Population Genomic Approaches for Weed Science

Abstract

Genomic approaches are opening avenues for understanding all aspects of biological life, especially as they begin to be applied to multiple individuals and populations. However, these approaches typically depend on the availability of a sequenced genome for the species of interest. While the number of genomes being sequenced is exploding, one group that has lagged behind are weeds. Although the power of genomic approaches for weed science has been recognized, what is needed to implement these approaches is unfamiliar to many weed scientists. In this review we attempt to address this problem by providing a primer on genome sequencing and provide examples of how genomics can help answer key questions in weed science such as: (1) Where do agricultural weeds come from; (2) what genes underlie herbicide resistance; and, more speculatively, (3) can we alter weed populations to make them easier to control? This review is intended as an introduction to orient weed scientists who are thinking about initiating genome sequencing projects to better understand weed populations, to highlight recent publications that illustrate the potential for these methods, and to provide direction to key tools and literature that will facilitate the development and execution of weed genomic projects.

Keywords: genome scans; genomics; non-target site resistance; plant genome assembly; population genetics; population genomics; weeds.

Figure 1

Plot of k-mer frequency by length produced for Camelina neglecta J.Brock, Mandáková, Lysak & Al-Shehbaz produced using Jellyfish and visualized using R. The position of the peak at a k-mer length of 22 is used to calculate genome size based on the area under the curve as represented by the light blue region. Here the genome size estimated is 248 Mb, while flow cytometry estimates indicate a genome size of 264 (±9) Mbp [80].

Figure 2

Blobplot generated for Conzya canadensis (Asteraceae) draft genome assembly showing the genera with the closest similarity to the sequenced genome (Laforest, Martin, and Page unpublished data). The first panel (A) indicates the percentage of reads that were mapped and the second panel (B) shows the taxonomic break down of hits at the taxonomic level requested. In this case the majority of hits are from other genera from the Asteraceae. The program generates a text file with more detailed information. The three part third panel (C) shows histograms for the proportion of G and C bases in the sequence which typically varies among species (top) and coverage (right) weighted by the cumulative length of sequences in each bin. The main panel has circles colored by taxonomic affiliation positioned on the x-axis by the GC proportion and on the y-axis by coverage within the raw data which gives a sense of the relative concentration of the sequences in the DNA sample.