NATIONAL GENETICS REFERENCE LABORATORY (Manchester)

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1 NATIONAL GENETICS REFERENCE LABORATORY (Manchester) MLPA analysis spreadsheets User Guide (updated October 2006) INTRODUCTION These spreadsheets are designed to assist with MLPA analysis using the kits available from MRC-Holland (see website at The spreadsheets have been created in Microsoft Excel They are intended to simplify and streamline the process of analysing complex MLPA data. Input data for the spreadsheets may be either peak heights or peak areas. We do however recommend using peak heights. Comparisons between the peak heights and peak areas as measures of peak intensity has shown that the variance of peak area measurements are consistently higher than those for peak heights. This may be due to peak smoothing or the arbitrary cut-off of peaks that occurs in fragment analysis programs. Peak heights appear to be a simpler and therefore more consistent measure than peak area If you have any suggestions for improvements or modifications to the spreadsheets I would be grateful for any feedback. I can be contacted on andrew.wallace@cmmc.nhs.uk. DESCRIPTION OF SPREADSHEETS The spreadsheets have been split into five worksheets or pages RAW DATA This page is used for data entry. The cells on the page have been laid out in order to ensure minimal user intervention in transferring data from the fragment analysis package. For instance, output from a Genotyper or GeneMapper table, if correctly configured, can be pasted directly onto the cells indicated on the RAW DATA sheet. Fig 1 shows a RAW DATA sheet with data from the BRCA1 MLPA analysis spreadsheet. FIG1:

2 RESULTS as the title suggests this page displays the results of the analysis. The results from the test samples are analysed in comparison with a group of 5 normal controls (see the analysis section for a more detailed description of the method of analysis). The results are displayed in four principal ways (i) as dosage quotients (DQs) gridded for each ligation product versus each control ligation product (ii) graphically as mean dosage quotient for each ligation product (iii) as a likelihood probability and odds for each ligation product calculated for one of three hypotheses that the dosage is normal (2n copies), that the dosage is deleted (n copies) or that the dosage is duplicated (3n copies). Fig 2 shows the RESULTS sheet for some typical data entered into the BRCA1 MLPA spreadsheet. FIG2: CALC1 this sheet is simply used for calculation. The dosage data is first normalised on this sheet depending on the signal strength of the control amplimers. The deviations of each test sample ligation product are also calculated on this sheet relative to the mean and standard deviation of the 5 normal controls. CALC2 this sheet is also used for calculation. On this sheet the peak heights of each ligation product are divided by every other peak height within a sample to yield a ratio. This is then divided by the equivalent figure derived from the average of the five normal controls to yield the dosage quotients displayed on the RESULTS worksheet. REGRESSIONS this sheet is used to correct for any data that slopes relative to increasing molecular weight of the product. We have found artefacts in data causing slope due to differences in the electrokinetic injection sample loading process used in capillary electrophoresis. Data is normalised on this page using a linear regression model based on the degree of sloping of the control ligation products. In kits where there are no clear control ligation products e.g. Human Telomere MLPA kits P069/P070, then all ligation products are used in the calculation of slope.

3 METHOD OF ANALYSIS Background Analysis of dosage data can be problematic. Dosage data is quantitative yet in diagnostics we require a binary (Yes or No) answer. Analysis of dosage data by the use of dosage quotients (DQs) has been generally accepted as the standard method of analysis for several years. These worksheets analyse data to produce DQs in the standard way; however, they also incorporate two novel features of analysis to aid with interpretation. The first generates a likelihood probability of concordance with one of three hypotheses. Namely that a ligation product is present at either one two or three copies within the test sample. This figure is generated by comparing the test sample to a series of five normal controls. The controls are used to give a measure of the variability for each ligation product and allows the probability of deviation from expectation of the test sample to be estimated using the t-statistic. The second acts as a control for the overall quality of an individual test by measuring the standard deviation of the DQs obtained for all the control ligation products. If the standard deviation exceeds 0.1 then the sample is deemed to be of poor quality. Studies carried out by Dr Ruth Charlton, Regional Genetics Service, Leeds have shown that there is no overlap between DQs of deleted, normal and duplicated DQs of samples where the standard deviation of the control ligation products do not exceed 0.1. Thus excluding samples with higher degrees of variability substantially reduces the possibility of making an incorrect diagnosis. The analysis process Firstly the data is input on the RAW DATA worksheet. The format of this sheet has been to designed to facilitate the input of data directly from fragment analysis applications with the minimum user intervention e.g. Genotyper/Genemapper. This data is then presented in a more amenable format either at the top or bottom of the RESULTS worksheet. Each test and control sample s data is normalised by summing the total control peak height and dividing each ligation product s peak height by this figure. Carrying out this step is necessary in order for meaningful measurements of the variability between control samples to be measured. The control and test data is then equalised by dividing the normalised peak height by the mean peak height of all five controls. Both these stages are carried out on the CALC1 sheet. The next step that is carried out is to correct for sloping. This is achieved by carrying out a linear regression of all the equalised control products (or all equalised products if there are no control products) against the mean of the five control samples and correcting the equalised peak heights for the slope of any regression calculated according to the molecular weight of each peak. This stage is carried out on the REGRESSIONS sheet. Dosage quotients (DQs) are next calculated firstly by dividing each slope corrected ligation product peak height by each slope corrected control ligation product peak height for the average of all five control samples to create a matrix or grid of values. The same set of calculations are carried out for each of the test samples. These matrices are displayed on the CALC2 sheet. The dosage quotients are then calculated by dividing the test sample matrix by the control mean matrix. Dosage quotients (DQs) are displayed on the RESULTS sheet. The mean and standard deviation of each ligation product is then calculated for the five normal controls. The fit to each of the three hypotheses (deleted, normal or duplicated) is then calculated as follows. For the normal (2n copies) hypothesis, the difference of each test sample s ligation product normalised peak height from the mean of the control samples is calculated as a number of standard deviations. For the deleted hypothesis (n copies) the assumption is made that if the test sample is heterozygously deleted for a ligation product that the peak should

4 be half-height and thus doubling the normalised peak height should therefore yield good fit with the control data. Thus doubled normalised peak heights are compared with the corresponding mean control amplimers to yield a difference as numbers of standard deviations for the deleted hypothesis. Finally, to test fit to the duplicated (3n) hypothesis the assumption is that if duplicated the test sample should be 1.5 times normal height thus multiplying the test sample normalised peak height by 2/3 should yield good fit with the control data. The three differences representing the three competing hypotheses are then converted into probabilities of deviation using the t-statistic. The precise probability for each amplimer is thus determined by two factors (i) the underlying variability in the batch of five normal controls for that particular ligation product and (ii) the size of the difference between the test sample for that ligation product and the control samples. Finally the relative likelihood of each of the three competing hypotheses is calculated for each ligation product as an odds ratio to indicate which hypothesis is more likely. For instance if the observed deviation from the normal hypothesis of the test sample is predicted to occur in 10% cases and the deviation from the deleted hypothesis is predicted to occur in 0.1% of cases then the relative odds of the normal to deleted hypotheses is 100:1 in favour of the normal hypothesis. Fig 3 illustrates the method used for calculating relative likelihoods. The three curves represent the relative probability distribution of dosage quotient for a given ligation product for each of the hypotheses, n deleted, 2n normal, 3n duplicated. The probability distribution is calculated in practice by the t-statistic. In the illustrated example the measured DQ of 0.9 equates to a probability of this being a normal result of 0.40, a probability of being a deleted result of and a probability of being a duplicated result of Dividing the Normal probability by the Deleted probability yields an odds ratio of 444:1 and dividing the Normal by the Duplicated probability yields an odds ratio of 667:1. Fig 3: p n 2n 3n DQ DQ = 0.9; p(2n) = 0.40 p(n) = ; p(3n) = Odds Norm:Del = 444:1 Odds Norm:Dup = 667:1 USE OF AND INTERPRETATION OF DATA ON THE WORKSHEETS The spreadsheet may be started by simply opening the file in Excel or double clicking the file icon in Windows Explorer. Depending on the levels of Macro Security set on your workstation you may or may not be informed that the spreadsheet contains macros and given the opportunity to enable or disable them but if macros are enabled a small dialog box is presented to the user (see Fig 4). The user must then enter both their name and corresponding laboratory worksheet number in order to proceed with analysis. The user name and Worksheet number are then entered on the worksheet within locked cells for audit purposes. This feature can be bypassed if preferred as some spreadsheets still function normally with macros disabled, however it is important to note that some do not e.g. P069 and P070 Human Telomeres. Any spreadsheets where macros need to be enabled in order for the spreadsheet to function normally will state this in the Release Notes. Should you need to

5 change the Macro security setting to allow macros to run, this can be done as follows in MS Excel 2003 FIG 4: On the Tools menu, click Options. Click the Security tab. Under Macro Security, click Macro Security. Click the Security Level tab, and then select the security level you want to use Medium is recommended, Data may now be copied and pasted directly from a Genotyper or equivalent output table in Excel format onto the RAW DATA sheet. The yellow cells indicate the cell(s) to select when pasting data. Fig 5 illustrates the appearance of the RAW DATA sheet prior to entering data. FIG 5: The spreadsheets must have five normal controls in order to function correctly. Once data has been pasted into position the Cross Ref column can be used to ensure that the data has been pasted into the sheet in the correct order provided the pasted data also includes data labels.

6 Fig 6 illustrates some data showing the concordance between the LABEL 1 field from the pasted data and the CROSS REF field. FIG 6: Space is allocated for a deletion or positive mutation control. Although this is not essential for the spreadsheet to function properly we strongly recommend that at least one positive control is run with each batch of samples. The data entry form is configured to accept up to 10 test samples. Once the test samples have been entered onto the RAW DATA sheet the results may be visualised by clicking on the RESULTS tab. On the RESULTS sheet, the control and test sample raw data are represented at the top of the sheet. Further down the sheet the analysed data are presented with the deletion control displayed first followed by up to 10 test samples. To the left of each sample s analysed data is a set of cells in which the sample name/lab no (from the raw data sheet), user and worksheet number (as entered in the dialog box when the worksheet was opened) is displayed for record keeping purposes. Beneath the worksheet information is a cell labelled Int QC Stand Dev. The cell directly below will either be coloured green if the sample quality is judged as good or red if it is judged poor. Sample quality is estimated by measuring the standard deviation of all the test ligation products measured against each other. As outlined previously retrospective analysis of MLPA data has shown that samples with control standard deviations less than 0.1 show no overlap between normal, duplicated and deleted ranges (Dr Ruth Charlton). Fig 7a illustrates the appearance of a typical sample where the data quality has been judged to be poor. FIG 7a: In worksheets where there are no control ligation products e.g. P069 and P070 Human telomeres, any cells that have been excluded from the quality control calculation due to them being possibly deleted or duplicated are listed in the cell below the data quality cell. If no cells have been excluded i.e. none have been judged as potentially deleted/duplicated the text Omitted;None appears.

7 FIG 7b: To the right of this sample information, the results are presented in a tabular format. For each sample, the upper rows are a series of dosage quotients gridded out for each ligation product (control and test) horizontally versus each control ligation product vertically. These cells are conditionally formatted to highlight deleted/duplicated and aberrant results. The actual settings that have been set for the conditional formats are given to the right of the raw data and may vary depending on the spreadsheet but typical ranges are as follows: Normal DQ Deleted DQ Duplicated DQ Equivocal DQ & Fig 8 illustrates a test sample showing a section of DQ results from a sample. The majority of DQs fall into the normal range and have a white background. A patch of three ligation products (BRCA1 Exons 1A, 1B and 2) all have reduced DQs within the deleted range of and are shaded aqua (ringed). A single DQ measurement, that between the control ligation product C11p13 and C12p12 lies in the equivocal range and is shaded a cream colour. Fig 8: Beneath the gridded DQ data lie (typically) two rows labelled in blue type above three rows labelled in green type. These rows hold the probabilistic analysis of the sample s mean DQ result for each ligation product. The green rows contain absolute probabilities measured by the t-statistic of the difference between the observed mean DQ for that ligation product and the expected DQ as estimated from the panel of 5 normal controls for each of the possible dosages (normal, deleted and duplicated). A 60% probability in the row for the normal hypothesis indicates that a random normal sample would be expected to deviate by at least this amount in 60% of tests. These cells are also conditionally formatted to highlight abnormal or equivocal results. The precise limits may vary from spreadsheet to spreadsheet but a key is given to the right of the control data at the top of the RESULTS sheet. The blue rows contain pairwise comparisons between the relative probabilities for the alternative hypotheses given as an odds ratio. With good quality data the odds ratios although varying should clearly favour one hypothesis over another. If the normal i.e. 2n hypothesis is clearly favoured (i.e. an odds ratio of >=20:1 for normal) the cell is conditionally formatted to have a green background. A clear odds ratio in favour of an abnormal hypothesis (i.e. an odds ration of >=1:20 in favour of a deleted or duplicated hypothesis) gives a cell with a red background. Any odds ratio giving equivocal results is highlighted with a cream background.

8 Figure 9 illustrates some typical results. Most of the odds ratios for the shown ligation products strongly favour the normal hypothesis and are consequently shaded in with a green backround. However the ligation product for BRCA1 Ex 13 is showing high DQs, consequently the fit is better to the duplicated hypothesis than either the normal or deleted hypotheses. Comparing the relative probabilities of fit to the duplicated and normal hypotheses the relative likelihood is calculated as 146:1 in favour of the sample being duplicated than the result being a normal outlier experienced by chance. Fig 9: The spreadsheets also incorporate a graphical representation of the results in the form of a histogram. This is located to the right of each test sample on the RESULTS sheet. Fig 10 illustrates a histogram from the HNPCC MLPA spreadsheet summarising the mean DQ data. This particular sample gave normal odds ratios for all ligation products except that for hmlh1 exon 2 which appeared to be deleted. This was subsequently confirmed by further analysis. FIG 10: INTERNAL QUALITY CONTROL Internal quality control is an important consideration for all diagnostic laboratories. Although the extra tiers of analysis given in the spreadsheets assist in the analysis of MLPA dosage data, the meaning and significance of some results will still remain a matter of professional judgement. The precise limits of what is an acceptable result must remain the responsibility of each laboratory to determine, however what follows are guidelines that have been found to be useful locally. 1) In order to be an acceptable result the Quality value (standard deviation of the control ligation products) should have a value of <=0.1. Samples with a Quality value exceeding 0.1 should be rejected. This condition applies to all categories of results and takes precedence over other quality criteria. 2) If all the odds ratios are for the normal hypothesis with >= 20:1 odds then the sample can be judged to be normal. 3) If two or more contiguous ligation products favour a deleted or duplicated hypothesis with >= 1:20 odds then the sample can be judged to be abnormal provided the remaining ligation products give odds ratios for the normal hypothesis of >= 20:1 odds. It still remains good

9 practice to confirm any mutations by repeating the analysis on a replicate MLPA analysis or on an affected first degree relative 4) If a single ligation product gives a >= 1:20 odds ratio in favour of deletion/duplication then further evidence must be obtained to confirm this result. Firstly the sample should be sequenced to establish that there are no mutations present beneath the ligation product hybridisation sites (this has been a frequently cause of a ligation product which appears deleted). Secondly the deletion/duplication should either (i) be confirmed using a separate assay e.g. long PCR, dosage PCR (ii) be confirmed on a separate DNA sample (not just a new replicate MLPA assay) or (iii) be confirmed on an affected first degree relative. 5) Where a sample otherwise appears normal yet the odds ratios for normality drop below >= 20:1 for some test ligation products this can be accepted to be a normal result provided (i) no one normal odds ratio drops below 5:1 in favour of normality (ii) that contiguous exons are not involved (iii) that no more than 10% of test ligation products are involved (iv) that none of the mean DQs falls into the transitional range ( or ). Please note that in the presence of a deletion the normal:duplication comparison is meaningless and will yield equivocal odds the same applies for the normal:deletion comparison for a duplicated sample. SPREADSHEETS FOR NEW MLPA ASSAYS If there is an MLPA assay in use in your laboratory for which you would like us to design a spreadsheet please contact me by at andrew.wallace@cmmc.nhs.uk CONFIGURATION OF EXISTING SPREADSHEETS If there is an existing spreadsheet for which the data entry page is not compatible with your fragment analysis output, I can usually quite simply alter these to suit your requirements. In order to do this please to me at andrew.wallace@cmmc.nhs.uk a sample of your data which you would like to import directly into the spreadsheets MODIFICATIONS TO EXISTING PROBE SETS MRC-Holland review their kits and often change the probe combinations in response to their customer demand. If you notice that this has occurred and the currently available spreadsheet will no longer analyse the data please let me know by at andrew.wallace@cmmc.nhs.uk as I can usually make minor modifications quite easily. ACKNOWLEDGEMENTS The quality control measure using the standard deviation of the control ligation products was developed by Dr Ruth Charlton, Regional Genetics Service, Leeds