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

1.0 1.1 1.2 1.30.90.8

0.7

DQ

Less variable

More variable

p

t-distributions of DQ values

0.5 1.0 1.5

0.5 1.0 1.5

Good quality data

Poorer quality data

p

p

n 2n 3n

n 2n 3n

Calculation of relative likelihood

0.5 1.0 1.5

p n 2n 3n

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

0.5 1.0 1.5

p n 2n 3n

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

0.5 1.0 1.5

Calculation of relative likelihood

p n 2n 3n

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)