High-dimensional supervised classification in a context of non-independence of observations to identify the determining SNPs in a phenotype

This work addresses the problem of supervised classification for highly correlated high-dimensional data describing non-independent observations to identify SNPs related to a phenotype. We use a general penalized linear mixed model with a single random effect that performs simultaneous SNP selection and population structure adjustment in high-dimensional prediction models. Specifically, the model simultaneously selects variables and estimates their effects, taking into account correlations between individuals.Single nucleotide polymorphisms (SNPs) are a type of genetic variation and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research has shown that SNPs can be used to identify the correct source population of an individual and can act in isolation or simultaneously to impact a phenotype. In this regard, the study of the contribution of genetics in infectious disease phenotypes is of great importance.In this study, we used uncorrelated variables from the construction of blocks of correlated variables done in a previous work to describe the most related observations of the dataset. The model was trained with 90% of the observations and tested with the remaining 10%. The best model obtained with the generalized information criterion (GIC) identified the SNP named rs2493311 located on the first chromosome of the gene called PRDM16 ((PR/SET domain 16)) as the most decisive factor in malaria attacks

The Spatiotemporal Distribution and Molecular Characterization of Circulating Dengue Virus Serotypes/Genotypes in Senegal from 2019 to 2023

Dengue virus is becoming a major public health threat worldwide, principally in Africa. From 2016 to 2020, 23 outbreaks were reported in Africa, principally in West Africa. In Senegal, dengue outbreaks have been reported yearly since 2017. Data about the circulating serotypes and their spatial and temporal distribution were limited to outbreaks that occurred between 2017 and 2018. Herein, we describe up-to-date molecular surveillance of circulating DENV serotypes in Senegal between 2019 to 2023 and their temporal and spatial distribution around the country. For this purpose, suspected DENV-positive samples were collected and subjected to dengue detection and serotyping using RT-qPCR methods. Positive samples were used for temporal and spatial mapping. A subset of DENV+ samples were then sequenced and subjected to phylogenetic analysis. Results show a co-circulation of three DENV serotypes with an overall predominance of DENV-3. In terms of abundance, DENV-3 is followed by DENV-1, with scarce cases of DENV-2 from February 2019 to February 2022. Interestingly, data show the extinction of both serotype 1 and serotype 2 and the only circulation of DENV-3 from March 2022 to February 2023. At the genotype level, the analysis shows that sequenced strains belong to same genotype as previously described: Senegalese DENV-1 strains belong to genotype V, DENV-2 strains to the cosmopolitan genotype, and DENV-3 strains to Genotype III. Interestingly, newly obtained DENV 1–3 sequences clustered in different clades within genotypes. This co-circulation of strains belonging to different clades could have an effect on virus epidemiology and transmission dynamics. Overall, our results highlight DENV serotype replacement by DENV-3, accompanied by a wider geographic distribution, in Senegal. These results highlight the importance of virus genomic surveillance and call for further viral fitness studies using both in vitro and in vivo models, as well as in-depth phylogeographic studies to uncover the virus dispersal patterns across the country

Analysis of contact tracing data showed contribution of asymptomatic and non-severe infections to the maintenance of SARS-CoV-2 transmission in Senegal

Abstract During the COVID-19 pandemic in Senegal, contact tracing was done to identify transmission clusters, their analysis allowed to understand their dynamics and evolution. In this study, we used information from the surveillance data and phone interviews to construct, represent and analyze COVID-19 transmission clusters from March 2, 2020, to May 31, 2021. In total, 114,040 samples were tested and 2153 transmission clusters identified. A maximum of 7 generations of secondary infections were noted. Clusters had an average of 29.58 members and 7.63 infected among them; their average duration was 27.95 days. Most of the clusters (77.3%) are concentrated in Dakar, capital city of Senegal. The 29 cases identified as super-spreaders, i.e., the indexes that had the most positive contacts, showed few symptoms or were asymptomatic. Deepest transmission clusters are those with the highest percentage of asymptomatic members. The correlation between proportion of asymptomatic and degree of transmission clusters showed that asymptomatic strongly contributed to the continuity of transmission within clusters. During this pandemic, all the efforts towards epidemiological investigations, active case-contact detection, allowed to identify in a short delay growing clusters and help response teams to mitigate the spread of the disease

Laboratory Evaluation and Field Testing of Dengue NS1 and IgM/IgG Rapid Diagnostic Tests in an Epidemic Context in Senegal

In Senegal, the burden of dengue is increasing and expanding. As case management and traditional diagnostic techniques can be difficult to implement, rapid diagnostic tests (RDTs) deployed at point of care are ideal for investigating active outbreaks. The aim of this study was to evaluate the diagnostic performance of the Dengue NS1 and Dengue IgM/IgG RDTs on the serum/plasma samples in a laboratory setting and in the field. During laboratory evaluation, performance of the NS1 RDT was assessed using NS1 ELISA as the gold standard. Sensitivity and specificity were 88% [75–95%] and 100% [97–100%], respectively. Performance of the IgM/IG RDT was assessed using the IgM Antibody Capture (MAC) ELISA, indirect IgG, and PRNT as gold standards. The IgM and IgG test lines respectively displayed sensitivities of 94% [83–99%] and 70% [59–79%] and specificities of 91% [84–95%] and 91% [79–98%]. In the field, the Dengue NS1 RDT sensitivity and specificity was 82% [60–95%] and 75% [53–90%], respectively. The IgM and IgG test lines displayed sensitivities of 86% [42–100%] and 78% [64–88%], specificities of 85% [76–92%] and 55% [36–73%], respectively. These results demonstrate that RDTs are ideal for use in a context of high prevalence or outbreak setting and can be implemented in the absence of a confirmatory test for acute and convalescent patients

Roots of <i>O</i>. <i>sativa</i> var. Sahel 202 with and without AMF structures.

<p>Roots free of AMF structures (A); root fragment colonized by <i>G</i>. <i>aggregatum</i>, with extraradical spores (B); root fragment colonized by <i>R</i>. <i>irregularis</i> presenting typical endospores (C); and root fragment colonized by <i>G</i>. <i>aggregatum</i>, with arbuscules (D).</p

<i>O</i>. <i>sativa</i> var. Sahel 202 heading and maturity dates and panicle weight for the 1^st and 2^nd year trials.

<p><i>O</i>. <i>sativa</i> var. Sahel 202 heading and maturity dates and panicle weight for the 1^st and 2^nd year trials.</p

Agronomic traits of inoculated and non-inoculated plants of the 8 rice varieties cultivated under irrigated filed conditions.

<p>Agronomic traits of inoculated and non-inoculated plants of the 8 rice varieties cultivated under irrigated filed conditions.</p

Matrix plot depicting the response to inoculation with AMF (MIE) of rice at variety and ecotype levels.

<p>HI: harvest index; 1000GWT: 1000 grain weight; GFD: grain filling duration. Numbers associated to the variety names (NE: NERICA; W: WAB; Sah: Sahel; TO: TOG) indicate the year of trial (1: first year and 2: second year).</p

Contribution of agronomic traits to the differences recorded in response to inoculation with AMF (MIE) of rice ecotypes in both first and second year trials, revealed by Similarity percentage (SIMPER).

<p>Contribution of agronomic traits to the differences recorded in response to inoculation with AMF (MIE) of rice ecotypes in both first and second year trials, revealed by Similarity percentage (SIMPER).</p

Ecotype and some agronomic traits of rice cultivars used in the present study.

<p>Ecotype and some agronomic traits of rice cultivars used in the present study.</p