IntroMap: a signal analysis based method for the detection of genomic introgressions

Daniel J. Shea, Motoki Shimizu, Namiko Nishida, Eigo Fukai, Takashi Abe, Ryo Fujimoto, Keiichi Okazaki

Breeding programs often rely on marker-assisted tests or variant calling of next generation sequence (NGS) data to identify regions of genomic introgression arising from the hybridization of two plant species. In this paper we present IntroMap, a bioinformatics pipeline for the screening of candidate plants through the application of signal processing techniques to NGS data, using alignment to a reference genome sequence (annotation is not required) that shares homology with the recurrent parental cultivar, and without the need for de novo assembly of the read data or variant calling. We show the accurate identification of introgressed genomic regions using both in silico simulated genomes, and a hybridized cultivar data set using our pipeline. Additionally we show, through targeted marker-based assays, validation of the IntroMap predicted regions for the hybrid cultivar. This approach can be used to automate the screening of large populations, reducing the time and labor required, and can improve the accuracy of the detection of introgressed regions in comparison to a marker-based approach. In contrast to other approaches that generally rely upon a variant calling step, our method achieves accurate identification of introgressed regions without variant calling, relying solely upon alignment.