PLoS ONE (Jan 2015)

The Use of Non-Variant Sites to Improve the Clinical Assessment of Whole-Genome Sequence Data.

  • Alberto Ferrarini,
  • Luciano Xumerle,
  • Francesca Griggio,
  • Marianna Garonzi,
  • Chiara Cantaloni,
  • Cesare Centomo,
  • Sergio Marin Vargas,
  • Patrick Descombes,
  • Julien Marquis,
  • Sebastiano Collino,
  • Claudio Franceschi,
  • Paolo Garagnani,
  • Benjamin A Salisbury,
  • John Max Harvey,
  • Massimo Delledonne

DOI
https://doi.org/10.1371/journal.pone.0132180
Journal volume & issue
Vol. 10, no. 7
p. e0132180

Abstract

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Genetic testing, which is now a routine part of clinical practice and disease management protocols, is often based on the assessment of small panels of variants or genes. On the other hand, continuous improvements in the speed and per-base costs of sequencing have now made whole exome sequencing (WES) and whole genome sequencing (WGS) viable strategies for targeted or complete genetic analysis, respectively. Standard WGS/WES data analytical workflows generally rely on calling of sequence variants respect to the reference genome sequence. However, the reference genome sequence contains a large number of sites represented by rare alleles, by known pathogenic alleles and by alleles strongly associated to disease by GWAS. It's thus critical, for clinical applications of WGS and WES, to interpret whether non-variant sites are homozygous for the reference allele or if the corresponding genotype cannot be reliably called. Here we show that an alternative analytical approach based on the analysis of both variant and non-variant sites from WGS data allows to genotype more than 92% of sites corresponding to known SNPs compared to 6% genotyped by standard variant analysis. These include homozygous reference sites of clinical interest, thus leading to a broad and comprehensive characterization of variation necessary to an accurate evaluation of disease risk. Altogether, our findings indicate that characterization of both variant and non-variant clinically informative sites in the genome is necessary to allow an accurate clinical assessment of a personal genome. Finally, we propose a highly efficient extended VCF (eVCF) file format which allows to store genotype calls for sites of clinical interest while remaining compatible with current variant interpretation software.