Genetic Variability of Palm Lethal Decline Phytoplasmas in the Caribbean Basin and Florida, U.S.A., Based on a Multilocus Analysis

Palm lethal decline phytoplasmas are an important group of plant pathogens that cause death in a variety of palm species throughout the Caribbean basin and the southeastern United States. The 16SrIV-D phytoplasma was introduced to the state of Florida, United States; it has since caused severe economic losses to the green industries of Florida and is threating natural ecosystems because of its ability to infect the native palm Sabal palmetto. In this study, the genetic variability of the 16SrIV-D phytoplasma was assessed over a 10-year period to determine if multiple introductions had occurred or if natural mutations were occurring. Furthermore, the genetic variability of the palm lethal decline phytoplasma group (16SrIV) was assessed with a multiple locus analysis using the 16S ribosomal RNA gene, the 16S-23S intergenic spacer region, and secA and groEL genes. Overall, no variability of the 16SrIV-D phytoplasma was documented in Florida over a 10-year period. The multilocus analysis showed support for three distinct species of the phytoplasma in the Caribbean basin that infect palms and further support that the 16SrIV-C from Tanzania is not closely related. Furthermore, 16SrIV-B and 16SrIV-D were found to be the same phytoplasma based on 100% identity between the two based on intergenic spacer region, secA, and groEL analysis. This study represents the first robust, multilocus analysis of palm-infecting phytoplasmas from the Caribbean and sheds light on the phylogeny and evolution of the group.

Genetic identification of the causative agent of Brucellosis

The development of animal husbandry suffers various kinds of losses due to the spread of infectious diseases among animals, in particular Brucellosis. A challenge faced by Brucella researchers has been the accurate identification of new isolates within the genus while preserving sufficient, and not excessive, biosafety and biosecurity requirements. The availability of discriminatory molecular typing tools to inform and assist conventional epidemiological approaches would be invaluable in controlling these infections, but efforts have been hampered by the genetic homogeneity of the genus. In this work, for better identification of infection, for control and monitor the source of outbreaks in prosperous areas was carried out identification of Brucella spp. strains which circulating in the Kostanay region. For this was used using multilocus analysis of a variable number of tandem repeats sequenced by 16 s – PNK on a genetic analyzer (sequencer). According to the results of a study of cattle, cultures of microorganisms were infected: No. 4, 5, 7, 8. Comparison of the obtained results with similar results of domestic and foreign works by A. Shevtsov, G. Borrello, P. Le Fleche, G. Garofolo suggest that the genotyping of local strains has an importance in the molecular epizootology of the Republic of Kazakhstan.

High-throughput and efficient multilocus genome-wide association study on longitudinal outcomes

Motivation With the emerging of high-dimensional genomic data, genetic analysis such as genome-wide association studies (GWAS) have played an important role in identifying disease-related genetic variants and novel treatments. Complex longitudinal phenotypes are commonly collected in medical studies. However, since limited analytical approaches are available for longitudinal traits, these data are often underutilized. In this article, we develop a high-throughput machine learning approach for multilocus GWAS using longitudinal traits by coupling Empirical Bayesian Estimates from mixed-effects modeling with a novel ℓ0-norm algorithm. Results Extensive simulations demonstrated that the proposed approach not only provided accurate selection of single nucleotide polymorphisms (SNPs) with comparable or higher power but also robust control of false positives. More importantly, this novel approach is highly scalable and could be approximately >1000 times faster than recently published approaches, making genome-wide multilocus analysis of longitudinal traits possible. In addition, our proposed approach can simultaneously analyze millions of SNPs if the computer memory allows, thereby potentially allowing a true multilocus analysis for high-dimensional genomic data. With application to the data from Alzheimer's Disease Neuroimaging Initiative, we confirmed that our approach can identify well-known SNPs associated with AD and were much faster than recently published approaches (≥6000 times). Availability and implementation The source code and the testing datasets are available at https://github.com/Myuan2019/EBE_APML0. Supplementary information Supplementary data are available at Bioinformatics online.