title: Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data.
abstract: Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing.
authors.affiliation: Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America; Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America; Center for Quantitative Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.
authors.forename: Bingshan
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America; Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Wei
authors.affiliation: Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Zhan
authors.affiliation: Center for Quantitative Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.
authors.forename: Xue
authors.initials: X
authors.lastname: Zhong
authors.affiliation: Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Institute of Computational Biology, Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
authors.forename: Chun
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America; Institute of Computational Biology, Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Haines
mesh_terms: D000465:Algorithms; D005190:Family; D005787:Gene Frequency; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010375:Pedigree; D017422:Sequence Analysis, DNA / Q000379:methods*