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 non-model 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-sib 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-sib populations of diploid species, implemented in a package called GUSMap. Our model is based on an extension of the Lander-Green hidden Markov model that accounts for errors present in sequencing data. Results show that GUSMap was able to give accurate estimates of the recombination fractions and overall map distance, 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.

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