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A Spatial Framework for Understanding Population Structure and Admixture.

ABSTRACT: Geographic patterns of genetic variation within modern populations, produced by complex histories of migration, can be difficult to infer and visually summarize. A general consequence of geographically limited dispersal is that samples from nearby locations tend to be more closely related than samples from distant locations, and so genetic covariance often recapitulates geographic proximity. We use genome-wide polymorphism data to build "geogenetic maps," which, when applied to stationary populations, produces a map of the geographic positions of the populations, but with distances distorted to reflect historical rates of gene flow. In the underlying model, allele frequency covariance is a decreasing function of geogenetic distance, and nonlocal gene flow such as admixture can be identified as anomalously strong covariance over long distances. This admixture is explicitly co-estimated and depicted as arrows, from the source of admixture to the recipient, on the geogenetic map. We demonstrate the utility of this method on a circum-Tibetan sampling of the greenish warbler (Phylloscopus trochiloides), in which we find evidence for gene flow between the adjacent, terminal populations of the ring species. We also analyze a global sampling of human populations, for which we largely recover the geography of the sampling, with support for significant histories of admixture in many samples. This new tool for understanding and visualizing patterns of population structure is implemented in a Bayesian framework in the program SpaceMix.

SUBMITTER: Bradburd GS

PROVIDER: S-EPMC4714911 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The Caribbean is a genetically diverse region with heterogeneous admixture compositions influenced by local island ecologies, migrations, colonial conflicts, and demographic histories. The Commonwealth of Dominica is a mountainous island in the Lesser Antilles historically known to harbor communities with unique patterns of migration, mixture, and isolation. This community-based population genetic study adds biological evidence to inform post-colonial narrative histories in a Dominican horticultural village. High density single nucleotide polymorphism data paired with a previously compiled genealogy provide the first genome-wide insights on genetic ancestry and population structure in Dominica. We assessed family-based clustering, inferred global ancestry, and dated recent admixture by implementing the fastSTRUCTURE clustering algorithm, modeling graph-based migration with TreeMix, assessing patterns of linkage disequilibrium decay with ALDER, and visualizing data from Dominica with Human Genome Diversity Panel references. These analyses distinguish family-based genetic structure from variation in African, European, and indigenous Amerindian admixture proportions, and analyses of linkage disequilibrium decay estimate admixture dates 5-6 generations (~160 years) ago. African ancestry accounts for the largest mixture components, followed by European and then indigenous components; however, our global ancestry inferences are consistent with previous mitochondrial, Y chromosome, and ancestry marker data from Dominica that show uniquely higher proportions of indigenous ancestry and lower proportions of African ancestry relative to known admixture in other French- and English-speaking Caribbean islands. Our genetic results support local narratives about the community's history and founding, which indicate that newly emancipated people settled in the steep, dense vegetation along Dominica's eastern coast in the mid-19th century. Strong genetic signals of post-colonial admixture and family-based structure highlight the localized impacts of colonial forces and island ecologies in this region, and more data from other groups are needed to more broadly inform on Dominica's complex history and present diversity.

Project description:Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as Rad-SpRL. In Rad-SpRL, common radiological entities tied to spatial relations are encoded through four spatial roles: Trajector, Landmark, Diagnosis, and Hedge, all identified in relation to a spatial preposition (or Spatial Indicator). We annotated a total of 2,000 chest X-ray reports following Rad-SpRL. We then propose a deep learning-based natural language processing (NLP) method involving word and character-level encodings to first extract the Spatial Indicators followed by identifying the corresponding spatial roles. Specifically, we use a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) neural network as the baseline model. Additionally, we incorporate contextualized word representations from pre-trained language models (BERT and XLNet) for extracting the spatial information. We evaluate both gold and predicted Spatial Indicators to extract the four types of spatial roles. The results are promising, with the highest average F1 measure for Spatial Indicator extraction being 91.29 (XLNet); the highest average overall F1 measure considering all the four spatial roles being 92.9 using gold Indicators (XLNet); and 85.6 using predicted Indicators (BERT pre-trained on MIMIC notes). The corpus is available in Mendeley at http://dx.doi.org/10.17632/yhb26hfz8n.1 and https://github.com/krobertslab/datasets/blob/master/Rad-SpRL.xml.

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A Spatial Framework for Understanding Population Structure and Admixture.

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