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

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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 How can we analyse dosage data to provide the clear cut Yes/No answers we want?

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

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

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

DQ likelihood distribution 1.01.11.21.30.90.80.7 DQ Less variable More variable p

t-distributions of DQ values 0.51.01.5 0.51.01.5 Good quality data Poorer quality data p p n2n3n n2n3n

Calculation of relative likelihood 0.51.01.5 p n2n3n DQ = 0.9 p(2n) = 0.40 p(n) = 0.0009 p(3n) = 0.0006 Odds Norm:Del = 444:1 Odds Norm:Dup = 667:1 Good data – normal DQ

Calculation of relative likelihood 0.51.01.5 p n2n3n DQ = 0.7 p(2n) = 0.0007 p(n) = 0.03 p(3n) = 0.00009 Odds Norm:Del = 1:42 Odds Norm:Dup = 7:1 Good data – deleted DQ

0.51.01.5 Calculation of relative likelihood p n2n3n DQ = 0.7 p(2n) = 0.007 p(n) = 0.021 p(3n) = 0.0007 Odds Norm:Del = 1:3 Odds Norm:Dup = 10:1 Poor data – ?deleted DQ

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

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

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

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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