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Reducing the Variability of Exome Sequencing

A look at interlaboratory variability of exome testing and ways labs can reduce this variability to improve patient care

Clinical laboratories are increasingly using exome testing to help reveal the genetic causes of disease. However, variability can arise between laboratories in their exome sequencing results and interpretation, which can impact patient care. Here, we look at how this interlaboratory variability arises and possible steps laboratories can take to reduce interlaboratory exome testing variability.

"Variability can arise between different laboratories carrying out exome sequencing, leading to differing interpretations of pathogenic variants that may have clinical impacts on patient diagnosis and management. "

The genome contains sections that code (exons) and do not code (introns) for proteins. The entire collection of exons is known as the exome and accounts for around 1–2 percent of the genome—yet 85 percent of disease-causing or pathogenic variants are thought to reside in the exome.1,2 Consequently, clinical laboratories are increasingly turning to next-generation sequencing strategies like exome sequencing (also referred to as whole-exome sequencing or WES) to help identify pathogenic variants.3 

Variability can arise between different laboratories carrying out exome sequencing, leading to differing interpretations of pathogenic variants that may have clinical impacts on patient diagnosis and management.3 Understanding how variability arises and steps to reduce this variability are key to improving consistency of exome sequencing across laboratories.

Exome sequencing workflow

A typical exome sequencing workflow can be split into three broad steps:4

1.    Genomic DNA samples are obtained from patients, typically in the form of blood leukocytes, saliva, or formalin-fixed paraffin-embedded samples.

2.    Exon regions of interest, or target exonic regions, are captured from DNA samples using exome capture kits. Here, DNA samples are fragmented, modified with adaptor molecules, and target enriched, where probes attach to adaptor molecules. Nontarget regions are then removed from the sample through washing, and the captured, target-enriched exonic regions are amplified through PCR. 

3.    Exome libraries are then sequenced. Through computational methods, sequence data is aligned to a reference sequence and any variants in target exonic regions of a given patient are identified. These variants are obtained through a series of steps, including variant calling, annotation, and filtering.

What contributes to interlaboratory differences in WES? 

Inconsistencies in coverage

Interlaboratory exome sequencing variability can arise at many different stages of the sequencing process.3 For example, laboratories can use different exome capture kits that vary in approach and ability at capturing target exonic regions, generating exome libraries with variable quality—currently, none of these kits can capture all exons in human samples.3,5 Using different exome sequencing methods can lead to variability in exome coverage, which can refer to:

Coverage breadth—how much of the target exome is sequenced (with each base sequenced a minimum number of times), and

Coverage depth—the average number of times a base is sequenced and aligned to a reference sequence.6

Some estimations suggest that more than half of clinically relevant exonic regions lack sufficient coverage depth (<20x) for interpretation.7

Differences in exome sequencing coverage can create false-positive and false-negative results. False-positive results can arise from poor coverage during trio-based exome sequencing of de novo variants (variants that are not present in an individual’s parents, arising after fertilization or while gametes form),8 which involves exome sequencing of DNA samples from a patient and their parents. Inconsistent coverage of variant regions can lead to variants incorrectly classed as de novo variants.7

"To reduce exome sequencing interlaboratory variability, laboratories can undertake strategies to monitor their methodology and coverage consistencies."

False-negative results can arise from poor coverage in regions that are underrepresented (GC-rich regions) or in regions that can be difficult to capture or align.3 For example, Londin et al. analyzed variants associated with drug response and showed that the coverage depth can change depending on the location of a variant,5 which can generate false-negative results when variants are in regions with poor coverage depth. 

Inconsistencies in interpretation

Clinical interpretation of exome sequencing analysis involves identification of pathogenic variants from non-pathogenic variants through detailed filtering strategies and assessment, and, using criteria, generation of an exome report that includes these identified variants.4 As many nonpathogenic variants can be found during exome sequencing analysis, filtering steps are critical to identifying pathogenic from nonpathogenic variants.4

Interpretation of variants can be a source of interlaboratory variability as laboratories may differ in their assessment of which variants to include or exclude during evaluation and reporting.4 SoRelle et al. found that laboratories differently interpreted around 47 percent of assessed variants in epileptic patients, with around 3 percent of variants being interpreted with “clinically substantial differences.”9

What steps can laboratories take to reduce exome sequencing variability?

To reduce exome sequencing interlaboratory variability, laboratories can undertake strategies to monitor their methodology and coverage consistencies. The College of American Pathologists Proficiency Testing Program for Next-Generation Sequencing assesses the ability of laboratories to locate variants in a given location. Gotway et al. suggest that this assessment system could also include data aspects as a way of checking for interlaboratory coverage consistencies. In addition, Gotway et al. suggest strategies for improving reporting by including additional information, such as the amount of “genes completely covered,” providing information on the percentage of genes covered at sufficient coverage depth (e.g., >20x).7 

To improve interpretation of exome sequencing analysis, laboratories can consistently follow available guidelines for variant interpretation, such as those outlined by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology 2015 variant interpretation guidelines.3


1.     Warr A, Robert C, Hume D, Archibald A, Deeb N, Watson M. Exome sequencing: current and future perspectives. G3. 2015;5(8). doi:10.1534/g3.115.018564

2.    Rabbani B, Tekin M, Mahdieh N. The promise of whole-exome sequencing in medical genetics. J Hum Genet. 2014;59(1). doi:10.1038/jhg.2013.114.

3.    Marcou CA, Baudhuin LM. All clinical exomes are not alike: coverage matters. Clin Chem. 2020;66(1). doi:10.1093/clinchem.2019.310615

4.    Seaby EG, Pengelly RJ, Ennis S. Exome sequencing explained: a practical guide to its clinical application. Brief Funct Genomics. 2016;15(5). doi:10.1093/bfgp/elv054

5.    Londin ER, Clark P, Sponziello M, Kricka LJ, Fortina P, Park JY. Performance of exome sequencing for pharmacogenomics. Per Med. 2015;12(2). doi:10.2217/pme.14.77

6.     Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014;15(2). doi:10.1038/nrg3642

7.     Gotway G, Crossley E, Kozlitina J, et al. Clinical exome studies have inconsistent coverage. Clin Chem. 2020;66(1). doi:10.1093/clinchem.2019.306795

8.    Acuna-Hidalgo R, Veltman JA, Hoischen A. New insights into the generation and role of de novo mutations in health and disease. Genome Bio. 2016;17(1). doi:10.1186/s13059-016-1110-1 

9.    Sorelle JA, Pascual JM, Gotway G, Park JY. Assessment of interlaboratory variation in the interpretation of genomic test results in patients with epilepsy. JAMA Netw Open. 2020;3(4). doi:10.1001/jamanetworkopen.2020.3812