Visual inspection reveals a novel pathogenic mutation in <em>PKD1</em> missed by the variant caller in whole‑exome sequencing

Koay,Bee Tee; Chiow,Mei Yee; Ismail,Jamiila; Fahmy,Norfarhana Khairul; Yee,Seow Yeing; Mustafa,Norhazlin; Arip,Masita; Ripen,Adiratna Mat; Mohamad,Saharuddin Bin

doi:10.3892/mmr.2022.12882

Visual inspection reveals a novel pathogenic mutation in PKD1 missed by the variant caller in whole‑exome sequencing

Authors:
- Bee Tee Koay
- Mei Yee Chiow
- Jamiila Ismail
- Norfarhana Khairul Fahmy
- Seow Yeing Yee
- Norhazlin Mustafa
- Masita Arip
- Adiratna Mat Ripen
- Saharuddin Bin Mohamad
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Affiliations: Allergy and Immunology Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health, Shah Alam, Selangor 40170, Malaysia, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia, Nephrology Department, Kuala Lumpur Hospital, Ministry of Health, Kuala Lumpur 50586, Malaysia
Published online on: October 24, 2022 https://doi.org/10.3892/mmr.2022.12882
Article Number: 365
Copyright: © Koay et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Autosomal dominant polycystic kidney disease (ADPKD) is the most common type of inherited cystic kidney disease. The feasibility of whole‑exome sequencing (WES) to obtain molecular diagnosis of ADPKD is still in question as previous studies showed conflicting results. Utilizing WES on a patient with ADPKD, standard bioinformatics pipeline demonstrated no pathogenic variant in the genes of interest. By visualizing read alignments using the Integrative Genomics Viewer, a region with atypical alignment of numerous soft‑clipped reads at exon 45 of polycystin 1, transient receptor potential channel interacting (PKD1) gene was demonstrated. A total of four visual inspection steps were outlined to assess the origin of these soft‑clipped reads as strand bias during capture, poor mapping, sequencing error or DNA template contamination. Following assessment, the atypical alignment at PKD1 was hypothesized to be caused by an insertion/deletion mutation. Sanger sequencing confirmed the presence of a novel 20‑bp insertion in PKD1 (NM_001009944.3; c.12143_12144insTCCCCGCAGTCTTCCCCGCA; p.Val4048LeufsTer157), which introduced a premature stop codon and was predicted to be pathogenic. The present study demonstrated that WES could be utilized as a molecular diagnostic tool for ADPKD. Furthermore, visual inspection of read alignments was key in identifying the pathogenic variant. The proposed visual inspection steps may be incorporated into a typical WES data analysis workflow to improve the diagnostic yield.

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1	Suwinski P, Ong C, Ling MHT, Poh YM, Khan AM and Ong HS: Advancing personalized medicine through the application of whole exome sequencing and big data analytics. Front Genet. 10:492019. View Article : Google Scholar : PubMed/NCBI
2	Schwarze K, Buchanan J, Taylor JC and Wordsworth S: Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature. Genet Med. 20:1122–1130. 2018. View Article : Google Scholar : PubMed/NCBI
3	Ma X, Shao Y, Tian L, Flasch DA, Mulder HL, Edmonson MN, Liu Y, Chen X, Newman S, Nakitandwe J, et al: Analysis of error profiles in deep next-generation sequencing data. Genome Biol. 20:502019. View Article : Google Scholar : PubMed/NCBI
4	Robinson JT, Thorvaldsdottir H, Wenger AM, Zehir A and Mesirov JP: Variant review with the integrative genomics viewer. Cancer Res. 77:e31–e34. 2017. View Article : Google Scholar : PubMed/NCBI
5	Koboldt DC: Best practices for variant calling in clinical sequencing. Genome Med. 12:912020. View Article : Google Scholar : PubMed/NCBI
6	Lanktree MB, Haghighi A, Guiard E, Iliuta IA, Song X, Harris PC, Paterson AD and Pei Y: Prevalence estimates of polycystic kidney and liver disease by population sequencing. J Am Soc Nephrol. 29:2593–2600. 2018. View Article : Google Scholar : PubMed/NCBI
7	Chebib FT and Torres VE: Autosomal dominant polycystic kidney disease: Core curriculum 2016. Am J Kidney Dis. 67:792–810. 2016. View Article : Google Scholar : PubMed/NCBI
8	National Library of Medicine (US), National Center for Biotechnology Information, . Nucleotide NM_001009944.3. https://www.ncbi.nlm.nih.gov/nuccore/NM_001009944.3December 30–2021
9	National Library of Medicine (US), National Center for Biotechnology Information, . Nucleotide NM_000297.4. https://www.ncbi.nlm.nih.gov/nuccore/NM_000297.4December 30–2021
10	Bogdanova N, Markoff A, Gerke V, McCluskey M, Horst J and Dworniczak B: Homologues to the first gene for autosomal dominant polycystic kidney disease are pseudogenes. Genomics. 74:333–341. 2001. View Article : Google Scholar : PubMed/NCBI
11	Audrézet MP, Cornec-Le Gall E, Chen JM, Redon S, Quéré I, Creff J, Bénech C, Maestri S, Le Meur Y and Férec C: Autosomal dominant polycystic kidney disease: Comprehensive mutation analysis of PKD1 and PKD2 in 700 unrelated patients. Hum Mutat. 33:1239–1250. 2012. View Article : Google Scholar : PubMed/NCBI
12	Yu G, Qian X, Wu Y, Li X, Chen J, Xu J and Qi J: Analysis of gene mutations in PKD1/PKD2 by multiplex ligation-dependent probe amplification: Some new findings. Ren Fail. 37:366–371. 2015. View Article : Google Scholar : PubMed/NCBI
13	Mallawaarachchi AC, Lundie B, Hort Y, Schonrock N, Senum SR, Gayevskiy V, Minoche AE, Hollway G, Ohnesorg T, Hinchcliffe M, et al: Genomic diagnostics in polycystic kidney disease: An assessment of real-world use of whole-genome sequencing. Eur J Hum Genet. 29:760–770. 2021. View Article : Google Scholar : PubMed/NCBI
14	Ali H, Al-Mulla F, Hussain N, Naim M, Asbeutah AM, AlSahow A, Abu-Farha M, Abubaker J, Al Madhoun A, Ahmad S and Harris PC: PKD1 duplicated regions limit clinical utility of whole exome sequencing for genetic diagnosis of autosomal dominant polycystic kidney disease. Sci Rep. 9:41412019. View Article : Google Scholar : PubMed/NCBI
15	Eisenberger T, Decker C, Hiersche M, Hamann RC, Decker E, Neuber S, Frank V, Bolz HJ, Fehrenbach H, Pape L, et al: An efficient and comprehensive strategy for genetic diagnostics of polycystic kidney disease. PLoS One. 10:e01166802015. View Article : Google Scholar : PubMed/NCBI
16	Cordido A, Besada-Cerecedo L and García-González MA: The genetic and cellular basis of autosomal dominant polycystic kidney disease-a primer for clinicians. Front Pediatr. 5:2792017. View Article : Google Scholar : PubMed/NCBI
17	Ripen AM, Chear CT, Baharin MF, Nallusamy R, Chan KC, Kassim A, Choo CM, Wong KJ, Fong SM, Tan KK, et al: A single-center pilot study in Malaysia on the clinical utility of whole-exome sequencing for inborn errors of immunity. Clin Exp Immunol. 206:119–128. 2021. View Article : Google Scholar : PubMed/NCBI
18	Ripen AM, Chiow MY, Rama Rao PR and Mohamad SB: Revealing chronic granulomatous disease in a patient with Williams-Beuren Syndrome using whole exome sequencing. Front Immunol. 12:7781332021. View Article : Google Scholar : PubMed/NCBI
19	Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, et al: From FastQ data to high confidence variant calls: The genome analysis toolkit best practices pipeline. Curr Protoc Bioinformatics. 43:11.10.1–11.10.33. 2013.PubMed/NCBI
20	Picard. Broad Institute, . GitHub repository. http://broadinstitute.github.io/picard/February 25–2020
21	Li H: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. https://arxiv.org/abs/1303.3997February 25–2020
22	Poplin R, Ruano-Rubio V, DePristo MA, Fennell TJ, Carneiro MO, Van der Auwera GA, Kling DE, Gauthier LD, Levy-Moonshine A, Roazen D, et al: Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv. 201178. 2018 February 27–2020
23	Chang X and Wang K: wANNOVAR: annotating genetic variants for personal genomes via the web. J Med Genet. 49:433–436. 2012. View Article : Google Scholar : PubMed/NCBI
24	Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G and Mesirov JP: Integrative genomics viewer. Nat Biotechnol. 29:24–26. 2011. View Article : Google Scholar : PubMed/NCBI
25	National Library of Medicine (US), National Center for Biotechnology Information, . BLAST. https://blast.ncbi.nlm.nih.gov/Blast.cgiNovember 24–2021
26	Ng PC and Henikoff S: SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31:3812–3814. 2003. View Article : Google Scholar : PubMed/NCBI
27	Schwarz JM, Cooper DN, Schuelke M and Seelow D: MutationTaster2: Mutation prediction for the deep-sequencing age. Nat Methods. 11:361–362. 2014. View Article : Google Scholar : PubMed/NCBI
28	Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS and Sunyaev SR: A method and server for predicting damaging missense mutations. Nat Methods. 7:248–249. 2010. View Article : Google Scholar : PubMed/NCBI
29	ADPKD Variant Database. https://pkdb.mayo.edu/28–January. 2022
30	Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM and Sirotkin K: dbSNP: The NCBI database of genetic variation. Nucleic Acids Res. 29:308–311. 2001. View Article : Google Scholar : PubMed/NCBI
31	Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, et al: The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 581:434–443. 2020. View Article : Google Scholar : PubMed/NCBI
32	Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hedge M, Lyon E, Spector E, 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. 17:405–424. 2015. View Article : Google Scholar : PubMed/NCBI
33	Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD and Bairoch A: ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31:3784–3788. 2003. View Article : Google Scholar : PubMed/NCBI
34	Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M and Rozen SG: Primer3-new capabilities and interfaces. Nucleic Acids Res. 40:e1152012. View Article : Google Scholar : PubMed/NCBI
35	Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S and Madden TL: Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 13:1342012. View Article : Google Scholar : PubMed/NCBI
36	Samson CA, Whitford W, Snell RG, Jacobsen JC and Lehnert K: Contaminating DNA in human saliva alters the detection of variants from whole genome sequencing. Sci Rep. 10:192552020. View Article : Google Scholar : PubMed/NCBI
37	Rossetti S, Consugar MB, Chapman AB, Torres VE, Guay-Woodford LM, Grantham JJ, Bennett WM, Meyers CM, Walker DL, Bae K, et al: Comprehensive molecular diagnostics in autosomal dominant polycystic kidney disease. J Am Soc Nephrol. 18:2143–2160. 2007. View Article : Google Scholar : PubMed/NCBI
38	Carrera P, Calzavara S, Magistroni R, den Dunnen JT, Rigo F, Stenirri S, Testa F, Messa P, Cerutti R, Scolari F, et al: Deciphering variability of PKD1 and PKD2 in an Italian cohort of 643 patients with autosomal dominant polycystic kidney disease (ADPKD). Sci Rep. 6:308502016. View Article : Google Scholar : PubMed/NCBI
39	Mantovani V, Bin S, Graziano C, Capelli I, Minardi R, Aiello V, Ambrosini E, Cristalli CP, Mattiaccio A, Pariali M, et al: Gene panel analysis in a large cohort of patients with autosomal dominant polycystic kidney disease allows the identification of 80 potentially causative novel variants and the characterization of a complex genetic architecture in a subset of families. Front Genet. 11:4642020. View Article : Google Scholar : PubMed/NCBI
40	Porath B, Gainullin VG, Cornec-Le Gall E, Dillinger EK, Heyer CM, Hopp K, Edwards ME, Madsen CD, Mauritz SR, Banks CJ, et al: Mutations in GANAB, encoding the glucosidase IIα subunit, cause autosomal-dominant polycystic kidney and liver disease. Am J Hum Genet. 98:1193–1207. 2016. View Article : Google Scholar : PubMed/NCBI
41	Cornec-Le Gall E, Olson RJ, Besse W, Heyer CM, Gainullin VG, Smith JM, Audrezet MP, Hopp K, Porath B, Shi B, et al: Monoallelic mutations to DNAJB11 cause atypical autosomal-dominant polycystic kidney disease. Am J Hum Genet. 102:832–844. 2018. View Article : Google Scholar : PubMed/NCBI
42	Hu HY, Zhang J, Qiu W, Liang C, Li CX, Wei TY, Feng ZK, Guo Q, Yang K and Liu ZG: Comprehensive strategy improves the genetic diagnosis of different polycystic kidney diseases. J Cell Mol Med. 25:6318–6332. 2021. View Article : Google Scholar : PubMed/NCBI
43	Mallawaarachchi AC, Hort Y, Cowley MJ, McCabe MJ, Minoche A, Dinger ME, Shine J and Furlong TJ: Whole-genome sequencing overcomes pseudogene homology to diagnose autosomal dominant polycystic kidney disease. Eur J Hum Genet. 24:1584–1590. 2016. View Article : Google Scholar : PubMed/NCBI
44	Al-Muhanna FA, Al-Rubaish AM, Vatte C, Mohiuddin SS, Cyrus C, Ahmad A, Shakil Akhtar M, Albezra MA, Alali RA, Almuhanna AF, et al: Exome sequencing of Saudi Arabian patients with ADPKD. Ren Fail. 41:842–849. 2019. View Article : Google Scholar : PubMed/NCBI
45	Torres VE, Harris PC and Pirson Y: Autosomal dominant polycystic kidney disease. Lancet. 369:1287–1301. 2007. View Article : Google Scholar : PubMed/NCBI
46	Khadangi F, Torkamanzehi A and Kerachian MA: Identification of missense and synonymous variants in Iranian patients suffering from autosomal dominant polycystic kidney disease. BMC Nephrol. 21:4082020. View Article : Google Scholar : PubMed/NCBI