CoChat
Sign Up
Cited by 20Genomics and Phylogenetic StudiesGenomics and Rare DiseasesMetabolism and Genetic Disorders

Next-generation sequencing of newborn screening genes: the accuracy of short-read mapping

Open access
Trier, C., Fournous, G., Strand, J. M., Stray-Pedersen, A., Pettersen, R. D., Rowe, A. D.·2020·npj Genomic Medicine
DOI 10.1038/s41525-020-00142-zPMC PMC7474066
Open this paper in CoChat — free accountOpen PDF

Abstract

Newborn screening programs are an integral part of public health systems aiming to save lives and improve the quality of life for infants with treatable disorders. Technological advancements have driven the expansion of newborn screening programs in the last two decades and the development of fast, accurate next-generation sequencing technology has opened the door to a range of possibilities in the field. However, technological challenges with short-read next-generation sequencing technologies remain significant in highly homologous genomic regions such as pseudogenes or paralogous genes and need to be considered when implemented in screening programs. Here, we simulate 50 genomes from populations around the world to test the extent to which high homology regions affect short-read mapping of genes related to newborn screening disorders and the impact of differential read lengths and ethnic backgrounds. We examine a 158 gene screening panel directly relevant to newborn screening and identify gene regions where read mapping is affected by homologous genomic regions at different read lengths. We also determine that the patient’s ethnic background does not have a widespread impact on mapping accuracy or coverage. Additionally, we identify newborn screening genes where alternative forms of sequencing or variant calling pipelines should be considered and demonstrate that alterations to standard variant calling can retrieve some formerly uncalled variants.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41525-020-00142-z

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The CYP21A2 locus remains technically challenging for short-read NGS platforms due to high sequence homology with the pseudogene CYP21A1P, leading to frequent mapping errors and potential allele misclassification.

    “However, the CYP21A2 locus remains technically challenging for short-read NGS platforms due to its high sequence homology with the pseudogene CYP21A1P, leading to frequent mapping errors and potential allele misclassification (85).”

    From Evolution of molecular diagnostic strategies for 21-hydroxylase deficiency: from classical methods to advanced genomic techniques (2026) · cites this paper as [85]

    The Results section reports extensive CYP21A2–CYP21A1P homology and shows that one simulated pathogenic variant caused reads to map to the pseudogene, preventing its detection. But variant calling was accurate for 10 of 11 simulated variants at 150 bp read lengths. The paper therefore supports a mapping-related challenge, not the claim that errors are frequent or that alleles were misclassified. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Short-read NGS struggles with misalignment due to the near-identical sequence of CYP21A1P, leading to false variant calls or allele drop-outs unless gene-specific enrichment and specialized bioinformatic filters are applied.

    “Short-read NGS struggles with misalignment due to the near-identical sequence of CYP21A1P, leading to false variant calls or allele drop-outs unless gene-specific enrichment and specialized bioinformatic filters are applied.”

    From Evolution of molecular diagnostic strategies for 21-hydroxylase deficiency: from classical methods to advanced genomic techniques (2026) · cites this paper as [85]

    The Results section on CYP21A2 reports that homology with CYP21A1P caused reads to map to the pseudogene and one simulated pathogenic variant to be missed. But default variant calling detected 10 of 11 simulated variants; the paper does not establish that gene-specific enrichment and specialized filters are required. Its masking-and-ploidy workaround recovered the missed variant but also produced extra variant calls. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • A combination of laboratory and bioinformatics strategies, including pseudogene masking, locus-specific long-range PCR, and targeted hybrid-capture panels, improves CYP21A2-specific resolution and enables accurate variant calling.

    “To overcome these limitations, a combination of laboratory and bioinformatics strategies is commonly employed to improve CYP21A2-specific resolution, including pseudogene masking during data analysis, locus-specific long-range PCR for selective amplification, and targeted hybrid-capture panels prior to library preparation (81, 82, 85).”

    From Evolution of molecular diagnostic strategies for 21-hydroxylase deficiency: from classical methods to advanced genomic techniques (2026) · cites this paper as [85]

    The Results (Pathogenic variant calling on CYP21A2; page unavailable) report that pseudogene masking with increased ploidy recovered one otherwise missed simulated variant, but also produced extra variant calls. The Discussion (page unavailable) describes gene-specific long-range PCR followed by short-read sequencing as an alternative strategy. Our copy does not establish that targeted hybrid-capture panels improve CYP21A2-specific resolution, that the three strategies are commonly used together, or that they collectively enable accurate variant calling. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 57

  1. [1]Centers for Disease Control and Prevention (CDC). Ten great public health achievements–United States, 2001–2010 Morb. Mortal. Wkly. Rep. 2011 60 619 623 21597455
  2. [2]Guthrie R Susi A A simple phenylalanine method for detecting phenylketonuria in large populations of newborn infants Pediatrics 1963 32 338 343 14063511
  3. [3]Guthrie R The origin of newborn screening Screening 1992 1 5 15 11615143
  4. [4]Wilcken B Wiley V Hammond J Carpenter K Screening newborns for inborn errors of metabolism by tandem mass spectrometry N. Engl. J. Med. 2003 348 2304 2312 12788994
  5. [5]Therrell BL Current status of newborn screening worldwide: 2015 Semin Perinatol. 2015 39 171 187 25979780
  6. [6]Millington DS Norwood DL Kodo N Roe CR Inouet F Application of fast atom bombardment with tandem mass spectrometry and liquid chromatography/ mass spectrometry to the analysis of acylcarnitines in human urine, blood, and tissue Anal. Biochem. 1989 180 331 339 2817363
  7. [7]Strand J Second-tier next generation sequencing integrated in nationwide newborn screening provides rapid molecular diagnostics of severe combined immunodeficiency Front. Immunol. 2020 11 1417 32754152 PMC7381310
  8. [8]Lundman E Implementation of newborn screening for cystic fibrosis in Norway. Results from the first three years J. Cyst. Fibros. 2016 15 318 324 26795017
  9. [9]Bodian DL Utility of whole-genome sequencing for detection of newborn screening disorders in a population cohort of 1,696 neonates Genet Med. 2016 18 221 230 26334177
  10. [10]Landau YE Lichter-Konecki U Levy HL Genomics in newborn screening J. Pediatr. 2014 164 14 19 23992678
  11. [11]Berg JS Powell CM Potential uses and inherent challenges of using genome-scale sequencing to augment current newborn screening Cold Spring Harb. Perspect. Med. 2015 5 a023150 26438605 PMC4665041
  12. [12]Bassaganyas L Whole exome and whole genome sequencing with dried blood spot DNA without whole genome amplification Hum. Mutat. 2018 39 167 171 29067733 PMC5738671
  13. [13]Saunders CJ Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units Sci. Transl. Med. 2012 4 154ra135 23035047 PMC4283791
  14. [14]Francescatto L Katsanis N Newborn screening and the era of medical genomics Semin Perinatol. 2015 39 617 622 26499764 PMC4644676
  15. [15]Almannai M Marom R Reid Sutton V Newborn screening: a review of history, recent advancements, and future perspectives in the era of next generation sequencing Curr. Opin. Pediatr. 2016 28 694 699 27552071
  16. [16]Boemer F A next-generation newborn screening pilot study: NGS on dried blood spots detects causal mutations in patients with inherited metabolic diseases Sci Rep. 2017 7 1 9 29247206 PMC5732277
  17. [17]Goodwin S McPherson JD McCombie WR Coming of age: Ten years of next-generation sequencing technologies Nat. Rev. Genet. 2016 17 333 351 27184599 PMC10373632
  18. [18]Mandelker D Navigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing Genet Med. 2016 18 1282 1289 27228465
  19. [20]Ebbert MTW Systematic analysis of dark and camouflaged genes reveals disease- relevant genes hiding in plain sight Genome Biol. 2019 20 97 31104630 PMC6526621
  20. [21]Madden TL BLAST+: architecture and applications BMC Bioinform. 2009 10 421 PMC2803857 20003500
  21. [22]Pearn J Incidence, prevalence, and gene frequency studies of chronic childhood spinal muscular atrophy J. Med. Genet. 1978 15 409 413 745211 PMC1013753
  22. [23]Prior TW Newborn and carrier screening for spinal muscular atrophy Am. J. Med. Genet A 2010 152A 1608 1616 20578137
  23. [24]Mailman MD Molecular analysis of spinal muscular atrophy and modification of the phenotype by SMN2 Genet Med. 2002 4 20 26 11839954
  24. [25]Swoboda KJ Natural history of denervation in SMA: relation to age, SMN2 copy number and function Ann. Neurol. 2005 57 704 712 15852397 PMC4334582
  25. [26]Zerres K Wirth B Rundik‐Schöneborn S Spinal muscular atrophy—clinical and genetic correlations Neuromuscul. Dis. 1997 7 202 207 9185186
  26. [27]Refsum H Fredriksen Å Meyer K Ueland P-M Kase BF Birth prevalence of homocystinuria J. Pediatr. 2004 144 830 832 15192637
  27. [28]Stray-Pedersen A Compound heterozygous CORO1A mutations in siblings with a mucocutaneous-immunodeficiency syndrome of epidermodysplasia verruciformis-HPV, molluscum contagiosum and granulomatous tuberculoid leprosy J. Clin. Immunol. 2014 34 871 890 25073507 PMC4386834
  28. [29]Moshous D Whole-exome sequencing identifies coronin-1A deficiency in 3 siblings with immunodeficiency and EBV-associated B-cell lymphoproliferation J. Allergy Clin. Immunol. 2013 131 1594 1603 23522482 PMC3824285
  29. [30]Shiow LR The actin regulator coronin-1A is mutated in a thymic egress deficient mouse strain and in a T-B+NK+SCID patient Nat. Immunol. 2008 9 1307 1315 18836449 PMC2672406
  30. [31]McKenna A The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data Genome Res. 2010 20 1297 1303 20644199 PMC2928508
  31. [32]Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013). · arXiv
  32. [33]Saunders CJ Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units Sci. Transl. Med. 2012 4 154ra135 23035047 PMC4283791
  33. [34]Campbell CL A global reference for human genetic variation Nature 2015 526 68 74 26432245 PMC4750478
  34. [35]Koren S Hybrid error correction and de novo assembly of single-molecule sequencing reads Nat. Biotechnol. 2012 30 693 700 22750884 PMC3707490
  35. [36]Fu S Wang A Au KF A comparative evaluation of hybrid error correction methods for error-prone long reads Genome Biol. 2019 20 26 30717772 PMC6362602
  36. [37]Frans G Conventional and single-molecule targeted sequencing method for specific variant detection in IKBKG while bypassing the IKBKGP1 pseudogene J. Mol. Diagn. 2018 20 195 202 29269280
  37. [39]Borràs DM Detecting PKD1 variants in polycystic kidney disease patients by single-molecule long-read sequencing Hum. Mutat. 2017 38 870 879 28378423 PMC5488171
  38. [42]Xue Y Ankala A Wilcox WR Hegde MR Solving the molecular diagnostic testing conundrum for Mendelian disorders in the era of next-generation sequencing: single-gene, gene panel, or exome/genome sequencing Genet Med. 2015 17 444 451 25232854
  39. [43]Christiaans I Mook ORF Alders M Bikker H Lekanne dit Deprez RH Large next- generation sequencing gene panels in genetic heart disease: challenges in clinical practice Neth. Heart J. 2019 27 299 303 30847665 PMC6533326
  40. [44]Samorodnitsky E Evaluation of hybridization capture versus amplicon-based methods for whole-exome sequencing Hum. Mutat. 2015 36 903 914 26110913 PMC4832303
  41. [45]Veal, C. D. et al. A mechanistic basis for amplification differences between samples and between genome regions. BMC Genomics.13, 455 (2012). PMC3469336 22950736
  42. [46]Meienberg J Bruggmann R Oexle K Matyas G Clinical sequencing: is WGS the better WES? Hum. Genet. 2016 135 359 362 26742503 PMC4757617
  43. [47]Mallawaarachchi AC Whole-genome sequencing overcomes pseudogene homology to diagnose autosomal dominant polycystic kidney disease Eur. J. Hum. Genet. 2016 24 1584 1590 27165007 PMC5110048
  44. [48]Li YR Rare copy number variants in over 100,000 European ancestry subjects reveal multiple disease associations Nat. Commun. 2020 11 255 31937769 PMC6959272
  45. [49]Vaz-Drago R Custódio N Carmo-Fonseca M Deep intronic mutations and human disease Hum. Genet. 2017 136 1093 1111 28497172
  46. [50]Jutzi D Akinyi MV Mechtersheimer J Frilander MJ Ruepp M-D The emerging role of minor intron splicing in neurological disorders Cell Stress 2018 2 40 54 31225466 PMC6558932
  47. [51]Flicek P Ensembl 2018 Nucleic Acids Res. 2017 46 D754 D761 PMC5753206 29155950
  48. [52]Durinck S BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis Bioinformatics 2005 21 3439 3440 16082012
  49. [53]O’Leary NA Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation Nucleic Acids Res. 2016 44 D733 D745 26553804 PMC4702849
  50. [54]Morgulis A Gertz EM Schäffer AA Agarwala R A fast and symmetric DUST implementation to mask low-complexity DNA sequences Comput Biol. 2006 13 1028 1040 16796549
  51. [55]Quinlan AR Hall IM BEDTools: a flexible suite of utilities for comparing genomic features Bioinformatics 2010 26 841 842 20110278 PMC2832824
  52. [56]Sandstrom R BEDOPS: high-performance genomic feature operations Bioinformatics 2012 28 1919 1920 22576172 PMC3389768
  53. [57]Li H A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data Bioinformatics 2011 27 2987 2993 21903627 PMC3198575
  54. [58]Pfeiffer F Systematic evaluation of error rates and causes in short samples in next- generation sequencing Sci. Rep. 2018 8 10950 30026539 PMC6053417
  55. [59]Korneliussen TS Albrechtsen A Nielsen R ANGSD: analysis of next-generation sequencing data BMC Bioinform. 2014 15 356 PMC4248462 25420514
  56. [60]Fumagalli M Quantifying population genetic differentiation from next-generation sequencing data Genetics 2013 195 979 992 23979584 PMC3813878
  57. [62]Danecek P The variant call format and VCFtools Bioinformatics 2011 27 2156 2158 21653522 PMC3137218

Cited by

Sorted by citations

Authors
Trier, C., Fournous, G., Strand, J. M., Stray-Pedersen, A., Pettersen, R. D., & Rowe, A. D.
Affiliations
Oslo University Hospital (NO)
Corresponding author
Trier, C.
ORCID
Venue
npj Genomic Medicine
Publisher
Springer Science and Business Media LLC
Published
2020-09-04
Volume
5
Issue
1
Pages
36-36
ISSN
2056-7944 (ISSN-L)
DOI
10.1038/s41525-020-00142-z
PMC
PMC7474066
OpenAlex
W3083564636
Cited by
20
References
4
Citations by year
2026: 2 · 2025: 3 · 2024: 2 · 2023: 2 · 2022: 6 · 2021: 4 · 2020: 1
FWCI
1.05
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Genomic Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Universitetet i Oslo (Uninett/Sigma2)
  • Helse Sør-Øst RHF
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
Keywords
newborn screening, pseudogenes, paralogs, variant calling, read length, ethnicity, simulated genomes, variant calling pipeline

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review