo G, Burge CB: Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol 2004, 11:377-394.
• [33]Ewing B, Green P: Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 1998, 8:186-194.
• [34]Worthey EA, Mayer AN, Syverson GD, Helbling D, Bonacci BB, Decker B, Serpe JM, Dasu T, Tschannen MR, Veith RL, et al.: Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet Med 2011, 13:255-262.
• [35]Cooper GM, Shendure J: Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat Rev Genet 2011, 12:628-640.
• [36]Osoegawa K, Mammoser AG, Wu C, Frengen E, Zeng C, Catanese JJ, de Jong PJ: A bacterial artificial chromosome library for sequencing the complete human genome. Genome Res 2001, 11:483-496.
• [37]Block R, Corsetti J, Goldenberg I, Vorobiof G, McNitt S, Ryan D, Wojciech Z, Moss AJ: The common apolipoprotein A-1 polymorphism -75A > G is associated with ethnic differences in recurrent coronary events after recovery from an acute myocardial infarction. Heart Int 2009, 4:e8.
• [38]Linder CC: The influence of genetic background on spontaneous and genetically engineered mouse models of complex diseases. Lab Anim (NY) 2001, 30:34-39.
• [39]Dewey FE, Chen R, Cordero SP, Ormond KE, Caleshu C, Karczewski KJ, Whirl-Carrillo M, Wheeler MT, Dudley JT, Byrnes JK, et al.: Phased whole-genome genetic risk in a family quartet using a major allele reference sequence. PLoS Genet 2011, 7:e1002280.