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Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes, and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a sin gle block, while the last approach considers one model by haplotype. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single-SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.

Forensic DNA signal is notoriously challenging to interpret and requires the implementation of computational tools that support its interpretation. While data from high-copy, low-contributor samples result in electropherogram signal that is readily interpreted by probabilistic methods, electropherogram signal from forensic stains is often garnered from low-copy, high-contributor-number samples and is frequently obfuscated by allele sharing, allele drop-out, stutter and noise. Since forensic DNA profiles are too complicated to quantitatively assess by manual methods, continuous, probabilistic frameworks that draw inferences on the Number of Contributors (NOC) and compute the Likelihood Ratio (LR) given the prosecution’s and defense’s hypotheses have been developed. In the current paper, we validate a new version of the NOCIt inference platform that determines an A Posteriori Probability (APP) distribution of the number of contributors given an electropherogram. NOCIt is a continuous inference system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise and allelic drop-out while taking into account allele frequencies in a reference population. We established the algorithm’s performance by conducting tests on samples that were representative of types often encountered in practice. In total, we tested NOCIt’s performance on 815 degraded, UV-damaged, inhibited, differentially degraded, or uncompromised DNA mixture samples containing up to 5 contributors. We found that the model makes accurate, repeatable and reliable inferences about the NOCs and significantly outperformed methods that rely on signal filtering. By leveraging recent theoretical results of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establish the NOC can be treated as a nuisance variable, we demonstrated that when NOCIt’s APP is used in conjunction with a downstream likelihood ratio (LR) inference system that employs the same probabilistic model, a full evaluation across multiple contributor numbers is rendered. This work, therefore, illustrates the power of modern probabilistic systems to report holistic and interpretable weights-of-evidence to the trier-of-fact without assigning a specified number of contributors or filtering signal.

In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph. In recent years, development of new methods for the construction and analysis of networks has helped to broaden the availability of these approaches from programmers to a diversity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.

International audience; In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph. In recent years, development of new methods for the construction and analysis of networks has helped to broaden the availability of these approaches from programmers to a diversity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.

International audience; Imaging-genetics is a growing popular research avenue which aims to find genetic variants associated with quantitative phenotypes that characterize a disease. In this work, we combine structural MRI with genetic data structured by prior knowledge of interactions in a Canonical Correlation Analysis (CCA) model with graph regularization. This results in improved prediction performance and yields a more inter-pretable model.

Imaging genetic studies of large control cohorts such as UK Biobank enable to assess the range of normal variations in brain structures. Previous studies by our group have shown that the width of several cortical sulci is associated with a variant in the upstream region of KCNK2 gene even if this effect is corrected with age. Here we propose to analyze in a multivariate setup the associations between sets of genetic variants and multiple sulci widths. The genetic variants we consider are sets of SNPs of known phase called haplotypes, taken from the upstream region of KCNK2 gene. To the best of our knowledge, multivariate analysis in imaging genetics has never been used in haplotype studies. Our method was able to recover the expected association signal and uncover new associations between imaging data and genetic variants.

International audience; Heritability of the regional thickness/surface in human cortex is established. Yet the estimates vary substantially depending on cohorts or the information they are derived from: pedigree or genotyping. Here we present three heritability studies of the cortex phenotype in two cohorts showing: i) both pedigree and genotyping or ii) genotyping only. We obtained clearly correlated heritability values between studies with a shift appearing between cohorts. Spatial pattern of heritability remains highly consistent across cohorts

International audience; Imaging-genetics cohorts allow to find associations between genotyped variants and brain imaging features. They are invaluable tools to evaluate the part of genetics and environment in the brain characteristic variance observed in normal and pathological populations. The present analyses were conducted using the 2018 UK Biobank (UKB) data release, and included 15,040 subjects for which sulcal opening, a measure of sulcus width, are extracted for 126 sulci using BrainVisa/Morphologist pipeline. Based on genetic maps, continuous blocks of high-confidence phase are extracted using the haplotype dataset of UK Biobank. The feasibility study analyzes 13,942 haplotypes of 1,756 blocks on chromosome 21, obtained with a stringent block definition, and shows that block-based test seems underpowered compared to haplotype-based association test.

The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications. However, the algorithmic tools for the analysis of such networks are usually restricted to people with high programming skills. We present an integrated suite of software tools named MultiTwin, aimed at the construction, structuring, and analysis of multipartite graphs for evolutionary biology. Typically, this kind of graph is useful for the comparative analysis of the gene content of genomes in microbial communities from the environment and for exploring patterns of gene sharing, for example between distantly related cellular genomes, pangenomes, or between cellular genomes and their mobile genetic elements. We illustrate the use of this tool with an application of the bipartite approach (using gene family–genome graphs) for the analysis of pathogenicity traits in prokaryotes.

The study of diseased human cells and of cells isolated from the natural environment will likely be revolutionized by single cell genomics (SCG). Here, we used protein similarity networks to explore within- and between-cell DNA differences from SCG data derived from six individual rhizarian cells related to Paulinella ovalis and proteins from the complete genome of another rhizarian, Bigelowiella natans. We identified shared and distinct DNA components within our SCG data and between P. ovalis and B. natans. We show that network properties such as assortativity and degree effectively discriminate genome features between SCG assemblies and that SCG data follow the power law with a small number of protein families dominating networks.

Background High-throughput sequencing technologies are lifting major limitations to molecular-based ecological studies of eukaryotic microbial diversity, but analyses of the resulting millions of short sequences remain a major bottleneck for these approaches. Here, we introduce the analytical and statistical framework of sequence similarity networks, increasingly used in evolutionary studies and graph theory, into the field of ecology to analyze novel pyrosequenced V4 small subunit rDNA (SSU-rDNA) sequence data sets in the context of previous studies, including SSU-rDNA Sanger sequence data from cultured ciliates and from previous environmental diversity inventories. Results Our broadly applicable protocol quantified the progress in the description of genetic diversity of ciliates by environmental SSU-rDNA surveys, detected a fundamental historical bias in the tendency to recover already known groups in these surveys, and revealed substantial amounts of hidden microbial diversity. Moreover, network measures demonstrated that ciliates are not globally dispersed, but are structured by habitat and geographical location at intermediate geographical scale, as observed for bacteria, plants, and animals. Conclusions Currently available ‘universal’ primers used for local in-depth sequencing surveys provide little hope to exhaust the significantly higher ciliate (and most likely microbial) diversity than previously thought. Network analyses such as presented in this study offer a promising way to guide the design of novel primers and to further explore this vast and structured microbial diversity. Electronic supplementary material The online version of this article (doi:10.1186/s12915-015-0125-5) contains supplementary material, which is available to authorized users.

Ancient transitions, such as between life that evolved in a reducing versus an oxidizing atmosphere precipitated by the Great Oxygenation Event (GOE) ca. 2.4 billion years ago, fundamentally altered the space in which prokaryotes could derive metabolic energy. Despite fundamental changes in Earth's redox state, there are very few comprehensive, proteome-wide analyses about the effects of these changes on gene content and evolution. Here, using a pan-proteome sequence similarity network applied to broadly sampled lifestyles of 84 prokaryotes that were categorized into four different redox groups (i.e., methanogens, obligate anaerobes, facultative anaerobes, and obligate aerobes), we reconstructed the genetic inventory of major respiratory communities. We show that a set of putative core homologs that is highly conserved in prokaryotic proteomes is characterized by the loss of canonical network connections and low conductance that correlates with differences in respiratory phenotypes. We suggest these different network patterns observed for different respiratory communities could be explained by two major evolutionary diversification events in the history of microbial life. The first event (M) is a divergence between methanogenesis and other anaerobic lifestyles in prokaryotes (archaebacteria and eubacteria). The second diversification event (OX) is from anaerobic to aerobic lifestyles that left a proteome-wide footprint among prokaryotes. Additional analyses revealed that oxidoreductase evolution played a central role in these two diversification events. Distinct cofactor binding domains were frequently recombined, allowing these enzymes to utilize increasingly oxidized substrates with high specificity.

Summary The Great Oxidation Event (GOE) ∼2.4 billion years ago resulted from the accumulation of oxygen by the ancestors of cyanobacteria [1–3]. Cyanobacteria continue to play a significant role in primary production [4] and in regulating the global marine and limnic nitrogen cycles [5, 6]. Relatively little is known, however, about the evolutionary history and gene content of primordial cyanobacteria [7, 8]. To address these issues, we used protein similarity networks [9], containing proteomes from 48 cyanobacteria as the test group, and reference proteomes from 84 microbes representing four distinct metabolic groups from most reducing to most oxidizing: methanogens, obligate anaerobes (nonmethanogenic), facultative aerobes, and obligate aerobes. These four metabolic groups represent extant bioinformatic proxies for ancient redox chemistries, extending from an anoxic origin through the GOE and ultimately to obligate aerobes [10–13]. Analysis of the network metric degree showed a strong relationship between cyanobacteria and obligate anaerobes, from which cyanobacteria presumably arose, for core functions that include translation, photosynthesis, energy conservation, and environmental interactions. These data were used to reconstruct primordial functions in cyanobacteria that included nine gene families involved in photosynthesis, hydrogenases, and proteins involved in defense from environmental stress. The presence of 60% of these genes in both reaction center I (RC-I) and RC-II-type bacteria may be explained by selective loss of either RC in the evolutionary history of some photosynthetic lineages. Finally, the network reveals that cyanobacteria occupy a unique position among prokaryotes as a hub between anaerobes and obligate aerobes.

Most brain functional connectivity methods in fMRI require a brain parcellation into functionally homogeneous regions. In this work we propose a novel parcellation approach based on a spatial hierarchical clustering, that provides clusters within a multi-level framework. The method has the advantage of producing several brain parcellations rather than a single one from a fixed size-homogeneity criterion. Results obtained on real data demonstrate the relevance of the approach. Finally, a connectivity study shows the benefit of a prior multi-level parcellation of the brain.