title: FAVR (Filtering and Annotation of Variants that are Rare): methods to facilitate the analysis of rare germline genetic variants from massively parallel sequencing datasets.
abstract: 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 genetic variants that are genuine from the millions of 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 the issues related to the analysis of the vast amount of resulting data, with a focus on relatively rare genetic variants. To the best of our knowledge, no equivalent method 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 and/or mapping-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. We applied conventional variant calling applied to whole-exome sequencing datasets, produced using both SOLiD and TruSeq chemistries, with or without downstream processing by FAVR methods. 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.
authors: Victorian Life Sciences Computation Initiative, The University of Melbourne, 187 Grattan Street Carlton, Melbourne, Victoria 3010, Australia.
authors: Bernard J
authors: BJ
authors: Pope
authors: Tú
authors: T
authors: Nguyen-Dumont
authors: Fabrice
authors: F
authors: Odefrey
authors: Fleur
authors: F
authors: Hammet
authors: Russell
authors: R
authors: Bell
authors: Kayoko
authors: K
authors: Tao
authors: Sean V
authors: SV
authors: Tavtigian
authors: David E
authors: DE
authors: Goldgar
authors: Andrew
authors: A
authors: Lonie
authors: Melissa C
authors: MC
authors: Southey
authors: Daniel J
authors: DJ
authors: Park
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D059472:Exome; D005260:Female; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D058977:Molecular Sequence Annotation; D010641:Phenotype; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D012984:Software