Kuismin, M, Saatoglu, D, Niskanen, AK, Jensen, H, Sillanpää, MJ. Genetic assignment of individuals to source populations using network estimation tools. Methods Ecol Evol. 2020; 11: 333– 344. https://doi.org/10.1111/2041-210X.13323
Genetic assignment of individuals to source populations using network estimation tools
|Author:||Kuismin, Markku1,2; Saatoglu, Dilan3; Niskanen, Alina K.3,4;|
1Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
2Biocenter Oulu, University of Oulu, Oulu, Finland
3Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
4Ecology and Genetics Research Unit, University of Oulu, Oulu, Finland
5Infotech Oulu, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020110989696
John Wiley & Sons,
|Publish Date:|| 2020-11-09
1. 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.
2. 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.
3. 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.
4. 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 licence and can be downloaded at GitHub. In addition to the R package, a tutorial for BONE is available at https://github.com/markkukuismin/BONE/.
Methods in ecology and evolution
|Pages:||333 - 344|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1184 Genetics, developmental biology, physiology
This work was supported by the University of Oulu's Exactus Doctoral Programme, Biocenter Oulu funding, the Technology Industries of Finland Centennial Foundation, the Jane and Aatos Erkko Foundation, and by grants from the Research Council of Norway (projects 221956 and 274930) and the Academy of Finland (project 295204 to A.K.N.). This work was also partly supported by the Research Council of Norway through its Centres of Excellence funding scheme (project 223257).
|Academy of Finland Grant Number:||
295204 (Academy of Finland Funding decision)
© 2019 The Authors. Methods in Ecology and Evolution © 2019 British Ecological Society. This is the peer reviewed version of the following article: Kuismin, M, Saatoglu, D, Niskanen, AK, Jensen, H, Sillanpää, MJ. Genetic assignment of individuals to source populations using network estimation tools. Methods Ecol Evol. 2020; 11: 333– 344, which has been published in final form at https://doi.org/10.1111/2041-210X.13323. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.