title: Comparison of Multi-Sample Variant Calling Methods for Whole Genome Sequencing.
abstract: Rapid advancement of next-generation sequencing (NGS) technologies has facilitated the search for genetic susceptibility factors that influence disease risk in the field of human genetics. In particular whole genome sequencing (WGS) has been used to obtain the most comprehensive genetic variation of an individual and perform detailed evaluation of all genetic variation. To this end, sophisticated methods to accurately call high-quality variants and genotypes simultaneously on a cohort of individuals from raw sequence data are required. On chromosome 22 of 818 WGS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which is the largest WGS related to a single disease, we compared two multi-sample variant calling methods for the detection of single nucleotide variants (SNVs) and short insertions and deletions (indels) in WGS: (1) reduce the analysis-ready reads (BAM) file to a manageable size by keeping only essential information for variant calling ("REDUCE") and (2) call variants individually on each sample and then perform a joint genotyping analysis of the variant files produced for all samples in a cohort ("JOINT").JOINTidentified 515,210 SNVs and 60,042 indels, whileREDUCEidentified 358,303 SNVs and 52,855 indels.JOINTidentified many more SNVs and indels compared toREDUCE. Both methods had concordance rate of 99.60% for SNVs and 99.06% for indels. For SNVs, evaluation with HumanOmni 2.5M genotyping arrays revealed a concordance rate of 99.68% forJOINTand 99.50% forREDUCE.REDUCEneeded more computational time and memory compared toJOINT. Our findings indicate that the multi-sample variant calling method using theJOINTprocess is a promising strategy for the variant detection, which should facilitate our understanding of the underlying pathogenesis of human diseases.
authors.affiliation: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA ; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Kwangsik
authors.initials: K
authors.lastname: Nho
authors.affiliation: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: West
authors.affiliation: University Information Technology Service, Indiana University, Indianapolis, IN, USA.
authors.forename: Huian
authors.initials: H
authors.lastname: Li
authors.affiliation: University Information Technology Service, Indiana University, Indianapolis, IN, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Henschel
authors.affiliation: Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Apoorva
authors.initials: A
authors.lastname: Bharthur
authors.affiliation: University Information Technology Service, Indiana University, Indianapolis, IN, USA.
authors.forename: Michel C
authors.initials: MC
authors.lastname: Tavares
authors.affiliation: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA ; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA ; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA ; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Saykin