CONE: Community Oriented Network Estimation Is a Versatile Framework for Inferring Population Structure in Large-Scale Sequencing Data

Abstract

Estimation of genetic population structure based on molecular markers is a common task in population genetics and ecology. We apply a generalized linear model with LASSO regularization to infer relationships between individuals and populations from molecular marker data. Specifically, we apply a neighborhood selection algorithm to infer population genetic structure and gene flow between populations. The resulting relationships are used to construct an individual-level population graph. Different network substructures known as communities are then dissociated from each other using a community detection algorithm. Inference of population structure using networks combines the good properties of: (i) network theory (broad collection of tools, including aesthetically pleasing visualization), (ii) principal component analysis (dimension reduction together with simple visual inspection), and (iii) model-based methods (e.g., ancestry coefficient estimates). We have named our process CONE (for community oriented network estimation). CONE has fewer restrictions than conventional assignment methods in that properties such as the number of subpopulations need not be fixed before the analysis and the sample may include close relatives or involve uneven sampling. Applying CONE on simulated data sets resulted in more accurate estimates of the true number of subpopulations than model-based methods, and provided comparable ancestry coefficient estimates. Inference of empirical data sets of teosinte single nucleotide polymorphism, bacterial disease outbreak, and the human genome diversity panel illustrate that population structures estimated with CONE are consistent with the earlier findings

Genetic assignment of individuals to source populations using network estimation tools

“…Puechmaille, ; Wang, ). Our previous study with the multinomial logit model suggests that our graph method is not sensitive to uneven sample sizes (Kuismin et al, ).…”

“…Meinshausen and Bühlmann, ; Tibshirani, ). Following the work of Kuismin et al (), we use the L 1 ‐regularized multinomial logit model (Friedman, Hastie, & Tibshirani, ) to select the graph and estimate the probability of the origin for each mixture individual under the restrictions illustrated in Figure .…”

“…Huson & Scornavacca, ) and in estimation of genetic population structure (see, e.g. Dyer & Nason, ; Greenbaum, Templeton, & Bar‐David, ; Kuismin, Ahlinder, & Sillanpӓӓ, ), to mention a few applications.…”

“…Here we show how a graph estimated with the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani, ) can be applied for population assignment using multilocus SNP data. We have used a similar approach previously for genetic population structure inference (Kuismin et al, ). The difference of genetic assignment compared to the population structure estimation is that some information on populations of origin (defined as ‘baseline’ or ‘source populations’) is known in advance, and these baseline populations function as reference data to which individuals without known origin (defined as ‘mixture individuals’, included in a ‘mixture population’) are assigned.…”

Network-based hierarchical population structure analysis for large genomic datasets

“…In recent years, network approaches have been introduced to model and analyze population structure (Dyer and Nason 2004;Rozenfeld et al 2008;Greenbaum et al 2016;Greenbaum and Fefferman 2017;Han et al 2017;Kuismin et al 2017). In network-based population structure inference, the genetic relations between individuals within the populations are formulated as a network, incorporating the many complexities that result from evolutionary processes in natural populations, with few prior assumptions regarding the biological processes involved in generating these relations.…”