ObStruct: A Method to Objectively Analyse Factors Driving Population Structure Using Bayesian Ancestry Profiles

Abstract

Bayesian inference methods are extensively used to detect the presence of population structure given genetic data. The primary output of software implementing these methods are ancestry profiles of sampled individuals. While these profiles robustly partition the data into subgroups, currently there is no objective method to determine whether the fixed factor of interest (e.g. geographic origin) correlates with inferred subgroups or not, and if so, which populations are driving this correlation. We present ObStruct, a novel tool to objectively analyse the nature of structure revealed in Bayesian ancestry profiles using established statistical methods. ObStruct evaluates the extent of structural similarity between sampled and inferred populations, tests the significance of population differentiation, provides information on the contribution of sampled and inferred populations to the observed structure and crucially determines whether the predetermined factor of interest correlates with inferred population structure. Analyses of simulated and experimental data highlight ObStruct's ability to objectively assess the nature of structure in populations. We show the method is capable of capturing an increase in the level of structure with increasing time since divergence between simulated populations. Further, we applied the method to a highly structured dataset of 1,484 humans from seven continents and a less structured dataset of 179 Saccharomyces cerevisiae from three regions in New Zealand. Our results show that ObStruct provides an objective metric to classify the degree, drivers and significance of inferred structure, as well as providing novel insights into the relationships between sampled populations, and adds a final step to the pipeline for population structure analyses.

European derivedSaccharomyces cerevisiaecolonisation of New Zealand vineyards aided by humans

“…To enable us to analyse a spectrum of possible population structures, from completely homogenised through to highly structured due to ancient divergences, we use complementary Bayesian-based population genetic methods capable of inferring finer degrees of population structure that account for recombination implemented in Structure (Pritchard, Stephens and Donnelly 2000), and the subsequent analyses of ancestry profiles by O bstruct (Gayevskiy et al . 2014). From the 11 059 143 nucleotide positions in the 93 aligned concatenated genomes, any which were uninformative or had missing data were conservatively removed leaving a total 66 316 robustly informative positions for population structure analysis.…”

“…2014) to determine whether geographic origin or niche of isolation might correlate most strongly with population structure. This analysis shows that variance in genetic structure in the NZ population correlated with niche of isolation (R 2 = 0.51, P < 0.0001) only marginally more than geographic origin (R 2 = 0.45, P < 0.0001).…”

“…Resulting ancestry profiles were objectively analysed using ObStruct (Gayevskiy et al . 2014) to determine the extent that geographic origin, niche of isolation and our phylogenomic analysis explains inferred population structure.…”

Cellar-Associated Saccharomyces cerevisiae Population Structure Revealed High-Level Diversity and Perennial Persistence at Sauternes Wine Estates

“…Ancestry profiles were drawn as bar plots from the Instruct output, using a different color for each inferred ancestral population under the R statistical environment. The contribution of each population was then evaluated with ObStruct software (47).…”

Drivers of Population Structure of the Bottlenose Dolphin (Tursiops truncatus) in the Eastern Mediterranean Sea

“…Correlation between genetic structures and basin of origin, was assessed using the software OBSTRUCT 43 (Gayevskiy et al 2014). This method analyses how much a given pattern of inferred population structure is …”