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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Genotyping Polyploids from Messy Sequencing Data

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Gerard, David [1] ; Ferrao, Luis Felipe Ventorim [2] ; Franco Garcia, Antonio Augusto [3] ; Stephens, Matthew [4, 5]
Total Authors: 4
[1] Amer Univ, Dept Math & Stat, 3501 Nebraska Ave NW, Don Myers Bldg, Washington, DC 20016 - USA
[2] Univ Florida, Hort Sci Dept, Gainesville, FL 32611 - USA
[3] Univ Sao Paulo, Luiz de Queiroz Coll Agr, Dept Genet, BR-13418900 Piracicaba - Brazil
[4] Univ Chicago, Dept Human Genet, Chicago, IL 60637 - USA
[5] Univ Chicago, Dept Stat, Chicago, IL 60637 - USA
Total Affiliations: 5
Document type: Journal article
Source: Genetics; v. 210, n. 3, p. 789-807, NOV 2018.
Web of Science Citations: 12

Detecting and quantifying the differences in individual genomes (i.e., genotyping), plays a fundamental role in most modern bioinformatics pipelines. Many scientists now use reduced representation next-generation sequencing (NGS) approaches for genotyping. Genotyping diploid individuals using NGS is a well-studied field, and similar methods for polyploid individuals are just emerging. However, there are many aspects of NGS data, particularly in polyploids, that remain unexplored by most methods. Our contributions in this paper are fourfold: (i) We draw attention to, and then model, common aspects of NGS data: sequencing error, allelic bias, overdispersion, and outlying observations. (ii) Many datasets feature related individuals, and so we use the structure of Mendelian segregation to build an empirical Bayes approach for genotyping polyploid individuals. (iii) We develop novel models to account for preferential pairing of chromosomes, and harness these for genotyping. (iv) We derive oracle genotyping error rates that may be used for read depth suggestions. We assess the accuracy of our method in simulations, and apply it to a dataset of hexaploid sweet potato (Ipomoea batatas). An R package implementing our method is available at (AU)

FAPESP's process: 14/20389-2 - Development of statistic genetic models for genomic selection in Coffea canephora and other species
Grantee:Luís Felipe Ventorim Ferrão
Support type: Scholarships in Brazil - Doctorate