Sparse Marker Research Articles

Assessing inter- and intra-rater reliability of movement scores and the effects of body-shape using a custom visualisation tool: an exploratory study

BackgroundThe literature shows conflicting results regarding inter- and intra-rater reliability, even for the same movement screen. The purpose of this study was to assess inter- and intra-rater reliability of movement scores within and between sessions of expert assessors and the effects of body-shape on reliability during a movement screen using a custom online visualisation software.MethodsKinematic data from 542 athletes performing seven movement tasks were used to create animations (i.e., avatar representations) using motion and shape capture from sparse markers (MoSh). For each task, assessors viewed a total of 90 animations. Using a custom developed visualisation tool, expert assessors completed two identical sessions where they rated each animation on a scale of 1–10. The arithmetic mean of weighted Cohen’s kappa for each task and day were calculated to test reliability.ResultsAcross tasks, inter-rater reliability ranged from slight to fair agreement and intra-rater reliability had slightly better reliability with slight to moderate agreement. When looking at the average kappa values, intra-rater reliability within session with and without body manipulation and between sessions were 0.45, 0.37, and 0.35, respectively.ConclusionsBased on these results, supplementary or alternative methods should be explored and are likely required to increase scoring objectivity and reliability even within expert assessors. To help future research and practitioners, the custom visualisation software has been made available to the public.

Local Geometric Indexing of High Resolution Data for Facial Reconstruction From Sparse Markers.

When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current trend towards using more and more data, our aim is not to fit the motion capture markers with a parameterized (blendshape) model or to smoothly interpolate a surface through the marker positions, but rather to find an instance in the high resolution dataset that contains local geometry to fit each marker. Just as is true for typical machine learning applications, this approach benefits from a plethora of data, and thus we also consider augmenting the dataset via specially designed physical simulations that target the high resolution dataset such that the simulation output lies on the same so-called manifold as the data targeted.

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations

BackgroundPlankton seascape genomics studies have revealed different trends from large-scale weak differentiation to microscale structures. Previous studies have underlined the influence of the environment and seascape on species differentiation and adaptation. However, these studies have generally focused on a few single species, sparse molecular markers, or local scales. Here, we investigated the genomic differentiation of plankton at the macro-scale in a holistic approach using Tara Oceans metagenomic data together with a reference-free computational method.ResultsWe reconstructed the FST-based genomic differentiation of 113 marine planktonic taxa occurring in the North and South Atlantic Oceans, Southern Ocean, and Mediterranean Sea. These taxa belong to various taxonomic clades spanning Metazoa, Chromista, Chlorophyta, Bacteria, and viruses. Globally, population genetic connectivity was significantly higher within oceanic basins and lower in bacteria and unicellular eukaryotes than in zooplankton. Using mixed linear models, we tested six abiotic factors influencing connectivity, including Lagrangian travel time, as proxies of oceanic current effects. We found that oceanic currents were the main population genetic connectivity drivers, together with temperature and salinity. Finally, we classified the 113 taxa into parameter-driven groups and showed that plankton taxa belonging to the same taxonomic rank such as phylum, class or order presented genomic differentiation driven by different environmental factors.ConclusionOur results validate the isolation-by-current hypothesis for a non-negligible proportion of taxa and highlight the role of other physicochemical parameters in large-scale plankton genetic connectivity. The reference-free approach used in this study offers a new systematic framework to analyse the population genomics of non-model and undocumented marine organisms from a large-scale and holistic point of view.

Genetic prediction of male pattern baldness based on large independent datasets

Genetic prediction of male pattern baldness (MPB) is important in science and society. Previous genetic MPB prediction models were limited by sparse marker coverage, small sample size, and/or data dependency in the different analytical steps. Here, we present novel models for genetic prediction of MPB based on a large set of markers and large independent subsample sets drawn among 187,435 European subjects. We selected 117 SNP predictors within 85 distinct loci from a list of 270 previously MPB-associated SNPs in 55,573 males of the UK Biobank Study (UKBB). Based on these 117 SNPs with and without age as additional predictor, we trained, by use of different methods, prediction models in a non-overlapping subset of 104,694 UKBB males and tested them in a non-overlapping subset of 26,177 UKBB males. Estimates of prediction accuracy were similar between methods with AUC ranges of 0.725–0.728 for severe, 0.631–0.635 for moderate, 0.598–0.602 for slight, and 0.708–0.711 for no hair loss with age, and slightly lower without, while prediction of any versus no hair loss gave 0.690–0.711 with age and slightly lower without. External validation in an early-onset enriched MPB dataset from the Bonn Study (N = 991) showed improved prediction accuracy without considering age such as AUC of 0.830 for no vs. any hair loss. Because of the large number of markers and the large independent datasets used for the different analytical steps, the newly presented genetic prediction models are the most reliable ones currently available for MPB or any other human appearance trait.

Development of a genome-wide marker design workflow for onions and its application in target amplicon sequencing-based genotyping.

Onions are one of the most widely cultivated vegetables worldwide; however, the development and utilization of molecular markers have been limited because of the large genome of this plant. We present a genome-wide marker design workflow for onions and its application in a high-throughput genotyping method based on target amplicon sequencing. The efficiency of the method was evaluated by genotyping of F2 populations. In the marker design workflow, unigene and genomic sequence data sets were constructed, and polymorphisms between parental lines were detected through transcriptome sequence analysis. The positions of polymorphisms detected in the unigenes were mapped onto the genome sequence, and primer sets were designed. In total, 480 markers covering the whole genome were selected. By genotyping an F2 population, 329 polymorphic sites were obtained from the estimated positions or the flanking sequences. However, missing or sparse marker regions were observed in the resulting genetic linkage map. We modified the markers to cover these regions by genotyping the other F2 populations. The grouping and order of markers on the linkages were similar across the genetic maps. Our marker design workflow and target amplicon sequencing are useful for genome-wide genotyping of onions owing to their reliability, cost effectiveness, and flexibility.

High-Resolution 3-Dimensional Contact Deformation Tracking for FingerVision Sensor With Dense Random Color Pattern

Recent studies on vision-based tactile sensing have shown promising results on the perception of contact information, which could improve the performance of dexterous manipulations. However, 3-dimensional contact deformation tracking at a higher resolution is desired and remains a challenge for vision-based tactile sensors with monocular configurations. In this work, a similar hardware structure to our previous FingerVision sensor is adopted. The dot markers are replaced with a novel random color pattern as the sensor's tracking target and a dense optical flow algorithm is used to track the deformation of its elastic contact interface. This results in a more accurate 2-dimensional deformation field estimation at a higher resolution in comparison with that obtained using sparse dot markers. Additionally, the denser and more accurate deformation field enables depth estimation with better fidelity. To achieve depth estimation purely from the optical flow field, Gaussian density feature extraction and processing framework are proposed. The resulting depth map can be used independently as a tactile sensing modality, or jointly with the accurate in-plane displacement field as a more complete deformation tracking of contact interfaces for vision-based tactile sensors.

Pollen Geochronology from the Atlantic Coast of the United States during the Last 500 Years

Building robust age–depth models to understand climatic and geologic histories from coastal sedimentary archives often requires composite chronologies consisting of multi-proxy age markers. Pollen chronohorizons derived from a known change in vegetation are important for age–depth models, especially those with other sparse or imprecise age markers. However, the accuracy of pollen chronohorizons compared to other age markers and the impact of pollen chronohorizons on the precision of age–depth models, particularly in salt marsh environments, is poorly understood. Here, we combine new and published pollen data from eight coastal wetlands (salt marshes and mangroves) along the Atlantic Coast of the United States (U.S.) from Florida to Connecticut to define the age and uncertainty of 17 pollen chronohorizons. We found that 13 out of 17 pollen chronohorizons were consistent when compared to other age markers (radiocarbon, radionuclide 137Cs and pollution markers). Inconsistencies were likely related to the hyperlocality of pollen chronohorizons, mixing of salt marsh sediment, reworking of pollen from nearby tidal flats, misidentification of pollen signals, and inaccuracies in or misinterpretation of other age markers. Additionally, in a total of 24 models, including one or more pollen chronohorizons, increased precision (up to 41 years) or no change was found in 18 models.

Induction of amyloid-\u03b2 deposits from serially transmitted, histologically silent, A\u03b2 seeds issued from human brains

In humans, iatrogenic transmission of cerebral amyloid-β (Aβ)-amyloidosis is suspected following inoculation of pituitary-derived hormones or dural grafts presumably contaminated with Aβ proteins as well as after cerebral surgeries. Experimentally, intracerebral inoculation of brain homogenate extracts containing misfolded Aβ can seed Aβ deposition in transgenic mouse models of amyloidosis or in non-human primates. The transmission of cerebral Aβ is governed by the host and by the inoculated samples. It is critical to better characterize the propensities of different hosts to develop Aβ deposition after contamination by an Aβ-positive sample as well as to better assess which biological samples can transmit this lesion. Aβ precursor protein (huAPPwt) mice express humanized non-mutated forms of Aβ precursor protein and do not spontaneously develop Aβ or amyloid deposits. We found that inoculation of Aβ-positive brain extracts from Alzheimer patients in these mice leads to a sparse Aβ deposition close to the alveus 18 months post-inoculation. However, it does not induce cortical or hippocampal Aβ deposition. Secondary inoculation of apparently amyloid deposit-free hippocampal extracts from these huAPPwt mice to APPswe/PS1dE9 mouse models of amyloidosis enhanced Aβ deposition in the alveus 9 months post-inoculation. This suggests that Aβ seeds issued from human brain samples can persist in furtive forms in brain tissues while maintaining their ability to foster Aβ deposition in receptive hosts that overexpress endogenous Aβ. This work emphasizes the need for high-level preventive measures, especially in the context of neurosurgery, to prevent the risk of iatrogenic transmission of Aβ lesions from samples with sparse amyloid markers.

A novel normalization and differential abundance test framework for microbiome data.

Microbial communities have been proved to have close relationship with many diseases. The identification of differentially abundant microbial species is clinically meaningful for finding disease-related pathogenic or probiotic bacteria. However, certain characteristics of microbiome data have hurdled the accuracy and effectiveness of differential abundance analysis. The abundances or counts of microbiome species are usually on different scales and exhibit zero-inflation and over-dispersion. Normalization is a crucial step before the differential abundance test. However, existing normalization methods typically try to adjust counts on different scales to a common scale by constructing size factors with the assumption that count distributions across samples are equivalent up to a certain percentile. These methods often yield undesirable results when differentially abundant species are of low to medium abundance level. For differential abundance analysis, existing methods often use a single distribution to model the dispersion of species which lacks flexibility to catch a single species' distinctiveness. These methods tend to detect a lot of false positives and often lack of power when the effect size is small. We develop a novel framework for differential abundance analysis on sparse high-dimensional marker gene microbiome data. Our methodology relies on a novel network-based normalization technique and a two-stage zero-inflated mixture count regression model (RioNorm2). Our normalization method aims to find a group of relatively invariant microbiome species across samples and conditions in order to construct the size factor. Another contribution of the paper is that our testing approach can take under-sampling and over-dispersion into consideration by separating microbiome species into two groups and model them separately. Through comprehensive simulation studies, the performance of our method is consistently powerful and robust across different settings with different sample size, library size and effect size. We also demonstrate the effectiveness of our novel framework using a published dataset of metastatic melanoma and find biological insights from the results. The R package 'RioNorm2' can be installed from Github athttps://github.com/yuanjing-ma/RioNorm2. Supplementary data are available at Bioinformatics online.

Brain Strain from Motion of Sparse Markers.

Brain strain secondary to head impact or inertial loading is closely associated with pathologic observations in the brain. The only experimental brain strain dataset under loadings close to traumatic levels was calculated by imposing the experimentally measured motion of markers embedded in the brain to an auxiliary model formed by triad elements (Hardy et al., 2007). However, fidelity of the calculated strain as well as the suitability of using triad elements for three-dimensional (3D) strain estimation remains to be verified. Therefore, this study proposes to use tetrahedron elements as a new approach to estimate the brain strain. Fidelity of this newly-proposed approach along with the previous triad-based approach is evaluated with the aid of three independently-developed finite element (FE) head models by numerically replicating the experimental impacts and strain estimation procedures. Strain in the preselected brain elements obtained from the whole head simulation exhibits good correlation with its tetra estimation and exceeds its triad estimation, indicating that the tetra approach more accurately estimates the strain in the preselected region. The newly calculated brain strain curves using tetra elements provide better approximations for the 3D experimental brain deformation and can be used for strain validation of FE models of human head.

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci (QTLs) is critical for assessing genetic effects and identifying candidate genes for quantitative traits. Interval and composite interval mappings have been the methods of choice for several decades, which have provided tools for identifying genomic regions harboring causal genes for quantitative traits. Historically, the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed. Currently, genomes of many organisms have been saturated with markers due to the new sequencing technologies. Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms (SNPs), which often include the causal polymorphisms. The concept of interval no longer exists, prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data. Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks, called bins. The new method detects associations of bins with quantitative traits. It borrows the mixed model methodology with a polygenic control from genome-wide association studies (GWAS) and can handle all kinds of experimental populations under the linear mixed model (LMM) framework. We tested the method using both simulated data and data from populations of rice. The results showed that this method has higher power than the current methods. An R package named binQTL is available from GitHub.

Principal Geodesic Analysis in the Space of Discrete Shells

AbstractImportant sources of shape variability, such as articulated motion of body models or soft tissue dynamics, are highly nonlinear and are usually superposed on top of rigid body motion which must be factored out. We propose a novel, nonlinear, rigid body motion invariant Principal Geodesic Analysis (PGA) that allows us to analyse this variability, compress large variations based on statistical shape analysis and fit a model to measurements. For given input shape data sets we show how to compute a low dimensional approximating submanifold on the space of discrete shells, making our approach a hybrid between a physical and statistical model. General discrete shells can be projected onto the submanifold and sparsely represented by a small set of coefficients. We demonstrate two specific applications: model‐constrained mesh editing and reconstruction of a dense animated mesh from sparse motion capture markers using the statistical knowledge as a prior.

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.

Centralized Networks to Generate Human Body Motions.

We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons’ states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers’ trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings.

Real-time labeling of non-rigid motion capture marker sets

Passive optical motion capture is one of the predominant technologies for capturing high fidelity human motion, and is a workhorse in a large number of areas such as bio-mechanics, film and video games. While most state-of-the-art systems can automatically identify and track markers on the larger parts of the human body, the markers attached to the fingers and face provide unique challenges and usually require extensive manual cleanup. In this work we present a robust online method for identification and tracking of passive motion capture markers attached to non-rigid structures. The method is especially suited for large capture volumes and sparse marker sets. Once trained, our system can automatically initialize and track the markers, and the subject may exit and enter the capture volume at will. By using multiple assignment hypotheses and soft decisions, it can robustly recover from a difficult situation with many simultaneous occlusions and false observations (ghost markers). In three experiments, we evaluate the method for labeling a variety of marker configurations for finger and facial capture. We also compare the results with two of the most widely used motion capture platforms: Motion Analysis Cortex and Vicon Blade. The results show that our method is better at attaining correct marker labels and is especially beneficial for real-time applications.

Design and evaluation of reduced marker layouts for hand motion capture

AbstractWe present a method for automatically generating reduced marker layouts for marker‐based optical motion capture of human hands. The employed motion reconstruction method is based on subspace‐constrained inverse kinematics, which allows for the recovery of realistic hand movements even from sparse input data. We additionally present a user‐specific hand model calibration procedure that fits an articulated hand model to point cloud data of the user's hand. Our marker layout optimization is sensitive to the kinematic structure and the subspace representations of hand articulations utilized in the reconstruction method, in order to generate sparse marker configurations that are optimal for solving the constrained inverse kinematics problem. We propose specific quality criteria for reduced marker sets that combine numerical stability with geometric feasibility of the resulting layout. These criteria are combined in an objective function that is minimized using a specialized surface‐constrained particle swarm optimization scheme, which generates marker layouts bound to the surface of an animated hand model. Our method provides a principled way for determining reduced marker layouts based on subspace representations of hand articulations. We demonstrate the effectiveness of our motion reconstruction and model calibration methods in a thorough evaluation.

Geomorphology, denudation rates, and stream channel profiles reveal patterns of mountain building adjacent to the San Andreas fault in northern California, USA

Relative horizontal motion along strike-slip faults can build mountains when motion is oblique to the trend of the strike-slip boundary. The resulting contraction and uplift pose off-fault seismic hazards, which are often difficult to detect because of the poor vertical resolution of satellite geodesy and difficulty of locating offset datable landforms in active mountain ranges. Sparse geomorphic markers, topographic analyses, and measurement of denudation allow us to map spatiotemporal patterns of uplift along the northern San Andreas fault. Between Jenner and Mendocino, California, emergent marine terraces found southwest of the San Andreas fault record late Pleistocene uplift rates between 0.20 and 0.45 mm yr−1 along much of the coast. However, on the northeast side of the San Andreas fault, a zone of rapid uplift (0.6−1.0 mm yr−1) exists adjacent to the San Andreas fault, but rates decay northeastward as the coast becomes more distant from the San Andreas fault. A newly dated 4.5 Ma shallow-marine deposit located at ∼500 m above sea level (masl) adjacent to the San Andreas fault is warped down to just 150 masl 15 km northeast of the San Andreas fault, and it is exposed at just 60−110 masl to the west of the fault. Landscape denudation rates calculated from abundance of cosmogenic radionuclides in fluvial sediment northeast of, and adjacent to, the San Andreas fault are 0.16−0.29 mm yr−1, but they are only 0.03−0.07 mm yr−1 west of the fault. Basin-average channel steepness and the denudation rates can be used to infer the erosive properties of the underlying bedrock. Calibrated erosion rates can then be estimated across the entire landscape using the spatial distribution of channel steepness with these erosive properties. The lower-elevation areas of this landscape that show high channel steepness (and hence calibrated erosion rate) are distinct from higher-elevation areas with systematically lower channel steepness and denudation rates. These two areas do not appear to be coincident with lithologic contacts. Assuming that changes in rock uplift rates are manifest in channel steepness values as an upstream-propagating kinematic wave that separates high and low channel steepness values, the distance that this transition has migrated vertically provides an estimate of the timing of rock uplift rate increase. This analysis suggests that rock uplift rates along the coast changed from 0.3 to 0.75 mm yr−1 between 450 and 350 ka. This zone of recent, relatively rapid crustal deformation along the plate boundary may be a result of the impingement of relatively strong crust underlying the Gualala block into the thinner, weaker oceanic crust left at the western margin of the North American plate by the westward migration of the subduction zone prior to establishment of the current transform plate boundary. The warped Pliocene marine deposits and the presence of a topographic ridge support the patterns indicated by the channel steepness analyses, and further indicate that the zone of rapid uplift may herald elevated off-fault seismic hazard if this uplift is created by periodic stick-slip motion on contractional structures.

On the Spatiotemporal Material Anisotropy of the Vitreous Body in Tension and Compression.

Although linked to several vitreoretinal pathologies including traumatic retinal tears, breaks, and symptomatic vitreomacular traction, the dynamic material behavior of the vitreous body in response to mechanical loads is not well understood. The purpose of this study was to evaluate spatiotemporal patterns of collagen fiber reorganization and vitreous deformation (strain) in response to tensile and compressive forces. Using thick slabs of bovine eyes we examined collagen fiber reorganization following tensile and compressive step-loading with quantitative polarized light imaging. Strains were measured from sparse marker arrays and temporal collagen behavior was estimated from creep compliance rheological tests. Results showed that under applied loads (1) collagen fibers became significantly more aligned at the vitreous base (near the pars plana and the ciliary body), (2) vitreous located directly behind the lens deformed significantly more than surrounding regions, and (3) changes in collagen fiber alignment occurred on a short (<5s) timescale. Together these results show that, despite a homogeneous visual appearance, the vitreous body exhibits anisotropic material behavior in tension and compression. Spatiotemporal patterns of collagen rearrangement were consistent with epidemiological patterns of traumatic retinal damage and vitreoretinal topology. High strains in the vitreous corresponded with locations of lower collagen content that are prone to age-related degeneration. These data suggest that differential fiber alignment and mechanical deformation could contribute to the pathogenesis of these diseases. Computational models that incorporate these experimental data will help improve our understanding of the biomechanical mechanisms that contribute to the pathogenesis of traumatic retinal damage, vitreous degeneration, and vitreoretinal disease.

Recent genomic heritage in Scotland.

BackgroundThe Generation Scotland Scottish Family Health Study (GS:SFHS) includes 23,960 participants from across Scotland with records for many health-related traits and environmental covariates. Genotypes at ~700 K SNPs are currently available for 10,000 participants. The cohort was designed as a resource for genetic and health related research and the study of complex traits. In this study we developed a suite of analyses to disentangle the genomic differentiation within GS:SFHS individuals to describe and optimise the sample and methods for future analyses.ResultsWe combined the genotypic information of GS:SFHS with 1092 individuals from the 1000 Genomes project and estimated their genomic relationships. Then, we performed Principal Component Analyses of the resulting relationships to investigate the genomic origin of different groups. We characterised two groups of individuals: those with a few sparse rare markers in the genome, and those with several large rare haplotypes which might represent relatively recent exogenous ancestors. We identified some individuals with likely Italian ancestry and a group with some potential African/Asian ancestry. An analysis of homozygosity in the GS:SFHS sample revealed a very similar pattern to other European populations. We also identified an individual carrying a chromosome 1 uniparental disomy. We found evidence of local geographic stratification within the population having impact on the genomic structure.ConclusionsThese findings illuminate the history of the Scottish population and have implications for further analyses such as the study of the contributions of common and rare variants to trait heritabilities and the evaluation of genomic and phenotypic prediction of disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1605-2) contains supplementary material, which is available to authorized users.