Genetic assignment of individuals to source populations using network estimation tools

Dispersal, the movement of individuals between populations, is crucial in
many ecological and genetic processes. However, direct identification of
dispersing individuals is difficult or impossible in natural populations.
By using genetic assignment methods, individuals with unknown genetic
origin can be assigned to source populations. This knowledge is necessary
in studying many key questions in ecology, evolution and conservation. We
introduce a network-based tool BONE (Baseline Oriented Network Estimation)
for genetic population assignment, which borrows concepts from undirected
graph inference. In particular, we use sparse multinomial Least Absolute
Shrinkage and Selection Operator (LASSO) regression to estimate
probability of the origin of all mixture individuals and their mixture
proportions without tedious selection of the LASSO tuning parameter. We
compare BONE with three genetic assignment methods implemented in R
packages radmixture, assignPOP and RUBIAS. Probability of the origin and
mixture proportion estimates of both simulated and real data (an insular
house sparrow metapopulation and Chinook salmon populations) given by BONE
are competitive or superior compared to other assignment methods. Our
examples illustrate how the network estimation method adapts to population
assignment, combining the efficiency and attractive properties of sparse
network representation and model selection properties of the L1
regularization. As far as we know, this is the first approach showing how
one can use network tools for genetic identification of individuals'
source populations. BONE is aimed at any researcher performing genetic
assignment and trying to infer the genetic population structure. Compared
to other methods, our approach also identifies outlying mixture
individuals that could originate outside of the baseline populations. BONE
is a freely available R package under the GPL license and can be
downloaded at GitHub. In addition to the R package, a tutorial for BONE is
available at https://github.com/markkukuismin/BONE/.