Dataset Information

Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations.

ABSTRACT: Next-generation sequencing is an efficient method that allows for substantially more markers than previous technologies, providing opportunities for building high-density genetic linkage maps, which facilitate the development of nonmodel species' genomic assemblies and the investigation of their genes. However, constructing genetic maps using data generated via high-throughput sequencing technology (e.g., genotyping-by-sequencing) is complicated by the presence of sequencing errors and genotyping errors resulting from missing parental alleles due to low sequencing depth. If unaccounted for, these errors lead to inflated genetic maps. In addition, map construction in many species is performed using full-sibling family populations derived from the outcrossing of two individuals, where unknown parental phase and varying segregation types further complicate construction. We present a new methodology for modeling low coverage sequencing data in the construction of genetic linkage maps using full-sibling populations of diploid species, implemented in a package called GUSMap. Our model is based on the Lander-Green hidden Markov model but extended to account for errors present in sequencing data. We were able to obtain accurate estimates of the recombination fractions and overall map distance using GUSMap, while most existing mapping packages produced inflated genetic maps in the presence of errors. Our results demonstrate the feasibility of using low coverage sequencing data to produce genetic maps without requiring extensive filtering of potentially erroneous genotypes, provided that the associated errors are correctly accounted for in the model.

SUBMITTER: Bilton TP

PROVIDER: S-EPMC5937187 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations.

Bilton Timothy P TP Schofield Matthew R MR Black Michael A MA Chagné David D Wilcox Phillip L PL Dodds Ken G KG

Genetics 20180227 1

Next-generation sequencing is an efficient method that allows for substantially more markers than previous technologies, providing opportunities for building high-density genetic linkage maps, which facilitate the development of nonmodel species' genomic assemblies and the investigation of their genes. However, constructing genetic maps using data generated via high-throughput sequencing technology (<i>e.g.</i>, genotyping-by-sequencing) is complicated by the presence of sequencing errors and ge ...[more]

PMID: 29487138

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Project description:Sorghum is an important target for plant genomic mapping because of its adaptation to harsh environments, diverse germplasm collection, and value for comparing the genomes of grass species such as corn and rice. The construction of an integrated genetic and physical map of the sorghum genome (750 Mbp) is a primary goal of our sorghum genome project. To help accomplish this task, we have developed a new high-throughput PCR-based method for building BAC contigs and locating BAC clones on the sorghum genetic map. This task involved pooling 24,576 sorghum BAC clones ( approximately 4x genome equivalents) in six different matrices to create 184 pools of BAC DNA. DNA fragments from each pool were amplified using amplified fragment length polymorphism (AFLP) technology, resolved on a LI-COR dual-dye DNA sequencing system, and analyzed using Bionumerics software. On average, each set of AFLP primers amplified 28 single-copy DNA markers that were useful for identifying overlapping BAC clones. Data from 32 different AFLP primer combinations identified approximately 2400 BACs and ordered approximately 700 BAC contigs. Analysis of a sorghum RIL mapping population using the same primer pairs located approximately 200 of the BAC contigs on the sorghum genetic map. Restriction endonuclease fingerprinting of the entire collection of sorghum BAC clones was applied to test and extend the contigs constructed using this PCR-based methodology. Analysis of the fingerprint data allowed for the identification of 3366 contigs each containing an average of 5 BACs. BACs in approximately 65% of the contigs aligned by AFLP analysis had sufficient overlap to be confirmed by DNA fingerprint analysis. In addition, 30% of the overlapping BACs aligned by AFLP analysis provided information for merging contigs and singletons that could not be joined using fingerprint data alone. Thus, the combination of fingerprinting and AFLP-based contig assembly and mapping provides a reliable, high-throughput method for building an integrated genetic and physical map of the sorghum genome.

Dataset Information

Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations.

Publications

Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations.

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