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 ]