Histoires d’identité (génétique)

Cette note présente un point de vue historique sur les conditions dans lesquelles est apparu le concept d’identité génétique entre quatre allèles de deux individus. Plusieurs définitions possibles des états d’identité ont été proposées entre les années 1940 et 1970, avec des regroupements de ces états qui différent d’un auteur à l’autre. L’analyse historique de la littérature montre que la définition et la description des états d’identité se sont mises en place quasi-simultanément, et très probablement indépendamment, en France et aux États-Unis.This note takes a historical perspective on the concept of genetic identity states between four alleles of two individuals. Several definitions of these states were proposed between 1940 and 1970, and states were diversely merged according to the authors. The historical analysis of the publications shows that the definition and the description of the identity states occurred quasi-simultaneously and most probably independently both in France and in the United States of America

Modeling the effect of a genetic factor for a complex trait in a simulated population

Genetic Analysis Workshop 14 simulated data have been analyzed with MASC(marker association segregation chi-squares) in which we implemented a bootstrap procedure to provide the variation intervals of parameter estimates. We model here the effect of a genetic factor, S, for Kofendrerd Personality Disorder in the region of the marker C03R0281 for the Aipotu population. The goodness of fit of several genetic models with two alleles for one locus has been tested. The data are not compatible with a direct effect of a single-nucleotide polymorphism (SNP) (SNP 16, 17, 18, 19 of pack 153) in the region. Therefore, we can conclude that the functional polymorphism has not been typed and is in linkage disequilibrium with the four studied SNPs. We obtained very large variation intervals both of the disease allele frequency and the degree of dominance. The uncertainty of the model parameters can be explained first, by the method used, which models marginal effects when the disease is due to complex interactions, second, by the presence of different sub-criteria used for the diagnosis that are not determined by S in the same way, and third, by the fact that the segregation of the disease in the families was not taken into account. However, we could not find any model that could explain the familial segregation of the trait, namely the higher proportion of affected parents than affected sibs

Comparative assessment of methods for estimating individual genome-wide homozygosity-by-descent from human genomic data

<p>Abstract</p> <p>Background</p> <p>Genome-wide homozygosity estimation from genomic data is becoming an increasingly interesting research topic. The aim of this study was to compare different methods for estimating individual homozygosity-by-descent based on the information from human genome-wide scans rather than genealogies. We considered the four most commonly used methods and investigated their applicability to single-nucleotide polymorphism (SNP) data in both a simulation study and by using the human genotyped data. A total of 986 inhabitants from the isolated Island of Vis, Croatia (where inbreeding is present, but no pedigree-based inbreeding was observed at the level of F > 0.0625) were included in this study. All individuals were genotyped with the Illumina HumanHap300 array with 317,503 SNP markers.</p> <p>Results</p> <p>Simulation data suggested that multi-point FEstim is the method most strongly correlated to true homozygosity-by-descent. Correlation coefficients between the homozygosity-by-descent estimates were high but only for inbred individuals, with nearly absolute correlation between single-point measures.</p> <p>Conclusions</p> <p>Deciding who is really inbred is a methodological challenge where multi-point approaches can be very helpful once the set of SNP markers is filtered to remove linkage disequilibrium. The use of several different methodological approaches and hence different homozygosity measures can help to distinguish between homozygosity-by-state and homozygosity-by-descent in studies investigating the effects of genomic autozygosity on human health.</p

Genetics of VEGF Serum Variation in Human Isolated Populations of Cilento: Importance of VEGF Polymorphisms

Vascular Endothelial Growth Factor (VEGF) is the main player in angiogenesis. Because of its crucial role in this process, the study of the genetic factors controlling VEGF variability may be of particular interest for many angiogenesis-associated diseases. Although some polymorphisms in the VEGF gene have been associated with a susceptibility to several disorders, no genome-wide search on VEGF serum levels has been reported so far. We carried out a genome-wide linkage analysis in three isolated populations and we detected a strong linkage between VEGF serum levels and the 6p21.1 VEGF region in all samples. A new locus on chromosome 3p26.3 significantly linked to VEGF serum levels was also detected in a combined population sample. A sequencing of the gene followed by an association study identified three common single nucleotide polymorphisms (SNPs) influencing VEGF serum levels in one population (Campora), two already reported in the literature (rs3025039, rs25648) and one new signal (rs3025020). A fourth SNP (rs41282644) was found to affect VEGF serum levels in another population (Cardile). All the identified SNPs contribute to the related population linkages (35% of the linkage explained in Campora and 15% in Cardile). Interestingly, none of the SNPs influencing VEGF serum levels in one population was found to be associated in the two other populations. These results allow us to exclude the hypothesis that the common variants located in the exons, intron-exon junctions, promoter and regulative regions of the VEGF gene may have a causal effect on the VEGF variation. The data support the alternative hypothesis of a multiple rare variant model, possibly consisting in distinct variants in different populations, influencing VEGF serum levels

TRAIP promotes DNA damage response during genome replication and is mutated in primordial dwarfism.

DNA lesions encountered by replicative polymerases threaten genome stability and cell cycle progression. Here we report the identification of mutations in TRAIP, encoding an E3 RING ubiquitin ligase, in patients with microcephalic primordial dwarfism. We establish that TRAIP relocalizes to sites of DNA damage, where it is required for optimal phosphorylation of H2AX and RPA2 during S-phase in response to ultraviolet (UV) irradiation, as well as fork progression through UV-induced DNA lesions. TRAIP is necessary for efficient cell cycle progression and mutations in TRAIP therefore limit cellular proliferation, providing a potential mechanism for microcephaly and dwarfism phenotypes. Human genetics thus identifies TRAIP as a component of the DNA damage response to replication-blocking DNA lesions.This work was supported by funding from the Medical Research Council and the European Research Council (ERC, 281847) (A.P.J.), the Lister Institute for Preventative Medicine (A.P.J. and G.S.S.), Medical Research Scotland (L.S.B.), German Federal Ministry of Education and Research (BMBF, 01GM1404) and E-RARE network EuroMicro (B.W), Wellcome Trust (M. Hurles), CMMC (P.N.), Cancer Research UK (C17183/A13030) (G.S.S. and M.R.H), Swiss National Science Foundation (P2ZHP3_158709) (O.M.), AIRC (12710) and ERC/EU FP7 (CIG_303806) (S.S.), Cancer Research UK (C6/A11224) and ERC/EU FP7 (HEALTH-F2- 2010-259893) (A.N.B. and S.P.J.).This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ng.345

Tracking patient clusters over time enables to extract all the information available in the medico-administrative databases

Abstract Context Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, the complexity of these databases, in particular due to the presence of truncated longitudinal data, requires adaptation of clustering approaches. Objective We propose here cluster-tracking approaches to identify clusters of patients from longitudinal data contained in medico-administrative databases. Material and Methods We first cluster patients at each age using either the Markov Cluster algorithm (MCL) from patient networks or Kmeans from raw data. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs prescribed from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort. Results Our cluster-tracking approaches allowed us to identify several cluster-trajectories having clinical significance. Silhouette score comparison between the different approaches reveals that the best score is obtained for the cluster-tracking approaches. Conclusion The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities

Tracking Temporal Clusters from Patient Networks

International audienceCreating homogeneous groups (clusters) of patients from medico-administrative databases provides a better understanding of health determinants. But in these databases, patients have truncated care pathways. We developed an approach based on patient networks to construct care trajectories from such truncated data. We tested this approach on antithrombotic treatments prescribed from 2008 to 2018 contained in the échantillon généraliste des bénéficiaires (EGB). We constructed a patient network for each patients’ age (years from birth). We then applied the Markov clustering algorithm in each network. The care trajectories were finally constructed by matching clusters identified in two consecutive networks. We calculated the silhouette score to assess the performance of this network approach compared to three existing approaches. We identified 12 care trajectories that we were able to associate with pathologies. The best silhouette score was obtained for the network approach. Our approach allowed to highlight care trajectories taking into account the longitudinal, multidimensional and truncated nature of data from medico-administrative databases

Improving patient clustering by incorporating structured label relationships in similarity measures

Context Patient stratification is the cornerstone of numerous health studies, serving to enhance medicine efficacy estimation and facilitate patient matching. To stratify patients, similarity measured between patients can be computed from medical health records databases, such as medico-administrative databases. Importantly, the variables included in medico-administrative databases can be associated with labels, which can be organized in ontologies or other classification systems. However, to the best of our knowledge, the relevance of considering such label classification in the computation of patient similarity measures has been poorly studied. Objective We propose and evaluate several weighted versions of the Cosine similarity that consider structured label relationships to compute patient similarities from a medico-administrative database. Material and Methods As a use case, we analyze medicine reimbursements contained in the Échantillon Généraliste des Bénéficiaires , a French medico-administrative database. We compute the standard Cosine similarity between patients based on their medicine reimbursement. In addition, we computed a weighted Cosine similarity measure that includes variable frequencies and two weighted Cosine similarity measures that consider label relationships. We construct patient networks from each similarity measure and identify clusters of patients. We evaluate the performance of the different similarity measures with enrichment tests using information on chronic diseases. Results The similarity measures that include label relationships perform better to identify similar patients. Indeed, using these weighted measures, we identify distinct patient clusters with a higher number of chronic disease enrichments as compared to the other measures. Importantly, the enrichment tests provide clinically interpretable insights into these patient clusters. Conclusion Considering label relationships when computing patient similarities improves stratification of patients regarding their health status

Tracking clusters of patients over time enables extracting information from medico-administrative databases

Context Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, these databases contain different types of longitudinal variables which are measured over different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering approaches that can handle this type of data. Objective We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases. Material and Methods We first cluster patients at each age. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three classical longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort. Results Our cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance without any imputation of data. The comparison of the silhouette scores obtained with the different approaches highlights the better performances of the cluster-tracking approaches. Conclusion The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities