Translational Innovation in Medicine and Complexity

Summary Based on population genomic and environmental data, genomewide ecological association studies aim at detecting allele frequencies that exhibit significant statistical association with ecological gradients. Ecological association studies can provide lists of genetic polymorphisms that are potentially involved in local adaptation to environmental conditions through natural selection. Here, we present the R package LEA that enables users to run ecological association studies from the R command line. The package can perform analyses of population structure and genome scans for adaptive alleles from large genomic data sets. It derives advantages from R programming functionalities to adjust significance values for multiple testing issues and to visualize results. This note also illustrates the main steps of ecological association studies and the typical use of LEA for analysing data sets based on R commands.

The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large-scale data. It can handle missing data and pooled sequencing data. By contrast to population-based approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved in terms of both statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2.0 and later versions of the package. When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other computer programs for genome scans (BayeScan, hapflk, OutFLANK, sNMF). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10% with the exception of BayeScan that generates 40% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful in scenarios of population divergence and range expansion. Because pcadapt handles next-generation sequencing data, it is a valuable tool for data analysis in molecular ecology.

Inference of individual ancestry coefficients, which is important for population genetic and association studies, is commonly performed using computer-intensive likelihood algorithms. With the availability of large population genomic data sets, fast versions of likelihood algorithms have attracted considerable attention. Reducing the computational burden of estimation algorithms remains, however, a major challenge. Here, we present a fast and efficient method for estimating individual ancestry coefficients based on sparse nonnegative matrix factorization algorithms. We implemented our method in the computer program sNMF and applied it to human and plant data sets. The performances of sNMF were then compared to the likelihood algorithm implemented in the computer program ADMIXTURE. Without loss of accuracy, sNMF computed estimates of ancestry coefficients with runtimes ∼10-30 times shorter than those of ADMIXTURE.

Plants generate effective responses to infection by recognizing both conserved and variable pathogen-encoded molecules. Pathogens deploy virulence effector proteins into host cells, where they interact physically with host proteins to modulate defense. We generated an interaction network of plant-pathogen effectors from two pathogens spanning the eukaryote-eubacteria divergence, three classes of Arabidopsis immune system proteins, and ~8000 other Arabidopsis proteins. We noted convergence of effectors onto highly interconnected host proteins and indirect, rather than direct, connections between effectors and plant immune receptors. We demonstrated plant immune system functions for 15 of 17 tested host proteins that interact with effectors from both pathogens. Thus, pathogens from different kingdoms deploy independently evolved virulence proteins that interact with a limited set of highly connected cellular hubs to facilitate their diverse life-cycle strategies.

Abstract On the basis of simulated data, this study compares the relative performances of the Bayesian clustering computer programs structure , geneland , geneclust and a new program named tess . While these four programs can detect population genetic structure from multilocus genotypes, only the last three ones include simultaneous analysis from geographical data. The programs are compared with respect to their abilities to infer the number of populations, to estimate membership probabilities, and to detect genetic discontinuities and clinal variation. The results suggest that combining analyses using tess and structure offers a convenient way to address inference of spatial population structure.

The Mediterranean diet has been linked to a number of health benefits, including reduced mortality risk and lower incidence of cardiovascular disease. Definitions of the Mediterranean diet vary across some settings, and scores are increasingly being employed to define Mediterranean diet adherence in epidemiological studies. Some components of the Mediterranean diet overlap with other healthy dietary patterns, whereas other aspects are unique to the Mediterranean diet. In this forum article, we asked clinicians and researchers with an interest in the effect of diet on health to describe what constitutes a Mediterranean diet in different geographical settings, and how we can study the health benefits of this dietary pattern.

Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.

Enzymatic fuel cells use enzymes to produce energy from bioavailable substrates. However, such biofuel cells are limited by the difficult electrical wiring of enzymes to the electrode. Here we show the efficient wiring of enzymes in a conductive pure carbon nanotube matrix for the fabrication of a glucose biofuel cell (GBFC). Glucose oxidase and laccase were respectively incorporated in carbon nanotube disks by mechanical compression. The characterization of each bioelectrode shows an open circuit potential corresponding to the redox potential of the respective enzymes, and high current densities for glucose oxidation and oxygen reduction. The mediatorless GBFC delivers a high power density up to 1.3 mW cm−2 and an open circuit voltage of 0.95 V. Moreover, the GBFC remains stable for 1 month and delivers 1 mW cm−2 power density under physiological conditions (5×10−3 mol l−1 glucose, pH 7). To date, these values are the best performances obtained for a GBFC. Glucose biofuel cells can be used to produce clean energy from renewable sources, but their use is limited by poor stability and low power output. In this study, bioelectrodes are fabricated using carbon nanotubes and the resulting biofuel cells have improved stability and power.

Thierry Wirth, Philip Supply, Stefan Niemann and colleagues analyze 4,987 Mycobacterium tuberculosis strains of the Beijing lineage isolated from 99 countries. They report whole-genome sequencing of 110 representative strains, characterize global population structure and reconstruct the evolutionary history of this lineage. Mycobacterium tuberculosis strains of the Beijing lineage are globally distributed and are associated with the massive spread of multidrug-resistant (MDR) tuberculosis in Eurasia. Here we reconstructed the biogeographical structure and evolutionary history of this lineage by genetic analysis of 4,987 isolates from 99 countries and whole-genome sequencing of 110 representative isolates. We show that this lineage initially originated in the Far East, from where it radiated worldwide in several waves. We detected successive increases in population size for this pathogen over the last 200 years, practically coinciding with the Industrial Revolution, the First World War and HIV epidemics. Two MDR clones of this lineage started to spread throughout central Asia and Russia concomitantly with the collapse of the public health system in the former Soviet Union. Mutations identified in genes putatively under positive selection and associated with virulence might have favored the expansion of the most successful branches of the lineage.

Most source separation algorithms are based on a model of stationary sources. However, it is a simple matter to take advantage of possible nonstationarities of the sources to achieve separation. This paper develops novel approaches in this direction based on the principles of maximum likelihood and minimum mutual information. These principles are exploited by efficient algorithms in both the off-line case (via a new joint diagonalization procedure) and in the on-line case (via a Newton-like procedure). Some experiments showing the good performance of our algorithms and evidencing an interesting feature of our methods are presented: their ability to achieve a kind of super-efficiency. The paper concludes with a discussion contrasting separating methods for non-Gaussian and nonstationary models and emphasizing that, as a matter of fact, "what makes the algorithms work" is-strictly speaking-not the nonstationarity itself but rather the property that each realization of the source signals has a time-varying envelope.

By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.

AIM: To monitor the trends in prevalence of cerebral palsy (CP) by birthweight in Europe, 1980 to 2003. METHOD: Data were collated from 20 population-based registers contributing to the Surveillance of Cerebral Palsy in Europe database. Trend analyses were conducted in four birthweight groups: <1000g (extremely low birthweight [ELBW]); 1000 to 1499g (very low birthweight [VLBW]); 1500 to 2499g (moderately low birthweight [MLBW]); and >2499g (normal birthweight [NBW]). RESULTS: The overall prevalence of CP decreased from 1.90 to 1.77 per 1000 live births, p<0.001, with a mean annual fall of 0.7% (95% confidence interval [CI] -0.3% to -1.0%). Prevalence in NBW children showed a non-significant trend from 1.17 to 0.89 per 1000 live births (p=0.22). Prevalence in MLBW children decreased from 8.5 to 6.2 per 1000 live births (p<0.001), but not linearly. Prevalence in VLBW children also declined from 70.9 to 35.9 per 1000 live births (p<0.001) with a mean annual fall of 3.4% (95% CI -2.4% to -4.3%). Prevalence in ELBW children remained stable, at a mean rate of 42.4 per 1000 live births. INTERPRETATION: The decline in prevalence of CP in children of VLBW continues, and confirms that previously reported. For the first time, there is also a significant decline among those of MLBW, resulting in a significant overall decrease in the prevalence of CP.

The author describes a system for dynamically maintaining a description of the limits to free space for a mobile robot using a belt of ultrasonic range sensors. A model is presented for the uncertainty inherent in such sensors, and the projection of range measurements into external Cartesian coordinates is described. Line segments are then expressed by a set of parameters represented by an estimate and a precision. A process is presented for extracting line segments from adjacent collinear range measurements, and a fast algorithm is presented for matching these line segments to a model of the limits to free space of the robot. A side effect of matching observations to a local model is a correction to the estimated position of the robot at the time that the observation was made. A Kalman filter update equation is developed to permit the correspondence of a line segment to the model to be applied as a correction to estimated position. Examples of segment extraction, position correction and modeling are presented using real ultrasonic data.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Gene-environment association (GEA) studies are essential to understand the past and ongoing adaptations of organisms to their environment, but those studies are complicated by confounding due to unobserved demographic factors. Although the confounding problem has recently received considerable attention, the proposed approaches do not scale with the high-dimensionality of genomic data. Here, we present a new estimation method for latent factor mixed models (LFMMs) implemented in an upgraded version of the corresponding computer program. We developed a least-squares estimation approach for confounder estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several order faster than existing GEA approaches and then our previous version of the LFMM program. In addition, the new method outperforms other fast approaches based on principal component or surrogate variable analysis. We illustrate the program use with analyses of the 1000 Genomes Project data set, leading to new findings on adaptation of humans to their environment, and with analyses of DNA methylation profiles providing insights on how tobacco consumption could affect DNA methylation in patients with rheumatoid arthritis. Software availability: Software is available in the R package lfmm at https://bcm-uga.github.io/lfmm/.

Motivation: Genome-wide datasets produced for association studies have dramatically increased in size over the past few years, with modern datasets commonly including millions of variants measured in dozens of thousands of individuals. This increase in data size is a major challenge severely slowing down genomic analyses, leading to some software becoming obsolete and researchers having limited access to diverse analysis tools. Results: Here we present two R packages, bigstatsr and bigsnpr, allowing for the analysis of large scale genomic data to be performed within R. To address large data size, the packages use memory-mapping for accessing data matrices stored on disk instead of in RAM. To perform data pre-processing and data analysis, the packages integrate most of the tools that are commonly used, either through transparent system calls to existing software, or through updated or improved implementation of existing methods. In particular, the packages implement fast and accurate computations of principal component analysis and association studies, functions to remove single nucleotide polymorphisms in linkage disequilibrium and algorithms to learn polygenic risk scores on millions of single nucleotide polymorphisms. We illustrate applications of the two R packages by analyzing a case-control genomic dataset for celiac disease, performing an association study and computing polygenic risk scores. Finally, we demonstrate the scalability of the R packages by analyzing a simulated genome-wide dataset including 500 000 individuals and 1 million markers on a single desktop computer. Availability and implementation: https://privefl.github.io/bigstatsr/ and https://privefl.github.io/bigsnpr/. Supplementary information: Supplementary data are available at Bioinformatics online.

Powering future generations of implanted medical devices will require cumbersome transcutaneous energy transfer or harvesting energy from the human body. No functional solution that harvests power from the body is currently available, despite attempts to use the Seebeck thermoelectric effect, vibrations or body movements. Glucose fuel cells appear more promising, since they produce electrical energy from glucose and dioxygen, two substrates present in physiological fluids. The most powerful ones, Glucose BioFuel Cells (GBFCs), are based on enzymes electrically wired by redox mediators. However, GBFCs cannot be implanted in animals, mainly because the enzymes they rely on either require low pH or are inhibited by chloride or urate anions, present in the Extra Cellular Fluid (ECF). Here we present the first functional implantable GBFC, working in the retroperitoneal space of freely moving rats. The breakthrough relies on the design of a new family of GBFCs, characterized by an innovative and simple mechanical confinement of various enzymes and redox mediators: enzymes are no longer covalently bound to the surface of the electron collectors, which enables use of a wide variety of enzymes and redox mediators, augments the quantity of active enzymes, and simplifies GBFC construction. Our most efficient GBFC was based on composite graphite discs containing glucose oxidase and ubiquinone at the anode, polyphenol oxidase (PPO) and quinone at the cathode. PPO reduces dioxygen into water, at pH 7 and in the presence of chloride ions and urates at physiological concentrations. This GBFC, with electrodes of 0.133 mL, produced a peak specific power of 24.4 microW mL(-1), which is better than pacemakers' requirements and paves the way for the development of a new generation of implantable artificial organs, covering a wide range of medical applications.

The emergence of farming during the Neolithic transition, including the domestication of livestock, was a critical point in the evolution of human kind. The goat (Capra hircus) was one of the first domesticated ungulates. In this study, we compared the genetic diversity of domestic goats to that of the modern representatives of their wild ancestor, the bezoar, by analyzing 473 samples collected over the whole distribution range of the latter species. This partly confirms and significantly clarifies the goat domestication scenario already proposed by archaeological evidence. All of the mitochondrial DNA haplogroups found in current domestic goats have also been found in the bezoar. The geographic distribution of these haplogroups in the wild ancestor allowed the localization of the main domestication centers. We found no haplotype that could have been domesticated in the eastern half of the Iranian Plateau, nor further to the east. A signature of population expansion in bezoars of the C haplogroup suggests an early domestication center on the Central Iranian Plateau (Yazd and Kerman Provinces) and in the Southern Zagros (Fars Province), possibly corresponding to the management of wild flocks. However, the contribution of this center to the current domestic goat population is rather low (1.4%). We also found a second domestication center covering a large area in Eastern Anatolia, and possibly in Northern and Central Zagros. This last domestication center is the likely origin of almost all domestic goats today. This finding is consistent with archaeological data identifying Eastern Anatolia as an important domestication center.

We describe the first implanted glucose biofuel cell (GBFC) that is capable of generating sufficient power from a mammal's body fluids to act as the sole power source for electronic devices. This GBFC is based on carbon nanotube/enzyme electrodes, which utilize glucose oxidase for glucose oxidation and laccase for dioxygen reduction. The GBFC, implanted in the abdominal cavity of a rat, produces an average open-circuit voltage of 0.57 V. This implanted GBFC delivered a power output of 38.7 μW, which corresponded to a power density of 193.5 μW cm(-2) and a volumetric power of 161 μW mL(-1). We demonstrate that one single implanted enzymatic GBFC can power a light-emitting diode (LED), or a digital thermometer. In addition, no signs of rejection or inflammation were observed after 110 days implantation in the rat.

Geography and landscape are important determinants of genetic variation in natural populations, and several ancestry estimation methods have been proposed to investigate population structure using genetic and geographic data simultaneously. Those approaches are often based on computer-intensive stochastic simulations and do not scale with the dimensions of the data sets generated by high-throughput sequencing technologies. There is a growing demand for faster algorithms able to analyse genomewide patterns of population genetic variation in their geographic context. In this study, we present TESS3, a major update of the spatial ancestry estimation program TESS. By combining matrix factorization and spatial statistical methods, TESS3 provides estimates of ancestry coefficients with accuracy comparable to TESS and with run-times much faster than the Bayesian version. In addition, the TESS3 program can be used to perform genome scans for selection, and separate adaptive from nonadaptive genetic variation using ancestral allele frequency differentiation tests. The main features of TESS3 are illustrated using simulated data and analysing genomic data from European lines of the plant species Arabidopsis thaliana.

The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.