**5. Quality management**

*Methods in Molecular Medicine*

**Pool A A** VAL-1

**Pool B E** VAL-1

**Table 3.** *Validation grid.* NA12878

**C VAL-11** VAL-1

NA12878

**H** VAL-5 VAL-2

*Copy number variant (CNV) samples are indicated in bold.*

**D** VAL-2 SG-063

**G** VAL-2 SG-063

**B VAL-3** VAL-10 **B** VAL-2

NA12878

**VAL-6 E** VAL-2

diluted to 20 nM (in a total volume of 20 μl) and subjected to sequencing using

**Run 001 Run 002 Run 003**

SG-063

SG-063

**VAL-9 G** VAL-14 VAL-1

**F** VAL-7 VAL-8 **F VAL-6 VAL-3 F VAL-23** VAL-2

**C VAL-17** VAL-2

VAL-4 **A** VAL-5 **VAL-13 A VAL-21** VAL-5

SG-063

VAL-12 **D** VAL-16 VAL-19 **D VAL-3** VAL-25

NA12878

**H VAL-18** VAL-4 **H** VAL-1

VAL-15 **B** VAL-28 VAL-1

**C** VAL-1 NA12878

VAL-20 **E VAL-22 VAL-6**

**G** VAL-4 VAL-27

NA12878

NA12878

VAL-24

SG-063

VAL-26

• Sequence analysis: performance metrics. Baseline performance metrics for the WES validation study must involve the analysis of well-characterized reference samples: the NIST sample (NA12878) and the SOPHiA™ Genetics control SG063. The sequence metrics for each sample in the run must be recorded and averages established using the reference samples. Samples must meet the sequencing metrics shown in **Table 4** in order to reach the threshold for clinical reporting.

Analytical sensitivity and specificity must be calculated separately for each variant type (SNV, indel, CNV, etc.). Additional runs may be required to meet acceptable confidence intervals for less frequent variant types of insertions and deletions. For 95% confidence and 95% reliability, 59 variants of each type (and insertion/deletion range) should be analyzed [5]. The variant types that do not have strong confidence intervals must be listed in the test limitations of the clinical report until such time that the desired confidence levels

**Selected sequencing metrics Must have Nice to have** Q30 score >80 >85 Total number of reads per sample >70 M 80–100 M Percentage of mapped reads >80% >85% Total percentage on-target reads >90% >95% Coverage 10% quantile (at this depth, 90% target covered) 20x 50x

an Illumina® HiSeq4000 Sequencing platform.

SG-063

**124**

**Table 4.** *Sequencing metrics.*

have been achieved.

The worksheets described by Santani et al. [4] set out very clear guidance for all quality aspects that need to be taken into consideration for the test to meet CAP requirements [4]. Through a validation study, the majority of a test's limitations will be discovered and can be recorded against the QC parameters. **Table 5** summarizes quality metrics that need to be addressed.


**Table 5.** *Quality management.*


**Table 6.**

**127**

*Clinical Validation of a Whole Exome Sequencing Pipeline*

reference data obtained from 1000 Genomes Project.

.

To assess accuracy, genetic variants must be compared against publicly available

Clinical association, gene validity and mutation spectrum are applied to the cre

ation of virtual gene panels in order to aid variant interpretation and reporting. The considerations associated with constructing virtual gene panels and the analysis of

The decision to implement WES in a clinical diagnostic environment is one that must take into account local context, which encompasses clinical complex

ity, staff resources, equipment resources and bioinformatic expertise. The deci

sions described here were made based on the above considerations with a view to establishing opportunity, the most important of which was to have a WES pipeline that could scale over time in terms of patients tested and with the potential to be a

It should be stressed, however, that a WES pipeline is sandwiched by two criti

cal elements: first, the need to focus on the quality and accurate quantitation of genomic DNA; which dictates the quality of everything that happens downstream, and second, to understand that the identification of DNA variants is technically demanding but the classification of those variants is not currently a fully auto

mated process. The former can sometimes be overlooked, while the latter can be a daunting exercise. It is perhaps the subject of another book chapter to discuss the

The authors wish to thank Mr. Duncan Kay of Custom Science (NZ) for his generous suggestions regarding commercial providers for WES data analysis and Javier Botet of Sophia Genetics for his advice regarding quality management -





*DOI: http://dx.doi.org/10.5772/intechopen.93251*

**6. Bioinformatics and IT**

variants are shown in **Table 6**

**7. Conclusions**

regional resource.

approaches to variant classification.

The authors declare no conflicts of interest.

**Conflicts of interest**

**Thanks**

considerations.

*Considerations for gene selection, analysis and virtual panel creation.*
