risk of misdiagnosis due to allele dropout in molecular diagnostics analysis of 30769 genotypes n.
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Risk of misdiagnosis due to allele dropout in molecular diagnostics: analysis of 30769 genotypes.

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risk of misdiagnosis due to allele dropout in molecular diagnostics analysis of 30769 genotypes

Risk of misdiagnosis due to allele dropout in molecular diagnostics: analysis of 30769 genotypes

Jonatan Blais1,2,3, Sébastien Lavoie1, Sylvie Giroux2, Johanne Bussières2, Carmen Lindsay2, Jacqueline Dionne1, Mélissa Laroche1, Yves Giguère1,3,4, François Rousseau1,2,3,41 Service de biochimie, Dépt. Biologie médicale, CHU de Québec, Québec, Canada 2 Unité de recherche en génétique humaine et moléculaire, Centre de recherche du CHU de Québec, Québec, Canada 3 Département de biologie moléculaire, biochimie médicale et pathologie, Faculté de Médecine, Université Laval, Québec, Canada 4 APOGEE-Net/CanGèneTest Research and Knowledge Network on Genetic Health Services and Policy, Canadian Institutes of Health Research

introduction
Introduction
  • Quality control in clinicalmolecular diagnostics ischallenging
  • Avoidingdiagnostic errorsis crucial: patients usuallytestedonly once in theirlifetime
  • Amplification basedmethodsremainnecessary for mostnucleicacid analyses
  • PCR is the mostfrequentlyused amplification technique
introduction1
Introduction
  • Althoughrelativelyrobust, PCR isaffected by occasionnal artefacts
  • Alleledropouts have been known to represent a potentialpitfall in PCR basedassays of diploidgenomic DNA
  • Recent CLSI guidelines recommendassaydevelopment and quality control to takethisphenomenonintoaccount
introduction2
Introduction
  • Potential sources of allele dropout:
    • Stochasticphenomenaincluding variations in:
      • DNA extraction quantity/quality (inhibitors…)
      • Pipetting volumes of reagents, templates
      • Thermocycler T°
    • Non-random allele-specific features (sequence/structure induced)
      • Mutations in primer binding sites
      • Polymerase hindering sequence-specific secondary structures, GC content, etc…
introduction3
Introduction
  • PCR-based genetic tests have been widespread in molecular diagnostics for over two decades
  • To our knowledge no systematic reliable estimate of the incidence of allele dropout in the context of clinical diagnostic applications has been published
objectives
Objectives
  • Evaluating the impact of allele dropout on diagnostic accuracy in the context of routine clinical molecular testing:
    • Estimating incidence of allele dropout
    • Estimating the relative frequency of random and non-random causes
methods pcr assay design
MethodsPCR assay design

Allele-specific oligonucleotide (ASO) PCR assays

Two independent sets of primers hybridizing on opposite strands of target DNA for each mutation, maximizing the likelihood of allele dropout detection

Forward and reverse target ASO PCR genotyping assay

  • Typical ASO PCR genotyping assay
methods targeted loci
MethodsTargetedloci
  • 8 autosomal recessive mutations in 4 different genes
    • Four mutations in the CFTR gene for the diagnosis of cystic fibrosis
    • C282Yand H63D mutations in the HFE gene for the diagnosis of hereditary hemochromatosis
    • IVS12+5G-Amutation in the FAH gene for the diagnosis of tyrosinemia type I
    • 2436delG mutation in the SLC12A6 gene for the diagnosis of agenesis of the corpus callosum with peripheral neuropathy
methods allele dropout drop in detection algorithm
MethodsAllele dropout & drop-in detection algorithm

Incidence of allele dropout & drop-in estimated from number of discordant results between forward and reverse assays

Alleles initially detected in one assay but shown to be false positives in confirmatory testing were also detected and referred to as "allele drop-in"

examples of aso pcr reaction results for the c282y mutation in the hfe gene
Examples of ASO PCR reaction results for the C282Y mutation in the HFE gene

Detection by SYBR Green 1 end point fluorescence, measured on a Fluoroskan Ascent (MTX Laboratory Syst.)

Raw fluorescence intensity for the forward and reverse assay

One patient with discordant genotype between the forward and reverse assay (heterozygous and mutant homozygous respectively)

results
Results
  • A total of 30769 patient genotypes compiled between 2004 and 2012 in the course of routine clinical testing
  • Each genotype determined by two independent assays and four PCR reactions:
    • 123 076 PCR reactions
results discordant cases
ResultsDiscordant cases
  • 135 cases of dropout & drop-in errors (0.44% of genotypes)
  • Concordance reached after replicate analysis in 94% (95% C.I. 89-97%) of cases, suggesting random causes
results summary of discordant results
ResultsSummary of discordant results
  • 8 discordant cases (6% of genotypes (95% C.I. 3%–11%)) associated with sequence variants after Sanger sequencing:
    • 6 mutations in primer binding sites (FAH, HFE genes)
    • 1 variant within exon 5 of HFE gene
    • 1 variant within intron 3 of CFTR gene
    • No variants known at time of primer design/assay validation, 6 of them now listed in public databases
assay error rate ranged from 0 0 53
Assay error rate ranged from 0-0.53%

Error rate (%) ± 95% CI for each of the 16 PCR assays

No significant difference between forward and reverse assays across loci (Wilcoxon matched pairs test: p=0.48)

Significant differences among assay targets (Fisher’s exact test: p < 0.0001, p-value estimated from Monte-Carlo simulation of 10000 replicates in R version 2.15.1.)

conclusion
Conclusion
  • Allele dropout & drop-in carrying misdiagnosis potential occurred in 0.44 % of genotyping results
      • 80 false positives
      • 55 false negatives
  • Assuming equal number of events on forward and reverse strands, ASO PCR assays relying on amplification of only one DNA strand would have produced 1:450 erroneous genotypes (0.22%)
  • Other PCR-based genotyping methods may have higher/lower rates
conclusion1
Conclusion
  • Some targeted regions are more affected then others: error rates are locus specific and can’t be extrapolated to an entire method
  • Most events appeared to be caused by stochastic phenomena, suggesting that careful primer design cannot prevent them
  • Best detection and minimizing strategy will vary depending on assay principle
acknowledgements
Acknowledgements
  • David Cole (University of Toronto) for helpful comments on this work
  • Funding agencies
thank you
Thank you
  • Questions ?
  • Further questions ?
  • Meet me at my poster (code 1145) !