title: An analytical workflow for accurate variant discovery in highly divergent regions.
abstract: Current variant discovery methods often start with the mapping of short reads to a reference genome; yet, their performance deteriorates in genomic regions where the reads are highly divergent from the reference sequence. This is particularly problematic for the human leukocyte antigen (HLA) region on chromosome 6p21.3. This region is associated with over 100 diseases, but variant calling is hindered by the extreme divergence across different haplotypes.
  
  
  
  We simulated reads from chromosome 6 exonic regions over a wide range of sequence divergence and coverage depth. We systematically assessed combinations between five mappers and five callers for their performance on simulated data and exome-seq data from NA12878, a well-studied individual in which multiple public call sets have been generated. Among those combinations, the number of known SNPs differed by about 5 % in the non-HLA regions of chromosome 6 but over 20 % in the HLA region. Notably, GSNAP mapping combined with GATK UnifiedGenotyper calling identified about 20 % more known SNPs than most existing methods without a noticeable loss of specificity, with 100 % sensitivity in three highly polymorphic HLA genes examined. Much larger differences were observed among these combinations in INDEL calling from both non-HLA and HLA regions. We obtained similar results with our internal exome-seq data from a cohort of chronic lymphocytic leukemia patients.
  
  
  
  We have established a workflow enabling variant detection, with high sensitivity and specificity, over the full spectrum of divergence seen in the human genome. Comparing to public call sets from NA12878 has highlighted the overall superiority of GATK UnifiedGenotyper, followed by GATK HaplotypeCaller and SAMtools, in SNP calling, and of GATK HaplotypeCaller and Platypus in INDEL calling, particularly in regions of high sequence divergence such as the HLA region. GSNAP and Novoalign are the ideal mappers in combination with the above callers. We expect that the proposed workflow should be applicable to variant discovery in other highly divergent regions.
authors: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
authors: Shulan
authors: S
authors: Tian
authors: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
authors: Huihuang
authors: H
authors: Yan
authors: Informatics Institute, University of Minnesota, Minneapolis, MN, 55455, USA.
authors: Claudia
authors: C
authors: Neuhauser
authors: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. Slager.Susan@mayo.edu.
authors: Susan L
authors: SL
authors: Slager
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D059472:Exome; D014644:Genetic Variation; D015894:Genome, Human; D023281:Genomics / Q000379:methods* / Q000592:standards; D006680:HLA Antigens / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D054643:INDEL Mutation; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D057188:Workflow
chemical_list: D006680:HLA Antigens
keywords: Alignment algorithm; Chronic lymphocytic leukemia; Exome sequencing; Human leukocyte antigen; Variant calling