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

<div><p>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.</p></div

Comparison of the content and size of different standard file formats for the storage of genomic data.

<p>Comparison of the content and size of different standard file formats for the storage of genomic data.</p

Compatibility of the eVCF file format with different variation analysis suites.

<p>Compatibility of the eVCF file format with different variation analysis suites.</p

Genotyping of known SNPs from dbSNP 141 using the VCF and gVCF file formats and the number of homozygous reference sites and no-calls based on WGS data.

<p>Genotyping of known SNPs from dbSNP 141 using the VCF and gVCF file formats and the number of homozygous reference sites and no-calls based on WGS data.</p

Genotyping of known SNPs from ClinVar using the VCF and gVCF file formats and the number of homozygous reference sites and no-calls based on WGS data.

<p>Genotyping of known SNPs from ClinVar using the VCF and gVCF file formats and the number of homozygous reference sites and no-calls based on WGS data.</p

Concordance of genotypes represented in VCF and gVCF files with those detected by the MI RISK Plus kit.

<p>Concordance of genotypes represented in VCF and gVCF files with those detected by the MI RISK Plus kit.</p

Genotyping of GWAS catalog sites using the VCF and gVCF file formats and the number of homozygous reference sites and no-calls based on WGS data.

<p>Genotyping of GWAS catalog sites using the VCF and gVCF file formats and the number of homozygous reference sites and no-calls based on WGS data.</p

Exonic regions coverage.

<p>Percentage of exonic regions covered at a read depth ≥ 5, an alignment score ≥ 10, a basecall quality ≥ 10 from WGS subsets of the original full set with different average X-fold coverage values.</p

Comparison of the number of dbSNP, ClinVar and GWAScat sites represented using VCF, gVCF and eVCF files.

<p>Comparison of the number of dbSNP, ClinVar and GWAScat sites represented using VCF, gVCF and eVCF files.</p