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In biomarker research, the goal is to construct an prediction rule on the basis of a small number of predictors. Formally, this means representing a macro-level response as a function of molecular features (DNA variants, transcript or protein abundancies) with minimal error. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 668858. This work was supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government. ; The information in this document is provided as is, and no guarantee or warranty is given that the information is fit for any particular purpose. The content of this document reflects only the author's view - the European Commission is not responsible for any use that may be made of the information it contains. The users use the information at their sole risk and liability.

The study of DNA to predict externally visible characteristics (EVCs) and the biogeographical ancestry (BGA) from unknown samples is gaining relevance in forensic genetics. Technical developments in Massively Parallel Sequencing (MPS) enable the simultaneous analysis of hundreds of DNA markers, which improves successful Forensic DNA Phenotyping (FDP). The EU-funded VISAGE (VISible Attributes through GEnomics) Consortium has developed various targeted MPS-based lab tools to apply FDP in routine forensic analyses. Here, we present an evaluation of the VISAGE Basic tool for appearance and ancestry prediction based on PowerSeq chemistry (Promega) on a MiSeq FGx System (Illumina). The panel consists of 153 single nucleotide polymorphisms (SNPs) that provide information about EVCs (41 SNPs for eye, hair and skin color from HIrisPlex-S) and continental BGA (115 SNPs; three overlap with the EVCs SNP set). The assay was evaluated for sensitivity, repeatability and genotyping concordance, as well as its performance with casework-type samples. This targeted MPS assay provided complete genotypes at all 153 SNPs down to 125 pg of input DNA and 99.67% correct genotypes at 50 pg. It was robust in terms of repeatability and concordance and provided useful results with casework-type samples. The results suggest that this MPS assay is a useful tool for basic appearance and ancestry prediction in forensic genetics for users interested in applying PowerSeq chemistry and MiSeq for this purpose ; The study received support from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 740580 within the framework of the VISible Attributes through GEnomics (VISAGE) Project and Consortium. L.P.-M. is a recipient of a postdoctoral fellowship from the Doctoral Research Staff Improvement Program of the Basque Government Department of Education (POS_2018_1_0037). M.d.l.P. is funded from a grant provided by the Consellería de Cultura, Educación e Ordenación Universitaria and the Consellería de Economía, ...

Understanding evolution requires detailed knowledge of genotype-phenotype maps; however, it can be a herculean task to measure every phenotype in a combinatorial map. We have developed a computational strategy to predict the missing phenotypes from an incomplete, combinatorial genotype-phenotype map. As a test case, we used an incomplete genotype-phenotype dataset previously generated for the malaria parasite's 'chloroquine resistance transporter' (PfCRT). Wild-type PfCRT (PfCRT3D7) lacks significant chloroquine (CQ) transport activity, but the introduction of the eight mutations present in the 'Dd2' isoform of PfCRT (PfCRTDd2) enables the protein to transport CQ away from its site of antimalarial action. This gain of a transport function imparts CQ resistance to the parasite. A combinatorial map between PfCRT3D7 and PfCRTDd2 consists of 256 genotypes, of which only 52 have had their CQ transport activities measured through expression in the Xenopus laevis oocyte. We trained a statistical model with these 52 measurements to infer the CQ transport activity for the remaining 204 combinatorial genotypes between PfCRT3D7 and PfCRTDd2. Our best-performing model incorporated a binary classifier, a nonlinear scale, and additive effects for each mutation. The addition of specific pairwise- and high-order-epistatic coefficients decreased the predictive power of the model. We evaluated our predictions by experimentally measuring the CQ transport activities of 24 additional PfCRT genotypes. The R2 value between our predicted and newly-measured phenotypes was 0.90. We then used the model to probe the accessibility of evolutionary trajectories through the map. Approximately 1% of the possible trajectories between PfCRT3D7 and PfCRTDd2 are accessible; however, none of the trajectories entailed eight successive increases in CQ transport activity. These results demonstrate that phenotypes can be inferred with known uncertainty from a partial genotype-phenotype dataset. We also validated our approach against a collection of previously published genotype-phenotype maps. The model therefore appears general and should be applicable to a large number of genotype-phenotype maps. ; This work was supported by a National Science Foundation CAREER Award (DEB-1844963 to MJH), an Australian Research Council Future Fellowship (FT160100226 to REM), and funding from the Australian National Health and Medical Research Council (Project Grant 1127338 and Fellowship 1053082 to REM; fellowship 1120690 to RLS). MJH is a Pew Scholar in the Biomedical Sciences, supported by The Pew Charitable Trusts. SHS was the recipient of an Australian Government Research Postgraduate Award

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole-genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep-sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and a specificity of 97.4%. 56 out of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol and ethionamide, and low-level rifampicin or isoniazid resistance mutations, all notoriously prone to phenotypic testing variability. Only two out of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and a specificity of 98.5/97.2/95.3%, respectively. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis or natural pyrazinamide resistance of globally rare "Mycobacterium canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 out of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

Background Borderline Personality Disorder (BPD) is a serious mental disorder characterized by marked interpersonal disturbances, including difficulties trusting others and volatile impressions of others' moral character, often resulting in premature relationship termination. We tested a hypothesis that moral character inference is disrupted in BPD and sensitive to Democratic Therapeutic Community (DTC) treatment. Methods BPD participants (20 treated and 23 DTC-treated) and non-BPD control participants (N=106) completed a moral inference task where they predicted the decisions of two agents with distinct moral preferences: the "bad" agent was more willing to harm others for money than the "good" agent. Periodically, participants rated their subjective impressions of the agent's moral character, and the certainty of those impressions. We fit a hierarchical Bayesian learning model to participants' trial-wise predictions to describe how beliefs about the morality of the agents were updated by new information. Results The computational mechanisms of moral inference differed for untreated BPD patients relative to matched non-BPD control participants and DTC-treated BPD patients. In BPD patients, beliefs about harmful agents were more certain and less amenable to updating relative to both non-BPD control participants and DTC-treated participants. Conclusions The findings suggest that DTC may help the maintenance of social relationships in BPD by increasing patients' openness to learning about adverse interaction partners. The results provide mechanistic insights into social deficits in BPD and demonstrate the potential for combining objective behavioral paradigms with computational modelling as a tool for assessing BPD pathology and treatment outcomes.

BACKGROUND: Borderline personality disorder (BPD) is a serious mental disorder characterized by marked interpersonal disturbances, including difficulties trusting others and volatile impressions of others' moral character, often resulting in premature relationship termination. We tested a hypothesis that moral character inference is disrupted in BPD and sensitive to democratic therapeutic community (DTC) treatment. METHODS: Participants with BPD (n = 43; 20 untreated and 23 DTC-treated) and control participants without BPD (n = 106) completed a moral inference task where they predicted the decisions of 2 agents with distinct moral preferences: the "bad" agent was more willing than the "good" agent to harm others for money. Periodically, participants rated their subjective impressions of the agent's moral character and the certainty of those impressions. We fit a hierarchical Bayesian learning model to participants' trialwise predictions to describe how beliefs about the morality of the agents were updated by new information. RESULTS: The computational mechanisms of moral inference differed for patients with untreated BPD relative to matched control participants and patients with DTC-treated BPD. In patients with BPD, beliefs about harmful agents were more certain and less amenable to updating relative to both control participants and participants who were treated with DTC. CONCLUSIONS: The findings suggest that DTC may help the maintenance of social relationships in BPD by increasing patients' openness to learning about adverse interaction partners. The results provide mechanistic insights into social deficits in BPD and demonstrate the potential for combining objective behavioral paradigms with computational modeling as a tool for assessing BPD pathology and treatment outcomes.

Although the relationship between grandiose and vulnerable narcissism and psychological health is widely accepted, little is known about whether and how coping strategies explain this relationship. We examined the mediating role of proactive coping in this relationship, with a sample
of 280 participants from five colleges in Korea. We used four parallel multiple mediation models, and controlled for the indirect effects of coping flexibility, self-esteem, and covariates (age and gender). Results show that grandiose narcissism predicted higher life satisfaction and lower
perceived stress, whereas vulnerable narcissism exhibited the opposite pattern, and all of these relationships were mediated by proactive coping. Overall, our results highlight the critical role of proactive coping in the prediction of psychological health among narcissists.

Purpose of the studyDetermination of HIV‐1 coreceptor usage is crucial for the clinical management of HIV‐infected patients and for optimizing patient selection prior to coreceptor antagonist use. HIV‐1 subtype CRF01‐AE predominates in south and south‐east Asia and has spread all around the world. As for other subtypes, the HIV‐1 subtype CRF01‐AE tropism must be assessed before CCR5 antagonists' usage. Genotypic methods would be useful for tropism determination but their correlation with the phenotypic approach has not been assessed.MethodsWe determined the HIV‐1 coreceptor usage in 44 subjects infected with subtype CRF01‐AE by both a recombinant phenotypic entry assay and sequencing of the V3 region to determine the correlation between them.Summary of resultsWe first used genotypic algorithms currently used for subtype B HIV‐1. The sensitivity of the Geno2pheno10 genotypic algorithm was 75% but the specificity was poor (46%). In contrast, the sensitivity of the combined 11/25 and net charge rule was poor (50%) but the specificity was 96%. We used a GenBank clonal data set of 69 CRF01‐AE V3 sequences of viruses with known phenotype to identify subtype CRF01‐AE determinants in the V3 region associated with CXCR4 use and built a new simple genotypic rule for optimizing the genotypic prediction of CRF01‐AE tropism. The data showed that loss of the N‐linked glycosylation site at the beginning of V3 was an independent determinant of CXCR4 use by the CRF01‐AE virus clones. The new genotypic tool based on the 11/25, net charge and glycosylation site mutation criteria was 96% concordant with the phenotype on the GenBank clonal data set. Lastly, this algorithm has been validated using our patients' data set in which the sensitivity was 70% and the specificity was 96% for predicting CXCR4 use.ConclusionsThe concordance between genotype and phenotype was 84% when using the CRF01‐AE genotypic tool, approaching the concordance obtained for the tropism prediction of HIV‐1 subtype B. The genotypic prediction of HIV‐1 subtype CRF01‐AE coreceptor usage requires an optimized genotypic tool for a safely use of CCR5 antagonists.