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.

Efficacy of praziquantel against urinary schistosomiasis and reinfection in Senegalese school children where there is a single well-defined transmission period.

BACKGROUND: Human schistosomiasis is a significant health problem in Sub-Saharan Africa. In Niakhar, West central Senegal, the transmission of S. haematobium occurs seasonally between July and November. No control measures have been implemented despite high prevalence reported in previous studies. This aim of this study was to i) determine the current prevalence of S. haematobium in children at Niakhar, ii) assess the efficacy of one dose of PZQ (40 mg/kg) against S. haematobium and iii) monitor reinfection. METHODS: The current study was carried out in a cohort of 329 children aged five to 15 years enrolled from six villages in Niakhar to determine the efficacy of one dose of PZQ, as well as reinfection. Parasitological screening was performed in June 2011 to determine the baseline prevalence of S. haematobium, and then a single dose of PZQ was administered to all selected subjects in the transmission season in August 2011. The efficacy of PZQ treatment and reinfection were monitored respectively five weeks after in September 2011 and from February to March 2012. RESULTS: At baseline, the overall prevalence and the heavy intensity of infection were 73.2% and 356.1 eggs/10 ml of urine. Significant differences in the prevalence and intensity of S. haematobium infection were noted between villages. A single dose of PZQ significantly reduced the prevalence of S. haematobium infection from 73.2% to 4.6% and the geometric mean intensity of infection from 356.1 to 43.3 eggs/10 ml of urine. The cure rates ranged from 89.4% to 100%. The egg reduction rates also ranged from 77.6% to 100%. Two to three months after the period of transmission, the overall rate of reinfection was 12.6% and was significantly higher in male children than in female children. The overall prevalence at this period was 13.8%, which was significantly lower than the prevalence at baseline (73.2%). CONCLUSION: The Niakhar study area remains a hot spot of urinary schistosomiasis in Senegal with differences in transmission between villages. This study suggests that when transmission is strictly seasonal, Praziquantel shows the expected efficacy in reducing the prevalence and intensity of infection, but also a significant effect on the occurrence of reinfection.