title: Leveraging ancestry to improve causal variant identification in exome sequencing for monogenic disorders.
abstract: Recent breakthroughs in exome-sequencing technology have made possible the identification of many causal variants of monogenic disorders. Although extremely powerful when closely related individuals (eg, child and parents) are simultaneously sequenced, sequencing of a single case is often unsuccessful due to the large number of variants that need to be followed up for functional validation. Many approaches filter out common variants above a given frequency threshold (eg, 1%), and then prioritize the remaining variants according to their functional, structural and conservation properties. Here we present methods that leverage the genetic structure across different populations to improve filtering performance while accounting for the finite sample size of the reference panels. We show that leveraging genetic structure reduces the number of variants that need to be followed up by 16% in simulations and by up to 38% in empirical data of 20 exomes from individuals with monogenic disorders for which the causal variants are known.
authors.affiliation: Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Brown
authors.affiliation: Department of Pathology and Laboratory Medicine, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Hane
authors.initials: H
authors.lastname: Lee
authors.affiliation: Department of Human Genetics, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Ascia
authors.initials: A
authors.lastname: Eskin
authors.affiliation: Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Gleb
authors.initials: G
authors.lastname: Kichaev
authors.affiliation: Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.|Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Kirk E
authors.initials: KE
authors.lastname: Lohmueller
authors.affiliation: Institute of Medical Biology, Human Genetics and Embryology Laboratory, A*STAR, Singapore, Singapore.|Institute of Molecular and Cellular Biology, A*STAR, Singapore, Singapore.|Department of Pediatrics, National University of Singapore, Singapore, Singapore.
authors.forename: Bruno
authors.identifier: http://orcid.org/0000-0002-4070-7997
authors.initials: B
authors.lastname: Reversade
authors.affiliation: Department of Pathology and Laboratory Medicine, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.|Department of Human Genetics, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Stanley F
authors.initials: SF
authors.lastname: Nelson
authors.affiliation: Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.|Department of Pathology and Laboratory Medicine, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.|Department of Human Genetics, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Bogdan
authors.initials: B
authors.lastname: Pasaniuc
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D059472:Exome; D005260:Female; D030342:Genetic Diseases, Inborn / Q000175:diagnosis / Q000208:ethnology / Q000235:genetics*; D014644:Genetic Variation; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D040582:Inheritance Patterns; D008297:Male; D015233:Models, Statistical; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D044469:Racial Groups; D017422:Sequence Analysis, DNA