title: Estimating individual admixture proportions from next generation sequencing data.
abstract: Inference of population structure and individual ancestry is important both for population genetics and for association studies. With next generation sequencing technologies it is possible to obtain genetic data for all accessible genetic variations in the genome. Existing methods for admixture analysis rely on known genotypes. However, individual genotypes cannot be inferred from low-depth sequencing data without introducing errors. This article presents a new method for inferring an individual's ancestry that takes the uncertainty introduced in next generation sequencing data into account. This is achieved by working directly with genotype likelihoods that contain all relevant information of the unobserved genotypes. Using simulations as well as publicly available sequencing data, we demonstrate that the presented method has great accuracy even for very low-depth data. At the same time, we demonstrate that applying existing methods to genotypes called from the same data can introduce severe biases. The presented method is implemented in the NGSadmix software available at http://www.popgen.dk/software.
authors.affiliation: The Bioinformatics Centre, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N.
authors.forename: Line
authors.initials: L
authors.lastname: Skotte
authors.forename: Thorfinn Sand
authors.initials: TS
authors.lastname: Korneliussen
authors.forename: Anders
authors.initials: A
authors.lastname: Albrechtsen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005787:Gene Frequency; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D005828:Genetics, Population / Q000706:statistics & numerical data*; D005838:Genotype; D060148:HapMap Project; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide
keywords: NGS; admixture; association studies; population structure; resequencing
references.citation: BMC Bioinformatics. 2011 Jun 11;12:231
references.pmid: 21663684
references.citation: Nature. 2012 Nov 1;491(7422):56-65
references.pmid: 23128226
references.citation: Nature. 2010 Oct 28;467(7319):1061-73
references.pmid: 20981092
references.citation: Genome Res. 2010 Sep;20(9):1297-303
references.pmid: 20644199
references.citation: Nature. 2010 Sep 2;467(7311):52-8
references.pmid: 20811451
references.citation: Am J Hum Genet. 2009 Dec;85(6):847-61
references.pmid: 19931040
references.citation: Bioinformatics. 2011 Nov 1;27(21):2987-93
references.pmid: 21903627
references.citation: Am J Hum Genet. 2007 Sep;81(3):559-75
references.pmid: 17701901
references.citation: Nat Genet. 2004 May;36(5):512-7
references.pmid: 15052271
references.citation: Nature. 2010 Feb 11;463(7282):757-62
references.pmid: 20148029
references.citation: Genet Epidemiol. 2012 Jul;36(5):430-7
references.pmid: 22570057
references.citation: Nat Rev Genet. 2011 Jun;12(6):443-51
references.pmid: 21587300
references.citation: Nat Rev Genet. 2010 Jul;11(7):459-63
references.pmid: 20548291
references.citation: Genome Res. 2008 Nov;18(11):1851-8
references.pmid: 18714091
references.citation: Evol Bioinform Online. 2011;7:55-9
references.pmid: 21698091
references.citation: Genome Res. 2009 Jun;19(6):1124-32
references.pmid: 19420381
references.citation: Science. 2002 Dec 20;298(5602):2381-5
references.pmid: 12493913
references.citation: Genetics. 2000 Jun;155(2):945-59
references.pmid: 10835412
references.citation: Genome Res. 2009 Sep;19(9):1655-64
references.pmid: 19648217
references.citation: Nat Genet. 2005 Nov;37(11):1243-6
references.pmid: 16228001
references.citation: Bioinformatics. 2009 Aug 15;25(16):2078-9
references.pmid: 19505943
references.citation: Genet Epidemiol. 2005 May;28(4):289-301
references.pmid: 15712363
references.citation: Nat Genet. 2012 May 20;44(6):631-5
references.pmid: 22610117