GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data

Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.Facultad de Ciencias Naturales y MuseoInstituto Multidisciplinario de Biología Celula

GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data

Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.Facultad de Ciencias Naturales y MuseoInstituto Multidisciplinario de Biología Celula

GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data

Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.Facultad de Ciencias Naturales y MuseoInstituto Multidisciplinario de Biología Celula

Reconstructing Native American population history

The peopling of the Americas has been the subject of extensive genetic, archaeological and linguistic research; however, central questions remain unresolved. One contentious issue is whether the settlement occurred by means of a single migration or multiple streams of migration from Siberia. The pattern of dispersals within the Americas is also poorly understood. To address these questions at a higher resolution than was previously possible, we assembled data from 52 Native American and 17 Siberian groups genotyped at 364,470 single nucleotide polymorphisms. Here we show that Native Americans descend from at least three streams of Asian gene flow. Most descend entirely from a single ancestral population that we call First American. However, speakers of Eskimog-Aleut languages from the Arctic inherit almost half their ancestry from a second stream of Asian gene flow, and the Na-Dene-speaking Chipewyan from Canada inherit roughly one-tenth of their ancestry from a third stream. We show that the initial peopling followed a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America. A major exception is in Chibchan speakers on both sides of the Panama isthmus, who have ancestry from both North and South America. © 2012 Macmillan Publishers Limited. All rights reserved.Fil: Reich, David. Harvard Medical School; Estados Unidos. Massachusetts Institute of Technology; Estados UnidosFil: Patterson, Nick. Massachusetts Institute of Technology; Estados UnidosFil: Campbell, Desmond. Colegio Universitario de Londres; Reino Unido. The University Of Hong Kong; Hong KongFil: Tandon, Arti. Harvard Medical School; Estados Unidos. Massachusetts Institute of Technology; Estados UnidosFil: Mazieres, Stéphane. Colegio Universitario de Londres; Reino UnidoFil: Ray, Nicolas. Universidad de Ginebra; SuizaFil: Parra, Maria V.. Colegio Universitario de Londres; Reino Unido. Universidad de Antioquia; ColombiaFil: Rojas, Winston. Colegio Universitario de Londres; Reino Unido. Universidad de Antioquia; ColombiaFil: Duque, Constanza. Universidad de Antioquia; Colombia. Colegio Universitario de Londres; Reino UnidoFil: Mesa, Natalia. Universidad de Antioquia; Colombia. Colegio Universitario de Londres; Reino UnidoFil: García, Luis F.. Universidad de Antioquia; ColombiaFil: Triana, Omar. Universidad de Antioquia; ColombiaFil: Blair, Silvia. Universidad de Antioquia; ColombiaFil: Maestre, Amanda. Universidad de Antioquia; ColombiaFil: Dib, Juan C.. Fundación Salud Para El Tró Pico; ColombiaFil: Bravi, Claudio Marcelo. Colegio Universitario de Londres; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Multidisciplinario de Biología Celular. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Multidisciplinario de Biología Celular. Universidad Nacional de La Plata. Instituto Multidisciplinario de Biología Celular; ArgentinaFil: Bailliet, Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Multidisciplinario de Biología Celular. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Multidisciplinario de Biología Celular. Universidad Nacional de La Plata. Instituto Multidisciplinario de Biología Celular; ArgentinaFil: Corach, Daniel. Universidad de Buenos Aires; ArgentinaFil: Hünemeier, Tábita. Colegio Universitario de Londres; Reino Unido. Universidade Federal do Rio Grande do Sul; BrasilFil: Bortolini, Maria Cátira. Universidade Federal do Rio Grande do Sul; BrasilFil: Salzano, Francisco M.. Universidade Federal do Rio Grande do Sul; BrasilFil: Petzl Erler, María Luiza. Universidade Federal do Paraná; BrasilFil: Acuña Alonzo, Victor. National Institute Of Anthropology And History; MéxicoFil: Aguilar Salinas, Carlos. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Canizales-Quinteros, Samuel. Universidad Nacional Autónoma de México; MéxicoFil: Tusié Luna, Teresa. Universidad Nacional Autónoma de México; MéxicoFil: Riba, Laura. Universidad Nacional Autónoma de México; MéxicoFil: Rodríguez Cruz, Maricela. Umae Hospital de Pediatría Centro Medico Nacional Siglo Xxi; MéxicoFil: Lopez Alarcón, Mardia. Umae Hospital de Pediatría Centro Medico Nacional Siglo Xxi; MéxicoFil: Coral Vazquez, Ramón. Instituto Politécnico Nacional; Méxic

Beringian Standstill and Spread of Native American Founders

Native Americans derive from a small number of Asian founders who likely arrived to the Americas via Beringia. However, additional details about the intial colonization of the Americas remain unclear. To investigate the pioneering phase in the Americas we analyzed a total of 623 complete mtDNAs from the Americas and Asia, including 20 new complete mtDNAs from the Americas and seven from Asia. This sequence data was used to direct high-resolution genotyping from 20 American and 26 Asian populations. Here we describe more genetic diversity within the founder population than was previously reported. The newly resolved phylogenetic structure suggests that ancestors of Native Americans paused when they reached Beringia, during which time New World founder lineages differentiated from their Asian sister-clades. This pause in movement was followed by a swift migration southward that distributed the founder types all the way to South America. The data also suggest more recent bi-directional gene flow between Siberia and the North American Arctic

Reconstructing Native American Population History

The peopling of the Americas has been the subject of extensive genetic, archaeological and linguistic research; however, central questions remain unresolved1–5. One contentious issue is whether the settlement occurred via a single6–8 or multiple streams of migration from Siberia9–15. The pattern of dispersals within the Americas is also poorly understood. To address these questions at higher resolution than was previously possible, we assembled data from 52 Native American and 17 Siberian groups genotyped at 364,470 single nucleotide polymorphisms. We show that Native Americans descend from at least three streams of Asian gene flow. Most descend entirely from a single ancestral population that we call “First American”. However, speakers of Eskimo-Aleut languages from the Arctic inherit almost half their ancestry from a second stream of Asian gene flow, and the Na-Dene-speaking Chipewyan from Canada inherit roughly one-tenth of their ancestry from a third stream. We show that the initial peopling followed a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America. A major exception is in Chibchan-speakers on both sides of the Panama Isthmus, who have ancestry from both North and South America