Exome Sequencing Analysis: A Guide to Disease Variant Detection

Abstract

Whole exome sequencing presents a powerful tool to study rare genetic disorders. The most challenging part of using exome sequencing for the purpose of disease-causing variant detection is analyzing, interpreting, and filtering the large number of detected variants. In this chapter we provide a comprehensive description of the various steps required for such an analysis. We address strategies in selecting samples to sequence, and technical considerations involved in exome sequencing. We then discuss how to identify variants, and methods for first annotating detected variants using characteristics such as allele frequency, location in the genome, and predicted severity, and then classifying and prioritizing the detected variants based on those annotations. Finally, we review possible gene annotations that may help to establish a relationship between genes carrying high-priority variants and the phenotype in question, in order to identify the most likely causative mutations.

Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations

“…37 Variant filtering is necessary because the initial variants identified are so numerous that effective analysis is impossible, and high false discovery rates will occur if variants are not prioritised. After variants are called in the WES analysis, approaches to variant filtering include assessment of variant frequencies and variant functionality (figure 1).…”

“…Through the 1000 Genomes Project, the majority (over 99%) of variants with a MAF above 1% in the general population were captured, and through imputing with the 1000 Genomes data, more common variants (>1% MAF) can be captured in combination with the GWAS design, which has been widely used for discovery of genetic aetiology of complex diseases. 37 Second, the 1000 Genomes Project showed that relatively rare variants (MAF <1% or even <0.5%) contain the majority of more 'deleterious' variants, and thus may represent an important source of disease aetiology. This is particularly true for the most highly conserved coding sites, in which 85% of non-synonymous variants and over 90% of stop-gain and splice-disrupting variants are within the category of rare variants.…”

“…This is particularly true for the most highly conserved coding sites, in which 85% of non-synonymous variants and over 90% of stop-gain and splice-disrupting variants are within the category of rare variants. 37 …”

GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS

“…This issue often implies additional costs for variants validation by Sanger sequencing, at least in diagnostic settings 5,16 . False positive calls pose serious challenges in downstream data analysis, introducing erroneous missense and loss of function variants, like frameshift INDELs, that are targets of most analysis work-flows 17,18 .…”

“…Overall, our tool effectively reduces false INDEL calls and could be useful to improve WES results interpretation considering that many work-flows search for variants that potentially alter gene function, especially loss of function variants like frameshift INDELs 17,18 . GARFELD-NGS can be successfully applied to SNPs filtering as well, with performances comparable to hard-filters or VQSR.…”