2.2: Making the most of genomic data
Population genomic data are best suited to analytical tools designed to work efficiently with large datasets and make the most of the available information. To this end, a number of new analytical approaches have been developed to infer the geographic origin of a genomic sample using continuous spatial models (e.g. , Battey, Ralph, & Kern, 2020; Guillot, Jónsson, Hinge, Manchih, & Orlando, 2016). Due to their computational efficiency, such measures can also be used to estimate the geographic origin of a sample in chromosomal windows. This feature is particularly useful when tracing the geographic origin of a candidate locus (e.g., a haplotype containing a pesticide resistance gene or a QTL known to be associated with invasiveness) or when investigating the contribution of different source populations across the genome. For example, Locator (Battey et al., 2020) has been used to identify the geographic origin of Anopheles samples (Figure 4).Locator is one of several new analytical tools in population genomics that make use of machine learning (reviewed by Schrider & Kern, 2018; see also Flagel, Brandvain, & Schrider, 2019). Modern tools for geographic inference have been taken up more readily in other fields to date (e.g. , forensic investigations into illegal wildlife trade), though they hold great potential in invasion science as a means of biomonitoring — for instance, determining the origin of invasive species intercepted at ports at a fine spatial scale.
[ FIGURE 4 ]
WGR increases our ability to infer the geographic origin of invasive species, though an associated increase in precision depends on the analytical tools used. Many analyses optimised for genomics can be used with reduced-representation datasets as well, so the application of WGR will depend on the nature of the system and the question being asked (see Box 1). For example, analysis of RAD was used to quantify ongoing migration rates of Aedes albopictus in Australia, and to identify the source of the incursions using Locator (Schmidt et al., 2020). WGR can also add value to geographic inference where there is a weak signature of population structure, or in studies of admixed invasive populations aiming to infer the geographic origin of specific loci (see Part 5).
[ BOX 2 ]