Evaluation of saliva as a source of accurate whole-genome and microbiome sequencing data

Validated WGS and WES protocols proved saliva-derived gDNA as an equivalent to blood-derived gDNA for clinical and population genomic analyses

BACKGROUND

Whole exome sequencing (WES) and whole genome sequencing (WGS) have become standard methods in human clinical diagnostics as well as in population genomics (POPGEN). Blood-derived genomic DNA (gDNA) is routinely used in the clinical environment. Conversely, many POPGEN studies and commercial tests benefit from easy saliva sampling. Here, we evaluated the quality of variant call sets and the level of genotype concordance of single nucleotide variants (SNVs) and small insertions and deletions (indels) for WES and WGS using paired blood- and saliva-derived gDNA isolates employing genomic reference-based validated protocols.

METHODS

The genomic reference standard Coriell NA12878 was repeatedly analyzed using optimized WES and WGS protocols, and data calls were compared with the truth dataset published by the Genome in a Bottle Consortium. gDNA was extracted from the paired blood and saliva samples of 10 participants and processed using the same protocols. A comparison of paired blood-saliva call sets was performed in the context of WGS and WES genomic reference-based technical validation results.

RESULTS

The quality pattern of called variants obtained from genomic-reference-based technical replicates correlates with data calls of paired blood-saliva-derived samples in all levels of tested examinations despite a higher rate of non-human contamination found in the saliva samples. The F1 score of 10 blood-to-saliva-derived comparisons ranged between 0.8030-0.9998 for SNVs and between 0.8883-0.9991 for small-indels in the case of the WGS protocol, and between 0.8643-0.999 for SNVs and between 0.7781-1.000 for small-indels in the case of the WES protocol.

CONCLUSION

Saliva may be considered an equivalent material to blood for genetic analysis for both WGS and WES under strict protocol conditions. The accuracy of sequencing metrics and variant-detection accuracy is not affected by choosing saliva as the gDNA source instead of blood but much more significantly by the genomic context, variant types, and the sequencing technology used.

RAVAQ: An integrative pipeline from quality control to region-based rare variant association analysis

Next-generation sequencing technologies have opened up the possibility to sequence large samples of cases and controls to test for association with rare variants. To limit cost and increase sample sizes, data from controls could be used in multiple studies and might thus be generated on different sequencing platforms. This could pose some problems of comparability between cases and controls due to batch effects that could be confounding factors, leading to false-positive association signals. To limit batch effects and ensure comparability of datasets, stringent quality controls are required. We propose an integrative five-steps pipeline, RAVAQ, that (a) performs a specific three-step quality control taking into account the case-control status to ensure data comparability, (b) selects qualifying variants as defined by the user, and (c) performs rare variant association tests per genomic region. The RAVAQ pipeline is wrapped in an R package. It is user-friendly and flexible in its arguments to adapt to the specificity of each research project. We provide examples showing how RAVAQ improves rare variant association tests. The default RAVAQ quality control outperformed the widely used Variant Quality Score Recalibration method, removing inflation due to spurious signals. RAVAQ is open source and freely available at https://gitlab.com/gmarenne/ravaq.