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Analysing MLPA Dosage Data

Analysing MLPA Dosage Data. Andrew Wallace National Genetics Reference Laboratory (Manchester). Problems with Dosage Analysis. Dosage data is quantitative – continuously variable Diagnostics requires a “binary” answer e.g. is the patient sample normal? Yes/No

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Analysing MLPA Dosage Data

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  1. Analysing MLPA Dosage Data Andrew Wallace National Genetics Reference Laboratory (Manchester)

  2. Problems with Dosage Analysis • Dosage data is quantitative – continuously variable • Diagnostics requires a “binary” answer e.g. is the patient sample normal? Yes/No • How can we analyse dosage data to provide the clear cut Yes/No answers we want?

  3. Problems with Dosage Analysis • Problem is compounded by the increasing numbers of analyses in newer tests e.g. MAPH and MLPA WHY? • If we use a standard statistical measure of significance for each exon tested the probability of a Type I error increases • Alternatively if we use an arbitrary cut-offs we fail to take into account variabilities between loci • Sample sizes limited to current experiment – too much variability between experiments

  4. Dosage Quotient (DQ) Expectations • We have one advantage - we know what results to expect i.e. for autosomal loci • normal expect a DQ = 1.0 • deleted then we expect a DQ = 0.5 • duplicated then we expect a DQ = 1.5

  5. Modified MLPA Dosage Analysis • Used a small series of reference normal samples (5) run at the same time as experimental samples to determine DQ variability of each amplimer • The deleted and duplicated values are inferred in relation to the control measurements (0.5x or 1.5x) • Use the t statistic to estimate agreement with three hypotheses (i) deleted (ii) duplicated (iii) normal • t statistic must be used rather than standard deviations due to small sample size

  6. Less variable More variable 0.7 0.8 0.9 1.0 1.1 1.2 1.3 DQ DQ likelihood distribution p

  7. n n 2n 2n 3n 3n 0.5 0.5 1.0 1.0 1.5 1.5 t-distributions of DQ values Good quality data p Poorer quality data p

  8. p n 2n 3n 0.5 1.0 1.5 Odds Norm:Del = 444:1 Odds Norm:Dup = 667:1 Calculation of relative likelihood Good data – normal DQ DQ = 0.9 p(2n) = 0.40 p(n) = 0.0009 p(3n) = 0.0006

  9. p n 2n 3n 0.5 1.0 1.5 Odds Norm:Del = 1:42 Odds Norm:Dup = 7:1 Calculation of relative likelihood Good data – deleted DQ DQ = 0.7 p(2n) = 0.0007 p(n) = 0.03 p(3n) = 0.00009

  10. n 2n 3n 0.5 1.0 1.5 Odds Norm:Del = 1:3 Odds Norm:Dup = 10:1 Calculation of relative likelihood p Poor data – ?deleted DQ DQ = 0.7 p(2n) = 0.007 p(n) = 0.021 p(3n) = 0.0007

  11. Good Quality Normal Data Showing Typical Variability MLH1 Exon 5 – although prob of deviation from normal is low (1.2249%) 147356:1 Normal: Deleted - thus not Deleted 797:1 Normal:Duplicated - thus not Duplicated

  12. Good Quality Data Giving an Unequivocal Odds Ratio for a Deletion MSH2 Exon 4 1:12460 Normal:Deleted thus Deleted 3:1 Normal:Duplicated – can discard this hypothesis due to evidence for deletion

  13. Poor Data Leading to Equivocal Odds Ratio MLH1 Exon 9 3419:1 Normal: Deleted Thus Not deleted 3:1 Normal:Duplicated ?Normal

  14. MLPA Dosage Analysis Spreadsheets CONCLUSIONS • New analysis which can attach a meaningful probability to dosage data – more objective • Unsuitable for detecting mosaic deletions/duplications – will give equivocal odds ratios • Can be applied to other quantitative PCR assays • Spreadsheets designed for BRCA1, HNPCC, VHL and DMD available from me – eventually from NGRL website (www.ngrl.co.uk)

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