Methodology article
FAVR (Filtering and Annotation of Variants that are Rare): methods to facilitate the analysis of rare germline genetic variants from massively parallel sequencing datasets
BMC Bioinformatics 2013, 14:65 doi:10.1186/1471-2105-14-65
Published: 25 February 2013Abstract (provisional)
Background
Characterising genetic diversity through the analysis of massively parallel sequencing (MPS) data offers enormous potential to significantly improve our understanding of the genetic basis for observed phenotypes, including predisposition to and progression of complex human disease. Great challenges remain in resolving which genetic variants are genuinely associated with disease from the millions of 'bystanders' and artefactual signals.
Results
FAVR is a suite of new methods designed to work with commonly used MPS analysis pipelines to assist in the resolution of some of these issues with a focus on relatively rare genetic variants. To the best of our knowledge, no equivalent has previously been described. The most important and novel aspect of FAVR is the use of signatures in comparator sequence alignment files during variant filtering, and annotation of variants potentially shared between individuals. The FAVR methods use these signatures to facilitate filtering of (i) platform-specific artefacts, (ii) common genetic variants, and, where relevant, (iii) artefacts derived from imbalanced paired-end sequencing, as well as annotation of genetic variants based on evidence of co-occurrence in individuals. By comparing conventional variant calling with or without downstream processing by FAVR methods applied to whole-exome sequencing datasets, we demonstrate a 3-fold smaller rare single nucleotide variant shortlist with no detected reduction in sensitivity. This analysis included Sanger sequencing of rare variant signals not evident in dbSNP131, assessment of known variant signal preservation, and comparison of observed and expected rare variant numbers across a range of first cousin pairs. The principles described herein were applied in our recent publication identifying XRCC2 as a new breast cancer risk gene and have been made publically available as a suite of software tools.
Conclusions
FAVR is a platform-agnostic suite of methods that significantly enhances the analysis of large volumes of sequencing data for the study of rare genetic variants and their influence on phenotypes.
